Skip to main content
NCBI home page
Springer Nature - PMC COVID-19 Collection logoLink to Springer Nature - PMC COVID-19 Collection
. 2022 Jul 30;46(1):225. doi: 10.1186/s42269-022-00917-7

A comprehensive in silico exploration of the impacts of missense variants on two different conformations of human pirin protein

Auroni Semonti Khan 1,#, Nahid Parvez 1,#, Tamim Ahsan 2,✉,#, Sabrina Samad Shoily 3, Abu Ashfaqur Sajib 3
PMCID: PMC9362109  PMID: 35967515

Abstract

Background

Pirin, a member of the cupin superfamily, is an iron-binding non-heme protein. It acts as a coregulator of several transcription factors, especially the members of NFκB transcription factor family. Based on the redox state of its iron cofactor, it can assume two different conformations and thereby act as a redox sensor inside the nucleus. Previous studies suggested that pirin may be associated with cancer, inflammatory diseases as well as COVID-19 severities. Hence, it is important to explore the pathogenicity of its missense variants. In this study, we used a number of in silico tools to investigate the effects of missense variants of pirin on its structure, stability, metal cofactor binding affinity and interactions with partner proteins. In addition, we used protein dynamics simulation to elucidate the effects of selected variants on its dynamics. Furthermore, we calculated the frequencies of haplotypes containing pirin missense variants across five major super-populations (African, Admixed American, East Asian, European and South Asian).

Results

Among a total of 153 missense variants of pirin, 45 were uniformly predicted to be pathogenic. Of these, seven variants can be considered for further experimental studies. Variants R59P and L116P were predicted to significantly destabilize and damage pirin structure, substantially reduce its affinity to its binding partners and alter pirin residue fluctuation profile via changing the flexibility of several key residues. Additionally, variants R59Q, F78V, G98D, V151D and L220P were found to impact pirin structure and function in multiple ways. As no haplotype was identified to be harboring more than one missense variant, further interrogation of the individual effects of these seven missense variants is highly recommended.

Conclusions

Pirin is involved in the transcriptional regulation of several genes and can play an important role in inflammatory responses. The variants predicted to be pathogenic in this study may thus contribute to a better understanding of the underlying molecular mechanisms of various inflammatory diseases. Future studies should be focused on clarifying if any of these variants can be used as disease biomarkers.

Supplementary Information

The online version contains supplementary material available at 10.1186/s42269-022-00917-7.

Keywords: Pirin, Pathogenic variants, Non-heme protein, Inflammation, Transcriptional regulation, Oxidative stress, Cancer, NFκB pathway

Background

Pirin (PIR) is a highly conserved protein across both eukaryotes and prokaryotes (Dunwell et al. 2001). It is widely present in sub-nuclear structures of cells (Pang et al. 2004). This protein is predominantly expressed in human liver, heart, kidneys, and muscles while low levels of expression are observed in brain and lungs (Wendler et al. 1997; Pang et al. 2004). Human pirin has been implicated in skin, breast, lung, head and neck, gastrointestinal, cervical cancers (Perez-Dominguez et al. 2021) and tumors of epithelial tissues, hematopoietic and neurological systems (Yoshikawa et al. 2004; Licciulli et al. 2010a; Jungk et al. 2016).

The human pirin is a non-heme iron-binding protein that comprises 290 amino acids and has a molecular weight of 32 kDa. Due to its primary sequence and structural similarity, pirin belongs to the cupin superfamily. It contains two structurally similar β-barrel domains facing each other (Wendler et al. 1997; Pang et al. 2004). The N-terminal domain is highly conserved in mammals, plants, fungi and prokaryotic organisms (Wendler et al. 1997), whereas the C-terminal domain varies. The C-terminal domain does not contain any metal-binding site, but has a cavity which is more compact than that of the N-terminal domain (Pang et al. 2004). The iron cofactor (Fe2+ or Fe3+) is coordinated by residues His-56, His-58, His-101 and Glu-103 within the negatively charged metal-binding cavity of the N-terminal domain (Pang et al. 2004; Liu et al. 2013). Depending on the redox state, this iron center within the R-shaped surface area allosterically controls the interactions between pirin and its binding partners (Pang et al. 2004; Liu et al. 2013).

Pirin was originally identified as an interactor of nuclear factor I/CCAAT box transcription factor (NFI/CTF1), which can regulate transcription initiation and DNA replication (Santoro et al. 1988; Wendler et al. 1997). Nuclear factor κB (NF-κB), a family of transcription factors, plays crucial roles in intracellular signaling for immune responses and consists of dimeric RelA (p65), RelB, c-Rel, p50, and p52 (Ghosh et al. 1998; Li and Verma 2002). Pirin functions as a transcriptional coregulator for the expression of NF-κB target genes through regulating the binding of NF-κB p65 to κB-sites (Liu et al. 2013). Nuclear translocation of NF-κB and its binding to DNA is prevented by members of the inhibitor of kappa-B (IκB) family (Bhatt and Ghosh 2014). B-cell lymphoma 3-encoded protein (BCL3), despite being a member of the IκB family, has both transactivation and transrepression roles in regulation of p50 or p52 (members of NF-κB family) homodimers mediated pathways (Lenardo and Siebenlist 1994; Maldonado and Melendez-Zajgla 2011). The difference in mechanism of inhibition by IκB and stimulation by BCL3 is not known, but it is evident that these bind to different protein partners. Pirin is one of the four binding partners of BCL3 and is known to enhance the DNA binding by BCL3-p50 via formation of a quaternary complex (Dechend et al. 1999).

The nuclear factor erythroid-derived 2-like 2 (Nrf2) transcription factor triggered in response to cellular oxidative stress conditions can modulate PIR gene expression via the functional antioxidant response elements (AREs) in its promoter region (Hübner et al. 2009; Chorley et al. 2012). Chronic cigarette smoking causes increased oxidative stress, which up-regulates expression of pirin and it has been implicated in bronchial epithelial cell apoptosis (Hübner et al. 2009; Chorley et al. 2012). Pirin is an oxidative stress sensor (Perez-Dominguez et al. 2021) and ferric conformation of this protein facilitates binding of NF-κB p65 to DNA (Liu et al. 2013). Although elevated activation of p65 is observed in chronic inflammation (Giridharan and Srinivasan 2018), canonical NF-κB pathway mediated by p65/p50 heterodimer is critically important for innate immunity and inflammatory responses (Oeckinghaus and Ghosh 2009; Liu et al. 2017). Furthermore, a recent study suggested possible association of pirin with the severity of COVID-19 (Shoily et al. 2021).

Pirin may be functionally important and associated with several diseases (Licciulli et al. 2010b; Perez-Dominguez et al. 2021). For example, terminal myeloid differentiation is impaired by down-regulation of PIR, and reduction in pirin activity may be involved in differentiation arrest characteristic of acute myeloid leukemia (AML) (Licciulli et al. 2010a). Additionally, loss of pirin function may lead to immunodeficiency by hampering binding of p65 to DNA. Therefore, identification of the pathogenic variants in this protein can help in determining potential genetic factors leading to immunodeficiency and AML as well as other pathological conditions resulting for disruption of pirin function. Missense variants involving amino acid substitutions at functionally important sites may affect protein structure, stability, flexibility, ligand binding and protein–protein interactions (Zhang et al. 2012; Tamura et al. 2017; Hernandez and Facelli 2021; Birolo et al. 2021; Qi et al. 2021). Studying all variants experimentally is time-consuming, and demands significant resources and efforts. In silico analyses can ease the process by prioritizing missense variants for further experimental studies. In the present study, the impacts of pirin missense variants were explored using various in silico tools.

Materials and methods

Retrieval of missense variants of pirin and prediction of pathogenicity of the missense variants

The list of missense variants in pirin was retrieved from the Ensembl Genome Browser (Howe et al. 2021). The amino acid sequence of pirin was retrieved from UniProt Knowledgebase (UniProtKB) (Accession Number: O00625). Five different pathogenicity predicting tools—Sorting Intolerant From Tolerant (SIFT) (Sim et al. 2012), Polymorphism Phenotyping-2 (PolyPhen-2) (Adzhubei et al. 2010), PMut (López-Ferrando et al. 2017), Meta-SNP (Capriotti et al. 2013) and Rhapsody (Ponzoni et al. 2020) were used to predict the pathogenicity of these variants. Only the variants which were predicted to be harmful by all of these five tools were selected for further analyses.

Determination of the effects of missense variants on the stability of pirin protein

First, 3D models of Fe2+ and Fe3+ bound conformations of pirin were generated by template-based modeling at SWISS-MODEL server (Waterhouse et al. 2018) using their X-ray crystallographic structures as templates (PDB IDs: 1J1L and 4GUL, respectively). These X-ray crystallographic structures were retrieved from the RCSB Protein Data bank (PDB) (Berman et al. 2000). These 3D structures were used as input in mutation Cutoff Scanning Matrix (mCSM) (Pires et al. 2014), DeepDDG (Cao et al. 2019), Impact of Nonsynonymous variations on Protein Stability 3D (INPS3D) (Savojardo et al. 2016), MAESTROweb (Laimer et al. 2015), and PremPS (Chen et al. 2020) to assess the destabilizing effects of the selected pirin variants.

