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Pred-AHCP: Robust Feature Selection-Enabled Sequence-Specific Prediction of Anti-Hepatitis C Peptides via Machine

Akash Saraswat1, Utsav Sharma2, Aryan Gandotra2

  • 1Department of Applied Sciences, School of Engineering and Technology, BML Munjal University, Gurugram, Haryana 122413, India.

Journal of Chemical Information and Modeling
|November 6, 2024
PubMed
Summary

Machine learning accurately predicts anti-Hepatitis C peptides (AHCPs) using amino acid sequences. This approach enhances the development of novel peptide-based therapeutics for Hepatitis C virus (HCV) infection.

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Area of Science:

  • Computational Biology
  • Biochemistry
  • Machine Learning

Background:

  • Hepatitis C virus (HCV) infection affects 1.5 million people annually, leading to significant liver disease.
  • Predicting antiviral peptides is crucial for therapeutic development, yet few studies focus on specific viruses like HCV.
  • Machine learning (ML) offers a promising avenue for identifying effective antiviral peptides.

Purpose of the Study:

  • To develop and fine-tune a machine learning model for predicting anti-Hepatitis C peptides (AHCPs).
  • To harness peptide amino acid sequences and physicochemical properties for predicting anti-HCV potential.
  • To provide a web server resource for predicting and re-engineering AHCPs.

Main Methods:

  • Feature computation based on peptide sequence and physicochemical properties.
  • Feature selection using mutual information and variance inflation factor to remove redundant features.
  • Development and evaluation of ML models, with Random Forest showing optimal performance.

Main Results:

  • A fine-tuned, explainable ML model achieved approximately 92% accuracy in predicting AHCPs.
  • Key predictive features include hydrophobicity, polarizability, coil-forming residues, glycine frequency, and specific dipeptide motifs (VL, LV, CC).
  • The Pred-AHCP web server (http://tinyurl.com/web-Pred-AHCP) is available for predicting and re-engineering AHCPs.

Conclusions:

  • The developed ML model effectively predicts anti-Hepatitis C peptides, aiding in the design of peptide-based therapeutics.
  • The methodology can be extended to predict peptide inhibitors for other viral infections.
  • The model serves to validate AI-generated peptide sequences for optimization.