D-SCRIPT translates genome to phenome with sequence-based, structure-aware, genome-scale predictions of protein-protein interactions
By Samuel Sledzieski, Rohit Singh, Lenore J. Cowen, Bonnie Berger. 2021
179 citations recorded by the source corpus.
Summary 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 to the state-of-the-art approach. Evaluating the same D-SCRIPT model on protein complexes with known 3-D 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 is available.
Subfields: Molecular Biology
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In Molecular Biology
- Jianzhu Ma
- Jian Peng
- Jinbo Xu
- Lenore J. Cowen
- Bonnie Berger
- Rohit Singh
- Samuel Sledzieski
- Kapil Devkota
Citations in the corpus
- High-resolution de novo structure prediction from primary sequence (2022, background): “The use of deep language models has recently led to major progress in language modeling ( 21 ) as well as elucidating protein properties ( 22 – 25 ), in protein-protein interaction prediction ( 26 , 27 ), and in viral escape prediction ( 28 ) from sequence data.”
- Topsy-Turvy: integrating a global view into sequence-based PPI prediction (2022, result): “For PIPR and DeepPPI, we report here the results from the study in Sledzieski et al. (2021) where the same”