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In Silico Approach for Predicting Toxicity of Peptides and Proteins

Author

Listed:
  • Sudheer Gupta
  • Pallavi Kapoor
  • Kumardeep Chaudhary
  • Ankur Gautam
  • Rahul Kumar
  • Open Source Drug Discovery Consortium
  • Gajendra P S Raghava

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/).

Suggested Citation

  • Sudheer Gupta & Pallavi Kapoor & Kumardeep Chaudhary & Ankur Gautam & Rahul Kumar & Open Source Drug Discovery Consortium & Gajendra P S Raghava, 2013. "In Silico Approach for Predicting Toxicity of Peptides and Proteins," PLOS ONE, Public Library of Science, vol. 8(9), pages 1-10, September.
  • Handle: RePEc:plo:pone00:0073957
    DOI: 10.1371/journal.pone.0073957
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    1. Abdulrahman Alshammari & Abdullah F. Alasmari & Metab Alharbi & Nemat Ali & Ziyad Tariq Muhseen & Usman Ali Ashfaq & Miraj Ud-din & Asad Ullah & Muhammad Arshad & Sajjad Ahmad, 2022. "Novel Chimeric Vaccine Candidate Development against Leptotrichia buccalis," IJERPH, MDPI, vol. 19(17), pages 1-15, August.
    2. Metab Alharbi & Abdulrahman Alshammari & Abdullah F. Alasmari & Salman Mansour Alharbi & Muhammad Tahir ul Qamar & Asad Ullah & Sajjad Ahmad & Muhammad Irfan & Atif Ali Khan Khalil, 2022. "Designing of a Recombinant Multi-Epitopes Based Vaccine against Enterococcus mundtii Using Bioinformatics and Immunoinformatics Approaches," IJERPH, MDPI, vol. 19(6), pages 1-19, March.
    3. Asad Ullah & Sajjad Ahmad & Saba Ismail & Zobia Afsheen & Muhammad Khurram & Muhammad Tahir ul Qamar & Naif AlSuhaymi & Mahdi H. Alsugoor & Khaled S. Allemailem, 2021. "Towards A Novel Multi-Epitopes Chimeric Vaccine for Simulating Strong Immune Responses and Protection against Morganella morganii," IJERPH, MDPI, vol. 18(20), pages 1-26, October.
    4. Hassan N. Althurwi & Khalid M. Alharthy & Faisal F. Albaqami & Ali Altharawi & Muhammad Rizwan Javed & Ziyad Tariq Muhseen & Muhammad Tahir ul Qamar, 2022. "mRNA-Based Vaccine Designing against Epstein-Barr Virus to Induce an Immune Response Using Immunoinformatic and Molecular Modelling Approaches," IJERPH, MDPI, vol. 19(20), pages 1-21, October.

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