Bioinformatics for Peptide Science

Computational approaches have become indispensable in modern peptide research. This section covers the bioinformatics tools and methodologies that accelerate peptide discovery, characterization, and optimization.

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Peptide Sequence Analysis Tools

BLAST, multiple sequence alignment, motif discovery, phylogenetic analysis, and physicochemical property prediction for peptide sequences.

→ NCBI BLAST and peptide databases

→ Clustal Omega and MUSCLE alignment

→ ProP, ProtParam, and peptide property tools

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In Silico Peptide Design

Computational peptide engineering — rational design, directed evolution simulation, de novo peptide generation, and structure-based optimization.

→ Rosetta peptide design suite

→ AlphaFold for peptide structure prediction

→ De novo peptide design algorithms

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Molecular Docking for Peptide Research

Principles and best practices for peptide-protein docking — AutoDock, HADDOCK, RosettaDock — including flexible peptide docking and scoring functions.

→ Rigid vs flexible docking protocols

→ Force field and scoring function selection

→ Validation and benchmarking

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Machine Learning in Peptide Science

ML and deep learning applications — antimicrobial peptide prediction, toxicity classification, binding affinity prediction, and generative peptide models.

→ Random forest and SVM classifiers

→ Deep learning with sequence embeddings

→ Generative adversarial networks for peptides

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Peptide Database Resources

Comprehensive survey of peptide databases — UniProt, PeptideAtlas, CAMP, DBAASP, PepBank — with guidance on data retrieval and integration.

→ UniProt and NCBI Protein

→ Antimicrobial peptide databases

→ Therapeutic peptide knowledge bases