Sequence, structure prediction, and epitope analysis of the polymorphic membrane protein family in Chlamydia trachomatis

Patrick W Cervantes; Brent W Segelke; Edmond Y Lau; Beverly V Robinson; Abisola Abisoye-Ogunniyan; Sukumar Pal; Luis M de la Maza; Matthew A Coleman; Patrik D’haeseleer

doi:10.1371/journal.pone.0304525

. 2024 Jun 11;19(6):e0304525. doi: 10.1371/journal.pone.0304525

Sequence, structure prediction, and epitope analysis of the polymorphic membrane protein family in Chlamydia trachomatis

Patrick W Cervantes ^1,^¤, Brent W Segelke ¹, Edmond Y Lau ¹, Beverly V Robinson ¹, Abisola Abisoye-Ogunniyan ¹, Sukumar Pal ², Luis M de la Maza ², Matthew A Coleman ¹, Patrik D’haeseleer ^1,^*

Editor: Agathe Subtil³

PMCID: PMC11166332 PMID: 38861498

Abstract

The polymorphic membrane proteins (Pmps) are a family of autotransporters that play an important role in infection, adhesion and immunity in Chlamydia trachomatis. Here we show that the characteristic GGA(I,L,V) and FxxN tetrapeptide repeats fit into a larger repeat sequence, which correspond to the coils of a large beta-helical domain in high quality structure predictions. Analysis of the protein using structure prediction algorithms provided novel insight to the chlamydial Pmp family of proteins. While the tetrapeptide motifs themselves are predicted to play a structural role in folding and close stacking of the beta-helical backbone of the passenger domain, we found many of the interesting features of Pmps are localized to the side loops jutting out from the beta helix including protease cleavage, host cell adhesion, and B-cell epitopes; while T-cell epitopes are predominantly found in the beta-helix itself. This analysis more accurately defines the Pmp family of Chlamydia and may better inform rational vaccine design and functional studies.

1. Introduction

Chlamydia trachomatis is the most common sexually transmitted bacterial infection in humans with an estimated prevalence of 1–2% in the United States and 4.2% globally [1–3]. However, this estimated prevalence is likely severely underestimated as nearly 50% of male and 80% of female Chlamydia infections are asymptomatic and consequently go unreported [1]. C. trachomatis infection poses a significant health risk to humans as untreated infections can lead to blindness, pelvic inflammatory disease, ectopic pregnancy, and infertility [1, 4]. C. trachomatis serovars are classified into three distinct groups, or biovars, based on tissues sites of isolation and disease outcomes. These include the etiological agents of ocular trachoma (serovars A, B, Ba, C), urogenital infection (serovars D, Da, E, F, G, Ga, H, I, Ia, J, K), and lymphogranuloma venereum (LVG) disease (serovars L1, L2, L2a, L3) [5–8]. Of the different C. trachomatis serovars, D, E and F are known as the most prevalent in humans [5, 9, 10]. Historically, the different serovars were distinguished from one another by immunotyping using antibodies to the Major Outer Membrane Protein (MOMP) [5, 11–13]. Direct lifetime medical cost of C. trachomatis infections were estimated to be around $691 million in the US alone in 2018 [14]. Treatment options rely on antibiotics for known exposures or symptomatic infection, but the high prevalence of asymptomatic infection necessitates the development of protective vaccines, reviewed by de la Maza et al. [1]. In short, vaccines that target chlamydial surface proteins show promise to elicit protection, decrease transmission, and prevent adverse health outcomes.

The polymorphic membrane protein (Pmp) family is a group of surface-exposed proteins in Chlamydia that have been highlighted as viable vaccine candidates [15–20]. Several laboratories have shown Pmps are immunogenic in naturally infected humans, as well as in non-human primates and mice infected with C. trachomatis or C. muridarum, respectively [7, 15, 21–24]. These studies showed protein subunit vaccinations with Pmp N-terminal domains were more immunogenic and suggest cross-serovar and cross-species protection against Chlamydia. To further support the potential use and development of a Pmp vaccine, the N-terminal domain was shown to contain novel T-cell epitopes that neutralize a vaginal Chlamydia challenge [25–27].

Pmps are predicted to be autotransporters, part of the Type Va secretion system in gram-negative bacteria [28, 29], characterized by 3 functional domains: (i) a secretory sequence that facilitates transport across the plasma membrane, (ii) an outer membrane β-barrel transmembrane region, and (iii) an extracellular passenger domain that carries out a number of biological roles, from enzymatic processes to pathogenesis [30]. The Pmp family is further defined by FxxN and GGA(I,L,V) tetrapeptide repeats that are concentrated in the extracellular passenger domain (see Fig 1). These motifs occur 13.6 and 6.5 times on average in C. trachomatis Pmps, respectively, and are thought to play a role in cellular adhesion and infectivity [15, 28, 31–35]. However, it remains unclear whether the tetrapeptide motifs directly mediate cellular adhesion or provide passenger domain structural support. Another characteristic feature of the Pmp family is the conserved PMP_M “Middle domain” (Pfam PF07548) at the C-terminal end of the passenger domain [36], the function of which is as yet unknown.

Fig 1 — The total PmpD protein length is 1530 amino acids. The protein map shows functional domains which include a secretory sequence, the extracellular β-helix passenger domain (grey), the Middle domain (aquamarine), and the transmembrane β-barrel autotransporter (purple). The FxxN and GGA(I,L,V) tetrapeptide motifs are shown in blue and yellow, respectively. The motif repeats are concentrated in the extracellular passenger domain but can also be found, less frequently, in the C-terminal domain.

The number of pmp genes vary among Chlamydia species. C. trachomatis has nine pmp genes in three chromosomal loci, comprising more than 3% of the ~1Mbp genome [37], all of which are known to be transcribed and translocated to the outer membrane [38–40]. The nine different Pmp proteins (PmpA-I) share less than 30% amino acid identity [28] with the exception of PmpB versus PmpC (58%), whether measured across the passenger domain or the beta barrel. However individual Pmps are highly conserved across serovars, with Pmps A and D being the most conserved (~98%) and Pmp F the least conserved (~85%) [41, 42]. Interestingly, phylogenetic analysis and sequence diversity of Pmp H and F groups C. trachomatis serovars into 3 clades that correspond to ocular, genital, and LGV disease types [41, 43], suggesting a role for Pmps in tissue specific infection.

The passenger domain has long been predicted to form a right-handed β-helix structure [44]. A recent paper by Debrine et al [45] uses Alphafold to study the predicted β-helical structure of the passenger domain in greater depth and demonstrated the presence of many non-canonical terapetide motifs. However there is currently no crystal structure to validate the computational model of the passenger domain β-helix or the overall Pmp native structure. The C. trachomatis Pmp passenger domains have been shown to form hetero- and homomeric oligomers in vitro [34, 37, 46]. Additionally, proteolytic cleavage sites have been observed in many Chlamydia Pmps [33, 37, 39, 47–50], giving rise to the idea that Pmps may form diverse oligomeric structures that could contribute antigenic diversity and immune evasion [32, 34].

In this study we utilize protein structure prediction algorithms to visualize, the Pmp family from C. trachomatis Serovar E. In addition, our Pmp amino acid sequence analysis shows that the tetrapeptide motifs, GGA(I,L,V) and FxxN, fit into a larger and predictable spacing pattern, matching the predicted β-helical structure of the passenger domain. Our sequence and structural analysis more accurately defines the Pmp family of Chlamydia which could be used to inform rational vaccine design and functional studies.

2. Methods

2.1 Identifying Pmp repeats

Tetrapeptide motifs and longer motifs with various spacings in the Pmp protein sequences were investigated using the ScanProsite tool [51]. The resulting longer motif instances were aligned using the Multiple Alignment using Fast Fourier Transform (MAFFT) tool [52] with the G-INS-i option for global alignment, and the resulting alignment was then turned into an HMM model using HMMSEARCH [53] to search against the Uniprot database. The Pmp amino acid sequences used included all 9 Pmps from C. trachomatis serovar E (Bour), all 9 C. muridarum (NiggII) Pmps, and all 16 C. pneumoniae (TWAR) Pmps. The complete list with protein accession numbers can be found in S1 Table. The ScanProsite motif hits, and the HMM model can be found in S2 Table and S1 File.

2.2 Protein structure prediction

After removing the signal peptides, protein sequences for all nine C. trachomatis serovar E Pmps, PmpD of C. trachomatis serovar L2, and Pmp21 of C. pneumoniae were submitted for protein structure prediction using both the TrRosetta [54] and RoseTTAFold [55] algorithms through the Robetta protein structure prediction service hosted by the Baker lab at https://robetta.bakerlab.org/. The five models generated by Robetta for each sequence were inspected visually and a representative model chosen. For proteins longer than 1000aa (the length limit on the Robetta server at the time these jobs were submitted), the first and last 1000aa were submitted for structure prediction separately. The resulting models were then aligned using Matchmaker in UCSF Chimera [56], and representative models with the best structural homology between the two partial models were spliced together into a single structure. AlphaFold2 structure predictions for all the proteins in the Swissprot database–including the Serovar D homologs of the Pmp proteins–have recently been made available through Uniprot or directly through the AlphaFold Protein Structure Database at https://alphafold.ebi.ac.uk/faq [57, 58], however these pregenerated structures typically include the signal peptide, which can impact the structure at the top of the passenger domain. We generated Alphafold2 structure predictions for the mature Pmp proteins without the signal peptides, using the Aphafold2 Colab notebook at https://colab.research.google.com/github/deepmind/alphafold/blob/main/notebooks/AlphaFold.ipynb. Likewise, we generated ESMFold structure predictions [59] using the Colab notebook provided by Sergey Ovchinnikov at https://colab.research.google.com/github/sokrypton/ColabFold/blob/main/ESMFold.ipynb).

2.3 Prediction of membrane binding domains

We used the DREAMM public web server at https://dreamm.ni4os.eu/ to predict the membrane-penetrating amino acids in Pmp proteins. DREAMM is a recently developed ensemble machine learning algorithm for predicting protein–membrane interfaces of peripheral membrane proteins [60].

2.4 Molecular dynamics

All molecular dynamics simulations utilized the MARTINI coarse-grain force field (version 2, the proteins used the version 2.2 parameters) [61, 62]. The system was prepared using the CHARMM-GUI [63]. Each protein was embedded into a POPC bilayer, solvated with the MARTINI polarizable water model [64], and the appropriate number of Na + /Cl—was added to neutralize the system and obtain a concentration of 150 mM. The program GROMACS (version 2021.1) was used for the MD simulations [65]. The system was energy minimized and 1 microsecond of production dynamics was performed. Simulations were performed in the NPT ensemble with weak temperature and pressure coupling using a velocity-rescaling thermostat at 303 K (coupling constant 1.0 ps) [66] and the Parrinello-Rahman barostat (coupling constant 12 ps) [67]. A timestep of 20 fs was used. Electrostatics were calculated with a reaction field (dielectric constant of 2.5) and cutoff at 1.1 nm. The van der Waals interactions were calculated with a cutoff at 1.1 nm.

2.5 Epitope prediction

We used analysis tools provided through the Immune Epitope Database (IEDB) web server [68] for prediction of B- and T-cell epitopes. The B-cell epitope predictions were generated with the protein sequence based tool BepiPred-2.0 [69], as well as the structure based tool Discotope [70], the latter using our RoseTTAFold-predicted structures of the Pmps as input. T-cell epitopes predictions were generated using the recommended settings for the T-cell epitope prediction tools at IEDB, which currently default to NetMHCPan 4.1 EL for MHC-I, and Consensus 2.22 for MHC-II. For MHC-II prediction, we focus on a set of 13 HLA alleles that incorporates some of the most frequent DRB1/3/4/5 alleles, and supertype alleles that cover well-over 95% of most HLA types present in human population groups [71–73]: DRB1*01:01, DRB1*03:01, DRB1*04:01, DRB1*07:01, DRB1*08:01, DRB1*09:01, DRB1*11:01, DRB1*13:01, DRB1*15:01, DRB3*01:01, DRB3*02:02, DRB4*01:01, DRB5*01:01.

2.6 Pmp amino acid sequence conservation

Multiple Alignment using Fast Fourier Transform (MAFFT, v7.487) [52] was used to align C. trachomatis Pmp amino acid sequences from serovars A-K, Ba, and L1-L3. Alignments were uploaded to the Multalign View tool in UCSF Chimera and the percent amino acid conservations were mapped onto the respective C. trachomatis Serovar E Pmp structural model generated in RoseTTAFold.

3. Results

3.1 Pmp passenger domains contain a larger repeat sequence that incorporates the GGA(I,L,V) and FxxN repeat motifs

To test the statement by Grimwood and Stephens that “the number of amino acids between the [GGA(I,L,V) and FxxN] repeat motifs does not appear to follow any characteristic spacing pattern” [28], we examined the spacing between motifs in all nine C. trachomatis serovar D and C. muridarum Pmps, and all 16 C. pneumoniae Pmps. We found that GGA(I,L,V) motifs are almost always followed by a FxxN motif (Fig 2A), and 75% of these pairs (169/226) are spaced only 14–18 amino acids apart (Fig 2B). The spacing following FxxN motifs is much more variable. In about 57% of the cases (122/214), an FxxN motif is followed by a GGA(I,L,V) motif with a gap of only 4-5aa, but much longer gaps or a second FxxN motif are also common. Out of the 34 Pmp proteins examined, we found 188 instances of the core pattern (G-G-A-[ILV]-x(14,18)-F-x-x-N in PROSITE notation), and 301 instances if we allowed one amino acid difference to the core pattern or allowed the gap to vary from 12–23 amino acids. Fig 2C shows the sequence logo for the HMM derived from these sequences, showing several conserved amino acid positions between the classical tetrapeptide motifs, including an asparagine at position 15 conserved in 55% of the sequences, and alternating hydrophobic amino acids at positions 4, 6, 10,12, 19, 21, which are likely facing toward the inside of the β-helix.

Fig 2 — (A) The GGA(I,L,V) and FxxN motifs tend to alternate, with GGA(I,L,V) almost always followed by FxxN. (B) GGA(I,L,V) and FxxN repeats show a regular spacing. (C) Sequence logo for the longer repeat we identified. (D) Sequence logo for PF02415.

Interestingly, Pfam contains a “Chlamydia polymorphic membrane protein (Chlamydia_Pmp) repeat” domain (PF02415) that incorporates both tetrapeptide motifs, but in the opposite order: FxxN, followed by a long and variable gap with little sequence specificity before the GGA(I,L,V) motif (Fig 2D), which omits many of the conserved positions we identified. Although this is supposedly a Chlamydia specific Pfam domain, less than a third of the proteins containing this domain (443 out of 1448 Uniprot proteins based on HMMSEARCH) belong to Chlamydia species, including almost 200 non-bacterial proteins. In contrast, the HMM we derived here is almost exclusive to Chlamydia, matches a larger number of Chlamydia proteins (675 out of 744 Uniprot proteins), and identifies more than double the number of repeats per protein (average 6.1 repeats per protein, versus 2.9 for PF02415).

