Prospectively Validated Proteochemometric Models for the Prediction of Small-Molecule Binding to Bromodomain Proteins.
Publication Date
2018-09-24Journal Title
J Chem Inf Model
ISSN
1549-9596
Publisher
American Chemical Society (ACS)
Volume
58
Issue
9
Pages
1870-1888
Language
eng
Type
Article
Physical Medium
Print-Electronic
Metadata
Show full item recordCitation
Giblin, K., Hughes, S. J., Boyd, H., Hansson, P., & Bender, A. (2018). Prospectively Validated Proteochemometric Models for the Prediction of Small-Molecule Binding to Bromodomain Proteins.. J Chem Inf Model, 58 (9), 1870-1888. https://doi.org/10.1021/acs.jcim.8b00400
Abstract
The bromodomain-containing proteins are a ligandable family of epigenetic readers, which play important roles in oncological, cardiovascular, and inflammatory diseases. Achieving selective inhibition of specific bromodomains is challenging, due to the limited understanding of compound and target selectivity features. In this study we build and benchmark proteochemometric (PCM) classification models on bioactivity data for 15,350 data points across 31 bromodomains, using both compound fingerprints and binding site protein descriptors as input variables, achieving a maximum performance as measured by the Matthew's Correlation Coefficient (MCC) of 0.83 on the external test set. We also find that histone peptide binding data can be used as a target descriptor to build a high performing PCM model (MCC 0.80), showing the transferability of peptide interaction information to modeling small-molecule bioactivity. 1,139 compounds were selected for prospective experimental testing by performing a virtual screen using model predictions and implementing conformal prediction, which resulted in 319 correctly predicted compound-target pair actives and the correct prediction for certain selectivity profile combinations of the four bromodomains tested against. We identify that conformal prediction can be used to fine-tune the balance between hit retrieval and hit structural diversity in a virtual screening setting. PCM can be applied to future virtual screening and compound design, including off-target prediction for bromodomains.
Keywords
Humans, Nuclear Proteins, Reproducibility of Results, Binding Sites, Protein Conformation, Protein Binding, Quantitative Structure-Activity Relationship, Models, Chemical, Models, Molecular, Computer Simulation, Drug Discovery
Sponsorship
European Research Council (336159)
Identifiers
External DOI: https://doi.org/10.1021/acs.jcim.8b00400
This record's URL: https://www.repository.cam.ac.uk/handle/1810/285050
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