Structural analysis of pirin variants

The impacts of these variants on pirin structure was analyzed using Missense3D (Ittisoponpisan et al. 2019; Khanna et al. 2021). This is a structure database resource which can predict structural changes due to buried Gly replacement, Gly introduction in a bend, buried Pro introduction, Cys-Pro replacement, buried H-bond breakage, buried charge introduction, buried charge replacement, buried charge switch, buried salt bridge breakage, di-sulfide bond breakage, buried hydrophilic introduction, disallowed phi/psi introduction, cavity alteration, clash and secondary structure alteration. These changes may lead to the disruption of alpha helices or beta sheets, alterations in inter-residue interactions in the structures as well as unfavorable energy changes that affect the protein’s structure (Kajander et al. 2000; Betts and Russell 2003; Krieger et al. 2005; Ho and Brasseur 2005; Hubbard and Haider 2010; Chan et al. 2011).

Assessment of the effects of variants on metal cofactor binding of pirin

To evaluate the impacts of the selected variants on pirin-iron interactions, the variant models were analyzed through metal ion-binding site prediction (MIB) (Lin et al. 2016) tool that uses fragment transformation method and can predict the metal-binding sites in a protein as well as perform metal ion docking (Lu et al. 2006).

Determination of effects of missense variants of pirin on protein–protein interactions

The Fe3+ conformation of pirin modulates DNA binding by NF-κB p65 (Liu et al. 2013) and in a previous study, Ankyrin (ANK) repeat number 5, 6 and 7 of BCL3 was shown to bind to the Fe2+ conformation of pirin (Pang et al. 2004). Amino acid sequence of BCL3 covering these repeats (275–367) was retrieved from UniProtKB (Accession no. P20749) (Bateman et al. 2021). X-ray crystallographic structure obtained from RCSB PDB (PDB ID: 1K1A) was used as a template to generate a 3D model of these three repeat regions using SWISS-MODEL (Waterhouse et al. 2018). For p65 (UniProtKB Accession no. Q04207), a 3D model of its Rel homology domain (RHD) (19–306 amino acid residues) was prepared using the template 1RAM (PDB ID). Docking was performed using HDOCK (Yan et al. 2020) based on previously published docking results (Pang et al. 2004; Liu et al. 2013).

In case of docking with BCL3, the top model was superimposed on BCL3-p50 homodimer structure using UCSF Chimera 1.14 (Pettersen et al. 2021). Previously described method was followed for this superimposition, and our observation was consistent with the published data (Pang et al. 2004). Interactions between PIR-p65 in the top model were evaluated with Protein Interaction Calculator (PIC) web server (Tina et al. 2007). PIR-p65 interactions were consistent with the available information (Liu et al. 2013).

PIR-BCL3 and PIR-p65 complexes thus predicted were then utilized to determine the impacts of pirin missense variants on its interactions with partners using mCSM-PPI2 (Rodrigues et al. 2019), MutaBind2 (Zhang et al. 2020), SAAMBE-3D (Pahari et al. 2020) and BeAtMuSiC V1.0 (Dehouck et al. 2013).

Simulation of the dynamics of pirin variants

To appraise the changes in protein dynamics caused by missense variants, the wild-type and mutant 3D models of pirin protein were subjected to simulation through a CABS coarse-grained protein model (Kmiecik et al. 2016). Previous study has suggested this method to be a proper alternative to conventional molecular dynamics that includes all atoms (Jamroz et al. 2013). Variants that showed significant negative impact in all the aforementioned analyses were selected for the simulation study. 3D models of variant structures were prepared using template-based modeling in SWISS-MODEL after manually substituting the wild-type residues with the mutant ones in the amino acid sequence of pirin. 3D models of both pirin conformations previously prepared with SWISS-MODEL were used as templates (Waterhouse et al. 2018). The dynamics of the wild-type and the variant structures were simulated with CABS-flex 2.0 using its default parameters (Kuriata et al. 2018).

Haplotyping of the PIR variants

We were interested to see if any haplotype of PIR contains more than one missense variant. For this purpose, all SNPs with global minor allele frequencies > 0.001 were identified from dbSNP (Sherry et al. 2001). Haplotypes containing these polymorphisms and their frequencies were analyzed with the LDhap module of LDlink (Machiela and Chanock 2015). This module was used to calculate haplotype frequencies in five super-populations (African, Admixed American, East Asian, European and South Asian).

Results

Pathogenicity of the missense variants

A total of 153 missense variants of pirin were identified (Additional file 1: Table S1). The pathogenicity of these variants was predicted using five different tools (SIFT, PolyPhen2, PMut, Meta-SNP and Rhapsody). Each tool follows a different algorithm to predict pathogenicity. SIFT uses sequence homology-based approach for classifying a missense variant as either deleterious or tolerated (Sim et al. 2012). PolyPhen-2 classifies an amino acid substitution into probably damaging, possibly damaging, and benign on the basis of sequence, phylogenetic and structural characteristics of the substitution(Adzhubei et al. 2010, 2013). PMut uses neural networks to predict structure and evolutionary properties resulting from change in amino acid sequence (López-Ferrando et al. 2017). Rhapsody utilizes sequence coevolution data along with structure- and dynamics-based methods to predict pathogenicity of target variants (Ponzoni et al. 2020). Meta-SNP discriminates between disease-related and polymorphic nonsynonymous SNVs (nsSNV) through a random forest-based binary classification method and utilizes several different prediction tools to derive a consensus result (Capriotti et al. 2013). In this study, 45 variants were predicted to have harmful effects by all tools (Table 1). These variants were selected for further analyses.

Table 1.

Pathogenic missense variants of pirin protein as predicted by five separate tools

Variant ID Variants SIFT PolyPhen-2a PMut Meta-SNP Rhapsody
rs372506134 G19A Deleterious Probably damaging Disease Disease Deleterious
rs758349788 V24F Deleterious Probably damaging Disease Disease Deleterious
rs1485928589 R25W Deleterious Possibly damaging Disease Disease Deleterious
rs766252248 I28T Deleterious Probably damaging Disease Disease Deleterious
rs746253345 P38L Deleterious Probably damaging Disease Disease Deleterious
rs1277921319 D43H Deleterious Probably damaging Disease Disease Deleterious
rs1356176104 H56Q Deleterious Probably damaging Disease Disease Deleterious
rs780168534 H58R Deleterious Probably damaging Disease Disease Deleterious
rs188288097 R59P Deleterious Probably damaging Disease Disease Deleterious
rs188288097 R59Q Deleterious Probably damaging Disease Disease Deleterious
rs1319331957 G60S Deleterious Probably damaging Disease Disease Deleterious
rs1459166472 G60V Deleterious Probably damaging Disease Disease Deleterious
rs780078643 G70A Deleterious Probably damaging Disease Disease Deleterious
rs748257098 G70R Deleterious Probably damaging Disease Disease Deleterious
rs780078643 G70V Deleterious Probably damaging Disease Disease Deleterious
rs750390136 D77E Deleterious Probably damaging Disease Disease Deleterious
rs1042818236 F78V Deleterious Probably damaging Disease Disease Deleterious
rs757045955 H81P Deleterious Probably damaging Disease Disease Deleterious
rs1464579620 G83D Deleterious Probably damaging Disease Disease Deleterious
rs866898423 L90F Deleterious Probably damaging Disease Disease Deleterious
rs149497039 A95V Deleterious Probably damaging Disease Disease Deleterious
rs778749014 G98D Deleterious Probably damaging Disease Disease Deleterious
rs140109164 G98S Deleterious Probably damaging Disease Disease Deleterious
rs1467570812 H101Y Deleterious Probably damaging Disease Disease Deleterious
rs752062795 Q115K Deleterious Probably damaging Disease Disease Deleterious
rs1484554733 L116P Deleterious Probably damaging Disease Disease Deleterious
rs1329364366 M126T Deleterious Probably damaging Disease Disease Deleterious
rs1996173 P129L Deleterious Probably damaging Disease Disease Deleterious
rs1294033379 V151D Deleterious Probably damaging Disease Disease Deleterious
rs772251328 S161Y Deleterious Probably damaging Disease Disease Deleterious
rs953093600 T167I Deleterious Possibly damaging Disease Disease Deleterious
rs768193675 D173G Deleterious Probably damaging Disease Disease Deleterious
rs780763035 D173N Deleterious Probably damaging Disease Disease Deleterious
rs779413343 G179V Deleterious Possibly damaging Disease Disease Deleterious
rs760795372 P187L Deleterious Possibly damaging Disease Disease Prob.delet
rs1569195774 W190S Deleterious Possibly damaging Disease Disease Deleterious
rs751833973 L220P Deleterious Probably damaging Disease Disease Deleterious
rs1272804008 P245S Deleterious Probably damaging Disease Disease Prob.delet
rs772771810 E248A Deleterious Probably damaging Disease Disease Deleterious
rs769242287 E248D Deleterious Probably damaging Disease Disease Deleterious
rs747391287 G254C Deleterious Probably damaging Disease Disease Deleterious
rs996737505 G254V Deleterious Probably damaging Disease Disease Deleterious
rs762648888 V257A Deleterious Probably damaging Disease Disease Deleterious
rs764770692 M258I Deleterious Probably damaging Disease Disease Deleterious
rs761242213 I264S Deleterious Probably damaging Disease Disease Deleterious
Open in a new tab

aPolyPhen-2 classifies variants as benign, possibly damaging and probably damaging. Only those variants that were predicted to be “probably damaging” were considered to be harmful to increase the accuracy of prediction

Destabilizing effects of the potentially pathogenic pirin variants

We used five different tools (mCSM, DeepDDG, INPS3D, MAESTROweb and PremPS) to evaluate the impacts of the variants on pirin stability. mCSM exploits the correlation between the impact of a mutation and atomic distance patterns surrounding the amino acid residue using graph-based signatures to predict stability changes (Pires et al. 2014). DeepDDG relies on neural network-based methods in the prediction of changes in protein stability due to point mutations (Cao et al. 2019). INPS3D uses descriptors to calculate ΔΔG values using a support vector regression (Savojardo et al. 2016). Descriptors extracted from the protein sequence to differentiate between wild and changed protein include a substitution score derived from the Blosum62 matrix, Kyte–Doolittle hydrophobicity scores of native and changed, the mutability index of the native residue, the molecular weights of native and changed residues, the difference in the alignment score between the native and variant sequences and an HMM, encoding evolutionary information of the wild-type sequence. MAESTROweb utilizes a multi-agent machine learning system based on protein structure to produce changes in unfolding free energy upon point mutation (Laimer et al. 2015). PremPS uses random forest regression scoring function to estimate effects of single mutations on protein stability. It employs an energy function to calculate unfolding Gibbs free energy (Chen et al. 2020).

