分子动力学
亲脂性
化学
对接(动物)
药物发现
分子模型
球状蛋白
分子识别
分配系数
蒙特卡罗方法
折叠(DSP实现)
分子
计算生物学
生物系统
计算化学
组合化学
立体化学
生物化学
有机化学
生物
工程类
护理部
电气工程
统计
医学
数学
作者
Roman G. Efremov,Anton O. Chugunov,Timothy V. Pyrkov,John P. Priestle,Alexander S. Arseniev,Edgar Jacoby
标识
DOI:10.2174/092986707779941050
摘要
Hydrophobic interactions play a key role in the folding and maintenance of the 3-dimensional structure of proteins, as well as in the binding of ligands (e.g. drugs) to protein targets. Therefore, quantitative assessment of spatial hydrophobic (lipophilic) properties of these molecules is indispensable for the development of efficient computational methods in drug design. One possible solution to the problem lies in application of a concept of the 3-dimensional molecular hydrophobicity potential (MHP). The formalism of MHP utilizes a set of atomic physicochemical parameters evaluated from octanol-water partition coefficients (log P) of numerous chemical compounds. It permits detailed assessment of the hydrophobic and/or hydrophilic properties of various parts of molecules and may be useful in analysis of protein-protein and protein-ligand interactions. This review surveys recent applications of MHP-based techniques to a number of biologically relevant tasks. Among them are: (i) Detailed assessment of hydrophobic/hydrophilic organization of proteins; (ii) Application of this data to the modeling of structure, dynamics, and function of globular and membrane proteins, membrane-active peptides, etc. (iii) Employment of the MHP-based criteria in docking simulations for ligands binding to receptors. It is demonstrated that the application of the MHP-based techniques in combination with other molecular modeling tools (e.g. Monte Carlo and molecular dynamics simulations, docking, etc.) permits significant improvement to the standard computational approaches, provides additional important insights into the intimate molecular mechanisms driving protein assembling in water and in biological membranes, and helps in the computer-aided drug discovery process.
科研通智能强力驱动
Strongly Powered by AbleSci AI