Literature DB >> 34051682

Protein sequence design with deep generative models.

Zachary Wu1, Kadina E Johnston2, Frances H Arnold3, Kevin K Yang4.   

Abstract

Protein engineering seeks to identify protein sequences with optimized properties. When guided by machine learning, protein sequence generation methods can draw on prior knowledge and experimental efforts to improve this process. In this review, we highlight recent applications of machine learning to generate protein sequences, focusing on the emerging field of deep generative methods.
Copyright © 2021 The Authors. Published by Elsevier Ltd.. All rights reserved.

Entities:  

Keywords:  Deep learning; Generative models; Protein engineering

Mesh:

Year:  2021        PMID: 34051682     DOI: 10.1016/j.cbpa.2021.04.004

Source DB:  PubMed          Journal:  Curr Opin Chem Biol        ISSN: 1367-5931            Impact factor:   8.822


  14 in total

Review 1.  Deep generative models for peptide design.

Authors:  Fangping Wan; Daphne Kontogiorgos-Heintz; Cesar de la Fuente-Nunez
Journal:  Digit Discov       Date:  2022-03-31

Review 2.  Machine learning to navigate fitness landscapes for protein engineering.

Authors:  Chase R Freschlin; Sarah A Fahlberg; Philip A Romero
Journal:  Curr Opin Biotechnol       Date:  2022-04-09       Impact factor: 10.279

3.  Protein-structure prediction revolutionized.

Authors:  Mohammed AlQuraishi
Journal:  Nature       Date:  2021-08       Impact factor: 49.962

Review 4.  Intelligent host engineering for metabolic flux optimisation in biotechnology.

Authors:  Lachlan J Munro; Douglas B Kell
Journal:  Biochem J       Date:  2021-10-29       Impact factor: 3.857

Review 5.  Protein Design with Deep Learning.

Authors:  Marianne Defresne; Sophie Barbe; Thomas Schiex
Journal:  Int J Mol Sci       Date:  2021-10-29       Impact factor: 5.923

6.  Enhancing computational enzyme design by a maximum entropy strategy.

Authors:  Wen Jun Xie; Mojgan Asadi; Arieh Warshel
Journal:  Proc Natl Acad Sci U S A       Date:  2022-02-15       Impact factor: 12.779

7.  Reduced antigenicity of Omicron lowers host serologic response.

Authors:  Jérôme Tubiana; Yufei Xiang; Li Fan; Haim J Wolfson; Kong Chen; Dina Schneidman-Duhovny; Yi Shi
Journal:  bioRxiv       Date:  2022-02-15

8.  In silico proof of principle of machine learning-based antibody design at unconstrained scale.

Authors:  Rahmad Akbar; Philippe A Robert; Cédric R Weber; Michael Widrich; Robert Frank; Milena Pavlović; Lonneke Scheffer; Maria Chernigovskaya; Igor Snapkov; Andrei Slabodkin; Brij Bhushan Mehta; Enkelejda Miho; Fridtjof Lund-Johansen; Jan Terje Andersen; Sepp Hochreiter; Ingrid Hobæk Haff; Günter Klambauer; Geir Kjetil Sandve; Victor Greiff
Journal:  MAbs       Date:  2022 Jan-Dec       Impact factor: 5.857

9.  Protein sequence design with a learned potential.

Authors:  Namrata Anand; Raphael Eguchi; Irimpan I Mathews; Carla P Perez; Alexander Derry; Russ B Altman; Po-Ssu Huang
Journal:  Nat Commun       Date:  2022-02-08       Impact factor: 14.919

10.  Interpretable pairwise distillations for generative protein sequence models.

Authors:  Christoph Feinauer; Barthelemy Meynard-Piganeau; Carlo Lucibello
Journal:  PLoS Comput Biol       Date:  2022-06-23       Impact factor: 4.779

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