The predicted changes in folding Gibbs free energy (ΔΔG) of both ferrous and ferric Pirin by these variants are listed in Table 2. ΔΔG value < − 1.0 kcal/mol (shown as bold) was considered to reflect destabilization and variants predicted by at least four tools to have ΔΔG value <  − 1.0 kcal/mol were considered to have significant destabilizing effects. Based on this criterion, nine variants identified to have significant destabilizing impacts on both conformations, whereas G98D may be more destabilizing for Fe2+ conformation as compared to the F3+ one.

Table 2.

Impacts of potentially pathogenic missense variants on pirin stability

Variantsa, Changes in Fe2+ bound Pirin Stability, ∆∆G (kcal/mol)b Variantsa, Changes in Fe3+ bound Pirin Stability, ∆∆G (kcal/mol)b
mCSM DeepDDG INPS3D MAESTROweb PremPS mCSM DeepDDG INPS3D MAESTROweb PremPS
G19A − 0.281 − 1.309 0.12 − 0.045 − 0.85 G19A − 0.348 − 1.419 0.099 − 0.036 − 0.97
V24F − 1.211 − 0.729 − 0.38 0.736 − 1.19 V24F − 1.27 − 1.047 − 0.395 0.723 − 1.35
R25W − 0.567 − 0.445 − 0.574 0.396 − 1.27 R25W − 0.577 − 0.109 − 0.276 0.348 − 1.24
I28T − 2.411 − 1.377 − 2.543 − 1.268 − 2.03 I28T − 3.068 − 1.927 − 2.511 − 1.317 − 2.4
P38L − 0.932 − 3.204 − 0.523 0.592 − 0.73 P38L − 0.965 − 3.391 − 0.523 0.426 − 0.73
D43H − 1.98 − 0.887 − 0.623 0.121 − 0.55 D43H − 1.564 − 0.509 − 0.679 0.212 − 0.71
H56Q − 0.754 − 0.396 − 0.853 1 − 1.33 H56Q − 0.9 − 0.529 − 0.853 0.246 − 1.54
H58R − 1.891 − 1.472 − 0.258 − 0.005 − 1.51 H58R − 1.97 − 1.444 − 0.321 − 0.016 − 1.59
R59P − 1.594 − 2.737 − 1.567 − 1.498 − 1.41 R59P − 1.571 − 2.829 − 1.612 − 1.767 − 1.56
R59Q − 1.478 − 1.578 − 1.013 − 1.66 − 1.94 R59Q − 1.485 − 1.538 − 1.068 − 1.781 − 2.09
G60S − 1.841 − 3.177 − 0.912 − 0.479 − 1.56 G60S − 1.889 − 3.076 − 0.912 − 0.531 − 1.69
G60V − 0.605 − 3.761 − 1.185 0.065 − 1.44 G60V − 0.6 − 3.656 − 1.185 0.252 − 1.52
G70A − 0.78 − 2.001 − 0.099 − 0.102 − 0.3 G70A − 0.811 − 1.701 − 0.125 − 0.014 − 0.4
G70R − 0.746 − 1.566 0.119 − 0.045 − 0.53 G70R − 0.823 − 1.287 0.227 − 0.027 − 0.62
G70V − 0.685 − 2.375 0.181 0.557 − 0.44 G70V − 0.693 − 2.049 0.21 0.218 − 0.52
D77E − 0.601 − 1.97 − 0.586 0.319 − 0.7 D77E − 0.422 − 2.908 − 0.65 0.328 − 0.77
F78V − 1.476 − 3.941 − 2.152 − 0.739 − 1.95 F78V − 1.549 − 4.009 − 2.086 − 1.03 − 2.23
H81P 0.725 − 1.264 − 1.286 0.019 − 0.95 H81P 0.696 − 1.189 − 1.242 0.069 − 1.13
G83D − 1.452 − 1.512 − 0.683 0.599 − 1.27 G83D − 1.473 − 1.529 − 0.698 0.697 − 1.45
L90F − 1.604 − 1.029 − 1.032 0.437 − 1.02 L90F − 1.585 − 0.804 − 1.011 0.441 − 1.16
A95V − 0.963 − 2.69 − 1.356 1.485 − 1.81 A95V − 0.899 − 2.24 − 1.356 1.541 − 1.9
G98D − 1.632 − 3.5 − 0.963 − 1.203 − 1.64 G98D − 1.789 − 3.266 − 0.987 − 0.548 − 1.78
G98S − 1.299 − 2.466 − 0.93 0.106 − 1.31 G98S − 1.421 − 2.281 − 0.938 0.182 − 1.45
H101Y − 0.182 − 0.845 − 0.116 0.454 − 1.02 H101Y − 0.016 − 0.744 − 0.116 0.44 − 1.08
Q115K − 0.339 − 1.437 − 0.796 − 0.556 − 1.46 Q115K − 0.263 − 1.195 − 0.607 − 0.355 − 1.34
L116P − 1.323 − 4.818 − 3.54 − 1.508 − 2.79 L116P − 1.437 − 4.762 − 3.54 − 1.645 − 3.02
M126T − 0.114 − 1.028 − 1.544 − 1.123 − 1.37 M126T − 0.145 − 0.798 − 1.533 − 1.237 − 1.27
P129L − 0.713 − 1.614 − 0.579 0.436 − 0.69 P129L − 0.748 − 1.719 − 0.579 0.303 − 0.85
V151D − 3.091 − 5.314 − 3.003 − 1.201 − 2.79 V151D − 3.117 − 5.492 − 3.003 − 1.18 − 2.94
S161Y − 0.62 − 1.719 − 0.524 0.85 − 0.84 S161Y − 0.607 − 1.933 − 0.551 0.947 − 0.89
T167I − 0.191 − 0.479 − 0.846 0.177 − 1.21 T167I − 0.158 − 0.518 − 0.846 0.512 − 1.21
D173G 0.64 − 2.656 − 0.949 − 0.026 0.01 D173G 0.482 − 2.415 − 1.005 − 0.021 − 0.09
D173N − 0.137 − 1.544 − 0.597 0.064 − 0.5 D173N − 0.336 − 1.255 − 0.742 0.141 − 0.48
G179V − 0.612 − 2.029 − 1.538 − 0.93 − 1.44 G179V − 0.56 − 2.096 − 1.542 − 0.361 − 1.52
P187L − 0.369 − 0.886 − 0.814 0.066 − 0.78 P187L − 0.343 − 0.91 − 0.814 0.037 − 0.85
W190S − 2.767 − 1.464 − 2.712 − 1.465 − 1.26 W190S − 2.707 − 1.415 − 2.654 − 1.568 − 1.42
L220P − 1.386 − 5.855 − 3.385 − 1.341 − 2.32 L220P − 1.379 − 5.872 − 3.402 − 1.261 − 2.49
P245S − 1.457 − 1.37 − 1.046 − 0.413 − 1.24 P245S − 1.554 − 1.494 − 1.046 − 0.419 − 1.45
E248A − 0.689 − 0.603 − 0.581 − 1.544 − 0.51 E248A − 0.645 − 0.614 − 0.575 − 1.524 − 0.67
E248D − 0.685 − 0.853 − 0.729 − 0.862 − 1.03 E248D − 0.597 − 0.835 − 0.704 − 0.879 − 1.19
G254C − 0.606 − 0.619 − 1.685 − 0.543 − 1.18 G254C − 0.511 − 0.649 − 1.636 − 0.435 − 1.2
G254V − 0.199 − 0.862 − 1.482 − 0.709 − 1.56 G254V − 0.149 − 0.94 − 1.4 − 0.623 − 1.4
V257A − 2.006 − 1.87 − 2.659 − 1.145 − 2.1 V257A − 2.155 − 1.948 − 2.671 − 1.143 − 2.24
M258I − 0.971 − 1.677 − 2.049 0.268 − 1.67 M258I − 0.921 − 1.283 − 2.079 0.352 − 1.82
I264S − 2.812 − 2.587 − 3.226 − 1.715 − 2.56 I264S − 2.676 − 2.499 − 3.258 − 1.757 − 2.69
Open in a new tab

aVariants that were predicted to significantly reduce pirin stability (∆∆G < − 1 kcal/mol) by all five tools are written in bold italics, whereas those that were predicted to have ∆∆G < − 1 kcal/mol by four tools are written in bold letters

bNegative ∆∆G values indicate decrease in pirin stability. ∆∆G values of < − 1 kcal/mol are written in bold letters, while ∆∆G values of > 1 kcal/mol are underlined

Structural changes caused by the potentially pathogenic variants

Missense3D was used to predict the effects of the missense variants on pirin structure. First, 21 variants were predicted to have structural damages in the Fe2+ bound state. For the Fe3+ bound conformation, 20 variants were predicted to alter pirin conformation (Table 3). Six of the missense variants with bound Fe2+ (D43H, R59P, G60V, F78V, H101Y and D173G) were predicted to cause buried hydrogen breakage (Additional file 2: Table S2). Variants H101Y and D173G were predicted to alter cavity volume by more than 70 Å3 in both the Fe2+ and Fe3+ bound states. Besides, H58R and G83D were predicted to have altered cavities only in the Fe3+ bound conformation (Table 3).

