De Novo Peptide Structure Prediction: An Overview -- Molecular Modeling of Peptides -- Improved Methods for Classification, Prediction, and Design of Antimicrobial Peptides -- Building MHC Class II Epitope Predictor Using Machine Learning Approaches -- Dynamics (UHBD) Program -- Computational Prediction of Short Linear Motifs from Protein Sequences -- Peptide Toxicity Prediction -- Synthetica Structural Routes For The Rational Conversion of Peptides Into Small Molecules -- In Silico Design Of Antimicrobial Peptides -- Information-Driven Modelling Of Protein-Peptide Complexes "Information-Driven Peptide Docking" -- Computational Approaches To Developing Short Cyclic Peptide Modulators Of Protein-Protein Interactions -- A Use of Homology Modeling And Molecular Docking Methods: To Explore Binding Mechanisms of Nonylphenol And Bisphenol a with Antioxidant Enzymes -- Computational Peptide Vaccinology -- Computational Modeling Of Peptide-Aptamer Binding.

In this volume expert researchers detail in silico methods widely used to study peptides. These include methods and techniques covering the database, molecular docking, dynamics simulation, data mining, de novo design and structure modeling of peptides and protein fragments. Chapters focus on integration and application of technologies to analyze, model, identify, predict, and design a wide variety of bioactive peptides, peptide analogues and peptide drugs, as well as peptide-based biomaterials. Written in the highly successful Methods in Molecular Biology series format, chapters include introductions to their respective topics, lists of the necessary materials and reagents, step-by-step, readily reproducible laboratory protocols, and key tips on troubleshooting and avoiding known pitfalls.   Authoritative and practical, Computational Peptidology seeks to aid scientists in the further study into this newly rising subfield.