Knowledge-Based Scoring Functions in Drug Design: 2. Can the Knowledge Base Be Enriched?
文献类型：期刊论文
| 作者 | Shen, Qiancheng1; Xiong, Bing1 ; Zheng, Mingyue1; Luo, Xiaomin1; Luo, Cheng1; Liu, Xian1; Du, Yun1; Li, Jing1; Zhu, Weiliang1; Shen, Jingkang
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| 刊名 | JOURNAL OF CHEMICAL INFORMATION AND MODELING
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| 出版日期 | 2011-02 |
| 卷号 | 51期号:2页码:386-397 |
| ISSN号 | 1549-9596 |
| DOI | 10.1021/ci100343j |
| 文献子类 | Article |
| 英文摘要 | 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 functions in binding affinity prediction. |
| WOS关键词 | PROTEIN-LIGAND INTERACTIONS ; ENERGY FUNCTION ; DOCKING ; RECOGNITION ; PREDICTION ; COMPLEXES ; ACCURACY ; DATABASE ; SET ; DNA |
| 资助项目 | State Key Program of Basic Research of China[2009CB918502] ; Hi-Tech Research and Development program of China[2006AA020404] ; National Natural Science Foundation of China[81001399] |
| WOS研究方向 | Pharmacology & Pharmacy ; Chemistry ; Computer Science |
| 语种 | 英语 |
| WOS记录号 | WOS:000287685700019 |
| 出版者 | AMER CHEMICAL SOC |
| 源URL | [http://119.78.100.183/handle/2S10ELR8/278616]  |
| 专题 | 药物发现与设计中心 中科院受体结构与功能重点实验室 新药研究国家重点实验室 |
| 通讯作者 | Zheng, Mingyue |
| 作者单位 | 1.Chinese Acad Sci, Shanghai Inst Mat Med, State Key Lab Drug Res, Drug Discovery & Design Ctr, Shanghai 201203, Peoples R China; 2.E China Univ Sci & Technol, Sch Pharm, Shanghai 200237, Peoples R China |
| 推荐引用方式 GB/T 7714 | Shen, Qiancheng,Xiong, Bing,Zheng, Mingyue,et al. Knowledge-Based Scoring Functions in Drug Design: 2. Can the Knowledge Base Be Enriched?[J]. JOURNAL OF CHEMICAL INFORMATION AND MODELING,2011,51(2):386-397. |
| APA | Shen, Qiancheng.,Xiong, Bing.,Zheng, Mingyue.,Luo, Xiaomin.,Luo, Cheng.,...&Jiang, Hualiang.(2011).Knowledge-Based Scoring Functions in Drug Design: 2. Can the Knowledge Base Be Enriched?.JOURNAL OF CHEMICAL INFORMATION AND MODELING,51(2),386-397. |
| MLA | Shen, Qiancheng,et al."Knowledge-Based Scoring Functions in Drug Design: 2. Can the Knowledge Base Be Enriched?".JOURNAL OF CHEMICAL INFORMATION AND MODELING 51.2(2011):386-397. |
入库方式： OAI收割
来源：上海药物研究所
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