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Computational Design of Ligand Binding Proteins

Overview of attention for book
Computational Design of Ligand Binding Proteins
Springer New York

Table of Contents

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    Book Overview
  2. Altmetric Badge
    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.
  19. Altmetric Badge
    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 10: Computational Design of Multinuclear Metalloproteins Using Unnatural Amino Acids.
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Chapter title
Computational Design of Multinuclear Metalloproteins Using Unnatural Amino Acids.
Chapter number 10
Book title
Computational Design of Ligand Binding Proteins
Published in
Methods in molecular biology, January 2016
DOI 10.1007/978-1-4939-3569-7_10
Pubmed ID
Book ISBNs
978-1-4939-3567-3, 978-1-4939-3569-7
Authors

William A. Hansen, Jeremy H. Mills, Sagar D. Khare

Editors

Barry L. Stoddard

Abstract

Multinuclear metal ion clusters, coordinated by proteins, catalyze various critical biological redox reactions, including water oxidation in photosynthesis, and nitrogen fixation. Designed metalloproteins featuring synthetic metal clusters would aid in the design of bio-inspired catalysts for various applications in synthetic biology. The design of metal ion-binding sites in a protein chain requires geometrically constrained and accurate placement of several (between three and six) polar and/or charged amino acid side chains for every metal ion, making the design problem very challenging to address. Here, we describe a general computational method to redesign oligomeric interfaces of symmetric proteins for the purpose of creating novel multinuclear metalloproteins with tunable geometries, electrochemical environments, and metal cofactor stability via first and second-shell interactions.The method requires a target symmetric organometallic cofactor whose coordinating ligands resemble the side chains of a natural or unnatural amino acid and a library of oligomeric protein structures featuring the same symmetry as the target cofactor. Geometric interface matches between target cofactor and scaffold are determined using a program that we call symmetric protein recursive ion-cofactor sampler (SyPRIS). First, the amino acid-bound organometallic cofactor model is built and symmetrically aligned to the axes of symmetry of each scaffold. Depending on the symmetry, rigid body and inverse rotameric degrees of freedom of the cofactor model are then simultaneously sampled to locate scaffold backbone constellations that are geometrically poised to incorporate the cofactor. Optionally, backbone remodeling of loops can be performed if no perfect matches are identified. Finally, the identities of spatially proximal neighbor residues of the cofactor are optimized using Rosetta Design. Selected designs can then be produced in the laboratory using genetically incorporated unnatural amino acid technology and tested experimentally for structure and catalytic activity.

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Mendeley readers

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The data shown below were compiled from readership statistics for 17 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 17 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 3 18%
Researcher 3 18%
Student > Bachelor 2 12%
Student > Postgraduate 2 12%
Student > Doctoral Student 1 6%
Other 4 24%
Unknown 2 12%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 6 35%
Agricultural and Biological Sciences 3 18%
Chemistry 3 18%
Neuroscience 1 6%
Psychology 1 6%
Other 0 0%
Unknown 3 18%
Attention Score in Context

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 30 September 2016.
All research outputs
#15,369,653
of 22,865,319 outputs
Outputs from Methods in molecular biology
#5,350
of 13,127 outputs
Outputs of similar age
#230,936
of 393,648 outputs
Outputs of similar age from Methods in molecular biology
#545
of 1,470 outputs
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