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Computational Peptidology
other
Editor(s):
Peng Zhou
,
Jian Huang
Publication date
(Print):
2015
Publisher:
Springer New York
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Computational epistasis
Author and book information
Book
ISBN (Print):
978-1-4939-2284-0
ISBN (Electronic):
978-1-4939-2285-7
Publication date (Print):
2015
DOI:
10.1007/978-1-4939-2285-7
SO-VID:
6ea44c1f-4174-466f-9869-ac8828a9c32b
License:
http://www.springer.com/tdm
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Book chapters
pp. 1
De Novo Peptide Structure Prediction: An Overview
pp. 15
Molecular Modeling of Peptides
pp. 43
Improved Methods for Classification, Prediction, and Design of Antimicrobial Peptides
pp. 67
Building MHC Class II Epitope Predictor Using Machine Learning Approaches
pp. 75
Brownian Dynamics Simulation of Peptides with the University of Houston Brownian Dynamics (UHBD) Program
pp. 89
Computational Prediction of Short Linear Motifs from Protein Sequences
pp. 143
Peptide Toxicity Prediction
pp. 159
Synthetic and Structural Routes for the Rational Conversion of Peptides into Small Molecules
pp. 195
In Silico Design of Antimicrobial Peptides
pp. 221
Information-Driven Modeling of Protein-Peptide Complexes
pp. 241
Computational Approaches to Developing Short Cyclic Peptide Modulators of Protein–Protein Interactions
pp. 273
A Use of Homology Modeling and Molecular Docking Methods: To Explore Binding Mechanisms of Nonylphenol and Bisphenol A with Antioxidant Enzymes
pp. 291
Computational Peptide Vaccinology
pp. 313
Computational Modeling of Peptide–Aptamer Binding
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