Significant Enhancement of Docking Sensitivity Using Implicit Ligand Sampling

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• 作者：Mengang Xu ; Markus A. Lill
• 刊名：Journal of Chemical Information and Modeling
• 出版年：2011
• 出版时间：March 28, 2011
• 年：2011
• 卷：51
• 期：3
• 页码：693-706
• 全文大小：1323K
• 年卷期：v.51,no.3(March 28, 2011)
• ISSN：1549-960X

文摘

The efficient and accurate quantification of protein鈭抣igand interactions using computational methods is still a challenging task. Two factors strongly contribute to the failure of docking methods to predict free energies of binding accurately: the insufficient incorporation of protein flexibility coupled to ligand binding and the neglected dynamics of the protein鈭抣igand complex in current scoring schemes. We have developed a new methodology, named the 鈥榣igand-model鈥?concept, to sample protein conformations that are relevant for binding structurally diverse sets of ligands. In the ligand-model concept, molecular-dynamics (MD) simulations are performed with a virtual ligand, represented by a collection of functional groups that binds to the protein and dynamically changes its shape and properties during the simulation. The ligand model essentially represents a large ensemble of different chemical species binding to the same target protein. Representative protein structures were obtained from the MD simulation, and docking was performed into this ensemble of protein conformation. Similar binding poses were clustered, and the averaged score was utilized to rerank the poses. We demonstrate that the ligand-model approach yields significant improvements in predicting native-like binding poses and quantifying binding affinities compared to static docking and ensemble docking simulations into protein structures generated from an apo MD simulation.