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Computational Design of Ligand Binding Proteins

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Cover of 'Computational Design of Ligand Binding Proteins'

Table of Contents

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    Book Overview
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    Chapter 1 In silico Identification and Characterization of Protein-Ligand Binding Sites
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    Chapter 2 Computational Modeling of Small Molecule Ligand Binding Interactions and Affinities.
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    Chapter 3 Binding Site Prediction of Proteins with Organic Compounds or Peptides Using GALAXY Web Servers.
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    Chapter 4 Computational Design of Ligand Binding Proteins
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    Chapter 5 PocketOptimizer and the Design of Ligand Binding Sites.
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    Chapter 6 Proteus and the Design of Ligand Binding Sites.
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    Chapter 7 A Structure-Based Design Protocol for Optimizing Combinatorial Protein Libraries.
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    Chapter 8 Computational Design of Ligand Binding Proteins
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    Chapter 9 Computational Design of Ligand Binding Proteins
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    Chapter 10 Computational Design of Multinuclear Metalloproteins Using Unnatural Amino Acids.
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    Chapter 11 De Novo Design of Metalloproteins and Metalloenzymes in a Three-Helix Bundle.
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    Chapter 12 Design of Light-Controlled Protein Conformations and Functions.
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    Chapter 13 Computational Introduction of Catalytic Activity into Proteins.
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    Chapter 14 Computational Design of Ligand Binding Proteins
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    Chapter 15 Design of Specific Peptide-Protein Recognition.
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    Chapter 16 Computational Design of DNA-Binding Proteins.
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    Chapter 17 Motif-Driven Design of Protein-Protein Interfaces.
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    Chapter 18 Computational Design of Ligand Binding Proteins
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    Chapter 19 Computational Design of Ligand Binding Proteins
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    Chapter 20 Computational Design of Protein Linkers.
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    Chapter 21 Modeling of Protein-RNA Complex Structures Using Computational Docking Methods.
Attention for Chapter 3: Binding Site Prediction of Proteins with Organic Compounds or Peptides Using GALAXY Web Servers.
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Chapter title
Binding Site Prediction of Proteins with Organic Compounds or Peptides Using GALAXY Web Servers.
Chapter number 3
Book title
Computational Design of Ligand Binding Proteins
Published in
Methods in molecular biology, January 2016
DOI 10.1007/978-1-4939-3569-7_3
Pubmed ID
Book ISBNs
978-1-4939-3567-3, 978-1-4939-3569-7
Authors

Lim Heo, Hasup Lee, Minkyung Baek, Chaok Seok

Editors

Barry L. Stoddard

Abstract

We introduce two GALAXY web servers called GalaxySite and GalaxyPepDock that predict protein complex structures with small organic compounds and peptides, respectively. GalaxySite predicts ligands that may bind the input protein and generates complex structures of the protein with the predicted ligands from the protein structure given as input or predicted from the input sequence. GalaxyPepDock takes a protein structure and a peptide sequence as input and predicts structures for the protein-peptide complex. Both GalaxySite and GalaxyPepDock rely on available experimentally resolved structures of protein-ligand complexes evolutionarily related to the target. With the continuously increasing size of the protein structure database, the probability of finding related proteins in the database is increasing. The servers further relax the complex structures to refine the structural aspects that are missing in the available structures or that are not compatible with the given protein by optimizing physicochemical interactions. GalaxyPepDock allows conformational change of the protein receptor induced by peptide binding. The atomistic interactions with ligands predicted by the GALAXY servers may offer important clues for designing new molecules or proteins with desired binding properties.

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 %
Researcher 6 33%
Student > Bachelor 2 11%
Student > Ph. D. Student 2 11%
Student > Master 2 11%
Professor 1 6%
Other 1 6%
Unknown 4 22%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 8 44%
Unspecified 1 6%
Immunology and Microbiology 1 6%
Medicine and Dentistry 1 6%
Neuroscience 1 6%
Other 1 6%
Unknown 5 28%