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Title: D-SCRIPT translates genome to phenome with sequence-based, structure-aware, genome-scale predictions of protein-protein interactions
Award ID(s):
1940169 1939263
NSF-PAR ID:
10374278
Author(s) / Creator(s):
; ; ;
Date Published:
Journal Name:
Cell Systems
Volume:
12
Issue:
10
ISSN:
2405-4712
Page Range / eLocation ID:
969 to 982.e6
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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  1. Abstract Background

    Protein–protein interactions play a crucial role in almost all cellular processes. Identifying interacting proteins reveals insight into living organisms and yields novel drug targets for disease treatment. Here, we present a publicly available, automated pipeline to predict genome-wide protein–protein interactions and produce high-quality multimeric structural models.

    Results

    Application of our method to the Human and Yeast genomes yield protein–protein interaction networks similar in quality to common experimental methods. We identified and modeled Human proteins likely to interact with the papain-like protease of SARS-CoV2’s non-structural protein 3. We also produced models of SARS-CoV2’s spike protein (S) interacting with myelin-oligodendrocyte glycoprotein receptor and dipeptidyl peptidase-4.

    Conclusions

    The presented method is capable of confidently identifying interactions while providing high-quality multimeric structural models for experimental validation. The interactome modeling pipeline is available at usegalaxy.org and usegalaxy.eu.

     
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