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Modeling Peptide-Protein Interactions

Overview of attention for book
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 6: Highly Flexible Protein-Peptide Docking Using CABS-Dock
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Chapter title
Highly Flexible Protein-Peptide Docking Using CABS-Dock
Chapter number 6
Book title
Modeling Peptide-Protein Interactions
Published in
Methods in molecular biology, February 2017
DOI 10.1007/978-1-4939-6798-8_6
Pubmed ID
Book ISBNs
978-1-4939-6796-4, 978-1-4939-6798-8

Maciej Pawel Ciemny, Mateusz Kurcinski, Konrad Jakub Kozak, Andrzej Kolinski, Sebastian Kmiecik, Maciej Paweł Ciemny


Ora Schueler-Furman, Nir London


Protein-peptide molecular docking is a difficult modeling problem. It is even more challenging when significant conformational changes that may occur during the binding process need to be predicted. In this chapter, we demonstrate the capabilities and features of the CABS-dock server for flexible protein-peptide docking. CABS-dock allows highly efficient modeling of full peptide flexibility and significant flexibility of a protein receptor. During CABS-dock docking, the peptide folding and binding process is explicitly simulated and no information about the peptide binding site or its structure is used. This chapter presents a successful CABS-dock use for docking a potentially therapeutic peptide to a protein target. Moreover, simulation contact maps, a new CABS-dock feature, are described and applied to the docking test case. Finally, a tutorial for running CABS-dock from the command line or command line scripts is provided. The CABS-dock web server is available from http://biocomp.chem.uw.edu.pl/CABSdock/ .

Twitter Demographics

The data shown below were collected from the profiles of 3 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 32 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 32 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 7 22%
Researcher 6 19%
Other 4 13%
Student > Master 4 13%
Student > Doctoral Student 2 6%
Other 4 13%
Unknown 5 16%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 9 28%
Agricultural and Biological Sciences 7 22%
Chemistry 4 13%
Psychology 2 6%
Computer Science 2 6%
Other 1 3%
Unknown 7 22%

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 31 May 2016.
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Altmetric has tracked 14,571,953 research outputs across all sources so far. This one is in the 24th percentile – i.e., 24% of other outputs scored the same or lower than it.
So far Altmetric has tracked 8,758 research outputs from this source. They receive a mean Attention Score of 2.5. This one has gotten more attention than average, scoring higher than 55% of its peers.
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