3.2 Structural modeling shows Pmps share many features with other autotransporters

We initially generated protein structure predictions for all C. trachomatis Pmps using the TrRosetta algorithm [54] available on the Baker lab’s public Robetta protein structure prediction service (https://robetta.bakerlab.org/), and then upgraded to their new RoseTTAFold algorithm [55], and then DeepMind’s record breaking Alphafold2 [57], and Meta’s ESMFold [59] as those became publicly available. The older TrRosetta algorithm (Fig 3A) cannot resolve some regions such as the passenger domain as well as the newer algorithm, and was not used for any further analysis. The newer algorithms all converge on a similar structure (Fig 3B–3D)–a promising finding as the methods to generate models by RoseTTAFold/AlphaFold2 are very different from the large language model-based ESMFold. Although all three modelers struggle to resolve the structure of the large side loops–with the AlphaFold structures often showing large completely unstructured side loops (Fig 3C)–we chose to focus primarily on the RoseTTAFold structure predictions, because they tended to predict less unstructured side loops with slightly higher confidence scores. However without any confirming experimental data, the exact structures of the side loops is speculative. PDB files with the RoseTTAFold structure predictions for PmpA to PmpI of C. trachomatis serovar E can be found in S2 File.

Fig 3 — Structure predictions using TrRosetta (A), RoseTTAFold (B), AlphaFold2 (C), and ESMFold (D). Confidence scores (pLDDT for AlphaFold and ESMFold, RMSD for TrRosetta and RoseTTAFold) were mapped to the same blue-to-red scale using the empirical formula $C_{α} R M S = 1.5 e x p (4 \times (0.7 - p L D D T))$ **[55, 74]**.

The resulting structures show many of the stereotypical features found in other autotransporters, including a 12-stranded β-barrel at the C-terminal. The predicted β-barrel tended to incorporate multiple mortise-tenon joints [75], and a β-hairpin structure as part of the fifth extracellular loop of the β-barrel shown to be important for correct folding of the passenger domain in other autotransporters [76] (Fig 4A). The predicted β-barrel averages approximately 16 residues per β-strand, for a total height of about 7nm, similar to known autotransporter β-barrel crystal structures such as E. coli EspP (PDB 3SLJ). However the width of the Chlamydia outer membrane bilayer is predicted to be only 4.23 nm (RB) to 4.41 nm (EB) [77], suggesting that only part of the β-barrel is embedded in the membrane. Indeed, only the lower 3–4 nm of the β-barrel is covered with hydrophobic residues (Fig 4B, 4C), suggesting that the remainder of the Pmp β-barrel protrudes extracellularly.

Fig 4 — (A) The β-barrel for PmpG showing six “mortise-tenon joints”, in which an aromatic side chain facing into the lumen of the barrel domain locks into a void created by the presence of a glycine residue on the neighboring β-strand. Blue: aromatic side chains. Red: glycine residues. Shown in green is a 4-stranded β-sheet formed by β-hairpins in extracellular loops L4 and L5. The β-hairpin in L5 has been shown to be important for correct folding of the passenger domain in other autotransporters. Prediction of transmembrane β-strands using TMbed (B) and visualization of the hydrophobic residues (C) indicates that only the bottom 3-4nm of the β-barrel is predicted to be embedded in the Chlamydia outer membrane.

The conserved Pmp_M “Middle domain” is predicted to include the first couple of coils of the β-helix most proximal to the β-barrel (even though it does not contain the conserved GGA(I,L,V)/FxxN motifs or HMM sequence pattern described above). The Middle domain flattens to a β-sandwich and is capped by a β-hairpin. This structure is similar to autochaperone domains found in many autotransporters with β-helical passenger domains such as pertactin [78–80], suggesting it may play a role in proper secretion and folding.

As shown by others, the passenger domain is predicted to fold into a tightly coiled β-helix formed by parallel β-strands (Fig 5A). Based on the structure predictions shown here, Pmps and their homologs in related Chlamydia species may be some of the longest known parallel β-helix proteins, with at least 27 complete coils for PmpB and 22 coils for PmpD, versus 16 coils for Pertactin.

Following the nomenclature introduced by Yoder et al. 1993 and Jenkins et al. 2001 [80, 81], the three parallel β-sheets (PB) in PmpD are labeled PB1 (yellow), PB2 (blue) and PB3 (red) and the turns (T) following the β-sheets labeled T1, T2, T3 (Fig 5C). Compared with the HMM pattern in Fig 2C, the initial conserved GGA constitutes the end of T3, followed by a short ~3 amino acid PB1 (Fig 3B). The two areas with sequence length variability between positions 6–9 and 16–17 (indicated by the vertical lines in Fig 2C) correspond to turns T1 and T2, while the conserved FxxN motif marks the transition from PB3 to T3 (Fig 3B). It is important to note that the side chain positions of the most conserved amino acids in the GGA(I,L,V) and FxxN tetrapeptide motifs point inward, within the β-helix core (Fig 5C), suggesting that the tetrapeptide motif provide a more structural role to the passenger domain β-helix. We also show that most of the variability in this pattern lies within the sometimes very long side loops inserted in T3 (also referred to as Ω-loops [45, 82]) and occasionally T1, while T2 typically only varies in length by one or two amino acids (Fig 2C).

All predicted Pmp structures showed a 12-stranded β-barrel at the C-terminal, connected to the passenger domain by an α-helix threaded through the center of the β-barrel (Fig 4). Surprisingly, most models favored an angle of 90 degrees or more between the β-helical passenger domain and the transmembrane β-barrel, rather than having the two domains in a straight line. Coarse-grained molecular dynamics simulations suggest a flexible hinge region between the membrane-embedded β-barrel and the base of the passenger domain, allowing the extracellular portion of the protein to take on a range of conformations, from standing straight out from the membrane to lying flat against it, as illustrated in Fig 6.

Fig 6 — Coarse-grained molecular dynamics simulation of PmpE with the β-barrel domain embedded in a POPC bilayer shows that the passenger domain is connected by a flexible hinge between the β-barrel and the Middle domain.

3.3 Protease cleavage sites are concentrated in side loops

[83] lists a total of 21 Pmp cleavage sites that have been experimentally detected in serovar L2 Pmp proteins by N-terminal sequencing [37], mass spectrometry [50], and identification of semitryptic peptides [39]. Mapping these cleavage sites to our serovar E structures, we found that three of these sites map to signal peptide cleavage sites, two cleave the alpha helix inside the transmembrane β-barrel (PmpD and PmpG), and two cleave the β-barrel itself. Of the remaining 14 cleavage sites that fall within the passenger domains, only two are located inside the β-helix coils, while the remaining 12 map to the side loops, the N-terminal cap, or the non-β-helical parts of the Middle domain (Fig 7).

Fig 7 — Pmp cleavage sites from serovar L2 were mapped by BLAST to the homologous locations on the serovar E PmPs, for purposes of illustrating their location in the side loops only. Note that these cleavage sites may not be present or active in the serovar E Pmps.

3.4 B-cell epitopes are concentrated in side loops, T-cell epitopes in the main β-helix of the passenger domain

The Immune Epitope Database (IEDB) [68] contains data on experimentally validated B-cell epitopes for 40 different C. trachomatis antigens, including 7 epitopes for PmpD and 5 for PmpC. All experimentally validated B-cell epitopes for PmpC and PmpD map to side loops of the β-helix or the Middle domain (Fig 8A). Assuming many Pmp epitopes remain to be discovered, we also utilized computational methods available through IEDB to predict additional epitopes. The first prediction method, Bepipred-2.0 [69], uses the linear sequences of proteins to predict epitopes. We input the C. trachomatis serovar E sequences for each member of the Pmp family (A-I). We also predicted B-cell epitopes using the protein structure based Discotope 2.0 algorithm [70] to predict B-cell epitopes for each Pmp based on surface accessibility and amino acid epitope propensity scores. Similar to the experimentally validated epitopes, epitopes predicted from either sequence or structure also localized predominantly in the passenger domain side-loops, and especially in some of the long extended loops (Fig 8B). B-cell epitope predictions on the Alphafold2 predicted structures were largely identical (data not shown). The extracellular face of the transmembrane β-barrel also scored high in the structure-based B-cell epitope prediction, even though IEDB contains no known epitopes in this area. However when taken in context of the protein in its biological setting (embedded into the membrane and shielded by the large passenger domain or other membrane components) these regions would likely not bind antibodies. B-cell epitope predictions using Bepipred-2.0 and Discotope 2.0 can be found in S3 and S4 Tables.

Experimentally validated T-cell epitopes in IEDB occur for 49 different C. trachomatis antigens, which include one epitope each in PmpC, D, E, G, H, and I. Two MHC-I epitopes map to different strands of β-barrel for PmpC and PmpI, while the four MHC-II epitopes in PmpD, E, G, and H map to various locations along the β-helix.

Computational prediction of MHC-II T-cell epitopes map predominantly to the β-helix of the passenger domains (Fig 8C), with some epitopes matching multiple HLA subtypes. Interestingly, epitopes for allele HLA-DRB3*02:02 seem strongly overrepresented, accounting for more than one third of all MHC-II T-cell epitopes (Fig 8D). Upon further examination, it appears that the core binding motif for this allele overlaps with the FxxN motif in the passenger domain β-helices (Fig 8E). This suggests that populations with this particular MHC-II allele may be able to mount a stronger T-cell immune response to Chlamydia infections. The number of MHC-II T-cell epitopes differs noticeable across Pmps, with the short PmpA containing 25 epitopes (20 not counting HLA-DRB3*02:02) for 12 of the 13 HLA subtypes tested, while the equally short PmpH only contains 9 (8 not counting HLA-DRB3*02:02), and the much longer PmpC contains 13 (6 not counting HLA-DRB3*02:02) (Fig 8D). This suggests that some Pmps may present better vaccine targets to stimulate a broad T-cell response. MHC-I T-cell epitopes were predicted to be fairly abundant in the Pmps and spread throughout the entire length of the protein.

MHC-II class T-cell epitope predictions using IEDB’s Consensus 2.22 can be found in S5 Table.

3.5 Side loops are predicted to be involved in host cell adhesion

Pmp side loops include proline-rich regions (PRRs) that may be involved in binding to host membrane proteins. PRRs are often involved in eukaryotic protein-protein interactions, and can be exploited by viral and bacterial pathogens to interact with host proteins [84, 85]. A wide range of different sequence motifs have been described for PRRs, but they tend to contain at least four prolines, separated by up to four other amino acids. In the Pmps, PRRs are predominantly found on the side loops and the Middle domain (Fig 9).

Fig 9 — Proline-rich regions are located primarily in the side loops and the Middle domain. Sites with at least four prolines separated by 0–4 residues are highlighted in red and labeled with the sequence.

Besides binding to membrane proteins, Pmps may also interact directly with the host membrane. As expected, prediction of protein-membrane interface peptides using DREAMM yields multiple hits in the transmembrane β-barrel for each Pmp. In addition, each Pmp has one or more predicted membrane-penetrating amino acids at the N terminal and in various side loops off the main β-helix, that may be involved in adhesion to host membrane (Fig 10). Membrane-penetrating amino acids predicted by DREAMM can be found in S6 Table.

Fig 10 — Predicted membrane-penetrating amino acids (highlighted in red) are located primarily in the side loops, and at the N terminal, as well as the transmembrane β-barrel (not shown).

Sequence variation between the C. trachomatis serovars, based on a multiple sequence alignment with MAFFT is also more concentrated in the passenger domain side loops (Fig 11), which may be correlated with the positive selective pressure on the B-cell epitopes by the host immune system, and with differences in tissue tropism. Multiple sequence alignment for each of the C. trachomatis Pmps using MAFFT can be found in S3 File.

Fig 11 — Highlighted in red are the amino acids with less than 70% sequence conservation based on a multiple sequence alignment across the C. *trachomatis* serovars by MAFFT.

4. Discussion

The Pmp family is a group of surface exposed chlamydial proteins that are naturally immunogenic in humans and may function as viable vaccine antigen candidates [15, 21–24]. In this study, we took advantage of the latest generation of protein structure prediction tools to create full-length protein structure predictions for all nine Pmp proteins (PmpA-I) from C. trachomatis serovar E (Bour). Although the GGA(I,L,V) and FxxN tetrapeptide motifs have been recognized as prominent features of the Pmp passenger domains for more than twenty years [28], their semi-regular spacing had not been previously appreciated. Our bioinformatic and structural analyses discovered that the tetrapeptide motifs fit into a larger repeat sequence that can be fit with an HMM model including several other partially conserved residues, and that these larger sequence repeats correspond to the individual coils of the predicted β-helical structure of the passenger domains. Since not all the β-helical coils include the canonical GGA(I,L,V) and FxxN motifs, as also pointed out by [45], it may be possible to derive a more inclusive HMM by starting from a structural alignment of the coils.

The availability of a high quality HMM also makes it trivial to identify additional Pmp-like proteins, where previous efforts had to resort to searching for the short tetrapeptide motifs [45], PSI-BLAST [86], or the use of a Pfam that misses around a third of Chlamydia’s own Pmp’s [87, 88]. For example, the highest scoring hit to our HMM from Fig 2C is not to a Chlamydia protein, but a pectin lyase fold/virulence factor from Akkermansia glycaniphila (accession A0A1C7PBX1, HMM E-value 3.6e-158) with 27% sequence identity to the nearest Chlamydia protein, predicted to fold into a classical autotransporter with a β-barrel and a passenger domain with a record breaking 43 β-helical coils. We also confirm the presence of Pmp-like proteins in Methanobacteria (best hit, accession A0A125RDR3, E-value 1.7e-17) [45], and Trichomonas (best hit, accession A2E8Q2, E-value 0.004) [88]. In contrast, the Chlamydia trachomatis “Pmp-like proteins” Pls1, Pls2 and Pls3 reported by Jorgensen and Valdivia [86] based on marginal PSI-BLAST similarity with the PmpC passenger domain do not show any significant match with our HMM, and do not show any structural homology with existing Pmps.

Cartoon representations of autotransporters typically show the passenger domain in line with the transmembrane β-barrel sticking out from the membrane at a right angle [79, 83, 89–91]. However structural modeling of the Pmps shows a variety of potential angles between the β-barrel and the passenger domain, and molecular dynamics shows the presence of a flexible hinge between the two domains. It is not clear whether this is a general feature of autotransporters or unique to the Pmp family. The potential for passenger domains to lay flat against the outer membrane also opens the possibility for Pmp’s to make longer-range protein-protein interactions with other members of the outer membrane complex and may help define their functional and cellular features in the life cycle of chlamydia.

