vSDC: a method to improve early recognition in virtual screening when limited experimental resources are available.
vSDC: a method to improve early recognition in virtual screening when limited experimental resources are available.
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DOI：
10.1186/s13321-016-0112-z
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发表时间：
2016
影响因子：
8.6
通讯作者：
Mouawad L
中科院分区：
文献类型：
--
作者：
Chaput L;Martinez-Sanz J;Quiniou E;Rigolet P;Saettel N;Mouawad L
在药物设计中，人们可能会遇到这样的问题，即找到靶点，而这些靶点没有已知的小抑制分子，只有低通量实验可用（如ITC或NMR研究），这是典型学术环境中遇到的两个常见困难。使用虚拟筛选策略如对接可以通过仅选择排名靠前的分子来缓解一些问题并节省相当多的时间，但前提是该方法非常有效，即当在1- 10%的排名最好的分子中发现良好比例的活性物质时。几个程序的使用（在我们的研究中，Gold，Surflex，FlexX和Glide被认为是）显示了结果的分歧，这在指导实验方面带来了困难。为了克服这种分歧并提高虚拟筛选的产量，我们创建了标准偏差一致性（SDC）和可变SDC（vSDC）方法，由来自几个虚拟筛选程序的分子集的交集组成，基于其排名分布的标准偏差。在药物设计中，对于给定的靶标和给定的化学库，使用不同的虚拟筛选程序获得的结果是不同的。那么，如何合理地指导实验测试，尤其是在实验次数很少的情况下？可变标准偏差共识（vSDC）方法的开发是为了回答这个问题。左图vSDC原理由交叉分子集组成，基于从各种虚拟筛选程序获得的其排序分布的标准偏差选择。在本研究中，使用了Glide、Gold、FlexX和Surflex，并在DUD-E数据库的102个靶点上进行了测试。右图：当仅考虑来自DUD-E数据库的102个化学文库中的每一个的10个分子时，用vSDC和四个程序中的每一个发现的命中的平均百分比的比较。平均而言，vSDC能够找到38%的可找到的命中，而Glide为34%，Gold为32%，FlexX为16%，Surflex为14%，这表明使用vSDC，SDC使我们能够通过测试仅9个和11个小分子来发现两个新蛋白质靶点的命中，一个包含大约15,000种化合物的化学图书馆此外，当应用于DUD-E基准数据库的102种蛋白质时，vSDC成功地找到了比四种分离程序中的任何一种更多的命中，达到13- 60%的目标。此外，当仅考虑102个化学文库中的每一个的10个分子时，vSDC在发现的命中数方面表现更好，比由单独的对接程序给出的10个最佳排序的分子提高6- 24%。本文的在线版本（doi：10.1186/s13321-016-0112-z）包含补充材料，可供授权用户使用。
In drug design, one may be confronted to the problem of finding hits for targets for which no small inhibiting molecules are known and only low-throughput experiments are available (like ITC or NMR studies), two common difficulties encountered in a typical academic setting. Using a virtual screening strategy like docking can alleviate some of the problems and save a considerable amount of time by selecting only top-ranking molecules, but only if the method is very efficient, i.e. when a good proportion of actives are found in the 1–10 % best ranked molecules. The use of several programs (in our study, Gold, Surflex, FlexX and Glide were considered) shows a divergence of the results, which presents a difficulty in guiding the experiments. To overcome this divergence and increase the yield of the virtual screening, we created the standard deviation consensus (SDC) and variable SDC (vSDC) methods, consisting of the intersection of molecule sets from several virtual screening programs, based on the standard deviations of their ranking distributions. In drug design, for a given target and a given chemical library, the results obtained with different virtual screening programs are divergent. So how to rationally guide the experimental tests, especially when only a few number of experiments can be made? The variable Standard Deviation Consensus (vSDC) method was developed to answer this issue. Left panel the vSDC principle consists of intersecting molecule sets, chosen on the basis of the standard deviations of their ranking distributions, obtained from various virtual screening programs. In this study Glide, Gold, FlexX and Surflex were used and tested on the 102 targets of the DUD-E database. Right panel Comparison of the average percentage of hits found with vSDC and each of the four programs, when only 10 molecules from each of the 102 chemical libraries of the DUD-E database were considered. On average, vSDC was capable of finding 38 % of the findable hits, against 34 % for Glide, 32 % for Gold, 16 % for FlexX and 14 % for Surflex, showing that with vSDC, it was possible to overcome the unpredictability of the virtual screening results and to improve them SDC allowed us to find hits for two new protein targets by testing only 9 and 11 small molecules from a chemical library of circa 15,000 compounds. Furthermore, vSDC, when applied to the 102 proteins of the DUD-E benchmarking database, succeeded in finding more hits than any of the four isolated programs for 13–60 % of the targets. In addition, when only 10 molecules of each of the 102 chemical libraries were considered, vSDC performed better in the number of hits found, with an improvement of 6–24 % over the 10 best-ranked molecules given by the individual docking programs. The online version of this article (doi:10.1186/s13321-016-0112-z) contains supplementary material, which is available to authorized users.