Mpamhanga Chidochangu P, Chen Beining, McLay Iain M, Ormsby Daniel L, Lindvall Mika K
Department of Chemistry, University of Sheffield, Dainton Building, Brookhill, Sheffield S3 7HF, U.K.
J Chem Inf Model. 2005 Jul-Aug;45(4):1061-74. doi: 10.1021/ci050044x.
Scoring forms a major obstacle to the success of any docking study. In general, fast scoring functions perform poorly when used to determine the relative affinity of ligands for their receptors. In this study, the objective was not to rank compounds with confidence but simply to identify a scoring method which could provide a 4-fold hit enrichment in a screening sample over random selection. To this end, LigandFit, a fast shape matching docking algorithm, was used to dock a variety of known inhibitors of type 4 phosphodiesterase (PDE4B) into its binding site determined crystallographically for a series of pyrazolopyridine inhibitors. The success of identifying good poses with this technique was explored through RMSD comparisons with 19 known inhibitors for which crystallographic structures were available. The effectiveness of five scoring functions (PMF, JAIN, PLP2, LigScore2, and DockScore) was then evaluated through consideration of the success in enriching the top ranked fractions of nine artificial databases, constructed by seeding 1980 inactive ligands (pIC50 < 5) with 20 randomly selected inhibitors (pIC50 > 6.5). PMF and JAIN showed high average enrichment factors (greater than 4 times) in the top 5-10% of the ranked databases. Rank-based consensus scoring was then investigated, and the rational combination of 3 scoring functions resulted in more robust scoring schemes with (cScore)-DPmJ (consensus score of DockScore, PMF, and JAIN) and (cScore)-PPmJ (PLP2, PMF, and JAIN) yielding particularly good results. These cScores are believed to be of greater general application. Finally, the analysis of the behavior of the scoring functions across different chemotypes uncovered the inherent bias of the docking and scoring toward compounds in the same structural family as that employed for the crystal structure, suggesting the need to use multiple versions of the binding site for more successful virtual screening strategies.
评分是任何对接研究成功的主要障碍。一般来说，快速评分函数在用于确定配体与其受体的相对亲和力时表现不佳。在本研究中，目标不是自信地对化合物进行排名，而只是确定一种评分方法，该方法在筛选样本中相对于随机选择能够提供4倍的命中富集。为此，使用快速形状匹配对接算法LigandFit，将多种已知的4型磷酸二酯酶（PDE4B）抑制剂对接至其通过晶体学确定的一系列吡唑并吡啶抑制剂的结合位点。通过与19种已知抑制剂（可获得晶体结构）进行RMSD比较，探索了使用该技术识别良好构象的成功率。然后，通过考虑在九个虚拟数据库的排名靠前部分中富集的成功率，评估了五种评分函数（PMF、JAIN、PLP2、LigScore2和DockScore）的有效性，这九个虚拟数据库是通过用20种随机选择的抑制剂（pIC50>6.5）播种1980种无活性配体（pIC50<5）构建的。PMF和JAIN在排名数据库的前5-10%中显示出高平均富集因子（大于4倍）。然后研究了基于排名的共识评分，三种评分函数的合理组合产生了更稳健的评分方案，（cScore）-DPmJ（DockScore、PMF和JAIN的共识评分）和（cScore）-PPmJ（PLP2、PMF和JAIN）产生了特别好的结果。这些cScore被认为具有更广泛的普遍适用性。最后，对不同化学类型的评分函数行为的分析揭示了对接和评分对与晶体结构所采用的结构家族相同的化合物的固有偏向，这表明需要使用结合位点的多个版本以实现更成功的虚拟筛选策略。
