Using molecular modeling to identify potential anti‐tubulin compounds

微管蛋白 秋水仙碱 化学 微管 对接(动物) 立体化学 结合位点 自动停靠 生物化学 生物 细胞生物学 生物信息学 医学 遗传学 基因 护理部
作者
Blake Evans,Kathryn Cole
出处
期刊:The FASEB Journal [Wiley]
卷期号:36 (S1)
标识
DOI:10.1096/fasebj.2022.36.s1.r2086
摘要

In 2020 the Center for Disease Control and Prevention reported cancer as the second leading cause of death in the United States with over 598,000 cases. Therefore, the need for new innovative cancer drugs is ever‐growing. Tubulin, a protein crucial to cell dynamics, has been the target for many anti‐cancer drugs since the 1960’s. Most anti‐tubulin compounds to date, however, are complex molecules that have issues with solubility, toxicity, multi‐drug resistance, and low efficacy amongst various cancers. The severity of these problems highlights the need to develop a new generation of anti‐tubulin molecules. Previously we synthesized a small vicinal diaryl heterocycle, PY‐407‐C, which showed potent antiproliferative activity against U‐937 cancer cells. Further investigations in vitro determined the mode of inhibition is by anti‐tubulin polymerization. Analysis of the known binding sites of tubulin led us to hypothesize PY‐407‐C binds in the tubulin colchicine‐binding site due the similarities of having vicinal diaryl groups. Initial molecular modeling studies were then carried out in AutoDock Vina which strongly supported our predictions. Most significantly, PY‐407‐C makes many hydrophobic and van der Waals interactions within a known hydrophobic pocket of the beta‐tubulin subunit congruent to colchicine, such as L242, L248, L252, L255, T314, V315, I318, N350, K352, and A354. Utilizing these inhibitory docking studies, we hypothesize analogs that theoretically have stronger binding energies than colchicine or PY‐407‐C will also make stronger inhibitors. This allows for an efficient system where synthetic targets are first determined through computational studies, to which they would be synthesized and then tested for biological activity. More specifically, by analyzing interactions between our potential inhibitors and nearby residues, we can make informed decisions on which changes will lead to stronger binding. For example, this methodology led to the design of a new compound for synthesis containing a large hydrophobic propyl‐benzene substituent, which when simulated gave the highest binding affinity of compounds tested thus far.

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