Mutating or deleting the conserved GGA(I,L,V) and FxxN motifs has been shown to disrupt Pmp oligomerization [33] and host cell adhesion [32, 33]. Our structural models clearly show that the conserved residues of the tetrapeptide motifs are actually located on the inside of the β-helix (see Fig 5C, 5D). This suggests that the motifs are more likely to play a purely structural role in ensuring the proper folding and stacking coils of the β-helical passenger domain coils, rather than being directly involved in binding to host membranes, host proteins, or other Pmp proteins. The Pmp21-D passenger domain fragment studied in [32, 33] consist of a total of 6 β-helical coils, only the first two of which have canonical FxxN motifs, although there are stacking interactions with phenylalanines in the next two coils as well. AlphaFold2-MULTIMER predicts that the β-helices of the monomers stack N-terminal to N-terminal and C-terminal to C-terminal, forming a progressively longer beta-helical oligomer (Fig 12A). ESMFold prediction of the Pmp21-D protein fragment with the two FxxN motifs mutated to SxxV (Pmp21-D-mut3) shows a distortion of the first two coils of the β-helix, which may inhibits stacking at the N-terminals and thus formation of longer oligomers. In addition, the first FxxN motif (FYGN) contains an outwards-facing tyrosine that is predicted by DREAMM to be a membrane-interacting residue for the wild type Pmp21-D, but not for the mutated version. So even though the phenylalanine and asparagine of the FxxN motif are internal to the β-helix, disrupting these key residues can affect both oligomerization and adhesion.

Fig 12 — (A) The predicted oligomer structure for Pmp21-D forms an extended β-helix, joined N-terminal to N-terminal and C-terminal to C-terminal. (B) The predicted PmpA^408-608:PmpD^269-918 heterodimer structure for also forms an extended β-helix, with the N-terminal of PmpA^408-608 meeting up with the C-terminal of PmpD^269-918. (C) the predicted PmpE19-459 homodimer structure shows an antiparallel configuration. All structures are shown in Rainbow coloring, with N-terminal in blue, C-terminal in red.

Several other Pmp passenger domain fragments have been shown to assemble into homomeric and heteromeric filaments as well [34]. We used AlphaFold2-MULTIMER to predict the dimer, trimer and tetramer structures of the PmpA, PmpD, PmpG and PmpI fragments from this study, as well as the full length PmpA-PmpI passenger domains, and the C. pneumoniae Pmp21 fragments from [32]. Preliminary results suggest that Pmp passenger domain fragments may be able to oligomerize in a number of different configurations. Fig 12B and 12C shows two of the dimer predictions with the highest model confidence: a PmpA^408-608:PmpD^269-918 heterodimer that forms a single long β-helix, and a PmpE^19-459 homodimer in an antiparallel configuration similar to that observed for the Ag43 passenger domain, involved in bacterial clumping [92].

A recent Genome-Wide Association study of C. trachomatis serovar G genomes identified polymorphisms in Pmp E, F, and H that are associated with rectal tissue tropism, localized specifically to the side loops in these Pmps [93]. Pmps are also known to be directly involved in invasion of host cells by binding to host EGFR in C. pneumoniae [94] and C. psittaci [95]. Although the precise mechanisms of these interactions are as yet poorly understood, the proline-rich regions and membrane interaction domains we identified in the side loops may be involved in mediating host EGFR interactions. PmpD also contains an integrin-binding RGD motif on a short loop in turn T1, that may be involved in attachment or host entry [37].

This enhanced understanding of the structural role of the tetrapeptide motifs shifts the perspective towards the areas between these conserved motifs. We have shown that the side loops jutting out from the β-helical backbone of the passenger domain contain many interesting features including protease cleavage sites, host cell adhesion, and B-cell epitopes. Conversely, T-cell epitopes are predominantly found in the β-helix itself. These results expand on the structural insights by Debrine et al [45], and highlight the modular structure encoded within Pmp passenger domains that may lend itself well to protein engineering and rational vaccine design.

The ideal Chlamydia vaccine would provide cross-serovar protection with strong cell-mediated and humoral responses. In this study, we extended the previously analyzed T- and B-cell epitopes in PmpD by Russi et al. [96] to the entire Pmp family. We are also leveraging much higher quality structure predictions that have recently become available, and especially benefit the B-cell epitope prediction. For example, several of the sequence-based B-cell epitope predictions derived by Russi et al using Bepipred [69] overlapped with the tetrapeptide motifs, which we predict are embedded in the β-helix and therefore unlikely to be accessible to antibodies. The study by Russi et al. focused on six peptides that included multiple B- and T-cell epitope predictions. However, our analysis shows that B- and T-cell epitopes are largely disjoint, with B-cell epitopes predominantly in side loops, while MHC-II T-cell epitopes are concentrated more inside the β-helix. For vaccine design it may therefore be more effective to choose B- and T-cell epitopes separately, rather than expect to be able to capture both with the same peptides. Given our current understanding of the structure and function of the Pmp passenger domains, a future peptide-based Pmp vaccine may need to combine parts of the β-helical scaffold that contains a good variety of T-cell epitopes, with specific side loops that carry the most promising B-cell epitopes. Conversely, it is possible that an effective Pmp vaccine will require the recombinant expression of the whole protein. The Pmp-based vaccines tested in animal models thus far have been based almost exclusively on passenger domain fragments–often based on the known protease cleavage products, or fragments with the highest number of canonical tetrapeptide motifs. Protection elicited by the current human papilloma virus, hepatitis B virus, and SARS-CoV-2 vaccines depend on tertiary conformation of the antigen. Therefore, a full-length and native conformation of the passenger domain or the entire Pmp protein may be required for optimal protection, possibly requiring engineering out some of the cleavage sites. This paper provides an in-depth analysis of the Pmp protein family that can serve as a foundation to rational vaccine design and a structural vaccinology approach to develop an effective Pmp vaccine.

Supporting information

S1 File. HMM model for the longer repeat pattern we identified.

(HMM)

pone.0304525.s001.hmm^{(12.8KB, hmm)}

S2 File. File ZIP file containing PDB files with the RoseTTAFold structure predictions for PmpA to PmpI of C. trachomatis serovar E.

(ZIP)

pone.0304525.s002.zip^{(2.7MB, zip)}

S3 File. File ZIP file with multiple sequence alignment for each of the C. trachomatis Pmps using MAFFT.

(ZIP)

pone.0304525.s003.zip^{(22.4KB, zip)}

S1 Table. Accession numbers for the C. trachomatis, C. muridarum and C. pneumoniae Pmp proteins used in this study.

(XLSX)

pone.0304525.s004.xlsx^{(10.9KB, xlsx)}

S2 Table. ScanProsite motif hits.

(XLSX)

pone.0304525.s005.xlsx^{(132.4KB, xlsx)}

S3 Table. B-cell epitope predictions using Bepipred-2.0.

(XLSX)

pone.0304525.s006.xlsx^{(573.7KB, xlsx)}

S4 Table. B-cell epitope predictions using Discotope 2.0.

(XLSX)

pone.0304525.s007.xlsx^{(472.9KB, xlsx)}

S5 Table. MHC-II class T-cell epitope predictions using IEDB’s Consensus 2.22.

(XLSX)

pone.0304525.s008.xlsx^{(40.4KB, xlsx)}

S6 Table. Membrane-penetrating residues predicted by DREAMM.

(XLSX)

pone.0304525.s009.xlsx^{(9.2KB, xlsx)}

Data Availability

All relevant data are within the manuscript and its Supporting Information files.

Funding Statement

This work was performed, in part, under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344 (PWC, BWS, EYL, BVR, AA-O, MAC, PD). Support was provided by NIH/NIAID grant U19AI144184 (PWC, BWS, BVR, AA-O, SP, LMM, MAC, PD). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

