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. 2013 Sep 13;8(9):e73957.
doi: 10.1371/journal.pone.0073957. eCollection 2013.

In silico approach for predicting toxicity of peptides and proteins

Sudheer Gupta  1 , Pallavi Kapoor, Kumardeep Chaudhary, Ankur Gautam, Rahul Kumar; Open Source Drug Discovery Consortium; Gajendra P S Raghava
Affiliations

In silico approach for predicting toxicity of peptides and proteins

Sudheer Gupta et al. PLoS One. .

Abstract

Background: Over the past few decades, scientific research has been focused on developing peptide/protein-based therapies to treat various diseases. With the several advantages over small molecules, including high specificity, high penetration, ease of manufacturing, peptides have emerged as promising therapeutic molecules against many diseases. However, one of the bottlenecks in peptide/protein-based therapy is their toxicity. Therefore, in the present study, we developed in silico models for predicting toxicity of peptides and proteins.

Description: We obtained toxic peptides having 35 or fewer residues from various databases for developing prediction models. Non-toxic or random peptides were obtained from SwissProt and TrEMBL. It was observed that certain residues like Cys, His, Asn, and Pro are abundant as well as preferred at various positions in toxic peptides. We developed models based on machine learning technique and quantitative matrix using various properties of peptides for predicting toxicity of peptides. The performance of dipeptide-based model in terms of accuracy was 94.50% with MCC 0.88. In addition, various motifs were extracted from the toxic peptides and this information was combined with dipeptide-based model for developing a hybrid model. In order to evaluate the over-optimization of the best model based on dipeptide composition, we evaluated its performance on independent datasets and achieved accuracy around 90%. Based on above study, a web server, ToxinPred has been developed, which would be helpful in predicting (i) toxicity or non-toxicity of peptides, (ii) minimum mutations in peptides for increasing or decreasing their toxicity, and (iii) toxic regions in proteins.

Conclusion: ToxinPred is a unique in silico method of its kind, which will be useful in predicting toxicity of peptides/proteins. In addition, it will be useful in designing least toxic peptides and discovering toxic regions in proteins. We hope that the development of ToxinPred will provide momentum to peptide/protein-based drug discovery (http://crdd.osdd.net/raghava/toxinpred/).

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Conflict of interest statement

Competing Interests: Gajendra P.S. Raghava is an academic editor of PLOS ONE. This does not alter the authors’ adherence to all the PLOS ONE policies on sharing data and materials.

Figures

Figure 1
Figure 1. Overview of datasets’ creation.
Figure 2
Figure 2. Comparison of average amino acid composition between various classes of therapeutic peptides.
Figure 3
Figure 3. Comparison of average amino acid composition of preferred residues between toxic and non-toxic peptides.
Figure 4
Figure 4. Sequence logos of (A) first ten residues of N-terminus and (B) last ten residues of C-terminus of toxic peptides, where size of residue is proportional to its propensity (main dataset).
Figure 5
Figure 5. Maximum and minimum scoring residues at every position as observed in quantitative matrix (main dataset).
Figure 6
Figure 6. ROC curves of support vector machine models based on (A) amino acid composition, (B), dipeptide composition, and (C) hybrid approach.
Figure 7
Figure 7. Schematic representation of ToxinPred webserver.
See this image and copyright information in PMC

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Publication types

Grants and funding

The authors are thankful to funding agencies Council of Scientific and Industrial Research (project Open Source Drug discovery and GENESIS BSC0121) and Department of Biotechnology (project BTISNET), Govt. of India financial support. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
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