Table 3.

Effects of potentially pathogenic missense variants on pirin structure

Varianta Fe2 + conformation Varianta Fe3 + conformation
Structural Changes Structural Changes
G19A No structural damage detected G19A No structural damage detected
V24F No structural damage detected V24F No structural damage detected
R25W No structural damage detected R25W No structural damage detected
I28T No structural damage detected I28T No structural damage detected
P38L Cis pro replaced P38L This substitution triggers clash alert
D43H Buried H-bond breakage, buried salt bridge breakage D43H Buried charge switch, Buried H-bond breakage, Buried salt bridge breakage
H56Q No structural damage detected H56Q No structural damage detected
H58R No structural damage detected H58R Cavity altered
R59P Buried Pro-introduced, secondary structure altered, disallowed phi/psi, buried charge replaced, buried H-bond breakage, buried salt bridge breakage R59P Buried Pro introduced, secondary structure altered, disallowed phi/psi, buried charge replaced, buried H-bond breakage
R59Q Buried charge replaced, buried salt bridge breakage R59Q Buried charge replaced, buried H-bond breakage
G60S Buried Gly replaced, Gly in a bend G60S Disallowed phi/psi, buried Gly replaced, Gly in a bend
G60V Buried Gly replaced, buried H-bond breakage, Gly in a bend G60V Clash, disallowed phi/psi, buried Gly replaced, buried H-bond breakage, Gly in a bend
G70A Disallowed phi/psi, Gly in a bend G70A Clash, disallowed phi/psi, buried Gly replaced, buried H-bond breakage, Gly in a bend
G70R Clash, disallowed phi/psi, Gly in a bend G70R Clash, disallowed phi/psi, Gly in a bend
G70V Disallowed phi/psi, Gly in a bend G70V Disallowed phi/psi, Gly in a bend
D77E No structural damage detected D77E No structural damage detected
F78V Buried H-bond breakage F78V No structural damage detected
H81P No structural damage detected H81P No structural damage detected
G83D No structural damage detected G83D Cavity altered
L90F No structural damage detected L90F No structural damage detected
A95V No structural damage detected A95V No structural damage detected
G98D Buried charge introduced, disallowed phi/psi, buried Gly replaced G98D Buried charge introduced, disallowed phi/psi, buried Gly replaced
G98S Disallowed phi/psi, buried Gly replaced G98S Disallowed phi/psi, buried Gly replaced
H101Y Buried charge replaced, buried H-bond breakage, cavity altered H101Y Buried charge replaced, buried H-bond breakage, cavity altered
Q115K No structural damage detected Q115K No structural damage detected
L116P Buried Pro introduced, Disallowed phi/psi L116P Buried Pro introduced, disallowed phi/psi
M126T No structural damage detected M126T No structural damage detected
P129L No structural damage detected P129L No structural damage detected
V151D Buried hydrophilic introduced, buried charge introduced V151D Buried hydrophilic introduced, buried charge introduced
S161Y No structural damage detected S161Y No structural damage detected
T167I No structural damage detected T167I No structural damage detected
D173G Buried H-bond breakage Cavity altered, buried / exposed switch D173G Buried H-bond breakage, cavity altered, buried / exposed switch
D173N Buried charge replaced D173N No structural damage detected
G179V No structural damage detected G179V No structural damage detected
P187L No structural damage detected P187L No structural damage detected
W190S No structural damage detected W190S No structural damage detected
L220P Buried Pro introduced L220P Buried Pro introduced
P245S No structural damage detected P245S No structural damage detected
E248A No structural damage detected E248A No structural damage detected
E248D No structural damage detected G254C Disallowed phi/psi
G254C Disallowed phi/psi G254V Disallowed phi/psi
G254V Disallowed phi/psi E248D No structural damage detected
V257A No structural damage detected V257A No structural damage detected
M258I No structural damage detected M258I No structural damage detected
I264S No structural damage detected I264S No structural damage detected
Open in a new tab

aVariants that were predicted to be damaging to pirin structure are written in bold letters

Effects of variants on metal cofactor binding

Since the iron-binding site of pirin is crucial for its biological activity, the MIB tool (Lin et al. 2016) was used to investigate the effects of missense variants of pirin on iron binding. This tool uses fragment transformation method for binding site prediction and docking of metal ions. The overall binding scores of Fe2+ to pirin protein were similar for wild-type structure and the missense variant, except H56Q, H58R and H101Y (Additional file 3: Table S3). This pattern was observed for Fe3+, too. Residues His-56, His-58 and His-101 are three of four residues reported in a previous study to be the constituents of metal-binding site (Pang et al. 2004).

Effects of missense variants on protein–protein interactions

After generation of models for BCL3 and p65 through template-based modeling using SWISS-MODEL, pirin was docked to each of these proteins using HDOCK (Fig. 1) (Yan et al. 2020). Interactions of pirin with BCL3 and p65 were then analyzed for the impacts of missense variants in pirin using mCSM-PPI2 (Rodrigues et al. 2019), MutaBind2 (Zhang et al. 2020), SAAMBE-3D (Pahari et al. 2020) and BeAtMuSiC V1.0 (Dehouck et al. 2013). mCSM-PPI2 predicts the effects of missense variants on protein–protein binding affinity by concentrating on the inter-residue non-covalent interaction network using optimized graph-based signatures like graph kernels, evolutionary information, complex network metrics and energetic terms (Rodrigues et al. 2019). MutaBind2 exploits molecular mechanics force fields, statistical potentials and fast side-chain optimization algorithms built via random forest method (Zhang et al. 2020). SAAMBE-3D uses machine learning (Pahari et al. 2020). BeAtMuSiC V1.0 utilizes a set of statistical potentials derived from known protein structures and combines the effect of the mutation on the strength of the interactions at the interface, and on the overall stability of the complex (Dehouck et al. 2013).

Fig. 1.

Fig. 1

Open in a new tab

Interactions between pirin and its binding partners. Docking was performed between Fe2+ conformation of pirin and three ankyrin repeat domains (ANK 5-7) of BCL3 (A), as well as, Fe3+ conformation of pirin and Rel homology domain (RHD) of p65 (residues 19-306) (B). The protein–protein complex structures were depicted using PyMOL (Schrodinger and Delano 2020) (Aa and Ba). Pirin is illustrated in deep teal and its partners in hot pink. Protein–protein interfaces are delineated in yellow. Inter-residue interactions are delineated with iCn3D (Wang et al. 2020) (Ab and Bb). Each gray square represents contacts/interactions within 6 (Å). Residues of pirin are displayed in the x-axis and those of its binding partners are in the y-axis

In this study, negative values for ∆∆G indicated reduction in protein–protein binding affinity, and variants with predicted ∆∆G < -− 1 kcal/mole (by at least three tools) were considered to significantly decrease such affinity. Four variants (R59P, F78V, H81P and L116P) in case of pirin-BCL3 were found to be substantially destabilizing in case of pirin-BCL3 complex (Table 4). However, no variant had such considerable effect on pirin-p65 interactions. Interestingly, several variants (G98D, P129L, W190S and L220P in case of pir-BCL3 complex, and R59P, L116P and G254C in case of pirin-p65 complex) were predicted to have ∆∆G < − 1 kcal/mol by two tools and < -− 0.85 kcal/mol by at least one of the remaining two tools (Table 4). Such variants might also have significant destabilizing effects.

Table 4.

Effects of potentially pathogenic missense variants on stability of pirin-BCL3 and pirin-p65 interactions

