Knowledge-Based Scoring Functions in Drug Design:2.Can the Knowledge Base Be Enriched?

【作者】 沈倩诚； 郑明月； 罗小民； 朱维良； 蒋华良；

【Author】 Qiancheng Shen~1,Bing Xiong~2,Mingyue Zheng~(*,1),Xiaomin Luo 1,Cheng Luo~1,Xian Liu~1,Yun Du~1,Jing Li~1,Weiliang Zhu 1,Jingkang Shen,Hualiang Jiang~(*,1,3) 1.Drug Discovery and Design Center,State Key Laboratory of Drug Research,Shanghai Institute of Materia Medica,Chinese Academy of Sciences,555 Zuchongzhi Road,Shanghai 201203,China, 2.State Key Laboratory of Drug Research,Shanghai Institute of Materia Medica,Chinese Academy of Sciences, 555 Zuchongzhi Road,Shanghai 201203,China 3.School of Pharmacy,East China University of Science and Technology,Shanghai 200237,China

【机构】 Drug Discovery and Design Center,State Key Laboratory of Drug Research,Shanghai Institute of Materia Medica,Chinese Academy of Sciences,555 Zuchongzhi Road,Shanghai 201203,China； School of Pharmacy,East China University of Science and Technology,Shanghai 200237,China；

【摘要】 <正>Fast and accurate predicting of the binding affinities of large sets of diverse protein-ligand complexes is an important,yet extremely challenging,task in drug discovery.The development of knowledge-based scoring functions exploiting structural information of known protein-ligand complexes represents a valuable contribution to such a computational prediction.In this study,we report a scoring function named IPMF that integrates additional experimental binding affinity information into the extracted potentials,on the assumption that a scoring function with the "enriched" knowledge base may achieve increased accuracy in binding affinity prediction.In our approach,the functions and atom types of PMF04 were inherited to implicitly capture binding effects that are hard to model explicitly,and a novel iteration device was designed to gradually tailor the initial potentials.We evaluated the performance of the resultant IPMF with a diverse set of 219 protein-ligand complexes and compared it with seven scoring functions commonly used in computer-aided drug design,including GLIDE,AutoDock4,VINA,PLP,LUDI,PMF,and PMF04.While the IPMF is only moderately successful in ranking native or near native conformations,it yields the lowest mean error of 1.41 log K_i/K_d units from measured inhibition affinities and the highest Pearson’s correlation coefficient of R_p~2 0.40 for the test set.These results corroborate our initial supposition about the role of "enriched" knowledge base.With the rapid growing volume of high-quality structural and interaction data in the public domain,this work marks a positive step toward improving the accuracy of knowledge-based scoring function in binding affinity prediction.更多还原

【Abstract】 Fast and accurate predicting of the binding affinities of large sets of diverse protein-ligand complexes is an important,yet extremely challenging,task in drug discovery.The development of knowledge-based scoring functions exploiting structural information of known protein-ligand complexes represents a valuable contribution to such a computational prediction.In this study,we report a scoring function named IPMF that integrates additional experimental binding affinity information into the extracted potentials,on the assumption that a scoring function with the "enriched" knowledge base may achieve increased accuracy in binding affinity prediction.In our approach,the functions and atom types of PMF04 were inherited to implicitly capture binding effects that are hard to model explicitly,and a novel iteration device was designed to gradually tailor the initial potentials.We evaluated the performance of the resultant IPMF with a diverse set of 219 protein-ligand complexes and compared it with seven scoring functions commonly used in computer-aided drug design,including GLIDE,AutoDock4,VINA,PLP,LUDI,PMF,and PMF04.While the IPMF is only moderately successful in ranking native or near native conformations,it yields the lowest mean error of 1.41 log K_i/K_d units from measured inhibition affinities and the highest Pearson’s correlation coefficient of R_p~2 0.40 for the test set.These results corroborate our initial supposition about the role of "enriched" knowledge base.With the rapid growing volume of high-quality structural and interaction data in the public domain,this work marks a positive step toward improving the accuracy of knowledge-based scoring function in binding affinity prediction.更多还原