Literature DB >> 26773478

Protein sequence design and its applications.

Sankaran Sandhya1, Richa Mudgal2, Gayatri Kumar1, Ramanathan Sowdhamini3, Narayanaswamy Srinivasan4.   

Abstract

Design of proteins has far-reaching potentials in diverse areas that span repurposing of the protein scaffold for reactions and substrates that they were not naturally meant for, to catching a glimpse of the ephemeral proteins that nature might have sampled during evolution. These non-natural proteins, either in synthesized or virtual form have opened the scope for the design of entities that not only rival their natural counterparts but also offer a chance to visualize the protein space continuum that might help to relate proteins and understand their associations. Here, we review the recent advances in protein engineering and design, in multiple areas, with a view to drawing attention to their future potential.
Copyright © 2015 Elsevier Ltd. All rights reserved.

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Year:  2016        PMID: 26773478     DOI: 10.1016/j.sbi.2015.12.004

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


  7 in total

1.  Profiles of Natural and Designed Protein-Like Sequences Effectively Bridge Protein Sequence Gaps: Implications in Distant Homology Detection.

Authors:  Gayatri Kumar; Narayanaswamy Srinivasan; Sankaran Sandhya
Journal:  Methods Mol Biol       Date:  2022

2.  Free-Energy-Based Protein Design: Re-Engineering Cellular Retinoic Acid Binding Protein II Assisted by the Moveable-Type Approach.

Authors:  Haizhen A Zhong; Elizabeth M Santos; Chrysoula Vasileiou; Zheng Zheng; James H Geiger; Babak Borhan; Kenneth M Merz
Journal:  J Am Chem Soc       Date:  2018-02-28       Impact factor: 15.419

3.  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

4.  Master Blaster: an approach to sensitive identification of remotely related proteins.

Authors:  Chintalapati Janaki; Venkatraman S Gowri; Narayanaswamy Srinivasan
Journal:  Sci Rep       Date:  2021-04-22       Impact factor: 4.379

Review 5.  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

6.  Use of designed sequences in protein structure recognition.

Authors:  Gayatri Kumar; Richa Mudgal; Narayanaswamy Srinivasan; Sankaran Sandhya
Journal:  Biol Direct       Date:  2018-05-09       Impact factor: 4.540

7.  Sibe: a computation tool to apply protein sequence statistics to predict folding and design in silico.

Authors:  Ngaam J Cheung; Wookyung Yu
Journal:  BMC Bioinformatics       Date:  2019-09-06       Impact factor: 3.169

  7 in total

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