References

1.de la Maza LM, Darville TL, Pal S. Chlamydia trachomatis vaccines for genital infections: where are we and how far is there to go? Expert Rev Vaccines. 2021;4: 421–435. doi: 10.1080/14760584.2021.1899817 [DOI] [PMC free article] [PubMed] [Google Scholar]
2.Gupta K, Harrison SA, Davis NA, Culp ML, Hand SC, Simpson T, et al. Prevalence of Chlamydia trachomatis Infection in Young Women and Associated Predictors. Sex Transm Dis. 2021;48: 529–535. doi: 10.1097/OLQ.0000000000001372 [DOI] [PMC free article] [PubMed] [Google Scholar]
3.Rodrigues R, Sousa C, Vale N. Chlamydia trachomatis as a Current Health Problem: Challenges and Opportunities. Diagnostics. 2022;12: 1795. doi: 10.3390/diagnostics12081795 [DOI] [PMC free article] [PubMed] [Google Scholar]
4.Van Gerwen OT, Muzny CA, Marrazzo JM. Sexually transmitted infections and female reproductive health. Nat Microbiol. 2022;7: 1116–1126. doi: 10.1038/s41564-022-01177-x [DOI] [PMC free article] [PubMed] [Google Scholar]
5.Lesiak-Markowicz I, Schötta A-M, Stockinger H, Stanek G, Markowicz M. Chlamydia trachomatis serovars in urogenital and ocular samples collected 2014–2017 from Austrian patients. Sci Rep. 2019;9: 18327. doi: 10.1038/s41598-019-54886-5 [DOI] [PMC free article] [PubMed] [Google Scholar]
6.Morré SA, Rozendaal L, van Valkengoed IGM, Boeke AJP, van Voorst Vader PC, Schirm J, et al. Urogenital Chlamydia trachomatis Serovars in Men and Women with a Symptomatic or Asymptomatic Infection: an Association with Clinical Manifestations? J Clin Microbiol. 2000;38: 2292–2296. doi: 10.1128/jcm.38.6.2292-2296.2000 [DOI] [PMC free article] [PubMed] [Google Scholar]
7.Lanfermann C, Kohn M, Laudeley R, Rheinheimer C, Klos A. Chlamydia trachomatis Cross-Serovar Protection during Experimental Lung Reinfection in Mice. Vaccines. 2021;9: 871. doi: 10.3390/vaccines9080871 [DOI] [PMC free article] [PubMed] [Google Scholar]
8.Pilo S, Zizelski Valenci G, Rubinstein M, Pichadze L, Scharf Y, Dveyrin Z, et al. High-resolution multilocus sequence typing for Chlamydia trachomatis: improved results for clinical samples with low amounts of C. trachomatis DNA. BMC Microbiol. 2021;21: 28. doi: 10.1186/s12866-020-02077-y [DOI] [PMC free article] [PubMed] [Google Scholar]
9.Byrne GI. Chlamydia trachomatis Strains and Virulence: Rethinking Links to Infection Prevalence and Disease Severity. J Infect Dis. 2010;201: S126–S133. doi: 10.1086/652398 [DOI] [PMC free article] [PubMed] [Google Scholar]
10.Ardizzone CM, Albritton HL, Lillis RA, Bagnetto CEL, Shen L, Cavacini LA, et al. Human genital antibody-mediated inhibition of Chlamydia trachomatis infection and evidence for ompA genotype-specific neutralization. PLoS ONE. 2021;16: e0258759. doi: 10.1371/journal.pone.0258759 [DOI] [PMC free article] [PubMed] [Google Scholar]
11.Kuo C, wang S-P, Holmes KK, Grayston TJ. Immunotypes of Chlamydia trachomatis isolates in Seattle, Washington. Infect Immun. 1983;41: 865–868. doi: 10.1128/iai.41.2.865-868.1983 [DOI] [PMC free article] [PubMed] [Google Scholar]
12.Stephens RS, Sanchez-Pescador R, Wagar EA, Inouye C, Urdea MS. Diversity of Chlamydia trachomatis major outer membrane protein genes. J Bacteriol. 1987;169: 3879–3885. doi: 10.1128/jb.169.9.3879-3885.1987 [DOI] [PMC free article] [PubMed] [Google Scholar]
13.Batteiger BE. The major outer membrane protein of a single Chlamydia trachomatis serovar can possess more than one serovar-specific epitope. Infect Immun. 1996;64: 542–547. doi: 10.1128/iai.64.2.542-547.1996 [DOI] [PMC free article] [PubMed] [Google Scholar]
14.Chesson HW, Spicknall IH, Bingham A, Brisson M, Eppink ST, Farnham PG, et al. The Estimated Direct Lifetime Medical Costs of Sexually Transmitted Infections Acquired in the United States in 2018. Sex Transm Dis. 2021;48: 215. doi: 10.1097/OLQ.0000000000001380 [DOI] [PMC free article] [PubMed] [Google Scholar]
15.Vasilevsky S, Stojanov M, Greub G, Baud D. Chlamydial polymorphic membrane proteins: regulation, function and potential vaccine candidates. Virulence. 2016;7: 11–22. doi: 10.1080/21505594.2015.1111509 [DOI] [PMC free article] [PubMed] [Google Scholar]
16.Paes W, Brown N, Brzozowski AM, Coler R, Reed S, Carter D, et al. Recombinant polymorphic membrane protein D in combination with a novel, second-generation lipid adjuvant protects against intra-vaginal Chlamydia trachomatis infection in mice. Vaccine. 2016;34: 4123–4131. doi: 10.1016/j.vaccine.2016.06.081 [DOI] [PMC free article] [PubMed] [Google Scholar]
17.Müller T, Becker E, Stallmann S, Waldhuber A, Römmler-Dreher F, Albrecht S, et al. Vaccination with the polymorphic membrane protein A reduces Chlamydia muridarum induced genital tract pathology. Vaccine. 2017;35: 2801–2810. doi: 10.1016/j.vaccine.2017.04.017 [DOI] [PubMed] [Google Scholar]
18.Inic-Kanada A, Stojanovic M, Schlacher S, Stein E, Belij-Rammerstorfer S, Marinkovic E, et al. Delivery of a Chlamydial Adhesin N-PmpC Subunit Vaccine to the Ocular Mucosa Using Particulate Carriers. Murthy AK, editor. PLOS ONE. 2015;10: e0144380. doi: 10.1371/journal.pone.0144380 [DOI] [PMC free article] [PubMed] [Google Scholar]
19.Pal S, Favaroni A, Tifrea DF, Hanisch PT, Luczak SET, Hegemann JH, et al. Comparison of the nine polymorphic membrane proteins of Chlamydia trachomatis for their ability to induce protective immune responses in mice against a C. muridarum challenge. Vaccine. 2017;35: 2543–2549. doi: 10.1016/j.vaccine.2017.03.070 [DOI] [PMC free article] [PubMed] [Google Scholar]
20.Lanfermann C, Wintgens S, Ebensen T, Kohn M, Laudeley R, Schulze K, et al. Prophylactic Multi-Subunit Vaccine against Chlamydia trachomatis: In Vivo Evaluation in Mice. Vaccines. 2021;9: 609. doi: 10.3390/vaccines9060609 [DOI] [PMC free article] [PubMed] [Google Scholar]
21.Tan C, Hsia R, Shou H, Haggerty CL, Ness RB, Gaydos CA, et al. Chlamydia trachomatis-Infected Patients Display Variable Antibody Profiles against the Nine-Member Polymorphic Membrane Protein Family. Infect Immun. 2009;77: 3218–3226. doi: 10.1128/IAI.01566-08 [DOI] [PMC free article] [PubMed] [Google Scholar]
22.Wang J, Zhang Y, Lu C, Lei L, Yu P, Zhong G. A Genome-Wide Profiling of the Humoral Immune Response to Chlamydia trachomatis Infection Reveals Vaccine Candidate Antigens Expressed in Humans. J Immunol. 2010;185: 1670–1680. doi: 10.4049/jimmunol.1001240 [DOI] [PubMed] [Google Scholar]
23.Cruz-Fisher MI, Cheng C, Sun G, Pal S, Teng A, Molina DM, et al. Identification of Immunodominant Antigens by Probing a Whole Chlamydia trachomatis Open Reading Frame Proteome Microarray Using Sera from Immunized Mice. Infect Immun. 2011;79: 246–257. doi: 10.1128/IAI.00626-10 [DOI] [PMC free article] [PubMed] [Google Scholar]
24.Patton DL, Teng A, Randall A, Liang X, Felgner PL, de la Maza LM. Whole genome identification of C. trachomatis immunodominant antigens after genital tract infections and effect of antibiotic treatment of pigtailed macaques. J Proteomics. 2014;108: 99–109. doi: 10.1016/j.jprot.2014.05.009 [DOI] [PMC free article] [PubMed] [Google Scholar]
25.Yu H, Karunakaran KP, Jiang X, Chan Q, Rose C, Foster LJ, et al. Comparison of Chlamydia outer membrane complex to recombinant outer membrane proteins as vaccine. Vaccine. 2020;38: 3280–3291. doi: 10.1016/j.vaccine.2020.02.059 [DOI] [PubMed] [Google Scholar]
26.Karunakaran KP, Rey-Ladino J, Stoynov N, Berg K, Shen C, Jiang X, et al. Immunoproteomic Discovery of Novel T Cell Antigens from the Obligate Intracellular Pathogen Chlamydia. J Immunol. 2008;180: 2459–2465. doi: 10.4049/jimmunol.180.4.2459 [DOI] [PubMed] [Google Scholar]
27.Karunakaran KP, Yu H, Jiang X, Chan Q, Moon K-M, Foster LJ, et al. Outer membrane proteins preferentially load MHC class II peptides: Implications for as a Chlamydia trachomatis T cell vaccine. Vaccine. 2015;33: 2159–2166. doi: 10.1016/j.vaccine.2015.02.055 [DOI] [PMC free article] [PubMed] [Google Scholar]
28.Grimwood J, Stephens RS. Computational analysis of the polymorphic membrane protein superfamily of Chlamydia trachomatis and Chlamydia pneumoniae. Microb Comp Genomics. 1999;4: 187–201. doi: 10.1089/omi.1.1999.4.187 [DOI] [PubMed] [Google Scholar]
29.Henderson IR, Lam AC. Polymorphic proteins of Chlamydia spp.–autotransporters beyond the Proteobacteria. Trends Microbiol. 2001;9: 573–578. doi: 10.1016/S0966-842X(01)02234-X [DOI] [PubMed] [Google Scholar]
30.Meuskens I, Saragliadis A, Leo JC, Linke D. Type V Secretion Systems: An Overview of Passenger Domain Functions. Front Microbiol. 2019;10: 1163. doi: 10.3389/fmicb.2019.01163 [DOI] [PMC free article] [PubMed] [Google Scholar]
31.Becker E, Hegemann JH. All subtypes of the Pmp adhesin family are implicated in chlamydial virulence and show species-specific function. MicrobiologyOpen. 2014;3: 544–556. doi: 10.1002/mbo3.186 [DOI] [PMC free article] [PubMed] [Google Scholar]
32.Molleken K, Schmidt E, Hegemann JH. Members of the Pmp protein family of Chlamydia pneumoniae mediate adhesion to human cells via short repetitive peptide motifs. Mol Microbiol. 2010;78: 1004–17. doi: 10.1111/j.1365-2958.2010.07386.x [DOI] [PMC free article] [PubMed] [Google Scholar]
33.Luczak SET, Smits SHJ, Decker C, Nagel-Steger L, Schmitt L, Hegemann JH. The Chlamydia pneumoniae Adhesin Pmp21 Forms Oligomers with Adhesive Properties. J Biol Chem. 2016;291: 22806–22818. doi: 10.1074/jbc.M116.728915 [DOI] [PMC free article] [PubMed] [Google Scholar]
34.Favaroni A, Hegemann JH. Chlamydia trachomatis Polymorphic Membrane Proteins (Pmps) Form Functional Homomeric and Heteromeric Oligomers. Front Microbiol. 2021;12. doi: 10.3389/fmicb.2021.709724 [DOI] [PMC free article] [PubMed] [Google Scholar]
35.Favaroni A, Trinks A, Weber M, Hegemann JH, Schnee C. Pmp Repertoires Influence the Different Infectious Potential of Avian and Mammalian Chlamydia psittaci Strains. Front Microbiol. 2021;12: 656209. doi: 10.3389/fmicb.2021.656209 [DOI] [PMC free article] [PubMed] [Google Scholar]
36.Thomson NR, Yeats C, Bell K, Holden MTG, Bentley SD, Livingstone M, et al. The Chlamydophila abortus genome sequence reveals an array of variable proteins that contribute to interspecies variation. Genome Res. 2005;15: 629–640. doi: 10.1101/gr.3684805 [DOI] [PMC free article] [PubMed] [Google Scholar]
37.Swanson KA, Taylor LD, Frank SD, Sturdevant GL, Fischer ER, Carlson JH, et al. Chlamydia trachomatis Polymorphic Membrane Protein D Is an Oligomeric Autotransporter with a Higher-Order Structure. Infect Immun. 2009;77: 508–516. doi: 10.1128/IAI.01173-08 [DOI] [PMC free article] [PubMed] [Google Scholar]
38.Tan C, Hsia R, Shou H, Carrasco JA, Rank RG, Bavoil PM. Variable expression of surface-exposed polymorphic membrane proteins in in vitro-grown Chlamydia trachomatis. Cell Microbiol. 2010;12: 174–187. doi: 10.1111/j.1462-5822.2009.01389.x [DOI] [PMC free article] [PubMed] [Google Scholar]
39.Saka HA, Thompson JW, Chen Y-S, Kumar Y, Dubois LG, Moseley MA, et al. Quantitative proteomics reveals metabolic and pathogenic properties of Chlamydia trachomatis developmental forms. Mol Microbiol. 2011;82: 1185–1203. doi: 10.1111/j.1365-2958.2011.07877.x [DOI] [PMC free article] [PubMed] [Google Scholar]
40.Tanzer R J, Hatch T P. Characterization of Outer Membrane Proteins in Chlamydia trachomatis LGV Serovar L2. J Bacteriol. 2001;183: 2686–2690. doi: 10.1128/JB.183.8.2686-2690.2001 [DOI] [PMC free article] [PubMed] [Google Scholar]
41.Gomes JP, Nunes A, Bruno WJ, Borrego MJ, Florindo C, Dean D. Polymorphisms in the Nine Polymorphic Membrane Proteins of Chlamydia trachomatis across All Serovars: Evidence for Serovar Da Recombination and Correlation with Tissue Tropism. J Bacteriol. 2006;188: 275–286. doi: 10.1128/JB.188.1.275–286.2006 [DOI] [PMC free article] [PubMed] [Google Scholar]
42.Van Lent S, Creasy HH, Myers GSA, Vanrompay D. The Number, Organization, and Size of Polymorphic Membrane Protein Coding Sequences as well as the Most Conserved Pmp Protein Differ within and across Chlamydia Species. J Mol Microbiol Biotechnol. 2016;26: 333–344. doi: 10.1159/000447092 [DOI] [PubMed] [Google Scholar]
43.Stothard DR, Toth GA, Batteiger BE. Polymorphic membrane protein H has evolved in parallel with the three disease-causing groups of Chlamydia trachomatis. Infect Immun. 2003;71: 1200–1208. doi: 10.1128/IAI.71.3.1200–1208.2003 [DOI] [PMC free article] [PubMed] [Google Scholar]
44.Bradley P, Cowen L, Menke M, King J, Berger B. BETAWRAP: Successful prediction of parallel -helices from primary sequence reveals an association with many microbial pathogens. Proc Natl Acad Sci. 2001;98: 14819–14824. doi: 10.1073/pnas.251267298 [DOI] [PMC free article] [PubMed] [Google Scholar]
45.Debrine AM, Karplus PA, Rockey DD. A structural foundation for studying chlamydial polymorphic membrane proteins. Microbiol Spectr. 2023;11: e03242–23. doi: 10.1128/spectrum.03242-23 [DOI] [PMC free article] [PubMed] [Google Scholar]
46.Paes W, Dowle A, Coldwell J, Leech A, Ganderton T, Brzozowski A. The Chlamydia trachomatis PmpD adhesin forms higher order structures through disulphide-mediated covalent interactions. PLoS ONE. 2018;13: e0198662. doi: 10.1371/journal.pone.0198662 [DOI] [PMC free article] [PubMed] [Google Scholar]
47.Vandahl BB, Pedersen AS, Gevaert K, Holm A, Vandekerckhove J, Christiansen G, et al. The expression, processing and localization of polymorphic membrane proteins in Chlamydia pneumoniae strain CWL029. BMC Microbiol. 2002;2: 36. doi: 10.1186/1471-2180-2-36 [DOI] [PMC free article] [PubMed] [Google Scholar]
48.Wehrl W, Brinkmann V, Jungblut PR, Meyer TF, Szczepek AJ. From the inside out—processing of the Chlamydial autotransporter PmpD and its role in bacterial adhesion and activation of human host cells. Mol Microbiol. 2004;51: 319–334. doi: 10.1046/j.1365-2958.2003.03838.x [DOI] [PubMed] [Google Scholar]
49.Kiselev AO, Stamm WE, Yates JR, Lampe MF. Expression, Processing, and Localization of PmpD of Chlamydia trachomatis Serovar L2 during the Chlamydial Developmental Cycle. PLoS ONE. 2007;2: e568. doi: 10.1371/journal.pone.0000568 [DOI] [PMC free article] [PubMed] [Google Scholar]
50.Kiselev AO, Skinner MC, Lampe MF. Analysis of pmpD Expression and PmpD Post-Translational Processing during the Life Cycle of Chlamydia trachomatis Serovars A, D, and L2. PLoS ONE. 2009;4: e5191. doi: 10.1371/journal.pone.0005191 [DOI] [PMC free article] [PubMed] [Google Scholar]
51.Sigrist CJA, de Castro E, Cerutti L, Cuche BA, Hulo N, Bridge A, et al. New and continuing developments at PROSITE. Nucleic Acids Res. 2013;41: D344–D347. doi: 10.1093/nar/gks1067 [DOI] [PMC free article] [PubMed] [Google Scholar]
52.Katoh K, Standley DM. MAFFT Multiple Sequence Alignment Software Version 7: Improvements in Performance and Usability. Mol Biol Evol. 2013;30: 772–780. doi: 10.1093/molbev/mst010 [DOI] [PMC free article] [PubMed] [Google Scholar]
53.Potter SC, Luciani A, Eddy SR, Park Y, Lopez R, Finn RD. HMMER web server: 2018 update. Nucleic Acids Res. 2018;46: W200–W204. doi: 10.1093/nar/gky448 [DOI] [PMC free article] [PubMed] [Google Scholar]
54.Anishchenko I, Baek M, Park H, Hiranuma N, Kim DE, Dauparas J, et al. Protein tertiary structure prediction and refinement using deep learning and Rosetta in CASP14. Proteins. 2021. doi: 10.1002/prot.26194 [DOI] [PMC free article] [PubMed] [Google Scholar]
55.Baek M, DiMaio F, Anishchenko I, Dauparas J, Ovchinnikov S, Lee GR, et al. Accurate prediction of protein structures and interactions using a 3-track neural network. Science. 2021;373: 871–876. doi: 10.1126/science.abj8754 [DOI] [PMC free article] [PubMed] [Google Scholar]
56.Pettersen EF, Goddard TD, Huang CC, Couch GS, Greenblatt DM, Meng EC, et al. UCSF Chimera—a visualization system for exploratory research and analysis. J Comput Chem. 2004;25: 1605–1612. doi: 10.1002/jcc.20084 [DOI] [PubMed] [Google Scholar]
57.Jumper J, Evans R, Pritzel A, Green T, Figurnov M, Ronneberger O, et al. Highly accurate protein structure prediction with AlphaFold. Nature. 2021;596: 583–589. doi: 10.1038/s41586-021-03819-2 [DOI] [PMC free article] [PubMed] [Google Scholar]
58.Varadi M, Anyango S, Deshpande M, Nair S, Natassia C, Yordanova G, et al. AlphaFold Protein Structure Database: massively expanding the structural coverage of protein-sequence space with high-accuracy models. Nucleic Acids Res. 2022;50: D439–D444. doi: 10.1093/nar/gkab1061 [DOI] [PMC free article] [PubMed] [Google Scholar]
59.Lin Z, Akin H, Rao R, Hie B, Zhu Z, Lu W, et al. Evolutionary-scale prediction of atomic-level protein structure with a language model. Science. 2023;379: 1123–1130. doi: 10.1126/science.ade2574 [DOI] [PubMed] [Google Scholar]
60.Chatzigoulas A, Cournia Z. Predicting protein–membrane interfaces of peripheral membrane proteins using ensemble machine learning. Brief Bioinform. 2022;23: bbab518. doi: 10.1093/bib/bbab518 [DOI] [PMC free article] [PubMed] [Google Scholar]
61.Marrink SJ, Risselada HJ, Yefimov S, Tieleman DP, de Vries AH. The MARTINI Force Field: Coarse Grained Model for Biomolecular Simulations. J Phys Chem B. 2007;111: 7812–7824. doi: 10.1021/jp071097f [DOI] [PubMed] [Google Scholar]
62.de Jong DH, Singh G, Bennett WFD, Arnarez C, Wassenaar TA, Schäfer LV, et al. Improved Parameters for the Martini Coarse-Grained Protein Force Field. J Chem Theory Comput. 2013;9: 687–697. doi: 10.1021/ct300646g [DOI] [PubMed] [Google Scholar]
63.Qi Y, Ingólfsson HI, Cheng X, Lee J, Marrink SJ, Im W. CHARMM-GUI Martini Maker for Coarse-Grained Simulations with the Martini Force Field. J Chem Theory Comput. 2015;11: 4486–4494. doi: 10.1021/acs.jctc.5b00513 [DOI] [PubMed] [Google Scholar]
64.Yesylevskyy SO, Schäfer LV, Sengupta D, Marrink SJ. Polarizable Water Model for the Coarse-Grained MARTINI Force Field. PLOS Comput Biol. 2010;6: e1000810. doi: 10.1371/journal.pcbi.1000810 [DOI] [PMC free article] [PubMed] [Google Scholar]
65.Van Der Spoel D, Lindahl E, Hess B, Groenhof G, Mark AE, Berendsen HJC. GROMACS: Fast, flexible, and free. J Comput Chem. 2005;26: 1701–1718. doi: 10.1002/jcc.20291 [DOI] [PubMed] [Google Scholar]
66.Bussi G, Donadio D, Parrinello M. Canonical sampling through velocity rescaling. J Chem Phys. 2007;126: 014101. doi: 10.1063/1.2408420 [DOI] [PubMed] [Google Scholar]
67.Parrinello M, Rahman A. Polymorphic transitions in single crystals: A new molecular dynamics method. J Appl Phys. 1981;52: 7182–7190. doi: 10.1063/1.328693 [DOI] [Google Scholar]
68.Vita R, Mahajan S, Overton JA, Dhanda SK, Martini S, Cantrell JR, et al. The Immune Epitope Database (IEDB): 2018 update. Nucleic Acids Res. 2019;47: D339–D343. doi: 10.1093/nar/gky1006 [DOI] [PMC free article] [PubMed] [Google Scholar]
69.Jespersen MC, Peters B, Nielsen M, Marcatili P. BepiPred-2.0: improving sequence-based B-cell epitope prediction using conformational epitopes. Nucleic Acids Res. 2017;45: W24–W29. doi: 10.1093/nar/gkx346 [DOI] [PMC free article] [PubMed] [Google Scholar]
70.Kringelum JV, Lundegaard C, Lund O, Nielsen M. Reliable B cell epitope predictions: impacts of method development and improved benchmarking. PLoS Comput Biol. 2012;8: e1002829. doi: 10.1371/journal.pcbi.1002829 [DOI] [PMC free article] [PubMed] [Google Scholar]
71.Southwood S, Sidney J, Kondo A, del Guercio MF, Appella E, Hoffman S, et al. Several common HLA-DR types share largely overlapping peptide binding repertoires. J Immunol Baltim Md 1950. 1998;160: 3363–3373. [PubMed] [Google Scholar]
72.Terry FE, Moise L, Martin RF, Torres M, Pilotte N, Williams SA, et al. Time for T? Immunoinformatics addresses vaccine design for neglected tropical and emerging infectious diseases. Expert Rev Vaccines. 2014;14: 21–35. doi: 10.1586/14760584.2015.955478 [DOI] [PMC free article] [PubMed] [Google Scholar]
73.Paul S, Lindestam Arlehamn CS, Scriba TJ, Dillon MBC, Oseroff C, Hinz D, et al. Development and validation of a broad scheme for prediction of HLA class II restricted T cell epitopes. J Immunol Methods. 2015;422: 28–34. doi: 10.1016/j.jim.2015.03.022 [DOI] [PMC free article] [PubMed] [Google Scholar]
74.Hiranuma N, Park H, Baek M, Anishchenko I, Dauparas J, Baker D. Improved protein structure refinement guided by deep learning based accuracy estimation. Nat Commun. 2021;12: 1340. doi: 10.1038/s41467-021-21511-x [DOI] [PMC free article] [PubMed] [Google Scholar]
75.Leyton DL, Johnson MD, Thapa R, Huysmans GHM, Dunstan RA, Celik N, et al. A mortise-tenon joint in the transmembrane domain modulates autotransporter assembly into bacterial outer membranes. Nat Commun. 2014;5: 4239. doi: 10.1038/ncomms5239 [DOI] [PubMed] [Google Scholar]
76.Yuan X, Johnson MD, Zhang J, Lo AW, Schembri MA, Wijeyewickrema LC, et al. Molecular basis for the folding of β-helical autotransporter passenger domains. Nat Commun. 2018;9: 1395. doi: 10.1038/s41467-018-03593-2 [DOI] [PMC free article] [PubMed] [Google Scholar]
77.Lim JB, Klauda JB. Lipid chain branching at the iso- and anteiso-positions in complex Chlamydia membranes: a molecular dynamics study. Biochim Biophys Acta. 2011;1808: 323–331. doi: 10.1016/j.bbamem.2010.07.036 [DOI] [PubMed] [Google Scholar]
78.Rojas-Lopez M, Zorgani MA, Kelley LA, Bailly X, Kajava AV, Henderson IR, et al. Identification of the Autochaperone Domain in the Type Va Secretion System (T5aSS): Prevalent Feature of Autotransporters with a β-Helical Passenger. Front Microbiol. 2017;8: 2607. doi: 10.3389/fmicb.2017.02607 [DOI] [PMC free article] [PubMed] [Google Scholar]
79.Wells TJ, Tree JJ, Ulett GC, Schembri MA. Autotransporter proteins: novel targets at the bacterial cell surface. FEMS Microbiol Lett. 2007;274: 163–172. doi: 10.1111/j.1574-6968.2007.00833.x [DOI] [PubMed] [Google Scholar]
80.Jenkins J, Pickersgill R. The architecture of parallel β-helices and related folds. Prog Biophys Mol Biol. 2001;77: 111–175. doi: 10.1016/S0079-6107(01)00013-X [DOI] [PubMed] [Google Scholar]
81.Yoder MD, Lietzke SE, Jurnak F. Unusual structural features in the parallel β-helix in pectate lyases. Structure. 1993;1: 241–251. doi: 10.1016/0969-2126(93)90013-7 [DOI] [PubMed] [Google Scholar]
82.Leszczynski JF, Rose GD. Loops in Globular Proteins: A Novel Category of Secondary Structure. Science. 1986;234: 849–855. doi: 10.1126/science.3775366 [DOI] [PubMed] [Google Scholar]
83.Favaroni A. Role of adhesin proteins in Chlamydia infection. Dissertation, Heinrich Heine University. 2017. Available: https://docserv.uni-duesseldorf.de/servlets/DocumentServlet?id=42486 [Google Scholar]
84.Williamson MP. The structure and function of proline-rich regions in proteins. Biochem J. 1994;297: 249–260. doi: 10.1042/bj2970249 [DOI] [PMC free article] [PubMed] [Google Scholar]
85.Ball LJ, Kühne R, Schneider-Mergener J, Oschkinat H. Recognition of proline-rich motifs by protein-protein-interaction domains. Angew Chem Int Ed Engl. 2005;44: 2852–2869. doi: 10.1002/anie.200400618 [DOI] [PubMed] [Google Scholar]
86.Jorgensen I, Valdivia RH. Pmp-Like Proteins Pls1 and Pls2 Are Secreted into the Lumen of the Chlamydia trachomatis Inclusion. Infect Immun. 2008;76: 3940–3950. doi: 10.1128/IAI.00632-08 [DOI] [PMC free article] [PubMed] [Google Scholar]
87.Hirt RP, de Miguel N, Nakjang S, Dessi D, Liu Y-C, Diaz N, et al. Trichomonas vaginalis Pathobiology. In: Rollinson D, Hay SI, editors. Advances in Parasitology. Academic Press; 2011. pp. 87–140. doi: 10.1016/B978-0-12-391429-3.00006-X [DOI] [PubMed] [Google Scholar]
88.Handrich MR, Garg SG, Sommerville EW, Hirt RP, Gould SB. Characterization of the BspA and Pmp protein family of trichomonads. Parasit Vectors. 2019;12: 406. doi: 10.1186/s13071-019-3660-z [DOI] [PMC free article] [PubMed] [Google Scholar]
89.Leyton DL, Rossiter AE, Henderson IR. From self sufficiency to dependence: mechanisms and factors important for autotransporter biogenesis. Nat Rev Microbiol. 2012;10: 213–225. doi: 10.1038/nrmicro2733 [DOI] [PubMed] [Google Scholar]
90.van Ulsen P, Rahman S ur, Jong WSP, Daleke-Schermerhorn MH, Luirink J. Type V secretion: from biogenesis to biotechnology. Biochim Biophys Acta. 2014;1843: 1592–1611. doi: 10.1016/j.bbamcr.2013.11.006 [DOI] [PubMed] [Google Scholar]
91.Bernstein HD. Type V Secretion in Gram-Negative Bacteria. EcoSal Plus. 2019;8. doi: 10.1128/ecosalplus.ESP-0031-2018 [DOI] [PMC free article] [PubMed] [Google Scholar]
92.Heras B, Totsika M, Peters KM, Paxman JJ, Gee CL, Jarrott RJ, et al. The antigen 43 structure reveals a molecular Velcro-like mechanism of autotransporter-mediated bacterial clumping. Proc Natl Acad Sci U S A. 2014;111: 457–462. doi: 10.1073/pnas.1311592111 [DOI] [PMC free article] [PubMed] [Google Scholar]
93.Suchland RJ, Carrell SJ, Celum CL, Sanchez J, Hybiske K, Rockey DD. Prevalence of Chlamidia trachomatis Serovar G Strains in MSM Rectal Infections are Temporally and Globally Consistent and Share a Phylogenetic Lineage. Proceedings of the Julius Schachter Fifteenth International Symposium on Human Chlamydial Infections. San Antonio, TX, USA: IMS/UT Print The University of Texas Health Science Center at San Antonio; 2022. pp. 80–84. [Google Scholar]
94.Mölleken K, Becker E, Hegemann JH. The Chlamydia pneumoniae Invasin Protein Pmp21 Recruits the EGF Receptor for Host Cell Entry. PLOS Pathog. 2013;9: e1003325. doi: 10.1371/journal.ppat.1003325 [DOI] [PMC free article] [PubMed] [Google Scholar]
95.Li X, Wang Y, Hegemann J, He C. Polymorphic Membrane 17G of Chlamydia psittaci Mediated the Binding and Invasion of Host Cells by Interacting and Activating EGFR. Proceedings of the Julius Schachter Fifteenth International Symposium on Human Chlamydial Infections. San Antonio, TX, USA: IMS/UT Print The University of Texas Health Science Center at San Antonio; 2022. pp. 177–183. [Google Scholar]
96.Russi RC, Bourdin E, García MI, Veaute CMI. In silico prediction of T- and B-cell epitopes in PmpD: First step towards to the design of a Chlamydia trachomatis vaccine. Biomed J. 2018;41: 109–117. doi: 10.1016/j.bj.2018.04.007 [DOI] [PMC free article] [PubMed] [Google Scholar]

