Deep representation learning improves prediction of LacI-mediated transcriptional repression
[1];
[1]
- Department of Genetics, Harvard Medical School, Boston, MA 02115,, Wyss Institute for Biologically Inspired Engineering, Harvard University, Cambridge, MA 02138,, Program in Bioinformatics and Integrative Genomics, Department of Biomedical Informatics, Harvard Medical School, Boston, MA 02115,
- Wyss Institute for Biologically Inspired Engineering, Harvard University, Cambridge, MA 02138,, Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Boston, MA 02139
Significance The understanding of protein function increases with new experimental and evolutionary datasets. A major challenge is to apply machine learning to these datasets to capture essential features of protein function. Here, we analyze the experimentally determined repression function for tens of thousands of mutants of the LacI protein. This study provides a continuous, noncategorical repression value across a majority of all single mutations and for thousands of higher-order mutations. To develop a top-performing model for the prediction of repression by LacI, we compare several leading variant effect prediction algorithms. A deep representation learning paradigm, first trained across millions of proteins from all known protein families and then fine-tuned using LacI experimental data, offers the highest predictive performance of repression function.
- Research Organization:
- Harvard Univ., Cambridge, MA (United States)
- Sponsoring Organization:
- National Institutes of Health (NIH); USDOE Office of Science (SC); USDOE Office of Science (SC), Biological and Environmental Research (BER)
- Grant/Contract Number:
- FG02-02ER63445
- OSTI ID:
- 1804183
- Journal Information:
- Proceedings of the National Academy of Sciences of the United States of America, Journal Name: Proceedings of the National Academy of Sciences of the United States of America Journal Issue: 27 Vol. 118; ISSN 0027-8424
- Publisher:
- Proceedings of the National Academy of SciencesCopyright Statement
- Country of Publication:
- United States
- Language:
- English
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