探索与过氧化物酶体增殖激活受体γ（PPARγ）调节剂有关的抗糖尿病疗法指纹：化学计量建模方法

IF 2.6 4区生物学 Q2 BIOLOGY

Computational Biology and Chemistry Pub Date : 2024-07-02 DOI:10.1016/j.compbiolchem.2024.108142

Subham Dawn , Prabir Manna , Totan Das , Prabhat Kumar , Moumita Ray , Shovanlal Gayen , Sk Abdul Amin

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引用次数: 0

摘要

这项研究证明了过氧化物酶体增殖激活受体γ（PPARγ）调节剂的分子结构与其生物活性的相关性。贝叶斯分类法和递归分区（RP）研究被应用于由 323 种具有不同支架的 PPARγ 调节剂组成的数据集。研究结果深入揭示了调节 PPARγ 的重要亚结构特征。分子对接分析再次证实了所发现的亚结构特征在调节 PPARγ 活性中的重要作用。分子动力学模拟进一步强调了所研究的调节剂与 PPARγ 形成的复合物的稳定性。总之，多种计算方法的整合揭示了 PPARγ 调节活性所必需的关键结构基团，这将为未来开发有效的调节剂提供启示。

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Exploring fingerprints for antidiabetic therapeutics related to peroxisome proliferator-activated receptor gamma (PPARγ) modulators: A chemometric modeling approach

This study demonstrated the correlation of molecular structures of Peroxisome proliferator-activated receptor gamma (PPARγ) modulators and their biological activities. Bayesian classification, and recursive partitioning (RP) studies have been applied to a dataset of 323 PPARγ modulators with diverse scaffolds. The results provide a deep insight into the important sub-structural features modulating PPARγ. The molecular docking analysis again confirmed the significance of the identified sub-structural features in the modulation of PPARγ activity. Molecular dynamics simulations further underscored the stability of the complexes formed by investigated modulators with PPARγ. Overall, the integration of many computational approaches unveiled key structural motifs essential for PPARγ modulatory activity that will shed light on the development of effective modulators in the future.

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来源期刊

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.