PLoS One. doi: 10.1371/journal.pone.0304525.r001

Decision Letter 0

Agathe Subtil

11 Dec 2023

PONE-D-23-36013Sequence, Structure, and Epitope Analysis of the Polymorphic Membrane Protein Family in Chlamydia trachomatisPLOS ONE

Dear Dr. D'haeseleer,

Thank you for submitting your manuscript to Traffic. Your study has been sent to two referees for evaluation, and their reports are enclosed below. As you can see, while both referees consider the findings interesting, they also raise important points that need to be addressed before they can support publication in PLoS One. The paper by Debrine et al (doi: 10.1128/spectrum.03242-23), that was published before you submitted your MS, needs to be acknowledged. It will be useful for readers to state clearly in the discussion whether your conclusions differ or not, considering that the same prediction tools have been used. Also, as stated by reviewer 2, it is expected in the discussion (or in the results section, if helps keeping the flow) that you validate (or not) some of your structural predictions based on published functional work.

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Agathe Subtil

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PLOS ONE

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Reviewer #1: Yes

Reviewer #2: Yes

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2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: I Don't Know

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Reviewer #1: Yes

Reviewer #2: Yes

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Reviewer #2: Yes

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Reviewer #1: The manuscript by Cervantes et al provides an excellent description of in-silico-based analysis of the chlamydial Pmp family of proteins. The depth of analysis is considerable and the paper is well written. I have no significant criticisms of the work. The molecular dynamics, epitope prediction, and hinge analysis are particularly unique.

A similar paper has recently been published that describes an Alphafold-based analysis of this family of proteins. This manuscript and that paper look at the same issue from slightly different perspectives and both will be useful to the research community. It is suggested that these authors cite the other manuscript and perhaps briefly describe differences between the documents, with a goal of helping researchers get a wider perspective on predicted Pmp structure. There are data in that manuscript that differ slightly from this one, and certain subjects are addressed by one and not the other one. Both documents will be valuable to people who wish to explore chlamydial Pmp structure.

Minor comments about the writing:

Lines 183 vs. 185, and elsewhere… the designation of I,L,V or ILV needs to be consistent.

Perhaps the MOMP presentation in the Introduction is too drawn out. Lines 32-43 could probably be condensed to one or two sentences.

Lines 80-83: A distinction between the sequence similarity in the beta barrel vs the beta helical domain, across the Pmp proteins, might be useful here.

Reviewer #2: Review of MS PONE-D-23-36013 by Cervantes et al.

This MS utilizes protein structure prediction algorithms to visualize, for the first time, the Pmp family from C. trachomatis Serovar E. A special focus has been put onto the tetrapeptide motifs, GGA(I,L,V) and FxxN.

The MS is interesting and adds to our understanding of Pmp protein function. My main critics is that the authors do not try to compare their Pmp structures with published functional data on Pmp proteins and protein fragments. We have published for C. pneumoniae Pmp21, that different protein fragments have different adhesion capacities and that protein fragments with 2 motifs, when point-mutated, lose this capacity (Mölleken et al ). Our study on C. trachomatis Pmps shows that certain motif-rich and motif-poor Pmp protein fragments have a different capacity for adhesion and oligomerization (Favaroni 2021). Finally, we have used large segments of certain C. trachomatis Pmp proteins for a vaccine study (Lanfermann et al; doi.org/10.3390/vaccines9060609). Finally, our biochemical work on a Pmp21 protein fragment has revealed that this protein domain forms oligomers that adopt an amyloid-like structure comparable in size and shape to protofibrils of the amyloid protein Abeta42 (Luczak et al). It would be really interesting to compare the detailed structures presented in this MS with some of the functional data published.

The introduction is detailed but needs a few corrections:

Line 47: “….viable vaccine candidates…”. The authors missed one recent publication which convincingly shows that a mix of different recombinant C. trachomatis Pmps induces a high degree of cross-serovar protection against urogenital and ocular strains of C. trachomatis: Lanfermann et al., DOI: 10.3390/vaccines9060609.

Line 56: “Pmps are autotransporters”. That is not true. Pmps are predicted to be autotransporters. So far no experimental prove. Needs to be corrected.

Fig. 6 shows a molecular dynamics simulation of PmpE with the β-barrel domain embedded in a POPC bilayer. I am wondering whether the simulation gives a similar result, when the authors take into account that a typical outer membrane of a G- - negative bacterium is very asymmetrical, with phospholipids in the inner leaflet and LPS in the outer leaflet, plus all of the transmembrane proteins with their β-barrel fold (outer membrane proteins (OMPs)) and the lipoproteins.

Fig. 7. I could not find a figure legend.

Line 359. The authors define proline-rich regions (PRRs) as having at least a P-X0-2-P motive (see Fig. 9 legend). What is the basis of this definition?

Fig. 9. The legend says: “…A few proline-rich regions in side loops of PmpC, PmpG, PmpH and PmpI are labeled.” . To make this point stronger, the authors should label all proline-rich regions - not only a few.

Johannes Hegemann

**********

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Reviewer #2: Yes: Johannes H. Hegemann

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PLoS One. 2024 Jun 11;19(6):e0304525. doi: 10.1371/journal.pone.0304525.r002

Author response to Decision Letter 0

6 Mar 2024

We thank the reviewer for pointing out this paper. We had actually shared a copy of the preprint of our manuscript with the authors earlier, but had missed the publication of their paper. We have added a number of references throughout the manuscript.

