Atomistic Simulation Research Line, Italian Institute of Technology, Genova GE 16152, Italy.
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.
Proc Natl Acad Sci U S A. 2024 Dec 3;121(49):e2416621121. doi: 10.1073/pnas.2416621121. Epub 2024 Nov 26.
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.
机器学习（ML）正在改变对复杂生物过程的研究。在酶催化中，一个重大挑战是识别酶-底物复合物的反应构象（RC）：其中底物在活性位点中呈现出精确的排列，这是引发反应所必需的。传统方法受到涉及从非反应性到反应性构象的多维自由能景观复杂性的限制。在这里，我们应用 ML 技术来解决这一挑战，重点是人类胰腺α-淀粉酶，这是治疗 II 型糖尿病的关键酶。使用基于 ML 的集体变量（CVs），我们将 RC 的概率与几种麦芽寡糖底物的实验催化活性相关联。我们的研究结果表明，这些 CV 在各种化合物之间具有显著的可转移性，大大简化了建模过程，并减少了为新底物设置模拟的计算需求和手动干预。这种方法不仅推进了我们对酶促过程的理解，而且通过能够快速准确地评估不同代抑制剂的药物疗效，为加速药物发现提供了巨大的潜力。
