Coherent Blending of Biophysics-Based Knowledge with Bayesian Neural Networks for Robust Protein Property Prediction-Reference-Cited by-同舟云学术

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Coherent Blending of Biophysics-Based Knowledge with Bayesian Neural Networks for Robust Protein Property Prediction

Published:2023-10-27 Issue:11 Volume:12 Page:3242-3251
ISSN:2161-5063
Container-title:ACS Synthetic Biology
language:en
Short-container-title:ACS Synth. Biol.

Author:

Nisonoff Hunter¹^ORCID,Wang Yixin²^ORCID,Listgarten Jennifer¹³

Affiliation:

1. Center for Computational Biology, University of California, Berkeley, Berkeley, California 94720-3220, United States

2. Department of Statistics, University of Michigan, Ann Arbor, Michigan 48109-1107, United States

3. Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, Berkeley, California 94720-1776, United States

Funder

Division of Chemistry

Division of Mathematical Sciences

Biological and Environmental Research

Office of Naval Research

Defense Threat Reduction Agency

Publisher

American Chemical Society (ACS)

Subject

Biochemistry, Genetics and Molecular Biology (miscellaneous),Biomedical Engineering,General Medicine

Link

https://pubs.acs.org/doi/pdf/10.1021/acssynbio.3c00217

Reference51 articles.

1. The Rosetta All-Atom Energy Function for Macromolecular Modeling and Design

2. Machine learning-aided engineering of hydrolases for PET depolymerization

3. Active and machine learning-based approaches to rapidly enhance microbial chemical production

4. Navigating the protein fitness landscape with Gaussian processes

5. Neural networks to learn protein sequence–function relationships from deep mutational scanning data

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1. Engineering of highly active and diverse nuclease enzymes by combining machine learning and ultra-high-throughput screening;2024-03-24

2. Biophysics-based protein language models for protein engineering;2024-03-17

3. Opportunities and Challenges for Machine Learning-Assisted Enzyme Engineering;ACS Central Science;2024-02-05

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