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PreprintCited by 16Computational Drug Discovery MethodsProtein Interaction Studies and Fluorescence AnalysisProtein Structure and Dynamics

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PrePCI: A structure- and chemical similarity-informed database of predicted protein compound interactions

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Trudeau, Stephen J., Hwang, Howook, Mathur, Deepika, Begum, Kamrun, Petrey, Donald, Murray, Diana, Honig, Barry·2022·Protein Science
DOI 10.1101/2022.09.17.508184PMC PMC10019447

Preprint — published as 10.1002/pro.4594 · Paper page

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Abstract

We describe the Predicting Protein Compound Interactions (PrePCI) database which comprises over 5 billion predicted interactions between nearly 7 million chemical compounds and 19,797 human proteins. PrePCI relies on a proteome-wide database of structural models based on both traditional modeling techniques and the AlphaFold Protein Structure Database. Sequence and structural similarity-based metrics are established between template proteins in the Protein Data Bank, T, that bind small molecules, C, and proteins in the models database, Q. When these metrics pass a sequence threshold value, it is assumed that C also binds to Q with a probability derived from machine learning. If the relationship is based on structure, this probability is based on a scoring function that measures the extent to which C is compatible with the binding site of Q as described in the LT-scanner algorithm. For every predicted complex derived in this way, chemical similarity based on the Tanimoto Coefficient identifies other small molecules that may bind to Q. A likelihood ratio for the binding of C to Q is obtained from naïve Bayesian statistics. The PrePCI algorithm performs well under different validations. It can be queried by entering a UniProt ID for a protein and obtaining a list of compounds predicted to bind to it along with associated probabilities. Alternatively, entering an identifier for the compound outputs a list of proteins it is predicted to bind. Specific applications of the database are described and a strategy is introduced to use PrePCI as a first step in a docking screen.

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Metadata

Authors
Trudeau, S. J., Hwang, H., Mathur, D., Begum, K., Petrey, D., Murray, D., & Honig, B.
Affiliations
  • Columbia University Irving Medical Center (US)
  • Schrodinger (United States) (US)
Corresponding author
  • Murray, Diana
  • Honig, Barry
ORCID
Venue
Protein Science
Publisher
openRxiv
Published
2022-09-19
Volume
32
Issue
4
Pages
e4594-e4594
ISSN
0961-8368 (ISSN-L)
DOI
10.1101/2022.09.17.508184
PMC
PMC10019447
OpenAlex
W4320494012
Cited by
16
References
1
Citations by year
2026: 2 · 2025: 2 · 2024: 6 · 2023: 5 · 2022: 1
FWCI
2.15
Type
journal article
Language
en
Open access
Locations
  • Protein Science (publishedVersion)
  • bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
  • PubMed Central (publishedVersion)
  • PubMed (publishedVersion)
Funders
  • National Institutes of Health (1s10od032433-01a1)
  • National Cancer Institute (U54CA209997)
  • National Institute of General Medical Sciences (S06GM008224, T32GM008224, T32GM145440, R35GM139585)
Topics
  • Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Drug Discovery › Computational Drug Discovery Methods
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Interaction Studies and Fluorescence Analysis
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
Keywords
protein structure model, AlphaFold, structural similarity, sequence similarity, molecular similarity, Tanimoto coefficient, likelihood ratios, drug target prediction, lead compound identification, mechanism of action
MeSH terms
Algorithms, Bayes Theorem, Humans, Proteins, Databases, Protein, Databases, Chemical

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