CAMPR4: a database of natural and synthetic antimicrobial peptides

Ulka Gawde; Shuvechha Chakraborty; Faiza Hanif Waghu; Ram Shankar Barai; Ashlesha Khanderkar; Rishikesh Indraguru; Tanmay Shirsat; Susan Idicula-Thomas

doi:10.1093/nar/gkac933

. 2022 Nov 12;51(D1):D377–D383. doi: 10.1093/nar/gkac933

CAMP_R4: a database of natural and synthetic antimicrobial peptides

Ulka Gawde ^1,², Shuvechha Chakraborty ^2,², Faiza Hanif Waghu ^3,², Ram Shankar Barai ⁴, Ashlesha Khanderkar ⁵, Rishikesh Indraguru ⁶, Tanmay Shirsat ⁷, Susan Idicula-Thomas ^8,^✉

PMCID: PMC9825550 PMID: 36370097

Abstract

There has been an exponential increase in the design of synthetic antimicrobial peptides (AMPs) for its use as novel antibiotics. Synthetic AMPs are substantially enriched in residues with physicochemical properties known to be critical for antimicrobial activity; such as positive charge, hydrophobicity, and higher alpha helical propensity. The current prediction algorithms for AMPs have been developed using AMP sequences from natural sources and hence do not perform well for synthetic peptides. In this version of CAMP database, along with updating sequence information of AMPs, we have created separate prediction algorithms for natural and synthetic AMPs. CAMP_R4 holds 24243 AMP sequences, 933 structures, 2143 patents and 263 AMP family signatures. In addition to the data on sequences, source organisms, target organisms, minimum inhibitory and hemolytic concentrations, CAMP_R4 provides information on N and C terminal modifications and presence of unusual amino acids, as applicable. The database is integrated with tools for AMP prediction and rational design (natural and synthetic AMPs), sequence (BLAST and clustal omega), structure (VAST) and family analysis (PRATT, ScanProsite, CAMPSign). The data along with the algorithms of CAMP_R4 will aid to enhance AMP research. CAMP_R4 is accessible at http://camp.bicnirrh.res.in/.

INTRODUCTION

Antimicrobial resistance (AMR) is one of the major health crises affecting the health care system worldwide (1). The COVID-19 pandemic has further amplified AMR risk due to the rampant use of antibiotics; especially in low- and middle-income countries (2). The dearth of novel antimicrobials is a significant bottleneck to combat drug-resistant infections.

Over the years, antimicrobial peptides (AMPs) have gained attention as novel antibiotics. These are potent, broad-spectrum and quick-acting defence molecules produced by living organisms ranging from bacteria to mammals, as part of the innate immune response (3,4). Owing to the reduced risk of AMR of AMPs as compared to conventional antibiotics (5), there has been accelerated research on characterisation, discovery and rational design of AMPs. Consequentially, a large volume of data on AMPs is now accessible through various online databases (6–12).

We had first developed CAMP, a manually curated database on AMPs, in 2010 followed by updated versions in 2014 and 2016 (13–15). CAMP_R3 contained 10 247 sequences, 757 structures and 114 family-specific signatures of AMPs along with tools for AMP analysis (15). The data available in CAMP has been used by several research groups to create secondary AMP databases and prediction servers (16–31). The prediction algorithms in CAMP have been widely used to identify AMPs from natural sources and for rational design (32–45). In the present release, along with updating AMP sequences and associated data extracted from literature post 2015, we have dedicated a separate section for data and prediction algorithms pertaining to synthetic AMPs. Information related to the N and C terminal modifications, that are known to alter antimicrobial activity, has also been incorporated (Figure 1). CAMP_R4 presently contains 24243 sequences of which 11827 are of natural origin, 12416 are synthetic; 2143 patents; 933 3D structures and 263 family specific signatures.

Figure 1. — Overview of CAMP_R4 database update.

MATERIALS AND METHODS

Data collection

CAMP_R4 database was updated using information available on AMPs from the NCBI protein (46), PDB (47), PubMed and Lens (patent) databases for the period post 2015. These databases were queried using keywords such as ‘antimicrobial’, ‘antibacterial’, ‘antifungal’ and ‘antiviral’. The obtained hits were manually curated to extract information on sequence, structure, protein definition, accession numbers, reference literature, activity, taxonomy of the source organism, target organisms with minimum inhibitory concentration (MIC) values, hemolytic activity of the peptide and protein family description. Information on N and C terminal residues and other modifications including presence of alkyl groups or modified amino acids has been included in the comments section.

