Computational Peptidology
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 integr...
| Other Authors: | , |
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| Format: | eBook |
| Language: | English |
| Published: |
New York, NY
Humana
2015, 2015
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| Edition: | 1st ed. 2015 |
| Series: | Methods in Molecular Biology
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| Subjects: | |
| Online Access: | |
| Collection: | Springer eBooks 2005- - Collection details see MPG.ReNa |
Table of Contents:
- 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