D-SCRIPT translates genome to phenome with sequence-based, structure-aware, genome-scale predictions of protein-protein interactions
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1-s2.0-S2405471221003331-main.pdf
Description
Published version
Size
2.42 MB
Format
Adobe PDF
Checksum (MD5)
0b8068e9e4ac51e842c4857a327181b6
Author(s)
Sledzieski, Samuel
Singh, Rohit
Cowen, Lenore
Berger, Bonnie
Date Issued
2021
Journal
Cell Systems
Publisher
Elsevier BV
Citation
Sledzieski, Samuel, Singh, Rohit, Cowen, Lenore and Berger, Bonnie. 2021. "D-SCRIPT translates genome to phenome with sequence-based, structure-aware, genome-scale predictions of protein-protein interactions." Cell Systems, 12 (10).
Version
Final published version
Abstract
We combine advances in neural language modeling and structurally motivated design to develop D-SCRIPT, an interpretable and generalizable deep-learning model, which predicts interaction between two proteins using only their sequence and maintains high accuracy with limited training data and across species. We show that a D-SCRIPT model trained on 38,345 human PPIs enables significantly improved functional characterization of fly proteins compared with the state-of-the-art approach. Evaluating the same D-SCRIPT model on protein complexes with known 3D structure, we find that the inter-protein contact map output by D-SCRIPT has significant overlap with the ground truth. We apply D-SCRIPT to screen for PPIs in cow (Bos taurus) at a genome-wide scale and focusing on rumen physiology, identify functional gene modules related to metabolism and immune response. The predicted interactions can then be leveraged for function prediction at scale, addressing the genome-to-phenome challenge, especially in species where little data are available.
MIT Department
Massachusetts Institute of Technology. Department of Mathematics
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Terms of Use
Creative Commons Attribution 4.0 International license
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1016/J.CELS.2021.08.010