Database architecture

CAMP_R4 was developed using MySQL Server 5.1.33 as back-end and the front-end is built using PHP, HTML, JavaScript and Perl. The prediction server was developed using statistical software R version 4.0.5. The database interface consists of sections as described previously (15).

Algorithm for prediction of natural and synthetic peptides

Dataset creation

Positive class: The positive class comprised of experimentally validated AMP sequences, from natural and synthetic origins, available in CAMP_R4. Synthetic AMPs, as defined in our study, are peptides that are rationally designed through single or multiple residue substitutions of natural AMPs or through de novo synthesis. The experimentally validated AMPs were further filtered to exclude sequences that had (i) non-standard amino acids (α,γ-diamino β-hydroxy butyric acid, D-ornithine, Z-α,β−dehydroarginine), stapled and circular peptides, and (ii) length >100 residues and ≤2. These sequences were further filtered through CD-HIT server (48), using a 90% sequence similarity cut-off, to generate non-redundant datasets comprising of 1592 synthetic and 2328 natural AMP sequences.

Negative class: As it is difficult to get substantial number of experimentally validated non-AMPs from published literature, a dataset of 4011 peptides as previously described in CAMP_R1 was used as a negative class (13). The dataset comprised of experimentally proven non-AMPs (25 sequences), non-secretory proteins searched from the UniProt database (49) without annotation as ‘antimicrobial’ (2413 sequences), arbitrary sequences generated using random numbers (1200 sequences) and proteins retrieved randomly without ‘antimicrobial’ annotation from the UniProt database (1200 sequences). These sequences were then further filtered using CD-HIT server (48), for eliminating sequences with >90% similarity and the remaining 4011 sequences were used as a negative class.

An identical number of sequences were maintained for the positive and negative classes to create balanced datasets for model generation. The positive and negative classes were further randomly divided into training (60%), test (30%) and external validation (10%) datasets. We ensured that the positive class of external dataset did not contain AMPs that were part of CAMP_R3 and this dataset was used to compare the performance of CAMP_R4 with CAMP_R3 and other existing prediction algorithms.

Feature selection and model generation

257 features, that represent sequence-based composition and physicochemical properties of AMPs, were used as descriptors for model building, as described previously (13). These 257 features were ranked using the Gini score based rigorous recursive feature elimination (RFE) method and RF models were generated by reducing 50% of the features at each step. Thus, classification models for synthetic and natural AMPs were generated using 257, 128, 64, 32, 16, 8 and 4 features. These models were evaluated using 10-fold cross validation accuracy and kappa values for selecting the optimum number of features. Kappa values compares observed accuracy and accuracy obtained by random chance. The models generated using subset of 64 and 32 features, respectively for natural and synthetic AMPs performed the best. These features were used for developing SVM, RF and ANN based prediction models.

All the models were generated by implementation of SVM, RF and ANN in R (version 4.0.5). Linear, polynomial and radial basis SVM kernel functions were evaluated using ‘Kernlab’ (50) package. Polynomial and radial basis kernels were found to perform best and thus retained respectively for the natural and synthetic AMP final model generation. Hyper parameters such as degree, scale and offset were set to 3, 0.01 and 1 for natural and sigma and offset were set to 0.03 and 1 for synthetic AMP prediction. ‘randomForest’ package (51) was used to train the RF classifier with a maximum of 500 trees. ANN-based prediction model for natural and synthetic AMPs were built using the ‘nnet’ (52) package with parameters size and decay set as 1 and 0.1, respectively. The models were evaluated through 10-fold cross-validation using Matthews correlation coefficient (MCC) and prediction accuracy scores.

Rational design of natural and synthetic AMPs

Algorithms for generating single residue substitutions of user-defined sequence/s followed by their AMP prediction using developed models (RF, SVM and ANN) for natural and synthetic AMPs were created using in-house Perl scripts.

Generation and validation of family-specific signatures

Family-specific signatures, represented by patterns and hidden Markov models (HMMs), were generated for the updated experimentally validated natural AMPs and validated as explained in Waghu et al. (15,53). Clustal-omega 1.2.2 (54) was used for multiple sequence alignment; ‘hmmbuild’ and ‘hmmsearch’ commands (with default parameters) of HMMER downloadable version 3.3.2 (55) were used for generation and search using HMMs respectively. Patterns and HMMs that had precision and recall values of ≥0.5 were included in the database.

