对接(动物)
血红素
分子动力学
工作流程
计算生物学
生物系统
计算机科学
辅因子
可扩展性
化学
管道(软件)
氨基酸残基
立体化学
注释
蛋白质结构
数据挖掘
构象集合
黄蛋白
寻找对接的构象空间
脱卤酶
分子模型
活动站点
结合位点
生物化学
残留物(化学)
分子识别
细胞色素P450
蛋白质-配体对接
结晶学
细胞色素
方向(向量空间)
大分子对接
蛋白质数据库
作者
Liang Feng,Chang-Bin Niu,Xindong Qing,Chunhui Zhang,Changsheng Li
出处
期刊:
[Cold Spring Harbor Laboratory]
日期:2026-05-15
被引量:1
标识
DOI:10.64898/2026.05.12.724510
摘要
Abstract Cytochrome P450 enzymes (CYPs) are the primary drivers of chemical diversification in plant secondary metabolism; however, fewer than 10% of the superfamily members have been functionally characterized. Computational docking provides a scalable strategy to prioritize candidates for experimental validation, yet prevailing workflows are poorly adapted to plant P450s because AlphaFold-predicted structures lack the essential heme cofactor and conventional flexible-residue selection relies on subjective geometric cutoffs. To address these limitations, we developed an automated pipeline—PlantP450Dock—that unifies heme cofactor implantation, molecular dynamics-based conformational sampling, data-driven flexible residue selection, and semi-flexible docking within a single integrated workflow. The heme is transferred from a crystallographic template to the AlphaFold model via a local coordinate transformation algorithm, achieving a positional deviation of less than 0.2 Å relative to the experimentally determined CYP73A33 structure (PDB: 6VBY). Subsequent 100 ns molecular dynamics simulations confirmed faithful preservation of the Fe–S coordination geometry (2.61 ± 0.08 Å) across all trajectory frames. A singular value decomposition-based heme-plane filtering strategy objectively identified distal active-site residues for flexible treatment, eliminating user-dependent subjectivity. Cross-family validation against four phylogenetically distinct P450s (CYP73, CYP711, CYP706, and CYP701) generated catalytically competent binding poses with substrate-to-heme-iron distances of 2.8–4.4 Å without enzyme-specific parameterization. Released as an open-source tool, this pipeline furnishes the plant science community with a standardized, reproducible computational framework to accelerate functional annotation of the largely unexplored plant P450 families.
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