Variantsa Changes in pirin-BCL3 complex stability, ∆∆G (kcal/mol)b Variantsa Changes in pirin/p65 complex stability, ∆∆G (kcal/mol)b
mCSM-PPI2 MutaBind2 SAAMBE3D BeAtMuSic V1.0 mCSM-PPI2 MutaBind2 SAAMBE3D BeAtMuSiC V1.0
G19A − 0.119 − 1 − 0.1 − 0.25 G19A − 0.881 − 0.88 − 0.43 − 1.29
V24F 0.229 − 0.98 0.29 − 0.08 V24F 0.407 − 0.66 0.03 − 0.2
R25W − 0.171 − 0.43 − 0.34 − 0.22 R25W 0.019 − 0.26 − 0.34 − 0.05
I28T − 0.195 − 0.74 0.5 − 0.84 I28T − 0.19 − 0.64 0.5 − 0.93
P38L − 0.51 − 0.93 0.1 − 0.58 P38L − 0.44 − 0.57 0.1 − 0.41
D43H − 0.11 − 0.63 − 0.07 0.09 D43H 0.171 − 1.09 − 0.15 0.15
H56Q − 0.339 − 0.47 − 0.15 − 0.31 H56Q − 0.459 − 0.46 − 0.38 − 0.47
H58R − 0.015 − 0.9 − 0.18 − 0.34 H58R − 0.055 − 0.39 − 0.29 − 0.44
R59P − 1.139 − 1.15 − 1.9 − 1.1 R59P − 1.102 − 0.66 − 1.9 − 0.97
R59Q − 0.656 − 0.74 − 0.72 − 0.65 R59Q − 0.462 − 0.3 − 0.72 − 0.48
G60S − 0.673 − 0.99 0.15 − 0.6 G60S − 0.438 − 0.42 0.15 − 0.59
G60V − 0.999 − 1.85 − 0.16 − 0.46 G60V − 0.866 − 0.74 − 0.16 − 0.46
G70A − 0.114 − 0.46 0 − 0.9 G70A − 0.017 − 0.25 0 − 0.67
G70R − 0.309 − 0.31 − 0.34 − 0.92 G70R − 0.248 − 0.08 − 0.34 − 0.81
G70V − 0.43 − 0.54 − 0.16 − 0.71 G70V − 0.308 0 − 0.16 − 0.68
D77E − 0.668 − 1.3 − 0.7 − 0.95 D77E − 0.059 − 0.62 − 0.6 − 0.65
F78V − 1.472 − 2.3 − 1.16 − 0.99 F78V − 0.47 − 0.36 − 0.85 − 0.7
H81P − 2.528 − 2.97 − 1.57 − 1.86 H81P − 0.674 − 0.49 − 0.79 − 0.81
G83D − 0.286 − 1.74 − 0.61 − 0.87 G83D 0.167 − 0.49 0.03 − 0.43
L90F 0.712 − 0.95 − 0.03 − 0.06 L90F 0.401 − 0.73 − 0.03 − 0.13
A95V 0.12 − 0.78 0.43 0.02 A95V 0.207 − 0.77 0.43 − 0.09
G98D − 0.959 − 1.84 0.05 − 1.47 G98D − 0.464 − 0.54 0.05 − 1.3
G98S − 0.938 − 0.82 0.04 − 1.18 G98S − 0.512 − 0.66 0.04 − 1.09
H101Y − 0.336 − 1.1 − 0.72 − 0.06 H101Y − 0.077 − 0.83 − 0.72 − 0.09
Q115K − 0.452 − 0.49 − 0.26 − 0.47 Q115K − 0.322 − 0.38 − 0.26 − 0.41
L116P − 1.048 − 1.3 − 1.11 − 1.71 L116P − 0.875 − 0.61 − 1.11 − 1.91
M126T − 0.026 − 0.25 − 0.35 − 0.14 M126T − 0.222 − 0.43 − 0.35 − 0.02
P129L − 1.058 − 1.19 0.13 − 0.99 P129L − 0.083 − 0.48 0.28 − 0.1
V151D − 0.418 − 0.81 − 0.15 − 1.05 V151D − 0.364 − 0.63 − 0.15 − 1.17
S161Y − 0.079 − 1.26 0.13 − 0.18 S161Y 0.142 − 0.5 0.13 − 0.24
T167I − 0.279 − 1.02 0.03 − 0.15 T167I − 0.154 − 0.43 0.03 − 0.09
D173G − 0.5 − 1.1 − 0.5 − 0.34 D173G − 0.38 − 0.42 − 0.5 − 0.38
D173N − 0.608 − 0.52 − 1.24 − 0.05 D173N − 0.792 − 0.71 − 1.24 − 0.13
G179V − 0.02 − 0.92 − 0.23 − 0.61 G179V − 0.075 − 0.38 − 0.23 − 0.57
P187L − 0.248 − 0.46 0.13 − 0.39 P187L − 0.23 − 0.4 0.13 − 0.45
W190S − 0.117 − 1.19 − 1.19 − 0.99 W190S − 0.21 − 0.51 − 1.19 − 0.9
L220P − 0.974 − 1.22 − 0.84 − 1.15 L220P − 0.931 − 0.45 − 0.84 − 1.11
P245S − 0.16 − 0.88 0.15 − 0.5 P245S − 0.131 − 0.51 0.15 − 0.53
E248A − 0.118 − 0.24 − 0.32 − 0.38 E248A − 0.157 − 0.17 − 0.32 − 0.3
E248D − 0.003 − 0.46 − 0.51 − 0.27 E248D 0.042 − 0.21 − 0.51 − 0.45
G254C − 0.153 − 0.61 0.03 − 0.48 G254C − 0.897 − 1.22 − 0.03 − 1.08
G254V − 0.079 − 0.95 − 0.09 − 0.72 G254V − 0.62 − 0.95 − 0.56 − 1.55
V257A − 0.316 − 0.99 − 0.37 − 1.04 V257A − 0.324 − 0.69 − 0.49 − 1.14
M258I − 0.357 − 0.9 − 0.23 − 0.07 M258I − 0.387 − 1.06 − 0.23 − 0.15
I264S − 0.301 − 0.95 0.08 − 0.98 I264S − 0.28 − 0.49 0.08 − 0.9
Open in a new tab

aVariants that were predicted to significantly reduce protein–protein binding affinity (∆∆G < − 1 kcal/mol) by at least three tools are written in bold italics. Variants with predicted ∆∆G < − 1 kcal/mol by two tools and with predicted ∆∆G values between − 0.85 and − 1 kcal/mol by at least one tool are written in bold letters

bNegative ∆∆G values indicate decrease in protein–protein binding affinity. ∆∆G values of < − 1 kcal/mol are written in bold letters, while ∆∆G values between − 0.85 and − 1 kcal/mol are written in italics

Alterations in the dynamics of pirin

Two variants (R59P and L116P) were found to destabilize pirin (Table 2), damage its structure (Table 3) and diminish pirin’s binding affinity to its partners (Table 4). Protein dynamics simulation was performed for these two variant structures, along with the wild-type ones, in both Fe2+ and Fe3+ bound conformations. Residue fluctuation profile revealed three areas with major alterations in fluctuation (Fig. 2). These areas are roughly from residue 28 to 37, 78 to 84, 120 to 125. The first and third regions were found to have altered fluctuation for both variants in both conformations. However, the second region, from78 to 84, showed large alteration for Fe2+ bond conformation only. Region of amino acids 28 to 37 overlap the R-shaped region responsible for p65 binding. 78 to 84 and 120 to 125 amino acid residues overlap two of the acidic patches responsible for BCL3 binding (Fig. 1). There are other regions for each of the two variants that show altered fluctuation but the two conserved iron-binding clusters show no major fluctuation in any case.

Fig. 2.

Fig. 2

Open in a new tab

Fluctuation profiles of the wild-type and two variant (R59P and L116P) structures of pirin. A comparison between the predicted residue fluctuation profiles of the wild-type and the variant structures in case of Fe2+ (A) and Fe3+ (B) conformations of pirin is graphically presented. RMSF denoting root mean square fluctuation in Å is displayed in the y-axis and pirin residues are displayed in the x-axis

Haplotyping of the variants

With the goal of identifying haplotypes containing more than one pirin missense variants, haplotype frequencies in five super-populations were calculated (Table 5). Three haplotypes were found to harbor two variant alleles. Each of these haplotypes however contained rs8094T allele, which is synonymous.

Table 5.

Frequencies of haplotypes containing pirin variants

SNP IDs Haplotypesa Haplotype frequencies
ALL African American East Asian European South Asian
rs75378219_rs34104000_rs8094_rs35715407_rs34149789 C_A_C_C_C 0.5272 0.5902 0.4828 0.5314 0.4295 0.571
C_A_T_C_C 0.4286 0.2602 0.4847 0.4686 0.5705 0.429
C_G_C_C_C 0.0238 0.0788 0.021 – – –
T_A_C_C_C 0.0056 0.0189 0.0038 – – –
C_A_T_C_G 0.0045 0.016 0.0019 – – –
C_A_C_A_C 0.0029 0.011 – – – –
T_A_T_C_C 0.0029 0.01 0.0019 – – –
C_A_C_C_G 0.0024 0.007 0.0038 – – –
C_G_T_C_C 0.0021 0.008 – – – –
Open in a new tab

aVariant DNA bases are written in bold letters, and haplotypes containing more than one variant are written in italics

Discussion

The present study aimed at prioritizing pathogenic missense variants of pirin protein for further experimental analysis. For this purpose, out of 153 missense variants or pirin, 45 were selected for further analyses as they were uniformly predicted to be pathogenic (Table 1). The impacts of these selected variants on pirin’s stability (Table 2), structure (Table 3), cofactor binding (Additional file 3: Table S3) and interactions with binding partners (Table 4) were predicted using multiple in silico tools to enhance prediction accuracy. Additionally, two variant structures (R59P and L116P) were subjected to protein dynamics simulation (Fig. 2) due to their predicted substantial effects on pirin structure and possibly function. Our findings indicate that these two variants, along with five other variants (R59Q, F78V, G98D, V151D and L220P), should be subjected to further experimental investigations.

Two domains of pirin are the N-terminal domain spanning residues 3–134 and the C-terminal domain comprising residues 143–290 (Pang et al. 2004). Sequence of the N-terminal domain has remained significantly conserved in mammals, plants and prokaryotes, notably in two regions spanning residues 52–70 (cluster 1) and residues 88–106 (cluster 2) (Wendler et al. 1997). Together these regions contain a total of four metal coordinating residues that are strictly conserved among species. N-terminal domain and iron-binding residues are thus important for pirin function. R59P lie in the conserved cluster 1 and L116P is close to conserved cluster 2, both residues located in the N-terminal. The C-terminal domain contain neither any metal-binding site nor conserved residues (Pang et al. 2004).

Earlier studies reported that pirin interacts with BCL3 and p65 which are members of the IκB and NF-κB family, respectively (Dechend et al. 1999; Liu et al. 2013). A large acidic patch with residues 77–82, 97–103, and 124–128 is present on the surface of pirin N-terminal domain. This patch has been shown to interact with the large basic patch on ankyrin repeats 6 and 7 of BCL3, whereas residues in C-terminal domain may interact with ankyrin repeat 5 (Pang et al. 2004). An R-shaped region is a notable pirin surface region which directly interacts with p65, comprising most of the central binding surface between them (Liu et al. 2013). It is made up of the residues 7–41 and 53–62, which also spans the surrounding area of the metal-binding cavity at the N-terminus (Liu et al. 2013). Arg59 lies in this R-shaped region needed for p65 binding. On the other hand, Leu116 is located in between two acidic patches on the surface of pirin required for interaction with BCL3. Therefore, R59P and L116P are likely to have significant impacts on pirin functionalities.

