Literature DB >> 34014526

Computational Design of PDZ-Peptide Binding.

Nicolas Panel1, Francesco Villa1, Vaitea Opuu1, David Mignon1, Thomas Simonson2.   

Abstract

This chapter describes two computational methods for PDZ-peptide binding: high-throughput computational protein design (CPD) and a medium-throughput approach combining molecular dynamics for conformational sampling with a Poisson-Boltzmann (PB) Linear Interaction Energy for scoring. A new CPD method is outlined, which uses adaptive Monte Carlo simulations to efficiently sample peptide variants that tightly bind a PDZ domain, and provides at the same time precise estimates of their relative binding free energies. A detailed protocol is described based on the Proteus CPD software. The medium-throughput approach can be performed with standard MD and PB software, such as NAMD and Charmm. For 40 complexes between Tiam1 and peptide ligands, it gave high a2ccuracy, with mean errors of around 0.5 kcal/mol for relative binding free energies and no large errors. It requires a moderate amount of parameter fitting before it can be applied, and its transferability to other protein families is still untested.

Entities:  

Keywords:  Implicit solvent; Ligand binding; MC simulation; Molecular mechanics; Protein design; Proteus program

Year:  2021        PMID: 34014526     DOI: 10.1007/978-1-0716-1166-1_14

Source DB:  PubMed          Journal:  Methods Mol Biol        ISSN: 1064-3745


  50 in total

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Review 2.  The many roles of computation in drug discovery.

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4.  Continuum solvation models in the linear interaction energy method.

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Journal:  J Phys Chem B       Date:  2006-06-22       Impact factor: 2.991

Review 5.  Computational protein design of ligand binding and catalysis.

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Review 6.  Recent theoretical and computational advances for modeling protein-ligand binding affinities.

Authors:  Emilio Gallicchio; Ronald M Levy
Journal:  Adv Protein Chem Struct Biol       Date:  2011       Impact factor: 3.507

7.  Computational design of ligand-binding proteins with high affinity and selectivity.

Authors:  Christine E Tinberg; Sagar D Khare; Jiayi Dou; Lindsey Doyle; Jorgen W Nelson; Alberto Schena; Wojciech Jankowski; Charalampos G Kalodimos; Kai Johnsson; Barry L Stoddard; David Baker
Journal:  Nature       Date:  2013-09-04       Impact factor: 49.962

8.  Calculating protein-ligand binding affinities with MMPBSA: Method and error analysis.

Authors:  Changhao Wang; Peter H Nguyen; Kevin Pham; Danielle Huynh; Thanh-Binh Nancy Le; Hongli Wang; Pengyu Ren; Ray Luo
Journal:  J Comput Chem       Date:  2016-08-11       Impact factor: 3.376

9.  Binding pocket optimization by computational protein design.

Authors:  Christoph Malisi; Marcel Schumann; Nora C Toussaint; Jorge Kageyama; Oliver Kohlbacher; Birte Höcker
Journal:  PLoS One       Date:  2012-12-27       Impact factor: 3.240

10.  Problems of robustness in Poisson-Boltzmann binding free energies.

Authors:  Robert C Harris; Travis Mackoy; Marcia O Fenley
Journal:  J Chem Theory Comput       Date:  2015-02-10       Impact factor: 6.006

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