Literature DB >> 27348345

Computational protein design for given backbone: recent progresses in general method-related aspects.

Haiyan Liu1, Quan Chen2.   

Abstract

To achieve high success rate in protein design requires a reliable sequence design method to find amino acid sequences that stably fold into a desired backbone structure. This problem is addressed by computational protein design through the approach of energy minimization. Here we review recent method progresses related to improving the accuracy of this approach. First, the quality of the energy model is a key factor. Second, with structure sensitive energy functions, whether and how backbone flexibility is considered can have large effects on design accuracy, although usually only small adjustments of the backbone structure itself are involved. Third, the effective accuracy of design results can be boosted by post-processing a small number of designed sequences with complementary models that may not be efficient enough for full sequence optimization. Finally, computational method development will benefit greatly from increasingly efficient experimental approaches that can be applied to obtain extensive feedbacks.
Copyright © 2016 Elsevier Ltd. All rights reserved.

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Year:  2016        PMID: 27348345     DOI: 10.1016/j.sbi.2016.06.013

Source DB:  PubMed          Journal:  Curr Opin Struct Biol        ISSN: 0959-440X            Impact factor:   6.809


  9 in total

1.  Comparing side chain packing in soluble proteins, protein-protein interfaces, and transmembrane proteins.

Authors:  J C Gaines; S Acebes; A Virrueta; M Butler; L Regan; C S O'Hern
Journal:  Proteins       Date:  2018-02-26

2.  Data driven flexible backbone protein design.

Authors:  Mark G F Sun; Philip M Kim
Journal:  PLoS Comput Biol       Date:  2017-08-24       Impact factor: 4.475

3.  Rosetta:MSF: a modular framework for multi-state computational protein design.

Authors:  Patrick Löffler; Samuel Schmitz; Enrico Hupfeld; Reinhard Sterner; Rainer Merkl
Journal:  PLoS Comput Biol       Date:  2017-06-12       Impact factor: 4.475

4.  Computational Protein Design with Deep Learning Neural Networks.

Authors:  Jingxue Wang; Huali Cao; John Z H Zhang; Yifei Qi
Journal:  Sci Rep       Date:  2018-04-20       Impact factor: 4.379

5.  Collective repacking reveals that the structures of protein cores are uniquely specified by steric repulsive interactions.

Authors:  J C Gaines; A Virrueta; D A Buch; S J Fleishman; C S O'Hern; L Regan
Journal:  Protein Eng Des Sel       Date:  2017-05-01       Impact factor: 1.650

6.  Toward a Computational NMR Procedure for Modeling Dipeptide Side-Chain Conformation.

Authors:  Jesús San Fabián; Ignacio Ema; Salama Omar; Jose Manuel García de la Vega
Journal:  J Chem Inf Model       Date:  2021-11-11       Impact factor: 4.956

7.  Binder design for targeting SARS-CoV-2 spike protein: An in silico perspective.

Authors:  Ali Etemadi; Hamid Reza Moradi; Farideh Mohammadian; Mohammad Hossein Karimi-Jafari; Babak Negahdari; Yazdan Asgari; Mohammadali Mazloomi
Journal:  Gene Rep       Date:  2021-11-26

Review 8.  Protein-protein interaction prediction with deep learning: A comprehensive review.

Authors:  Farzan Soleymani; Eric Paquet; Herna Viktor; Wojtek Michalowski; Davide Spinello
Journal:  Comput Struct Biotechnol J       Date:  2022-09-19       Impact factor: 6.155

9.  Ten quick tips for homology modeling of high-resolution protein 3D structures.

Authors:  Yazan Haddad; Vojtech Adam; Zbynek Heger
Journal:  PLoS Comput Biol       Date:  2020-04-02       Impact factor: 4.475

  9 in total

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