Residue fluctuation profiles indicated both R59P and L116P cause significant fluctuation in amino acid residues 28 to 37, which is part of the R-shaped region responsible for p65 binding. The two variants also alter fluctuation in regions of 78 to 84 and 120 to 125 amino acid residues which overlap the acidic patches of residues 77 to 82 and 124 to 128 (Fig. 2). Based on previously reported roles of R-shaped region and acidic patch in interaction of pirin with P65 and BCL3 (Pang et al. 2004; Liu et al. 2013), it can be stated that the altered fluctuation due to these variants might hamper the protein–protein interactions of pirin. Our findings are consistent with such assumptions (Table 4).

The metal-binding pocket is situated in the N-terminal domain of pirin and contains residues His-56, His-58, His-101 and Glu-103 which coordinate with ferrous/ferric ion. Metal binding has been reported to be crucial for interactions between pirin and its binding partners. A small-molecule inhibitor TPh A inserts into the Fe2+ containing pocket and prevents the cellular activity of pirin by disrupting the formation of pirin-Bcl3 complex (Miyazaki et al. 2010). Moreover, the binding of Fe3+ instead of Fe2+ alters pirin conformation and thus helps it to bind to p65 of NF-κB family. This iron center plays a role in the allosteric control of the R-shaped surface loop region (Liu et al. 2013). Pirin may therefore stimulate gene expression by BCL3.-p50 complex and p65 in the Fe2+ and Fe3+ bound states, respectively. Fe2+ to Fe3+ conversion occurs as the nuclear environment becomes more oxidizing. Hence, the activity of pirin and its related gene expression is dependent on the redox state. The iron-binding center contributes significantly in this process. Since these interactions can be disrupted by the aforementioned missense variants, pathways of the immune system and cell division, modulated by BCL3 and p65 can thus be adversely impacted, leading to disease conditions.

One of the variants that alter the metal-binding residues, H101Y, was found to cause structural damage (Table 3). In addition, the H101Y variant replaces the buried charge, breaks a buried H-bond and alters cavity by 70 Å, in both Fe2+ and Fe3+ bound conformations (Table 3 and Additional file 2: Table S2). Only in the Fe3+ bound state, the variant H58R, another iron coordinating residue, was predicted to cause an alteration in the cavity. In the metal-binding analysis, variants H56Q, H58R and H101Y had scores inconsistent with the wild-type, which may be suggestive of the possible effects of these variants on pirin–iron associations.

L220P in the less conserved C-terminal domain was predicted to alter stability and structure of both Fe2+ and Fe3+ bound conformations (Table 2 and 3). It may also interfere with interactions with BCL3, but not with p65 (Table 4).

Two other variants of pirin (F78V and G98D) are of considerable interest on the basis of our findings. The F78V variant was found to break buried H-bond in Fe2+ bound state only (Additional file 2: Table S2), but it was predicted to destabilize both Fe2+ and Fe3+ bond conformations. It is part of the acidic patches that bind with ARD of BCL3. In agreement with this information, our study predicted the F78V variant to hamper binding of pirin with BCL3. No such relationship has been observed for p65. In the case of variant G98D, introduction of buried charge and replacement of glycine create structural changes in Fe2+ as well as Fe3+ bound conformations (Table 3). Destabilization of both conformations and hindrance in interaction with BCL3 is significant for this variant (Tables 2 and 4).

In spite of having no effect on protein–protein interactions, variants R59Q and V151D draw attention as both of these significantly destabilize and alter structures for the Fe2+ and Fe3+ bound conformations (Table 2 and 3). Four variants (I28T, W190S, V257A and I264S) were identified to significantly destabilize the protein. However, no effect on structure or protein–protein interaction was predicted. In contrast, P38L, D43H, H58R, G60S, G60V, G70A, G70R, G70V, G98S, H101Y, D173G, D173N, G254C and G254V were found to cause structural damages whereas the stability of those structures was predicted to be unaffected. Here, buried hydrogen bonds were disrupted by D43H, R59P, G60V and D173G (Additional file 2: Table S2). On the other hand, most of the variants displayed similar results regarding redox state of iron, showing damages in both conformations. Strikingly, D173N and H58R were exceptions. Structural damage has been predicted for D173N only when bound to Fe2+ and for H58R only when bound to Fe3+. This phenomenon and the differential roles of Asp-173 and His-58 in two pirin conformations should be clarified in future studies.

Although variants P38L, H58R, G60S and G60V are located in the R-shaped region responsible for p65 binding, these residues did not appear to exert any effect on protein–protein interactions (Table 4). The absence of their direct interactions with p65 might explain these results (Fig. 1). His-58 and His-101 are two of the iron coordinating residues and variants at these sites (H58R and H101Y) were found to minimize the binding affinity of iron compared to wild-type protein. According to MIB tool prediction, variants other than the four metal coordinating ones were found to show no adverse effect in iron binding (Additional file 3: Table S3).

Another variant can be of consideration. H81P has been reported by four tools to disrupt interaction with BCL3. Although it has no other negative effect, being part of the acidic patch makes it a significant residue for BCL3 interaction and functionality of pirin.

Absence of any haplotype with more than one missense variant (Table 5) indicates that the presence of multiple missense variants in the same individual is unlikely. So, exploring the combinatorial effect of more than one missense variant in pirin protein may not be necessary, and identifying individual effects of missense variants may be sufficient in this connection.

It should be noted that a recent study identified V257A, I28T and I264S variants to have significant destabilizing effects on pirin structure (Suleman et al. 2021). Our study also found these variants to substantially reduce the stability of both pirin conformations (Table 2). However, the previous study had a lower number of initial missense variants (119, as compared to 153 in our study), and chose less variants for further analysis (24, as compared to 45 in our study). Besides, impacts of variants on pirin structure, cofactor binding and interactions with other proteins were not elaborated. Furthermore, the aforementioned study did not differentiate between the two conformations of pirin. Therefore, our study appears to be the most comprehensive exploration of the effects of pirin missense variants so far. The variants identified in this study for further experimental clarifications thus contribute to the existing list of prioritized pirin variants.

Conclusions

Since pirin plays a crucial role in regulation of multiple gene expressions, variants that alter its structure and impede its functions can contribute to the pathogenesis of various diseases. Prioritizing these variants for further experimentation is therefore essential. In the present study, we used multiple in silico tools to assess the possible pathogenicity of a total of 153 missense variants and appraise the impacts of a selected set of variants on pirin’s structure and functions. Based on our findings, we propose that seven variants (R59P, L116P, L220P, F78V, G98D, R59Q and V151D) should be considered for further investigations. In addition, four other variants (H58R, H101Y, D173N and H81P) can also be important targets of analysis. Since haplotypes with more than one pirin missense variant could not be found, exploring effects of individual variants should be enough for identifying roles of variants in disrupting pirin’s functions. Our findings thus significantly contribute to the existing knowledge regarding pathogenic variants of pirin. Future studies should focus on the possibility of using these variants as disease biomarkers.

Supplementary Information

42269_2022_917_MOESM1_ESM.docx (19.5KB, docx)

Additional file 1: Table S1. List of missense variants of pirin.

42269_2022_917_MOESM2_ESM.docx (14.6KB, docx)

Additional file 2: Table S2. Buried hydrogen bonds broken by pirin missense variants.

42269_2022_917_MOESM3_ESM.docx (18.4KB, docx)

Additional file 3: Table S3. Effects of missense variants on metal binding of pirin according to MIB tool.

Acknowledgements

Not applicable.

Abbreviations

NFI/CTF1

Nuclear factor I/CCAAT box transcription factor

NF- κB

Nuclear factor κB

I κB

Inhibitor of kappa-B

BCL3

B-cell lymphoma 3-encoded protein

Nrf2

Nuclear factor erythroid-derived 2-like 2

ARE

Antioxidant response elements

AML

Acute myeloid leukemia

SNP

Single nucleotide polymorphism

nsSNV

Nonsynonymous single nucleotide variants

Author contributions

ASK took part in the acquisition, analysis and interpretation of data, manuscript preparation and reviewing. NP participated in the acquisition, analysis and interpretation of data, manuscript preparation and reviewing. TA was responsible for conception, acquisition, analysis, interpretation of data, manuscript preparation and reviewing. SSS was involved in the analysis, interpretation of data and manuscript reviewing. AAS took part in the analysis, interpretation of data and manuscript reviewing. All authors have read and approved the final manuscript.

Funding

This research was supported by a grant from special allocation in science and technology from the Ministry of Science and Technology, Bangladesh.

Availability of data and materials

All data generated or analyzed during this study are included in this published article (and its Additional files).