Minor comments about the writing:

Lines 183 vs. 185, and elsewhere… the designation of I,L,V or ILV needs to be consistent.

After reviewing dozens of papers on the Pmp repeat motifs, we found that “GGA(I,L,V)” is the most common notation used in text, and we have tried to use it consistently throughout the paper. We did correct one instance on line 74. On line 185, we list the corresponding pattern in PROSITE notation to be used on the ScanProsite search engine, which does look significantly different. We have edited that line to better explain the difference in notation.

Perhaps the MOMP presentation in the Introduction is too drawn out. Lines 32-43 could probably be condensed to one or two sentences.

We have left out the mention of immunotyping using MOMP, which is not really relevant here.

Lines 80-83: A distinction between the sequence similarity in the beta barrel vs the beta helical domain, across the Pmp proteins, might be useful here.

We’ve updated the text to clarify that the majority of Pmps (except PmpB vs PmpC) share less than 30% amino acid identity, and that sequence identity is similar in the beta barrel versus the passenger domain. We tend to think of the beta barrel as well conserved, but that is mainly because there is a reliable Pfam domain for it.

Reviewer #2: Review of MS PONE-D-23-36013 by Cervantes et al.

We have included a section on Pmp21-D, using ESMfold to predict the effect of the FxxN mutations on the structure, AlphaFold-MULTIMER to predict how this may affect oligomerization, and DREAMM to show the effect on a predicted membrane-interacting tyrosine within the first mutated FxxN.

We had hoped to find a simple oligomerization mechanism across all Pmp passenger domain fragments, like the one observed for the H.influenza Hap adhesin (see https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3173798/figure/f5/). AlphaFold-MULTIMER predictions of oligomer structure of the PmpA-PmpI passenger domains, as well as the Pmp21, PmpA, PmpD, PmpG and PmpI fragments described in these papers do not tend to yield a consistent oligomerization pattern. Out of the 34 AlphaFold-MULTIMER dimer, trimer and tetramer runs, only 10 yielded moderate to good confidence structures (pTM = 0.685 – 0.874), including 7 where the two beta-helices were in line with each other (4 C-terminal to C-terminal, 2 N-terminal to N-terminal, and 1 N-terminal to C-terminal), and 3 with the beta-helices next to each other (1 parallel, 2 antiparallel). Interestingly, the PmpA+G, PmpA+I, and especially PmpA+D heterodimers from Favaroni 2021 all show higher model confidence scores than their respective homodimers, in line with the oligomerization results from that paper. Also note that the two “motif-poor” Pmp protein fragments in Favaroni 2021 (A and G) are actually predicted to have an equivalent number of beta-helical coils as the “motif-poor” ones, with an average of around 38 amino acids per coil. The main difference lies in the fact that the “motif-poor” fragments are shorter, and overlap the Middle domain, which has a different sequence specificity (but still largely beta-helical structure). We have included a figure with the Pmp21-D predicted tetramer model, and the two most high-confidence dimer models (the PmpA+D heterodimer, and PmpE homodimer).

The introduction is detailed but needs a few corrections:

Reference has been added

Line 56: “Pmps are autotransporters”. That is not true. Pmps are predicted to be autotransporters. So far no experimental prove. Needs to be corrected.

Corrected.

The main purpose of doing the molecular dynamics simulations was to explain why many of the structure predictions show the passenger domain at a physiologically unrealistic sharp angle to the beta barrel, where it would intersect the membrane if the beta barrel was embedded in the OM. Clearly the presence of LPS and abundant transmembrane proteins such as MOMP would affect the angle as well, but such simulations would be much more expensive and may not add much additional insight to the topic in this manuscript. We hope to include some of this in a future paper on the Chlamydia Outer Membrane Complex.

Fig. 7. I could not find a figure legend.

We have reinserted the missing figure legend.

Line 359. The authors define proline-rich regions (PRRs) as having at least a P-X0-2-P motive (see Fig. 9 legend). What is the basis of this definition?

We were not able to find a unique definition of what constitutes the minimum requirement for a proline-rich region. Examining the wide range of different repeating and non-repeating proline-rich regions listed in Williamson 1994, the vast majority seem to contain at least four prolines, separated by no more than four other amino acids. We’ve decided to adopt this as our criterion for Figure 9, which also allows us to label all the corresponding regions on that figure.

Attachment

Submitted filename: Response to Reviewers.pdf

pone.0304525.s010.pdf^{(90.7KB, pdf)}

PLoS One. doi: 10.1371/journal.pone.0304525.r003

Decision Letter 1

Agathe Subtil

16 Apr 2024

PONE-D-23-36013R1Sequence, Structure, and Epitope Analysis of the Polymorphic Membrane Protein Family in Chlamydia trachomatisPLOS ONE

Dear Dr. D'haeseleer,

Thank you for submitting your revised manuscript to PLOS ONE. You have satisfactorily responded to all the reviewers's comments. However, reviewer 2 made a finale point that I would appreciate you to address by sending a revised version (very minor revision).

Please submit your revision by May 31 2024 11:59PM.

We look forward to receiving your revised manuscript.

Kind regards,

Agathe Subtil

Academic Editor

PLOS ONE

Journal Requirements:

Please review your reference list to ensure that it is complete and correct. If you have cited papers that have been retracted, please include the rationale for doing so in the manuscript text, or remove these references and replace them with relevant current references. Any changes to the reference list should be mentioned in the rebuttal letter that accompanies your revised manuscript. If you need to cite a retracted article, indicate the article’s retracted status in the References list and also include a citation and full reference for the retraction notice.

Additional Editor Comments:

Congratulation for this paper. PLease take into account the comments from reviewer 2 for minor revision of your manuscript.

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Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #2: All comments have been addressed

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

Reviewer #2: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #2: I Don't Know

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

Reviewer #2: Yes

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

Reviewer #2: Yes

**********

6. Review Comments to the Author

Reviewer #2: Review of MS PONE-D-23-36013_R1 by Cervantes et al.

In the revised MS, the authors dealt satisfactorily with all the points I had raised. In particular, I like the data and the discussion about the oligomerization potential of Pmp proteins.

One important aspect I did not mention in my first review concerns the terminology: The authors use a structure prediction program as a basis for their work. Therefore, all conclusions from these structural predictions are also predictions and not experimentally documented facts. This should be clearly indicated throughout the MS, e.g.

- the title of this MS omits this fact,

- Line 11. Under ´Keywords` no mentioning of the use of a structure prediction program….

- in the second line of the abstract (lines 15-16) the authors state “Here we show that the 16 characteristic GGA(I,L,V) and FxxN tetrapeptide repeats fit into a larger repeat sequence….” . Up to here, no mentioning of a structure prediction program. I suggest starting the abstract with sentence # three of the current abstract (Analysis of the protein using structure prediction algorithms provided novel insight to the chlamydial Pmp family of proteins.).

- line 23. The authors state `This structural analysis….` . It is a predicted structure…

Minor points:

- Line 54. The Greek symbol for beta is missing.

- Figures 1+2. Some of the text in the figures are not readable…..

Johannes Hegemann

**********

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Reviewer #2: Yes: Johannes Hegemann

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PLoS One. 2024 Jun 11;19(6):e0304525. doi: 10.1371/journal.pone.0304525.r004

Author response to Decision Letter 1

11 May 2024

Reviewer #2:

In the revised MS, the authors dealt satisfactorily with all the points I had raised. In particular, I like the data and the discussion about the oligomerization potential of Pmp proteins.

- the title of this MS omits this fact,

- Line 11. Under ´Keywords` no mentioning of the use of a structure prediction program….

Note that structure prediction was explicitly mentioned in the 2nd and 3rd sentences of the abstract. But for completeness, we have included it in the title and keywords, as suggested.

The analysis of the tetrapepdtide motifs (section 4.1), showing that they fit into a longer repeat pattern is indeed purely sequence based. It is rather surprising that this was not caught by Grimwood and Stephens back in 1999 when they claimed that the motifs did “not appear to follow any characteristic spacing pattern”, because to our eyes the alternating pattern and semi-regular spacing on Pmp sequence maps was quite striking. We feel that the flow of the manuscript works better by starting with the purely sequence based analysis, then follow it up with how the longer sequence repeat pattern that we identified matches with the rungs of the beta-helix in the predicted structures. We decided to keep the order in the abstract the same as the order in which these analyses are presented in the Results section.

- line 23. The authors state `This structural analysis….` . It is a predicted structure…

We have omitted “structural” here, because the analysis is based both on sequence and predicted structure

Minor points:

- Line 54. The Greek symbol for beta is missing.

We could not find a missing beta symbol on line 54, or anywhere else in this paragraph.

- Figures 1+2. Some of the text in the figures are not readable…..

We recreated Fig 1 by hand in vector graphics with slightly larger font sizes, so the text should be both larger and sharper. Likewise, we recreated the table with HMM occupancy / insertion frequencies and insertion lengths in the Fig 2C and 2D sequence logos. The reader may still need to download the full size image if they really want to read the HMM weights, but at least the numbers are larger and far more crisp in the new version.

Attachment

Submitted filename: Response to Reviewers2.docx

pone.0304525.s011.docx^{(14.3KB, docx)}

PLoS One. doi: 10.1371/journal.pone.0304525.r005

Decision Letter 2

Agathe Subtil

14 May 2024

Sequence, Structure Prediction, and Epitope Analysis of the Polymorphic Membrane Protein Family in Chlamydia trachomatis

PONE-D-23-36013R2

Dear Dr. D'haeseleer,

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Agathe Subtil

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Reviewers' comments:

PLoS One. doi: 10.1371/journal.pone.0304525.r006

Acceptance letter

Agathe Subtil

31 May 2024

PONE-D-23-36013R2

PLOS ONE

Dear Dr. D'haeseleer,

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now being handed over to our production team.

At this stage, our production department will prepare your paper for publication. This includes ensuring the following:

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

S1 File. HMM model for the longer repeat pattern we identified.

(HMM)

pone.0304525.s001.hmm^{(12.8KB, hmm)}

S2 File. File ZIP file containing PDB files with the RoseTTAFold structure predictions for PmpA to PmpI of C. trachomatis serovar E.

(ZIP)

pone.0304525.s002.zip^{(2.7MB, zip)}

S3 File. File ZIP file with multiple sequence alignment for each of the C. trachomatis Pmps using MAFFT.

(ZIP)

pone.0304525.s003.zip^{(22.4KB, zip)}

S1 Table. Accession numbers for the C. trachomatis, C. muridarum and C. pneumoniae Pmp proteins used in this study.

(XLSX)

pone.0304525.s004.xlsx^{(10.9KB, xlsx)}

S2 Table. ScanProsite motif hits.

(XLSX)

pone.0304525.s005.xlsx^{(132.4KB, xlsx)}

S3 Table. B-cell epitope predictions using Bepipred-2.0.

(XLSX)

pone.0304525.s006.xlsx^{(573.7KB, xlsx)}

S4 Table. B-cell epitope predictions using Discotope 2.0.

(XLSX)

pone.0304525.s007.xlsx^{(472.9KB, xlsx)}

S5 Table. MHC-II class T-cell epitope predictions using IEDB’s Consensus 2.22.

(XLSX)

pone.0304525.s008.xlsx^{(40.4KB, xlsx)}

S6 Table. Membrane-penetrating residues predicted by DREAMM.

(XLSX)

pone.0304525.s009.xlsx^{(9.2KB, xlsx)}

Attachment

Submitted filename: Response to Reviewers.pdf

pone.0304525.s010.pdf^{(90.7KB, pdf)}

Attachment

Submitted filename: Response to Reviewers2.docx

pone.0304525.s011.docx^{(14.3KB, docx)}

Data Availability Statement

All relevant data are within the manuscript and its Supporting Information files.

[pone.0304525.ref001] 1.de la Maza LM, Darville TL, Pal S. Chlamydia trachomatis vaccines for genital infections: where are we and how far is there to go? Expert Rev Vaccines. 2021;4: 421–435. doi: 10.1080/14760584.2021.1899817 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0304525.ref002] 2.Gupta K, Harrison SA, Davis NA, Culp ML, Hand SC, Simpson T, et al. Prevalence of Chlamydia trachomatis Infection in Young Women and Associated Predictors. Sex Transm Dis. 2021;48: 529–535. doi: 10.1097/OLQ.0000000000001372 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0304525.ref003] 3.Rodrigues R, Sousa C, Vale N. Chlamydia trachomatis as a Current Health Problem: Challenges and Opportunities. Diagnostics. 2022;12: 1795. doi: 10.3390/diagnostics12081795 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0304525.ref004] 4.Van Gerwen OT, Muzny CA, Marrazzo JM. Sexually transmitted infections and female reproductive health. Nat Microbiol. 2022;7: 1116–1126. doi: 10.1038/s41564-022-01177-x [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0304525.ref005] 5.Lesiak-Markowicz I, Schötta A-M, Stockinger H, Stanek G, Markowicz M. Chlamydia trachomatis serovars in urogenital and ocular samples collected 2014–2017 from Austrian patients. Sci Rep. 2019;9: 18327. doi: 10.1038/s41598-019-54886-5 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0304525.ref006] 6.Morré SA, Rozendaal L, van Valkengoed IGM, Boeke AJP, van Voorst Vader PC, Schirm J, et al. Urogenital Chlamydia trachomatis Serovars in Men and Women with a Symptomatic or Asymptomatic Infection: an Association with Clinical Manifestations? J Clin Microbiol. 2000;38: 2292–2296. doi: 10.1128/jcm.38.6.2292-2296.2000 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0304525.ref007] 7.Lanfermann C, Kohn M, Laudeley R, Rheinheimer C, Klos A. Chlamydia trachomatis Cross-Serovar Protection during Experimental Lung Reinfection in Mice. Vaccines. 2021;9: 871. doi: 10.3390/vaccines9080871 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0304525.ref008] 8.Pilo S, Zizelski Valenci G, Rubinstein M, Pichadze L, Scharf Y, Dveyrin Z, et al. High-resolution multilocus sequence typing for Chlamydia trachomatis: improved results for clinical samples with low amounts of C. trachomatis DNA. BMC Microbiol. 2021;21: 28. doi: 10.1186/s12866-020-02077-y [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0304525.ref009] 9.Byrne GI. Chlamydia trachomatis Strains and Virulence: Rethinking Links to Infection Prevalence and Disease Severity. J Infect Dis. 2010;201: S126–S133. doi: 10.1086/652398 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0304525.ref010] 10.Ardizzone CM, Albritton HL, Lillis RA, Bagnetto CEL, Shen L, Cavacini LA, et al. Human genital antibody-mediated inhibition of Chlamydia trachomatis infection and evidence for ompA genotype-specific neutralization. PLoS ONE. 2021;16: e0258759. doi: 10.1371/journal.pone.0258759 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0304525.ref011] 11.Kuo C, wang S-P, Holmes KK, Grayston TJ. Immunotypes of Chlamydia trachomatis isolates in Seattle, Washington. Infect Immun. 1983;41: 865–868. doi: 10.1128/iai.41.2.865-868.1983 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0304525.ref012] 12.Stephens RS, Sanchez-Pescador R, Wagar EA, Inouye C, Urdea MS. Diversity of Chlamydia trachomatis major outer membrane protein genes. J Bacteriol. 1987;169: 3879–3885. doi: 10.1128/jb.169.9.3879-3885.1987 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0304525.ref013] 13.Batteiger BE. The major outer membrane protein of a single Chlamydia trachomatis serovar can possess more than one serovar-specific epitope. Infect Immun. 1996;64: 542–547. doi: 10.1128/iai.64.2.542-547.1996 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0304525.ref014] 14.Chesson HW, Spicknall IH, Bingham A, Brisson M, Eppink ST, Farnham PG, et al. The Estimated Direct Lifetime Medical Costs of Sexually Transmitted Infections Acquired in the United States in 2018. Sex Transm Dis. 2021;48: 215. doi: 10.1097/OLQ.0000000000001380 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0304525.ref015] 15.Vasilevsky S, Stojanov M, Greub G, Baud D. Chlamydial polymorphic membrane proteins: regulation, function and potential vaccine candidates. Virulence. 2016;7: 11–22. doi: 10.1080/21505594.2015.1111509 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0304525.ref016] 16.Paes W, Brown N, Brzozowski AM, Coler R, Reed S, Carter D, et al. Recombinant polymorphic membrane protein D in combination with a novel, second-generation lipid adjuvant protects against intra-vaginal Chlamydia trachomatis infection in mice. Vaccine. 2016;34: 4123–4131. doi: 10.1016/j.vaccine.2016.06.081 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0304525.ref017] 17.Müller T, Becker E, Stallmann S, Waldhuber A, Römmler-Dreher F, Albrecht S, et al. Vaccination with the polymorphic membrane protein A reduces Chlamydia muridarum induced genital tract pathology. Vaccine. 2017;35: 2801–2810. doi: 10.1016/j.vaccine.2017.04.017 [DOI] [PubMed] [Google Scholar]

