Literature DB >> 27914053

Computational Protein Design Under a Given Backbone Structure with the ABACUS Statistical Energy Function.

Peng Xiong1, Quan Chen2, Haiyan Liu3,4.   

Abstract

An important objective of computational protein design is to identify amino acid sequences that stably fold into a given backbone structure. A general approach to this problem is to minimize an energy function in the sequence space. We have previously reported a method to derive statistical energies for fixed-backbone protein design and showed that it led to de novo proteins that fold as expected. Here, we present the usage of the program that implements this method, which we now name as ABACUS (A Backbone-based Amino aCid Usage Survey).

Keywords:  Backbone structure; Mutation analysis; Protein design; Statistical energy function

Mesh:

Substances:

Year:  2017        PMID: 27914053     DOI: 10.1007/978-1-4939-6637-0_10

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


  3 in total

1.  Selection and analyses of variants of a designed protein suggest importance of hydrophobicity of partially buried sidechains for protein stability at high temperatures.

Authors:  Mingjie Han; Sanhui Liao; Xiong Peng; Xiaoqun Zhou; Quan Chen; Haiyan Liu
Journal:  Protein Sci       Date:  2019-05-23       Impact factor: 6.725

2.  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

3.  A general-purpose protein design framework based on mining sequence-structure relationships in known protein structures.

Authors:  Jianfu Zhou; Alexandra E Panaitiu; Gevorg Grigoryan
Journal:  Proc Natl Acad Sci U S A       Date:  2019-12-31       Impact factor: 11.205

  3 in total

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