Learning protein fitness models from evolutionary and assay-labeled data

Machine learning-based models of protein fitness typically learn from either unlabeled, evolutionarily related sequences or variant sequences with experimentally measured labels. For regimes where only limited experimental data are available, recent work has suggested methods for combining both sour...

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Bibliographic Details
Published in:Nature biotechnology Vol. 40; no. 7; pp. 1114 - 1122
Main Authors: Hsu, Chloe, Nisonoff, Hunter, Fannjiang, Clara, Listgarten, Jennifer
Format: Journal Article
Language:English
Published: New York Nature Publishing Group US 01.07.2022
Nature Publishing Group
Springer Nature
Subjects:
631/114/1305
631/114/469
631/61/338/469
Agriculture
Amino acids
BASIC BIOLOGICAL SCIENCES
Bioinformatics
Biomedical and Life Sciences
Biomedical Engineering/Biotechnology
Biomedicine
Biotechnology
Competition
Computer engineering
Datasets
Density
Enzymes
Evolution
Evolutionary algorithms
Experimental data
Fitness
Laboratories
Learning algorithms
Life Sciences
Machine learning
protein design
Proteins
Regression analysis
Reproductive fitness
Statistical analysis
ISSN:1087-0156, 1546-1696, 1546-1696
Online Access:Get full text
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Summary:Machine learning-based models of protein fitness typically learn from either unlabeled, evolutionarily related sequences or variant sequences with experimentally measured labels. For regimes where only limited experimental data are available, recent work has suggested methods for combining both sources of information. Toward that goal, we propose a simple combination approach that is competitive with, and on average outperforms more sophisticated methods. Our approach uses ridge regression on site-specific amino acid features combined with one probability density feature from modeling the evolutionary data. Within this approach, we find that a variational autoencoder-based probability density model showed the best overall performance, although any evolutionary density model can be used. Moreover, our analysis highlights the importance of systematic evaluations and sufficient baselines. A simple machine learning algorithm combines evolutionary and experimental data for improved protein fitness prediction.
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AC52-07NA27344
USDOE Office of Science (SC), Biological and Environmental Research (BER)
ISSN:1087-0156
1546-1696
1546-1696
DOI:10.1038/s41587-021-01146-5