[pone.0304525.ref018] 18.Inic-Kanada A, Stojanovic M, Schlacher S, Stein E, Belij-Rammerstorfer S, Marinkovic E, et al. Delivery of a Chlamydial Adhesin N-PmpC Subunit Vaccine to the Ocular Mucosa Using Particulate Carriers. Murthy AK, editor. PLOS ONE. 2015;10: e0144380. doi: 10.1371/journal.pone.0144380 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0304525.ref019] 19.Pal S, Favaroni A, Tifrea DF, Hanisch PT, Luczak SET, Hegemann JH, et al. Comparison of the nine polymorphic membrane proteins of Chlamydia trachomatis for their ability to induce protective immune responses in mice against a C. muridarum challenge. Vaccine. 2017;35: 2543–2549. doi: 10.1016/j.vaccine.2017.03.070 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0304525.ref020] 20.Lanfermann C, Wintgens S, Ebensen T, Kohn M, Laudeley R, Schulze K, et al. Prophylactic Multi-Subunit Vaccine against Chlamydia trachomatis: In Vivo Evaluation in Mice. Vaccines. 2021;9: 609. doi: 10.3390/vaccines9060609 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0304525.ref021] 21.Tan C, Hsia R, Shou H, Haggerty CL, Ness RB, Gaydos CA, et al. Chlamydia trachomatis-Infected Patients Display Variable Antibody Profiles against the Nine-Member Polymorphic Membrane Protein Family. Infect Immun. 2009;77: 3218–3226. doi: 10.1128/IAI.01566-08 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0304525.ref022] 22.Wang J, Zhang Y, Lu C, Lei L, Yu P, Zhong G. A Genome-Wide Profiling of the Humoral Immune Response to Chlamydia trachomatis Infection Reveals Vaccine Candidate Antigens Expressed in Humans. J Immunol. 2010;185: 1670–1680. doi: 10.4049/jimmunol.1001240 [DOI] [PubMed] [Google Scholar]

[pone.0304525.ref023] 23.Cruz-Fisher MI, Cheng C, Sun G, Pal S, Teng A, Molina DM, et al. Identification of Immunodominant Antigens by Probing a Whole Chlamydia trachomatis Open Reading Frame Proteome Microarray Using Sera from Immunized Mice. Infect Immun. 2011;79: 246–257. doi: 10.1128/IAI.00626-10 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0304525.ref024] 24.Patton DL, Teng A, Randall A, Liang X, Felgner PL, de la Maza LM. Whole genome identification of C. trachomatis immunodominant antigens after genital tract infections and effect of antibiotic treatment of pigtailed macaques. J Proteomics. 2014;108: 99–109. doi: 10.1016/j.jprot.2014.05.009 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0304525.ref025] 25.Yu H, Karunakaran KP, Jiang X, Chan Q, Rose C, Foster LJ, et al. Comparison of Chlamydia outer membrane complex to recombinant outer membrane proteins as vaccine. Vaccine. 2020;38: 3280–3291. doi: 10.1016/j.vaccine.2020.02.059 [DOI] [PubMed] [Google Scholar]

[pone.0304525.ref026] 26.Karunakaran KP, Rey-Ladino J, Stoynov N, Berg K, Shen C, Jiang X, et al. Immunoproteomic Discovery of Novel T Cell Antigens from the Obligate Intracellular Pathogen Chlamydia. J Immunol. 2008;180: 2459–2465. doi: 10.4049/jimmunol.180.4.2459 [DOI] [PubMed] [Google Scholar]

[pone.0304525.ref027] 27.Karunakaran KP, Yu H, Jiang X, Chan Q, Moon K-M, Foster LJ, et al. Outer membrane proteins preferentially load MHC class II peptides: Implications for as a Chlamydia trachomatis T cell vaccine. Vaccine. 2015;33: 2159–2166. doi: 10.1016/j.vaccine.2015.02.055 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0304525.ref028] 28.Grimwood J, Stephens RS. Computational analysis of the polymorphic membrane protein superfamily of Chlamydia trachomatis and Chlamydia pneumoniae. Microb Comp Genomics. 1999;4: 187–201. doi: 10.1089/omi.1.1999.4.187 [DOI] [PubMed] [Google Scholar]

[pone.0304525.ref029] 29.Henderson IR, Lam AC. Polymorphic proteins of Chlamydia spp.–autotransporters beyond the Proteobacteria. Trends Microbiol. 2001;9: 573–578. doi: 10.1016/S0966-842X(01)02234-X [DOI] [PubMed] [Google Scholar]

[pone.0304525.ref030] 30.Meuskens I, Saragliadis A, Leo JC, Linke D. Type V Secretion Systems: An Overview of Passenger Domain Functions. Front Microbiol. 2019;10: 1163. doi: 10.3389/fmicb.2019.01163 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0304525.ref031] 31.Becker E, Hegemann JH. All subtypes of the Pmp adhesin family are implicated in chlamydial virulence and show species-specific function. MicrobiologyOpen. 2014;3: 544–556. doi: 10.1002/mbo3.186 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0304525.ref032] 32.Molleken K, Schmidt E, Hegemann JH. Members of the Pmp protein family of Chlamydia pneumoniae mediate adhesion to human cells via short repetitive peptide motifs. Mol Microbiol. 2010;78: 1004–17. doi: 10.1111/j.1365-2958.2010.07386.x [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0304525.ref033] 33.Luczak SET, Smits SHJ, Decker C, Nagel-Steger L, Schmitt L, Hegemann JH. The Chlamydia pneumoniae Adhesin Pmp21 Forms Oligomers with Adhesive Properties. J Biol Chem. 2016;291: 22806–22818. doi: 10.1074/jbc.M116.728915 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0304525.ref034] 34.Favaroni A, Hegemann JH. Chlamydia trachomatis Polymorphic Membrane Proteins (Pmps) Form Functional Homomeric and Heteromeric Oligomers. Front Microbiol. 2021;12. doi: 10.3389/fmicb.2021.709724 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0304525.ref035] 35.Favaroni A, Trinks A, Weber M, Hegemann JH, Schnee C. Pmp Repertoires Influence the Different Infectious Potential of Avian and Mammalian Chlamydia psittaci Strains. Front Microbiol. 2021;12: 656209. doi: 10.3389/fmicb.2021.656209 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0304525.ref036] 36.Thomson NR, Yeats C, Bell K, Holden MTG, Bentley SD, Livingstone M, et al. The Chlamydophila abortus genome sequence reveals an array of variable proteins that contribute to interspecies variation. Genome Res. 2005;15: 629–640. doi: 10.1101/gr.3684805 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0304525.ref037] 37.Swanson KA, Taylor LD, Frank SD, Sturdevant GL, Fischer ER, Carlson JH, et al. Chlamydia trachomatis Polymorphic Membrane Protein D Is an Oligomeric Autotransporter with a Higher-Order Structure. Infect Immun. 2009;77: 508–516. doi: 10.1128/IAI.01173-08 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0304525.ref038] 38.Tan C, Hsia R, Shou H, Carrasco JA, Rank RG, Bavoil PM. Variable expression of surface-exposed polymorphic membrane proteins in in vitro-grown Chlamydia trachomatis. Cell Microbiol. 2010;12: 174–187. doi: 10.1111/j.1462-5822.2009.01389.x [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0304525.ref039] 39.Saka HA, Thompson JW, Chen Y-S, Kumar Y, Dubois LG, Moseley MA, et al. Quantitative proteomics reveals metabolic and pathogenic properties of Chlamydia trachomatis developmental forms. Mol Microbiol. 2011;82: 1185–1203. doi: 10.1111/j.1365-2958.2011.07877.x [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0304525.ref040] 40.Tanzer R J, Hatch T P. Characterization of Outer Membrane Proteins in Chlamydia trachomatis LGV Serovar L2. J Bacteriol. 2001;183: 2686–2690. doi: 10.1128/JB.183.8.2686-2690.2001 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0304525.ref041] 41.Gomes JP, Nunes A, Bruno WJ, Borrego MJ, Florindo C, Dean D. Polymorphisms in the Nine Polymorphic Membrane Proteins of Chlamydia trachomatis across All Serovars: Evidence for Serovar Da Recombination and Correlation with Tissue Tropism. J Bacteriol. 2006;188: 275–286. doi: 10.1128/JB.188.1.275–286.2006 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0304525.ref042] 42.Van Lent S, Creasy HH, Myers GSA, Vanrompay D. The Number, Organization, and Size of Polymorphic Membrane Protein Coding Sequences as well as the Most Conserved Pmp Protein Differ within and across Chlamydia Species. J Mol Microbiol Biotechnol. 2016;26: 333–344. doi: 10.1159/000447092 [DOI] [PubMed] [Google Scholar]

[pone.0304525.ref043] 43.Stothard DR, Toth GA, Batteiger BE. Polymorphic membrane protein H has evolved in parallel with the three disease-causing groups of Chlamydia trachomatis. Infect Immun. 2003;71: 1200–1208. doi: 10.1128/IAI.71.3.1200–1208.2003 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0304525.ref044] 44.Bradley P, Cowen L, Menke M, King J, Berger B. BETAWRAP: Successful prediction of parallel -helices from primary sequence reveals an association with many microbial pathogens. Proc Natl Acad Sci. 2001;98: 14819–14824. doi: 10.1073/pnas.251267298 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0304525.ref045] 45.Debrine AM, Karplus PA, Rockey DD. A structural foundation for studying chlamydial polymorphic membrane proteins. Microbiol Spectr. 2023;11: e03242–23. doi: 10.1128/spectrum.03242-23 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0304525.ref046] 46.Paes W, Dowle A, Coldwell J, Leech A, Ganderton T, Brzozowski A. The Chlamydia trachomatis PmpD adhesin forms higher order structures through disulphide-mediated covalent interactions. PLoS ONE. 2018;13: e0198662. doi: 10.1371/journal.pone.0198662 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0304525.ref047] 47.Vandahl BB, Pedersen AS, Gevaert K, Holm A, Vandekerckhove J, Christiansen G, et al. The expression, processing and localization of polymorphic membrane proteins in Chlamydia pneumoniae strain CWL029. BMC Microbiol. 2002;2: 36. doi: 10.1186/1471-2180-2-36 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0304525.ref048] 48.Wehrl W, Brinkmann V, Jungblut PR, Meyer TF, Szczepek AJ. From the inside out—processing of the Chlamydial autotransporter PmpD and its role in bacterial adhesion and activation of human host cells. Mol Microbiol. 2004;51: 319–334. doi: 10.1046/j.1365-2958.2003.03838.x [DOI] [PubMed] [Google Scholar]

[pone.0304525.ref049] 49.Kiselev AO, Stamm WE, Yates JR, Lampe MF. Expression, Processing, and Localization of PmpD of Chlamydia trachomatis Serovar L2 during the Chlamydial Developmental Cycle. PLoS ONE. 2007;2: e568. doi: 10.1371/journal.pone.0000568 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0304525.ref050] 50.Kiselev AO, Skinner MC, Lampe MF. Analysis of pmpD Expression and PmpD Post-Translational Processing during the Life Cycle of Chlamydia trachomatis Serovars A, D, and L2. PLoS ONE. 2009;4: e5191. doi: 10.1371/journal.pone.0005191 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0304525.ref051] 51.Sigrist CJA, de Castro E, Cerutti L, Cuche BA, Hulo N, Bridge A, et al. New and continuing developments at PROSITE. Nucleic Acids Res. 2013;41: D344–D347. doi: 10.1093/nar/gks1067 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0304525.ref052] 52.Katoh K, Standley DM. MAFFT Multiple Sequence Alignment Software Version 7: Improvements in Performance and Usability. Mol Biol Evol. 2013;30: 772–780. doi: 10.1093/molbev/mst010 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0304525.ref053] 53.Potter SC, Luciani A, Eddy SR, Park Y, Lopez R, Finn RD. HMMER web server: 2018 update. Nucleic Acids Res. 2018;46: W200–W204. doi: 10.1093/nar/gky448 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0304525.ref054] 54.Anishchenko I, Baek M, Park H, Hiranuma N, Kim DE, Dauparas J, et al. Protein tertiary structure prediction and refinement using deep learning and Rosetta in CASP14. Proteins. 2021. doi: 10.1002/prot.26194 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0304525.ref055] 55.Baek M, DiMaio F, Anishchenko I, Dauparas J, Ovchinnikov S, Lee GR, et al. Accurate prediction of protein structures and interactions using a 3-track neural network. Science. 2021;373: 871–876. doi: 10.1126/science.abj8754 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0304525.ref056] 56.Pettersen EF, Goddard TD, Huang CC, Couch GS, Greenblatt DM, Meng EC, et al. UCSF Chimera—a visualization system for exploratory research and analysis. J Comput Chem. 2004;25: 1605–1612. doi: 10.1002/jcc.20084 [DOI] [PubMed] [Google Scholar]

