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通过分子动力学和机器学习阐明配体与嘌呤核苷磷酸化酶的结合机制。

The ligand binding mechanism to purine nucleoside phosphorylase elucidated via molecular dynamics and machine learning.

作者信息

Decherchi Sergio, Berteotti Anna, Bottegoni Giovanni, Rocchia Walter, Cavalli Andrea

机构信息

1] CONCEPT Lab, Istituto Italiano di Tecnologia, via Morego 30, 16163 Genova, Italy [2] BiKi Technologies s.r.l., via XX Settembre 33, 16121 Genova, Italy.

CompuNet, Istituto Italiano di Tecnologia, via Morego 30, 16163 Genova, Italy.

出版信息

Nat Commun. 2015 Jan 27;6:6155. doi: 10.1038/ncomms7155.

DOI:10.1038/ncomms7155
PMID:25625196
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC4308819/
Abstract

The study of biomolecular interactions between a drug and its biological target is of paramount importance for the design of novel bioactive compounds. In this paper, we report on the use of molecular dynamics (MD) simulations and machine learning to study the binding mechanism of a transition state analogue (DADMe-immucillin-H) to the purine nucleoside phosphorylase (PNP) enzyme. Microsecond-long MD simulations allow us to observe several binding events, following different dynamical routes and reaching diverse binding configurations. These simulations are used to estimate kinetic and thermodynamic quantities, such as kon and binding free energy, obtaining a good agreement with available experimental data. In addition, we advance a hypothesis for the slow-onset inhibition mechanism of DADMe-immucillin-H against PNP. Combining extensive MD simulations with machine learning algorithms could therefore be a fruitful approach for capturing key aspects of drug-target recognition and binding.

摘要

研究药物与其生物靶点之间的生物分子相互作用对于新型生物活性化合物的设计至关重要。在本文中，我们报告了使用分子动力学（MD）模拟和机器学习来研究过渡态类似物（DADMe-immucillin-H）与嘌呤核苷磷酸化酶（PNP）酶的结合机制。长达微秒级的MD模拟使我们能够观察到几个结合事件，这些事件遵循不同的动力学途径并达到不同的结合构型。这些模拟用于估计动力学和热力学量，如结合速率常数kon和结合自由能，与现有的实验数据取得了良好的一致性。此外，我们提出了一个关于DADMe-immucillin-H对PNP的慢发性抑制机制的假设。因此，将广泛的MD模拟与机器学习算法相结合可能是一种富有成效的方法，用于捕捉药物-靶点识别和结合的关键方面。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/70aa/4317499/1eb224bcdd28/ncomms7155-f7.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/70aa/4317499/98eb0ade2362/ncomms7155-f1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/70aa/4317499/36753cb1f78b/ncomms7155-f2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/70aa/4317499/101d8ef260dc/ncomms7155-f3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/70aa/4317499/db7f4f3ff766/ncomms7155-f4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/70aa/4317499/9f277b9c2f68/ncomms7155-f5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/70aa/4317499/55a160ddc5ba/ncomms7155-f6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/70aa/4317499/1eb224bcdd28/ncomms7155-f7.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/70aa/4317499/98eb0ade2362/ncomms7155-f1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/70aa/4317499/36753cb1f78b/ncomms7155-f2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/70aa/4317499/101d8ef260dc/ncomms7155-f3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/70aa/4317499/db7f4f3ff766/ncomms7155-f4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/70aa/4317499/9f277b9c2f68/ncomms7155-f5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/70aa/4317499/55a160ddc5ba/ncomms7155-f6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/70aa/4317499/1eb224bcdd28/ncomms7155-f7.jpg

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