Chemical neighborhood exploration for substrate discovery in biocatalysis

Predicting the substrate reactivity strength for a given biocatalyst remains a central challenge in computational biocatalysis. Here, we present Subdate, a modular workflow that combines descriptor-guided organization of substrate analogs with ab initio metadynamics simulations to prioritize reactive candidates. The workflow integrates i) substrate library construction, ii) conformer generation and descriptors set definition, iii) library clustering, iv) representative-substrate selection, and v) reaction-barrier quantitative prediction. Applied to selected biocatalysts (human butyrylcholinesterase and the catalytic antibody A17) sharing an SN2 reaction mechanism, Subdate quantitatively identifies reactivity trends that match experimental kinetic measurements. The developed workflow provides a mechanism-aware strategy for reactive substrate prioritization for efficient sampling through the chemical library in biocatalysis.

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Publication Details

Journal
Proceedings of the National Academy of Sciences
Published
2026-06-08
DOI
https://doi.org/10.1073/pnas.2535430123
Primary Topic
Machine Learning in Materials Science
Type
article
Field-Weighted Citation Impact
0.00

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article

Chemical neighborhood exploration for substrate discovery in biocatalysis

Igor A. Yaroshevich, Patrick Masson, N. N. Kostin, Yaroslav V. Solovev et al.
Proceedings of the National Academy of Sciences
Machine Learning in Materials Science
article

Chemical neighborhood exploration for substrate discovery in biocatalysis

Igor A. Yaroshevich, Patrick Masson, N. N. Kostin, Yaroslav V. Solovev, I. V. Smirnov, Petr Popov, Hongkai Zhang, А. В. Степанов, Yuri Prokopenko, Wei Zheng, Alexander G. Gabibov
article en

Abstract

Predicting the substrate reactivity strength for a given biocatalyst remains a central challenge in computational biocatalysis. Here, we present Subdate, a modular workflow that combines descriptor-guided organization of substrate analogs with ab initio metadynamics simulations to prioritize reactive candidates. The workflow integrates i) substrate library construction, ii) conformer generation and descriptors set definition, iii) library clustering, iv) representative-substrate selection, and v) reaction-barrier quantitative prediction. Applied to selected biocatalysts (human butyrylcholinesterase and the catalytic antibody A17) sharing an SN2 reaction mechanism, Subdate quantitatively identifies reactivity trends that match experimental kinetic measurements. The developed workflow provides a mechanism-aware strategy for reactive substrate prioritization for efficient sampling through the chemical library in biocatalysis.

Proceedings of the National Academy of SciencesVol. 123(24)
Scripps Research Institute (US), Statistics Austria (AT), Constructor University (DE), Kazan Federal University (RU), Constructing Excellence (GB), Institute of Bioorganic Chemistry (RU), Moscow State University (TJ)
National Natural Science Foundation of China
Openalex Percentile: Top 13%
Machine Learning in Materials Science
DOISourceOpenAlex
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