[pone.0304525.ref057] 57.Jumper J, Evans R, Pritzel A, Green T, Figurnov M, Ronneberger O, et al. Highly accurate protein structure prediction with AlphaFold. Nature. 2021;596: 583–589. doi: 10.1038/s41586-021-03819-2 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0304525.ref058] 58.Varadi M, Anyango S, Deshpande M, Nair S, Natassia C, Yordanova G, et al. AlphaFold Protein Structure Database: massively expanding the structural coverage of protein-sequence space with high-accuracy models. Nucleic Acids Res. 2022;50: D439–D444. doi: 10.1093/nar/gkab1061 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0304525.ref059] 59.Lin Z, Akin H, Rao R, Hie B, Zhu Z, Lu W, et al. Evolutionary-scale prediction of atomic-level protein structure with a language model. Science. 2023;379: 1123–1130. doi: 10.1126/science.ade2574 [DOI] [PubMed] [Google Scholar]

[pone.0304525.ref060] 60.Chatzigoulas A, Cournia Z. Predicting protein–membrane interfaces of peripheral membrane proteins using ensemble machine learning. Brief Bioinform. 2022;23: bbab518. doi: 10.1093/bib/bbab518 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0304525.ref061] 61.Marrink SJ, Risselada HJ, Yefimov S, Tieleman DP, de Vries AH. The MARTINI Force Field: Coarse Grained Model for Biomolecular Simulations. J Phys Chem B. 2007;111: 7812–7824. doi: 10.1021/jp071097f [DOI] [PubMed] [Google Scholar]

[pone.0304525.ref062] 62.de Jong DH, Singh G, Bennett WFD, Arnarez C, Wassenaar TA, Schäfer LV, et al. Improved Parameters for the Martini Coarse-Grained Protein Force Field. J Chem Theory Comput. 2013;9: 687–697. doi: 10.1021/ct300646g [DOI] [PubMed] [Google Scholar]

[pone.0304525.ref063] 63.Qi Y, Ingólfsson HI, Cheng X, Lee J, Marrink SJ, Im W. CHARMM-GUI Martini Maker for Coarse-Grained Simulations with the Martini Force Field. J Chem Theory Comput. 2015;11: 4486–4494. doi: 10.1021/acs.jctc.5b00513 [DOI] [PubMed] [Google Scholar]

[pone.0304525.ref064] 64.Yesylevskyy SO, Schäfer LV, Sengupta D, Marrink SJ. Polarizable Water Model for the Coarse-Grained MARTINI Force Field. PLOS Comput Biol. 2010;6: e1000810. doi: 10.1371/journal.pcbi.1000810 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0304525.ref065] 65.Van Der Spoel D, Lindahl E, Hess B, Groenhof G, Mark AE, Berendsen HJC. GROMACS: Fast, flexible, and free. J Comput Chem. 2005;26: 1701–1718. doi: 10.1002/jcc.20291 [DOI] [PubMed] [Google Scholar]

[pone.0304525.ref066] 66.Bussi G, Donadio D, Parrinello M. Canonical sampling through velocity rescaling. J Chem Phys. 2007;126: 014101. doi: 10.1063/1.2408420 [DOI] [PubMed] [Google Scholar]

[pone.0304525.ref067] 67.Parrinello M, Rahman A. Polymorphic transitions in single crystals: A new molecular dynamics method. J Appl Phys. 1981;52: 7182–7190. doi: 10.1063/1.328693 [DOI] [Google Scholar]

[pone.0304525.ref068] 68.Vita R, Mahajan S, Overton JA, Dhanda SK, Martini S, Cantrell JR, et al. The Immune Epitope Database (IEDB): 2018 update. Nucleic Acids Res. 2019;47: D339–D343. doi: 10.1093/nar/gky1006 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0304525.ref069] 69.Jespersen MC, Peters B, Nielsen M, Marcatili P. BepiPred-2.0: improving sequence-based B-cell epitope prediction using conformational epitopes. Nucleic Acids Res. 2017;45: W24–W29. doi: 10.1093/nar/gkx346 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0304525.ref070] 70.Kringelum JV, Lundegaard C, Lund O, Nielsen M. Reliable B cell epitope predictions: impacts of method development and improved benchmarking. PLoS Comput Biol. 2012;8: e1002829. doi: 10.1371/journal.pcbi.1002829 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0304525.ref071] 71.Southwood S, Sidney J, Kondo A, del Guercio MF, Appella E, Hoffman S, et al. Several common HLA-DR types share largely overlapping peptide binding repertoires. J Immunol Baltim Md 1950. 1998;160: 3363–3373. [PubMed] [Google Scholar]

[pone.0304525.ref072] 72.Terry FE, Moise L, Martin RF, Torres M, Pilotte N, Williams SA, et al. Time for T? Immunoinformatics addresses vaccine design for neglected tropical and emerging infectious diseases. Expert Rev Vaccines. 2014;14: 21–35. doi: 10.1586/14760584.2015.955478 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0304525.ref073] 73.Paul S, Lindestam Arlehamn CS, Scriba TJ, Dillon MBC, Oseroff C, Hinz D, et al. Development and validation of a broad scheme for prediction of HLA class II restricted T cell epitopes. J Immunol Methods. 2015;422: 28–34. doi: 10.1016/j.jim.2015.03.022 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0304525.ref074] 74.Hiranuma N, Park H, Baek M, Anishchenko I, Dauparas J, Baker D. Improved protein structure refinement guided by deep learning based accuracy estimation. Nat Commun. 2021;12: 1340. doi: 10.1038/s41467-021-21511-x [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0304525.ref075] 75.Leyton DL, Johnson MD, Thapa R, Huysmans GHM, Dunstan RA, Celik N, et al. A mortise-tenon joint in the transmembrane domain modulates autotransporter assembly into bacterial outer membranes. Nat Commun. 2014;5: 4239. doi: 10.1038/ncomms5239 [DOI] [PubMed] [Google Scholar]

[pone.0304525.ref076] 76.Yuan X, Johnson MD, Zhang J, Lo AW, Schembri MA, Wijeyewickrema LC, et al. Molecular basis for the folding of β-helical autotransporter passenger domains. Nat Commun. 2018;9: 1395. doi: 10.1038/s41467-018-03593-2 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0304525.ref077] 77.Lim JB, Klauda JB. Lipid chain branching at the iso- and anteiso-positions in complex Chlamydia membranes: a molecular dynamics study. Biochim Biophys Acta. 2011;1808: 323–331. doi: 10.1016/j.bbamem.2010.07.036 [DOI] [PubMed] [Google Scholar]

[pone.0304525.ref078] 78.Rojas-Lopez M, Zorgani MA, Kelley LA, Bailly X, Kajava AV, Henderson IR, et al. Identification of the Autochaperone Domain in the Type Va Secretion System (T5aSS): Prevalent Feature of Autotransporters with a β-Helical Passenger. Front Microbiol. 2017;8: 2607. doi: 10.3389/fmicb.2017.02607 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0304525.ref079] 79.Wells TJ, Tree JJ, Ulett GC, Schembri MA. Autotransporter proteins: novel targets at the bacterial cell surface. FEMS Microbiol Lett. 2007;274: 163–172. doi: 10.1111/j.1574-6968.2007.00833.x [DOI] [PubMed] [Google Scholar]

[pone.0304525.ref080] 80.Jenkins J, Pickersgill R. The architecture of parallel β-helices and related folds. Prog Biophys Mol Biol. 2001;77: 111–175. doi: 10.1016/S0079-6107(01)00013-X [DOI] [PubMed] [Google Scholar]

[pone.0304525.ref081] 81.Yoder MD, Lietzke SE, Jurnak F. Unusual structural features in the parallel β-helix in pectate lyases. Structure. 1993;1: 241–251. doi: 10.1016/0969-2126(93)90013-7 [DOI] [PubMed] [Google Scholar]

[pone.0304525.ref082] 82.Leszczynski JF, Rose GD. Loops in Globular Proteins: A Novel Category of Secondary Structure. Science. 1986;234: 849–855. doi: 10.1126/science.3775366 [DOI] [PubMed] [Google Scholar]

[pone.0304525.ref083] 83.Favaroni A. Role of adhesin proteins in Chlamydia infection. Dissertation, Heinrich Heine University. 2017. Available: https://docserv.uni-duesseldorf.de/servlets/DocumentServlet?id=42486 [Google Scholar]

[pone.0304525.ref084] 84.Williamson MP. The structure and function of proline-rich regions in proteins. Biochem J. 1994;297: 249–260. doi: 10.1042/bj2970249 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0304525.ref085] 85.Ball LJ, Kühne R, Schneider-Mergener J, Oschkinat H. Recognition of proline-rich motifs by protein-protein-interaction domains. Angew Chem Int Ed Engl. 2005;44: 2852–2869. doi: 10.1002/anie.200400618 [DOI] [PubMed] [Google Scholar]

[pone.0304525.ref086] 86.Jorgensen I, Valdivia RH. Pmp-Like Proteins Pls1 and Pls2 Are Secreted into the Lumen of the Chlamydia trachomatis Inclusion. Infect Immun. 2008;76: 3940–3950. doi: 10.1128/IAI.00632-08 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0304525.ref087] 87.Hirt RP, de Miguel N, Nakjang S, Dessi D, Liu Y-C, Diaz N, et al. Trichomonas vaginalis Pathobiology. In: Rollinson D, Hay SI, editors. Advances in Parasitology. Academic Press; 2011. pp. 87–140. doi: 10.1016/B978-0-12-391429-3.00006-X [DOI] [PubMed] [Google Scholar]

[pone.0304525.ref088] 88.Handrich MR, Garg SG, Sommerville EW, Hirt RP, Gould SB. Characterization of the BspA and Pmp protein family of trichomonads. Parasit Vectors. 2019;12: 406. doi: 10.1186/s13071-019-3660-z [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0304525.ref089] 89.Leyton DL, Rossiter AE, Henderson IR. From self sufficiency to dependence: mechanisms and factors important for autotransporter biogenesis. Nat Rev Microbiol. 2012;10: 213–225. doi: 10.1038/nrmicro2733 [DOI] [PubMed] [Google Scholar]

[pone.0304525.ref090] 90.van Ulsen P, Rahman S ur, Jong WSP, Daleke-Schermerhorn MH, Luirink J. Type V secretion: from biogenesis to biotechnology. Biochim Biophys Acta. 2014;1843: 1592–1611. doi: 10.1016/j.bbamcr.2013.11.006 [DOI] [PubMed] [Google Scholar]

[pone.0304525.ref091] 91.Bernstein HD. Type V Secretion in Gram-Negative Bacteria. EcoSal Plus. 2019;8. doi: 10.1128/ecosalplus.ESP-0031-2018 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0304525.ref092] 92.Heras B, Totsika M, Peters KM, Paxman JJ, Gee CL, Jarrott RJ, et al. The antigen 43 structure reveals a molecular Velcro-like mechanism of autotransporter-mediated bacterial clumping. Proc Natl Acad Sci U S A. 2014;111: 457–462. doi: 10.1073/pnas.1311592111 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0304525.ref093] 93.Suchland RJ, Carrell SJ, Celum CL, Sanchez J, Hybiske K, Rockey DD. Prevalence of Chlamidia trachomatis Serovar G Strains in MSM Rectal Infections are Temporally and Globally Consistent and Share a Phylogenetic Lineage. Proceedings of the Julius Schachter Fifteenth International Symposium on Human Chlamydial Infections. San Antonio, TX, USA: IMS/UT Print The University of Texas Health Science Center at San Antonio; 2022. pp. 80–84. [Google Scholar]

[pone.0304525.ref094] 94.Mölleken K, Becker E, Hegemann JH. The Chlamydia pneumoniae Invasin Protein Pmp21 Recruits the EGF Receptor for Host Cell Entry. PLOS Pathog. 2013;9: e1003325. doi: 10.1371/journal.ppat.1003325 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0304525.ref095] 95.Li X, Wang Y, Hegemann J, He C. Polymorphic Membrane 17G of Chlamydia psittaci Mediated the Binding and Invasion of Host Cells by Interacting and Activating EGFR. Proceedings of the Julius Schachter Fifteenth International Symposium on Human Chlamydial Infections. San Antonio, TX, USA: IMS/UT Print The University of Texas Health Science Center at San Antonio; 2022. pp. 177–183. [Google Scholar]

[pone.0304525.ref096] 96.Russi RC, Bourdin E, García MI, Veaute CMI. In silico prediction of T- and B-cell epitopes in PmpD: First step towards to the design of a Chlamydia trachomatis vaccine. Biomed J. 2018;41: 109–117. doi: 10.1016/j.bj.2018.04.007 [DOI] [PMC free article] [PubMed] [Google Scholar]

PERMALINK

Sequence, structure prediction, and epitope analysis of the polymorphic membrane protein family in Chlamydia trachomatis

Patrick W Cervantes

Brent W Segelke

Edmond Y Lau

Beverly V Robinson

Abisola Abisoye-Ogunniyan

Sukumar Pal

Luis M de la Maza

Matthew A Coleman

Patrik D’haeseleer

Roles

Abstract

1. Introduction

Fig 1. C. trachomatis serovar E (Bour) PmpD protein linear map.

2. Methods

2.1 Identifying Pmp repeats

2.2 Protein structure prediction

2.3 Prediction of membrane binding domains

2.4 Molecular dynamics

2.5 Epitope prediction

2.6 Pmp amino acid sequence conservation

3. Results

3.1 Pmp passenger domains contain a larger repeat sequence that incorporates the GGA(I,L,V) and FxxN repeat motifs

Fig 2. The Pmp tetrapeptide repeats, GGA(I,L,V) and FxxN, fit in a larger repeat.

3.2 Structural modeling shows Pmps share many features with other autotransporters

Fig 3. Comparison of structure predictions for PmpC serovar E.

Fig 4. The Pmps include a characteristic C-terminal transmembrane β-barrel.

Fig 5. Pmp passenger domains form a regular β-helical structure.

Fig 6. The Pmp passenger domains are connected to the transmembrane β-barrel by a flexible hinge.

3.3 Protease cleavage sites are concentrated in side loops

Fig 7. Pmp cleavage sites for serovar L2 Pmp proteins mapped to the serovar E passenger domain structures.

3.4 B-cell epitopes are concentrated in side loops, T-cell epitopes in the main β-helix of the passenger domain

Fig 8. B-cell and T-cell epitopes on Pmp structure.

3.5 Side loops are predicted to be involved in host cell adhesion

Fig 9. Proline-rich regions in the Pmp passenger domains may be involved in adhesion.

Fig 10. Putative membrane-penetrating amino acids, predicted by DREAMM.

Fig 11. Areas of high sequence variation between serovars are located primarily in the side loops.

4. Discussion

Fig 12. Predicted Pmp oligomers.

Supporting information

Data Availability

Funding Statement

References

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Agathe Subtil

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Author response to Decision Letter 1

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Agathe Subtil

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Associated Data

Supplementary Materials

Data Availability Statement

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