Correlating enzymatic reactivity for different substrates using transferable data-driven collective variables.

Autor:	Das S; Atomistic Simulation Research Line, Italian Institute of Technology, Genova GE 16152, Italy., Raucci U; Atomistic Simulation Research Line, Italian Institute of Technology, Genova GE 16152, Italy., Neves RPP; Laboratório Associado para a Química Verde, Rede de Química e Tecnologia, Departamento de Química e Bioquímica, Faculdade de Ciências, Universidade do Porto, Porto 4169-007, Portugal., Ramos MJ; Laboratório Associado para a Química Verde, Rede de Química e Tecnologia, Departamento de Química e Bioquímica, Faculdade de Ciências, Universidade do Porto, Porto 4169-007, Portugal., Parrinello M; Atomistic Simulation Research Line, Italian Institute of Technology, Genova GE 16152, Italy.
Jazyk:	angličtina
Zdroj:	Proceedings of the National Academy of Sciences of the United States of America [Proc Natl Acad Sci U S A] 2024 Dec 03; Vol. 121 (49), pp. e2416621121. Date of Electronic Publication: 2024 Nov 26.
DOI:	10.1073/pnas.2416621121
Abstrakt:	Machine learning (ML) is transforming the investigation of complex biological processes. In enzymatic catalysis, one significant challenge is identifying the reactive conformations (RC) of the enzyme:substrate complex where the substrate assumes a precise arrangement in the active site necessary to initiate a reaction. Traditional methods are hindered by the complexity of the multidimensional free energy landscape involved in the transition from nonreactive to reactive conformations. Here, we applied ML techniques to address this challenge, focusing on human pancreatic α-amylase, a crucial enzyme in type-II diabetes treatment. Using ML-based collective variables (CVs), we correlated the probability of being in a RC with the experimental catalytic activity of several malto-oligosaccharide substrates. Our findings demonstrate a remarkable transferability of these CVs across various compounds, significantly streamlining the modeling process and reducing both computational demand and manual intervention in setting up simulations for new substrates. This approach not only advances our understanding of enzymatic processes but also holds substantial potential for accelerating drug discovery by enabling rapid and accurate evaluation of drug efficacy across different generations of inhibitors. Competing Interests: Competing interests statement:The authors declare no competing interest.
Databáze:	MEDLINE
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