利用机器学习和分子动力学技术发现二肽基肽酶-4 抑制先导化合物的计算方法。

IF 2.6 4区 生物学 Q2 BIOLOGY Computational Biology and Chemistry Pub Date : 2024-07-10 DOI:10.1016/j.compbiolchem.2024.108145
Sandra De La Torre , Sebastián A. Cuesta , Luis Calle , José R. Mora , Jose L. Paz , Patricio J. Espinoza-Montero , Máryury Flores-Sumoza , Edgar A. Márquez
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引用次数: 0

摘要

从已知药物中预测可能具有 DPP-4 抑制活性的先导化合物,意味着药物开发在寻找治疗 2 型糖尿病(T2DM)的替代药物方面具有时间和成本上的优势。抑制二肽基肽酶-4(DPP-4)一直是开发治疗这种疾病的潜在药物的最常用策略之一。我们构建了一个由对 DPP-4 具有已知实验抑制活性的分子组成的多样化数据集,并利用不同的机器学习算法开发了预测模型。根据内部和外部性能,模型 M36 的 Q2CV = 0.813,Q2EXT = 0.803,是最有前途的模型。对 M36 进行了适用性领域评估和 Tropsha 分析验证,结果表明其在预测既定领域内有机分子的 pIC50 值方面具有稳健性和准确性。配体的理化性质(包括电负性、极化性和范德华体积)与预测抑制过程相关。该模型随后被用于潜在 DPP4 抑制剂的虚拟筛选,从 DrugBank 中发现了 448 种化合物,从 DiaNat 中发现了 9 种具有潜在抑制活性的化合物。分子对接和分子动力学模拟用于深入了解配体与蛋白质之间的相互作用。从筛选和有利的分子动力学结果来看,包括 Skimmin(pIC50 = 3.54,结合能 = -8.86 kcal/mol)、bergenin(pIC50 = 2.69,结合能 = -13.90 kcal/mol)和 DB07272(pIC50 = 3.97,结合能 = -25.28 kcal/mol)在内的几个化合物似乎有望在治疗 T2DM 方面进行测试和优化。这些结果意味着应用这种药物的成本和时间大大降低,因为有关其代谢的所有信息都已经存在。
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Computational approaches for lead compound discovery in dipeptidyl peptidase-4 inhibition using machine learning and molecular dynamics techniques

The prediction of possible lead compounds from already-known drugs that may present DPP-4 inhibition activity imply a advantage in the drug development in terms of time and cost to find alternative medicines for the treatment of Type 2 Diabetes Mellitus (T2DM). The inhibition of dipeptidyl peptidase-4 (DPP-4) has been one of the most explored strategies to develop potential drugs against this condition. A diverse dataset of molecules with known experimental inhibitory activity against DPP-4 was constructed and used to develop predictive models using different machine-learning algorithms. Model M36 is the most promising one based on the internal and external performance showing values of Q2 CV = 0.813, and Q2 EXT = 0.803. The applicability domain evaluation and Tropsha's analysis were conducted to validate M36, indicating its robustness and accuracy in predicting pIC50 values for organic molecules within the established domain. The physicochemical properties of the ligands, including electronegativity, polarizability, and van der Waals volume were relevant to predict the inhibition process. The model was then employed in the virtual screening of potential DPP4 inhibitors, finding 448 compounds from the DrugBank and 9 from DiaNat with potential inhibitory activity. Molecular docking and molecular dynamics simulations were used to get insight into the ligand-protein interaction. From the screening and the favorable molecular dynamic results, several compounds including Skimmin (pIC50 = 3.54, Binding energy = −8.86 kcal/mol), bergenin (pIC50 = 2.69, Binding energy = −13.90 kcal/mol), and DB07272 (pIC50 = 3.97, Binding energy = −25.28 kcal/mol) seem to be promising hits to be tested and optimized in the treatment of T2DM. This results imply a important reduction in cost and time on the application of this drugs because all the information about the its metabolism is already available.

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来源期刊
Computational Biology and Chemistry
Computational Biology and Chemistry 生物-计算机:跨学科应用
CiteScore
6.10
自引率
3.20%
发文量
142
审稿时长
24 days
期刊介绍: Computational Biology and Chemistry publishes original research papers and review articles in all areas of computational life sciences. High quality research contributions with a major computational component in the areas of nucleic acid and protein sequence research, molecular evolution, molecular genetics (functional genomics and proteomics), theory and practice of either biology-specific or chemical-biology-specific modeling, and structural biology of nucleic acids and proteins are particularly welcome. Exceptionally high quality research work in bioinformatics, systems biology, ecology, computational pharmacology, metabolism, biomedical engineering, epidemiology, and statistical genetics will also be considered. Given their inherent uncertainty, protein modeling and molecular docking studies should be thoroughly validated. In the absence of experimental results for validation, the use of molecular dynamics simulations along with detailed free energy calculations, for example, should be used as complementary techniques to support the major conclusions. Submissions of premature modeling exercises without additional biological insights will not be considered. Review articles will generally be commissioned by the editors and should not be submitted to the journal without explicit invitation. However prospective authors are welcome to send a brief (one to three pages) synopsis, which will be evaluated by the editors.
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