Recent Advances in Machine Learning Variant Effect Prediction Tools for Protein Engineering-Reference-Cited by-同舟云学术

Recent Advances in Machine Learning Variant Effect Prediction Tools for Protein Engineering

Published:2022-04-06 Issue:19 Volume:61 Page:6235-6245
ISSN:0888-5885
Container-title:Industrial & Engineering Chemistry Research
language:en
Short-container-title:Ind. Eng. Chem. Res.

Author:

Horne Jesse¹^ORCID,Shukla Diwakar¹²³⁴⁵^ORCID

Affiliation:

1. Department of Chemical and Biomolecular Engineering, University of Illinois Urbana−Champaign, Champaign, Illinois 61801, United States

2. Department of Bioengineering, University of Illinois Urbana−Champaign, Champaign, Illinois 61801, United States

3. Department of Plant Biology, University of Illinois Urbana−Champaign, Champaign, Illinois 61801, United States

4. Cancer Center at Illinois, University of Illinois Urbana−Champaign, Champaign, Illinois 61801, United States

5. Center for Biophysics and Quantitative Biology, University of Illinois Urbana−Champaign, Champaign, Illinois 61801, United States

Funder

National Institute of General Medical Sciences

University of Illinois

Cancer Center at Illinois, University of Illinois at Urbana-Champaign

Publisher

American Chemical Society (ACS)

Subject

Industrial and Manufacturing Engineering,General Chemical Engineering,General Chemistry

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.iecr.1c04943

Reference95 articles.

1. The Gene Ontology (GO) database and informatics resource

2. Sinai, S.; Kelsic, E. D. A Primer on Model-Guided Exploration of Fitness Landscapes for Biological Sequence Design. arXiv.org, 2020 (accessed on Nov. 17, 2020).

3. Predicting the Effects of Amino Acid Substitutions on Protein Function

4. The coming of age of de novo protein design

5. A Brief History of De Novo Protein Design: Minimal, Rational, and Computational

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