RESULTS AND DISCUSSION

The CAMP database has been updated to incorporate the large number of natural and synthetic AMPs that have been discovered and designed in the last five years after the release of CAMP_R3. Natural AMPs were majorly extracted from NCBI protein database (46). Synthetic AMPs were retrieved from published literature in PubMed database. A total of ∼65000 entries were retrieved from PubMed using keyword-based search. These entries were further filtered to 18355 PubMed articles using an in-house text mining code executed on the abstract of these publications. Subsequently, each of these articles was carefully reviewed to retrieve manually curated information on AMPs. A detailed description of the contents in updated CAMP_R4 can be viewed in Table 1. There has been a massive increase in the number of AMPs, especially in the discovery of synthetic AMPs as compared to the earlier years, 12170 of the 16079 new AMPs were of synthetic origin. This is expected as AMPs are being increasingly explored as new antibiotics. The databases on AMPs and subsequent sequence analysis have led to the identification of many sequence-related features of AMPs such as positive charge, hydrophobicity and helical propensity which could be exploited for rational design of AMPs (56,57).

Table 1.

Comparison of CAMP_R3 and CAMP_R4

Sr. No	Parameters of AMPs	CAMP_R3	CAMP_R4
1.	Sequences	8164	24243
	Experimentally validated	2766	16945
	Natural origin	2574	4839
	Synthetic origin	192	12106
	Predicted AMPs	5398	7298
2.	Patents	2083	2143
3.	3D structures	757	933
4.	Signatures	114	263
	Patterns	36	84
	HMMs	78	179
	Families	45	53
5.	Prediction algorithms	Single algorithm for prediction of natural and synthetic AMPs	Separate algorithms for prediction of natural and synthetic AMPs
6.	Rational design tool	Single tool for rational design of AMPs	Separate tools for rational design of natural and synthetic AMPs

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Probably for the same reason, synthetic AMPs were found to be significantly enriched with residues that are known to be critical for antimicrobial activity such as positively charged (K, R), hydrophobic (W, L), higher alpha helical propensity (L, K) and flexibility (W, L) as compared to natural AMPs (Figure 2). This observation prompted us to investigate the effectiveness of the current AMP prediction algorithms, that are trained on natural AMP sequences, for predicting synthetic AMPs. A dataset of 159 synthetic AMPs and 159 sequences from negative dataset that were not part of the training models (external validation dataset; see Methods) was predicted with an accuracy of 92.5% using CAMP_R3 and 71.7% using DBAASP (Table 2). In this update, taking cognizance of the difference in the sequence composition of these two classes of AMPs, we created independent prediction algorithms for natural and synthetic AMPs which have also been applied for rational design of natural and synthetic AMPs. The performance metrics and the top features used for these algorithms are provided in Supplementary Tables S1 and S2.

Figure 2. — Enrichment and depletion analysis of amino acids of synthetic AMPs (n = 955) as compared to (A) natural AMPs (n = 1397) and (B) SwissProt 51 datasets (58). Amino acid composition of natural AMPs was compared with SwissProt 51 dataset in (C). The analysis was performed with Composition profiler online tool (59) using 10 000 bootstrap iterations; alpha value was set to 0.05 for statistical significance. Error bars represent standard deviations of observed relative frequencies of the bootstrap samples. Amino acids are arranged left to right in x-axis in order of increasing hydrophobicity (60), alpha helix and beta structure frequency (61) for each of the respective plots. Disorder propensity of amino acids are as defined by Dunker *et al.* (62).

Table 2.

Comparison of prediction accuracy of CAMP_R4 algorithms with other AMP prediction algorithms

External dataset*	Algorithms	CAMP_R4	CAMP_R3	DBAASP
Natural source	RF	86.5%	85.0%	68.5%
	SVM	84.1%	82.4%
	ANN	82.2%	79.8%
Synthetic source	RF	94.3%	92.5%	71.7%
	SVM	90.3%	89.3%
	ANN	90.6%	85.5%

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*Number of natural peptides used as external dataset each for Positive and Negative class is 233. Number of synthetic peptides used as external dataset each for Positive and Negative class is 159.

Conclusion

CAMP_R4 contains updated information on sequences (natural and synthetic), structures and families of AMPs. The database hosts algorithms for predicting natural and synthetic AMPs. Comparison of CAMP_R4 with presently available manually curated AMP databases is provided in Supplementary Table S3.

The highlights of this update are as follows:

Comprehensive update on AMP-related data: The updated version has information on 24243 sequences (of which 11827 are natural and 12416 are synthetic), 2143 patents, 933 structures and 263 AMP family signatures.

