Computational Peptidology [Hardcover]

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.

1. De Novo Peptide Structure Prediction: An Overview

Pierre Th?venet, Julien Rey, Gautier Moroy, and Pierre Tuffery

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2. Molecular Modeling of Peptides

Krzysztof Kuczera

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3. Improved Methods for Classification, Prediction, and Design of Antimicrobial Peptides

Guangshun Wang

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4. Building MHC Class II Epitope Predictor Using Machine Learning Approaches

Loan Ping Eng, Tin Wee Tan, And Joo Chuan Tong

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5. Brownian Dynamics Simulation Of Peptides with The University of Houston Brownian Dynamics (UHBD) Program

Tongye Shen and Chung F. Wong

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6. Computational Prediction of Short Linear Motifs from Protein Sequences

Richard J. Edwards And Nicolas Palopol1