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通过拓机器学习来识别针对细菌 propionate 代谢的分子化合物

Astrit Tola¹, Shan Aziz², Dannie Zhabilov²

¹Department of Mathematics, Florida State University, 222 S. Copeland St, Tallahassee, 32306, FL, USA.

Journal of molecular graphics & modelling

|January 16, 2026

概括

机器学习将伊塔康酸确定为细菌甲基酸脱水酶 (AcnD) 的强有力的抑制剂. 这一发现为开发新的抗微生物药物提供了一种新的策略,通过破坏必要的细菌代谢途径来开发新的抗微生物药物.

科学领域:

计算化学是一种计算化学.
机器学习机器学习
药物发现药物发现

背景情况:

酸的代谢对于致病细菌至关重要,使其酶如甲基酸脱水酶 (AcnD) 成为潜在的抗菌标.
酸代谢效率低下可能导致毒性积累,突出显示该途径的脆弱性.

研究的目的:

应用拓机器学习方法来识别针对AcnD的新药化合物.
验证已识别的化合物对病原细菌的抗微生物潜力.

主要方法:

综合比较蛋白质和连接体分析与拓机器学习.
翻译了连接体结构成拓向量,用于优先选化合物.
利用分子对接模拟来评估化合物-蛋白质相互作用.
进行细菌生长测试以确认抗微生物药物的有效性.

主要成果:

鉴定出 itaconic 酸作为 AcnD 抑制的一个有前途的分子支架.
对接模拟证实了优先化合物的相互作用与关键的AcnD活性部位残留物.
伊塔科尼酸在29.13毫米时完全抑制了Pseudomonas aeruginosa和Acinetobacter baumannii的生长.

结论:

机器学习,特别是基于拓的配体建模,在药物发现中有效用于识别抗微生物支架.
伊塔科尼酸显示出作为一种抗菌剂的显著潜力,通过通过AcnD抑制来破坏细菌 propionate 的代谢.
这项研究验证了用于指导实验药物发现工作的计算方法.

关键词:

抗微生物药物的发展.伊他康酸盐是什么意思基于连接体的虚拟查.甲基酸盐的途径拓学数据分析的分析.

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