Prediction algorithms for natural and synthetic AMPs: Independent algorithms for prediction of synthetic and natural AMPs based on physicochemical properties and sequence composition have been developed. These algorithms have better prediction accuracy for natural (86.5%) and synthetic AMPs (94.3%) as compared to the currently available online algorithms (Table 2).

Tool for rational design of natural and synthetic AMPs: The tool allows the rational design of AMPs by generating single residue mutant sequences for a user-defined sequence and predicts the effect of single residue substitutions on antimicrobial activity using separate models generated for predicting natural and synthetic AMPs.

Updated family information and signatures: The database now contains information on 53 AMP families (8 new families included) and has 263 AMP family-specific signatures that can promote AMP family-based studies and novel AMP discovery. Signatures for 8 AMP families namely gurmarin, macin, magainin, nigrocin, pardaxin, piscidin, ranacyclin and stomoxyn have been included in this update.

Improved annotations: Information on features such as N and C terminal modification of amino acids, presence of unusual amino acids, cyclic nature of peptides that are important determinants of antimicrobial activity; have been included in this update. Information relating to other functions of AMPs such as anticancer, antiviral activity has also been added.

DATA AVAILABILITY

CAMP_R4 is freely accessible at http://camp.bicnirrh.res.in/.

Supplementary Material

gkac933_Supplemental_File

Click here for additional data file.^{(22.4KB, docx)}

ACKNOWLEDGEMENTS

The authors are grateful to Dr Geetanjali Sachdeva, Director, ICMR-NIRRCH for support. The authors thank Ms. Indra Kundu, Ms. Karishma Desai, Dr Chandan Kumar, Dr Prayagraj Fandilolu, Ms. Anam Arshi, and Ms. Komal Chaudhari at ICMR-NIRRCH, Ms. Rekha Chataule, Ms. Rohini Balmiki, Mr. Rakesh Poojari, Ms. Priya Pandey, Ms. Shabari Prakashan at Guru Nanak Khalsa College, Mumbai for assistance with data curation.

Contributor Information

Ulka Gawde, Biomedical Informatics Centre, ICMR-National Institute for Research in Reproductive and Child Health, Mumbai 400012, Maharashtra, India.

Shuvechha Chakraborty, Biomedical Informatics Centre, ICMR-National Institute for Research in Reproductive and Child Health, Mumbai 400012, Maharashtra, India.

Faiza Hanif Waghu, Biomedical Informatics Centre, ICMR-National Institute for Research in Reproductive and Child Health, Mumbai 400012, Maharashtra, India.

Ram Shankar Barai, Biomedical Informatics Centre, ICMR-National Institute for Research in Reproductive and Child Health, Mumbai 400012, Maharashtra, India.

Ashlesha Khanderkar, Department of Bioinformatics, Guru Nanak Khalsa College, Nathalal Parekh Marg, Matunga, Mumbai 400019, Maharashtra, India.

Rishikesh Indraguru, Department of Bioinformatics, Guru Nanak Khalsa College, Nathalal Parekh Marg, Matunga, Mumbai 400019, Maharashtra, India.

Tanmay Shirsat, Biomedical Informatics Centre, ICMR-National Institute for Research in Reproductive and Child Health, Mumbai 400012, Maharashtra, India.

Susan Idicula-Thomas, Biomedical Informatics Centre, ICMR-National Institute for Research in Reproductive and Child Health, Mumbai 400012, Maharashtra, India.

SUPPLEMENTARY DATA

Supplementary Data are available at NAR Online.

FUNDING

This work [RA/1121/09-2021] was supported by research funds from Department of Biotechnology (DBT), India [BT/PR40165/BTIS/137/12/2021] and Indian Council of Medical Research. The open access publication charge for this paper has been waived by Oxford University Press - NAR.

Conflict of interest statement. None declared.

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Associated Data

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

Supplementary Materials

gkac933_Supplemental_File

Click here for additional data file.^{(22.4KB, docx)}

Data Availability Statement

CAMP_R4 is freely accessible at http://camp.bicnirrh.res.in/.

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PERMALINK

CAMPR4: a database of natural and synthetic antimicrobial peptides

Ulka Gawde

Shuvechha Chakraborty

Faiza Hanif Waghu

Ram Shankar Barai

Ashlesha Khanderkar

Rishikesh Indraguru

Tanmay Shirsat

Susan Idicula-Thomas