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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 7: AnchorDock for Blind Flexible Docking of Peptides to Proteins
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Chapter title
AnchorDock for Blind Flexible Docking of Peptides to Proteins
Chapter number 7
Book title
Modeling Peptide-Protein Interactions
Published in
Methods in molecular biology, February 2017
DOI 10.1007/978-1-4939-6798-8_7
Pubmed ID
Book ISBNs
978-1-4939-6796-4, 978-1-4939-6798-8
Authors

Michal Slutzki, Avraham Ben-Shimon, Masha Y. Niv

Editors

Ora Schueler-Furman, Nir London

Abstract

Due to increasing interest in peptides as signaling modulators and drug candidates, several methods for peptide docking to their target proteins are under active development. The "blind" docking problem, where the peptide-binding site on the protein surface is unknown, presents one of the current challenges in the field. AnchorDock protocol was developed by Ben-Shimon and Niv to address this challenge.This protocol narrows the docking search to the most relevant parts of the conformational space. This is achieved by pre-folding the free peptide and by computationally detecting anchoring spots on the surface of the unbound protein. Multiple flexible simulated annealing molecular dynamics (SAMD) simulations are subsequently carried out, starting from pre-folded peptide conformations, constrained to the various precomputed anchoring spots.Here, AnchorDock is demonstrated using two known protein-peptide complexes. A PDZ-peptide complex provides a relatively easy case due to the relatively small size of the protein, and a typical peptide conformation and binding region; a more challenging example is a complex between USP7N-term and a p53-derived peptide, where the protein is larger, and the peptide conformation and a binding site are generally assumed to be unknown. AnchorDock returned native-like solutions ranked first and third for the PDZ and USP7 complexes, respectively. We describe the procedure step by step and discuss possible modifications where applicable.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 18 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 18 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 4 22%
Researcher 3 17%
Other 2 11%
Professor > Associate Professor 2 11%
Student > Doctoral Student 2 11%
Other 3 17%
Unknown 2 11%
Readers by discipline Count As %
Agricultural and Biological Sciences 5 28%
Chemistry 3 17%
Biochemistry, Genetics and Molecular Biology 2 11%
Engineering 2 11%
Medicine and Dentistry 2 11%
Other 1 6%
Unknown 3 17%