Declarations

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Footnotes

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Auroni Semonti Khan, Nahid Parvez and Tamim Ahsan have contributed equally

References

  1. Adzhubei I, Jordan DM, Sunyaev SR. Predicting functional effect of human missense mutations using PolyPhen-2. Curr Protoc Hum Genet Chapter. 2013;7(Unit7):20. doi: 10.1002/0471142905.hg0720s76. [DOI] [PMC free article] [PubMed] [Google Scholar]
  2. Adzhubei IA, Schmidt S, Peshkin L, et al. A method and server for predicting damaging missense mutations. Nat Methods. 2010;7:248–249. doi: 10.1038/nmeth0410-248. [DOI] [PMC free article] [PubMed] [Google Scholar]
  3. Bateman A, Martin MJ, Orchard S, et al. UniProt: the universal protein knowledgebase in 2021. Nucleic Acids Res. 2021;49:D480–D489. doi: 10.1093/NAR/GKAA1100. [DOI] [PMC free article] [PubMed] [Google Scholar]
  4. Berman HM, Westbrook J, Feng Z, et al. The protein data bank. Nucleic Acids Res. 2000;28:235–242. doi: 10.1093/nar/28.1.235. [DOI] [PMC free article] [PubMed] [Google Scholar]
  5. Betts MJ, Russell RB. Amino acid properties and consequences of substitutions. Bioinform Genet. 2003;317:10–1002. [Google Scholar]
  6. Bhatt D, Ghosh S. Regulation of the NF-κB-mediated transcription of inflammatory genes. Front Immunol. 2014 doi: 10.3389/FIMMU.2014.00071. [DOI] [PMC free article] [PubMed] [Google Scholar]
  7. Birolo G, Benevenuta S, Fariselli P, et al. Protein stability perturbation contributes to the loss of function in haploinsufficient genes. Front Mol Biosci. 2021;8:10–15. doi: 10.3389/fmolb.2021.620793. [DOI] [PMC free article] [PubMed] [Google Scholar]
  8. Cao H, Wang J, He L, et al. DeepDDG: predicting the stability change of protein point mutations using neural networks. J Chem Inf Model. 2019;59:1508–1514. doi: 10.1021/ACS.JCIM.8B00697/SUPPL_FILE/CI8B00697_SI_003.XLSX. [DOI] [PubMed] [Google Scholar]
  9. Capriotti E, Altman RB, Bromberg Y. Collective judgment predicts disease-associated single nucleotide variants. BMC Genomics. 2013;14(Suppl 3):S2. doi: 10.1186/1471-2164-14-S3-S2. [DOI] [PMC free article] [PubMed] [Google Scholar]
  10. Chan C-H, Yu T-H, Wong K-B. Stabilizing salt-bridge enhances protein thermostability by reducing the heat capacity change of unfolding. PLoS ONE. 2011;6:e21624. doi: 10.1371/journal.pone.0021624. [DOI] [PMC free article] [PubMed] [Google Scholar]
  11. Chen Y, Lu H, Zhang N, et al. PremPS: predicting the impact of missense mutations on protein stability. PLoS Comput Biol. 2020;16:e1008543. doi: 10.1371/journal.pcbi.1008543. [DOI] [PMC free article] [PubMed] [Google Scholar]
  12. Chorley BN, Campbell MR, Wang X, et al. Identification of novel NRF2-regulated genes by ChIP-Seq: influence on retinoid X receptor alpha. Nucleic Acids Res. 2012;40:7416–7429. doi: 10.1093/NAR/GKS409. [DOI] [PMC free article] [PubMed] [Google Scholar]
  13. Dechend R, Hirano F, Lehmann K, et al. The Bcl-3 oncoprotein acts as a bridging factor between NF-κB/Rel and nuclear co-regulators. Oncogene. 1999;1822(18):3316–3323. doi: 10.1038/sj.onc.1202717. [DOI] [PubMed] [Google Scholar]
  14. Dehouck Y, Kwasigroch JM, Rooman M, Gilis D. BeAtMuSiC: prediction of changes in protein–protein binding affinity on mutations. Nucleic Acids Res. 2013;41:W333–W339. doi: 10.1093/nar/gkt450. [DOI] [PMC free article] [PubMed] [Google Scholar]
  15. Dunwell JM, Culham A, Carter CE, et al. Evolution of functional diversity in the cupin superfamily. Trends Biochem Sci. 2001;26:740–746. doi: 10.1016/s0968-0004(01)01981-8. [DOI] [PubMed] [Google Scholar]
  16. Ghosh S, May MJ, Kopp EB. NF-kappa B and Rel proteins: evolutionarily conserved mediators of immune responses. Annu Rev Immunol. 1998;16:225–260. doi: 10.1146/annurev.immunol.16.1.225. [DOI] [PubMed] [Google Scholar]
  17. Giridharan S, Srinivasan M. Mechanisms of NF-κB p65 and strategies for therapeutic manipulation. J Inflamm Res. 2018;11:407–419. doi: 10.2147/JIR.S140188. [DOI] [PMC free article] [PubMed] [Google Scholar]
  18. Hernandez R, Facelli JC. Understanding protein structural changes for oncogenic missense variants. Heliyon. 2021;7:e06013. doi: 10.1016/j.heliyon.2021.e06013. [DOI] [PMC free article] [PubMed] [Google Scholar]
  19. Ho BK, Brasseur R. The Ramachandran plots of glycine and pre-proline. BMC Struct Biol. 2005 doi: 10.1186/1472-6807-5-14. [DOI] [PMC free article] [PubMed] [Google Scholar]
  20. Howe KL, Achuthan P, Allen JJJJJ, et al. Ensembl 2021. Nucleic Acids Res. 2021;49:D884–D891. doi: 10.1093/NAR/GKAA942. [DOI] [PMC free article] [PubMed] [Google Scholar]
  21. Hubbard RE, Haider MK. Hydrogen bonds in proteins: role and strength. Encycl Life Sci. 2010 doi: 10.1002/9780470015902.A0003011.PUB2. [DOI] [Google Scholar]
  22. Hübner R-H, Schwartz JD, De Bishnu P, et al. Coordinate control of expression of Nrf2-modulated genes in the human small airway epithelium is highly responsive to cigarette smoking. Mol Med. 2009;15:203–219. doi: 10.2119/molmed.2008.00130. [DOI] [PMC free article] [PubMed] [Google Scholar]
  23. Ittisoponpisan S, Islam SA, Khanna T, et al. Can predicted protein 3D structures provide reliable insights into whether missense variants are disease associated? J Mol Biol. 2019;431:2197–2212. doi: 10.1016/j.jmb.2019.04.009. [DOI] [PMC free article] [PubMed] [Google Scholar]
  24. Jamroz M, Orozco M, Kolinski A, Kmiecik S. Consistent view of protein fluctuations from all-atom molecular dynamics and coarse-grained dynamics with knowledge-based force-field. J Chem Theory Comput. 2013;9:119–125. doi: 10.1021/ct300854w. [DOI] [PubMed] [Google Scholar]
  25. Jungk C, Mock A, Exner J, et al. Spatial transcriptome analysis reveals Notch pathway-associated prognostic markers in IDH1 wild-type glioblastoma involving the subventricular zone. BMC Med. 2016;14:170. doi: 10.1186/s12916-016-0710-7. [DOI] [PMC free article] [PubMed] [Google Scholar]
  26. Kajander T, Kahn PC, Passila SH, et al. Buried charged surface in proteins. Structure. 2000;8:1203–1214. doi: 10.1016/S0969-2126(00)00520-7. [DOI] [PubMed] [Google Scholar]
  27. Khanna T, Hanna G, Sternberg MJEE, David A. Missense3D-DB web catalogue: an atom-based analysis and repository of 4M human protein-coding genetic variants. Hum Genet. 2021;140:805–812. doi: 10.1007/s00439-020-02246-z. [DOI] [PMC free article] [PubMed] [Google Scholar]
  28. Kmiecik S, Gront D, Kolinski M, et al. Coarse-grained protein models and their applications. Chem Rev. 2016;116:7898–7936. doi: 10.1021/acs.chemrev.6b00163. [DOI] [PubMed] [Google Scholar]
  29. Krieger F, Möglich A, Kiefhaber T. Effect of proline and glycine residues on dynamics and barriers of loop formation in polypeptide chains. J Am Chem Soc. 2005;127:3346–3352. doi: 10.1021/JA042798I. [DOI] [PubMed] [Google Scholar]
  30. Kuriata A, Gierut AM, Oleniecki T, et al. CABS-flex 2.0: a web server for fast simulations of flexibility of protein structures. Nucleic Acids Res. 2018;46:W338–W343. doi: 10.1093/nar/gky356. [DOI] [PMC free article] [PubMed] [Google Scholar]
  31. Laimer J, Hofer H, Fritz M, et al. MAESTRO–multi agent stability prediction upon point mutations. BMC Bioinform. 2015 doi: 10.1186/S12859-015-0548-6. [DOI] [PMC free article] [PubMed] [Google Scholar]
  32. Lenardo M, Siebenlist U. Bcl-3-mediated nuclear regulation of the NF-кB trans-activating factor. Immunol Today. 1994;15:145–147. doi: 10.1016/0167-5699(94)90308-5. [DOI] [PubMed] [Google Scholar]
  33. Li Q, Verma IM. NF-κB regulation in the immune system. Nat Rev Immunol. 2002;2:725–734. doi: 10.1038/nri910. [DOI] [PubMed] [Google Scholar]
  34. Licciulli S, Cambiaghi V, Scafetta G, et al. Pirin downregulation is a feature of AML and leads to impairment of terminal myeloid differentiation. Leukemia. 2010;24:429–437. doi: 10.1038/LEU.2009.247. [DOI] [PubMed] [Google Scholar]
  35. Licciulli S, Luise C, Zanardi A, et al. Pirin delocalization in melanoma progression identified by high content immuno-detection based approaches. BMC Cell Biol. 2010 doi: 10.1186/1471-2121-11-5. [DOI] [PMC free article] [PubMed] [Google Scholar]
