Literature DB >> 27270240

Using natural sequences and modularity to design common and novel protein topologies.

Aron Broom1, Kyle Trainor1, Duncan Ws MacKenzie1, Elizabeth M Meiering2.   

Abstract

Protein design is still a challenging undertaking, often requiring multiple attempts or iterations for success. Typically, the source of failure is unclear, and scoring metrics appear similar between successful and failed cases. Nevertheless, the use of sequence statistics, modularity and symmetry from natural proteins, combined with computational design both at the coarse-grained and atomistic levels is propelling a new wave of design efforts to success. Here we highlight recent examples of design, showing how the wealth of natural protein sequence and topology data may be leveraged to reduce the search space and increase the likelihood of achieving desired outcomes.
Copyright © 2016 Elsevier Ltd. All rights reserved.

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Year:  2016        PMID: 27270240     DOI: 10.1016/j.sbi.2016.05.007

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


  4 in total

1.  Computational tools help improve protein stability but with a solubility tradeoff.

Authors:  Aron Broom; Zachary Jacobi; Kyle Trainor; Elizabeth M Meiering
Journal:  J Biol Chem       Date:  2017-07-14       Impact factor: 5.157

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.  Self-assembly and regulation of protein cages from pre-organised coiled-coil modules.

Authors:  Fabio Lapenta; Jana Aupič; Marco Vezzoli; Žiga Strmšek; Stefano Da Vela; Dmitri I Svergun; José María Carazo; Roberto Melero; Roman Jerala
Journal:  Nat Commun       Date:  2021-02-11       Impact factor: 14.919

Review 4.  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

  4 in total

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