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Modeling Peptide-Protein Interactions

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Cover of 'Modeling Peptide-Protein Interactions'

Table of Contents

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    Book Overview
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    Chapter 1 The Usage of ACCLUSTER for Peptide Binding Site Prediction
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    Chapter 2 Detection of Peptide-Binding Sites on Protein Surfaces Using the Peptimap Server
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    Chapter 3 Peptide Suboptimal Conformation Sampling for the Prediction of Protein-Peptide Interactions
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    Chapter 4 Template-Based Prediction of Protein-Peptide Interactions by Using GalaxyPepDock
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    Chapter 5 Application of the ATTRACT Coarse-Grained Docking and Atomistic Refinement for Predicting Peptide-Protein Interactions
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    Chapter 6 Highly Flexible Protein-Peptide Docking Using CABS-Dock
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    Chapter 7 AnchorDock for Blind Flexible Docking of Peptides to Proteins
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    Chapter 8 Information-Driven, Ensemble Flexible Peptide Docking Using HADDOCK
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    Chapter 9 Modeling Peptide-Protein Structure and Binding Using Monte Carlo Sampling Approaches: Rosetta FlexPepDock and FlexPepBind
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    Chapter 10 Flexible Backbone Methods for Predicting and Designing Peptide Specificity
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    Chapter 11 Simplifying the Design of Protein-Peptide Interaction Specificity with Sequence-Based Representations of Atomistic Models
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    Chapter 12 Binding Specificity Profiles from Computational Peptide Screening
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    Chapter 13 Enriching Peptide Libraries for Binding Affinity and Specificity Through Computationally Directed Library Design
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    Chapter 14 Investigating Protein–Peptide Interactions Using the Schrödinger Computational Suite
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    Chapter 15 Identifying Loop-Mediated Protein–Protein Interactions Using LoopFinder
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    Chapter 16 Protein-Peptide Interaction Design: PepCrawler and PinaColada
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    Chapter 17 Modeling and Design of Peptidomimetics to Modulate Protein–Protein Interactions
Attention for Chapter 9: Modeling Peptide-Protein Structure and Binding Using Monte Carlo Sampling Approaches: Rosetta FlexPepDock and FlexPepBind
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Chapter title
Modeling Peptide-Protein Structure and Binding Using Monte Carlo Sampling Approaches: Rosetta FlexPepDock and FlexPepBind
Chapter number 9
Book title
Modeling Peptide-Protein Interactions
Published in
Methods in molecular biology, February 2017
DOI 10.1007/978-1-4939-6798-8_9
Pubmed ID
Book ISBNs
978-1-4939-6796-4, 978-1-4939-6798-8
Authors

Nawsad Alam, Ora Schueler-Furman

Editors

Ora Schueler-Furman, Nir London

Abstract

Many signaling and regulatory processes involve peptide-mediated protein interactions, i.e., the binding of a short stretch in one protein to a domain in its partner. Computational tools that generate accurate models of peptide-receptor structures and binding improve characterization and manipulation of known interactions, help to discover yet unknown peptide-protein interactions and networks, and bring into reach the design of peptide-based drugs for targeting specific systems of medical interest.Here, we present a concise overview of the Rosetta FlexPepDock protocol and its derivatives that we have developed for the structure-based characterization of peptide-protein binding. Rosetta FlexPepDock was built to generate precise models of protein-peptide complex structures, by effectively addressing the challenge of the considerable conformational flexibility of the peptide. Rosetta FlexPepBind is an extension of this protocol that allows characterizing peptide-binding affinities and specificities of various biological systems, based on the structural models generated by Rosetta FlexPepDock. We provide detailed descriptions and guidelines for the usage of these protocols, and on a specific example, we highlight the variety of different challenges that can be met and the questions that can be answered with Rosetta FlexPepDock.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 47 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 47 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 11 23%
Student > Ph. D. Student 7 15%
Student > Bachelor 6 13%
Student > Master 5 11%
Other 3 6%
Other 9 19%
Unknown 6 13%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 15 32%
Agricultural and Biological Sciences 8 17%
Chemistry 5 11%
Chemical Engineering 3 6%
Medicine and Dentistry 2 4%
Other 6 13%
Unknown 8 17%