  36. Lin Y-F, Cheng C-W, Shih C-S, et al. MIB: metal ion-binding site prediction and docking server. J Chem Inf Model. 2016;56:2287–2291. doi: 10.1021/acs.jcim.6b00407. [DOI] [PubMed] [Google Scholar]
  37. Liu F, Rehmani I, Esaki S, et al. Pirin is an iron-dependent redox regulator of NF-κB. Proc Natl Acad Sci. 2013;110:9722–9727. doi: 10.1073/pnas.1221743110. [DOI] [PMC free article] [PubMed] [Google Scholar]
  38. Liu T, Zhang L, Joo D, Sun SC. NF-κB signaling in inflammation. Signal Transduct Target Ther. 2017 doi: 10.1038/SIGTRANS.2017.23. [DOI] [PMC free article] [PubMed] [Google Scholar]
  39. López-Ferrando V, Gazzo A, de la Cruz X, et al. PMut: a web-based tool for the annotation of pathological variants on proteins, 2017 update. Nucleic Acids Res. 2017;45:W222–W228. doi: 10.1093/nar/gkx313. [DOI] [PMC free article] [PubMed] [Google Scholar]
  40. Lu CH, Lin YS, Chen YC, et al. The fragment transformation method to detect the protein structural motifs. Proteins. 2006;63:636–643. doi: 10.1002/PROT.20904. [DOI] [PubMed] [Google Scholar]
  41. Machiela MJ, Chanock SJ. LDlink: a web-based application for exploring population-specific haplotype structure and linking correlated alleles of possible functional variants. Bioinformatics. 2015;31:3555–3557. doi: 10.1093/bioinformatics/btv402. [DOI] [PMC free article] [PubMed] [Google Scholar]
  42. Maldonado V, Melendez-Zajgla J. Role of Bcl-3 in solid tumors. Mol Cancer. 2011 doi: 10.1186/1476-4598-10-152. [DOI] [PMC free article] [PubMed] [Google Scholar]
  43. Miyazaki I, Simizu S, Okumura H, et al. A small-molecule inhibitor shows that pirin regulates migration of melanoma cells. Nat Chem Biol. 2010;6:667–673. doi: 10.1038/nchembio.423. [DOI] [PubMed] [Google Scholar]
  44. Oeckinghaus A, Ghosh S. The NF-kappaB family of transcription factors and its regulation. Cold Spring Harb Perspect Biol. 2009 doi: 10.1101/CSHPERSPECT.A000034. [DOI] [PMC free article] [PubMed] [Google Scholar]
  45. Pahari S, Li G, Murthy AK, et al. SAAMBE-3D: predicting effect of mutations on protein-protein interactions. Int J Mol Sci. 2020;21:2563. doi: 10.3390/ijms21072563. [DOI] [PMC free article] [PubMed] [Google Scholar]
  46. Pang H, Bartlam M, Zeng Q, et al. Crystal structure of human pirin: an iron-binding nuclear protein and transcription cofactor. J Biol Chem. 2004;279:1491–1498. doi: 10.1074/jbc.M310022200. [DOI] [PubMed] [Google Scholar]
  47. Perez-Dominguez F, Carrillo-Beltrán D, Blanco R, et al. Role of pirin, an oxidative stress sensor protein, in epithelial carcinogenesis. Biology (basel) 2021;10:1–13. doi: 10.3390/BIOLOGY10020116. [DOI] [PMC free article] [PubMed] [Google Scholar]
  48. Pettersen EF, Goddard TD, Huang CC, et al. UCSF ChimeraX: structure visualization for researchers, educators, and developers. Protein Sci. 2021;30:70–82. doi: 10.1002/PRO.3943. [DOI] [PMC free article] [PubMed] [Google Scholar]
  49. DE Pires V, Ascher DB, Blundell TL. mCSM: predicting the effects of mutations in proteins using graph-based signatures. Bioinformatics. 2014;30:335–342. doi: 10.1093/bioinformatics/btt691. [DOI] [PMC free article] [PubMed] [Google Scholar]
  50. Ponzoni L, Peñaherrera DA, Oltvai ZN, Bahar I. Rhapsody: predicting the pathogenicity of human missense variants. Bioinformatics. 2020;36:3084–3092. doi: 10.1093/BIOINFORMATICS/BTAA127. [DOI] [PMC free article] [PubMed] [Google Scholar]
  51. Qi H, Zhang H, Zhao Y, et al. MVP predicts the pathogenicity of missense variants by deep learning. Nat Commun. 2021;12:510. doi: 10.1038/s41467-020-20847-0. [DOI] [PMC free article] [PubMed] [Google Scholar]
  52. Rodrigues CHM, Myung Y, Pires DEV, Ascher DB. mCSM-PPI2: predicting the effects of mutations on protein–protein interactions. Nucleic Acids Res. 2019;47:W338–W344. doi: 10.1093/NAR/GKZ383. [DOI] [PMC free article] [PubMed] [Google Scholar]
  53. Santoro C, Mermod N, Andrews PC, Tjian R. A family of human CCAAT-box-binding proteins active in transcription and DNA replication: cloning and expression of multiple cDNAs. Nature. 1988;334:218–224. doi: 10.1038/334218A0. [DOI] [PubMed] [Google Scholar]
  54. Savojardo C, Fariselli P, Martelli PL, Casadio R. INPS-MD: a web server to predict stability of protein variants from sequence and structure. Bioinformatics. 2016;32:2542–2544. doi: 10.1093/BIOINFORMATICS/BTW192. [DOI] [PubMed] [Google Scholar]
  55. Schrodinger L, Delano W (2020) PyMOL
  56. Sherry ST, Ward M-H, Kholodov M, et al. dbSNP: the NCBI database of genetic variation. Nucleic Acids Res. 2001;29:308–311. doi: 10.1093/nar/29.1.308. [DOI] [PMC free article] [PubMed] [Google Scholar]
  57. Shoily SS, Ahsan T, Fatema K, Sajib AA. Disparities in COVID-19 severities and casualties across ethnic groups around the globe and patterns of ACE2 and PIR variants. Infect Genet Evol. 2021;92:104888. doi: 10.1016/j.meegid.2021.104888. [DOI] [PMC free article] [PubMed] [Google Scholar]
  58. Sim N-L, Kumar P, Hu J, et al. SIFT web server: predicting effects of amino acid substitutions on proteins. Nucleic Acids Res. 2012;40:W452–W457. doi: 10.1093/NAR/GKS539. [DOI] [PMC free article] [PubMed] [Google Scholar]
  59. Suleman M, Tahirul QM, Saleem S, et al. Mutational landscape of pirin and elucidation of the impact of most detrimental missense variants that accelerate the breast cancer pathways: a computational modelling study. Front Mol Biosci. 2021 doi: 10.3389/FMOLB.2021.692835. [DOI] [PMC free article] [PubMed] [Google Scholar]
  60. Tamura M, Ishizawa M, Isojima T, et al. Functional analyses of a novel missense and other mutations of the vitamin D receptor in association with alopecia. Sci Rep. 2017;7:5102. doi: 10.1038/s41598-017-05081-x. [DOI] [PMC free article] [PubMed] [Google Scholar]
  61. Tina KG, Bhadra R, Srinivasan N. PIC: protein interactions calculator. Nucleic Acids Res. 2007;35:W473–W476. doi: 10.1093/NAR/GKM423. [DOI] [PMC free article] [PubMed] [Google Scholar]
  62. Wang J, Youkharibache P, Zhang D, et al. iCn3D, a web-based 3D viewer for sharing 1D/2D/3D representations of biomolecular structures. Bioinformatics. 2020;36:131–135. doi: 10.1093/bioinformatics/btz502. [DOI] [PMC free article] [PubMed] [Google Scholar]
  63. Waterhouse A, Bertoni M, Bienert S, et al. SWISS-MODEL: homology modelling of protein structures and complexes. Nucleic Acids Res. 2018;46:W296–W303. doi: 10.1093/nar/gky427. [DOI] [PMC free article] [PubMed] [Google Scholar]
  64. Wendler WMF, Kremmer E, Förster R, Winnacker E-LL. Identification of pirin, a novel highly conserved nuclear protein. J Biol Chem. 1997;272:8482–8489. doi: 10.1074/jbc.272.13.8482. [DOI] [PubMed] [Google Scholar]
  65. Yan Y, Tao H, He J, Huang S-Y. The HDOCK server for integrated protein–protein docking. Nat Protoc. 2020;15:1829–1852. doi: 10.1038/s41596-020-0312-x. [DOI] [PubMed] [Google Scholar]
  66. Yoshikawa R, Yanagi H, Hashimoto-Tamaoki T, et al. Gene expression in response to anti-tumour intervention by polysaccharide-K (PSK) in colorectal carcinoma cells. Oncol Rep. 2004;12:1287–1293. [PubMed] [Google Scholar]
  67. Zhang N, Chen Y, Lu H, et al. MutaBind2: predicting the impacts of single and multiple mutations on protein-protein interactions. iScience. 2020;23:100939. doi: 10.1016/j.isci.2020.100939. [DOI] [PMC free article] [PubMed] [Google Scholar]
  68. Zhang Z, Miteva MA, Wang L, Alexov E. Analyzing effects of naturally occurring missense mutations. Comput Math Methods Med. 2012;2012:1–15. doi: 10.1155/2012/805827. [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

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

Supplementary Materials

42269_2022_917_MOESM1_ESM.docx (19.5KB, docx)

Additional file 1: Table S1. List of missense variants of pirin.

42269_2022_917_MOESM2_ESM.docx (14.6KB, docx)

Additional file 2: Table S2. Buried hydrogen bonds broken by pirin missense variants.

42269_2022_917_MOESM3_ESM.docx (18.4KB, docx)

Additional file 3: Table S3. Effects of missense variants on metal binding of pirin according to MIB tool.

Data Availability Statement

All data generated or analyzed during this study are included in this published article (and its Additional files).

Articles from Bulletin of the National Research Centre are provided here courtesy of Nature Publishing Group

RESOURCES