FRAGSITE: A Fragment-Based Approach for Virtual Ligand Screening.
FRAGSITE: A Fragment-Based Approach for Virtual Ligand Screening.
复制标题
DOI：
10.1021/acs.jcim.0c01160
复制
发表时间：
2021-04-26
影响因子：
5.6
通讯作者：
Skolnick J
中科院分区：
文献类型：
--
作者：
Zhou H;Cao H;Skolnick J
为了减少时间和成本，在现代药物发现中，虚拟配体筛选（VLS）通常先于实验配体筛选。传统上，基于高分辨率结构的对接方法依赖于实验结构，而基于配体的方法需要已知的靶蛋白结合剂，并且仅探索其附近的化学空间。相比之下，我们基于结构的FINDSITEcomb2.0方法利用了预测的低分辨率结构和配体的信息，这些配体结合了其结合位点与靶蛋白相似的远亲蛋白。使用提升树回归机器学习框架，我们通过整合分子指纹编码的配体片段得分与FINDSITEcomb2.0的全局配体相似性得分，显着改进了FINDSITEcomb2.0。新的方法，FRAGSITE，利用我们的观察，配体片段，例如，环，往往与立体化学保守的蛋白质亚口袋，也发生在进化无关的蛋白质相互作用。FRAGSITE以102蛋白DUD-E组为基准，其中排除与靶序列同一性>30%的任何模板蛋白。在排名前100的分子中，相对于FINDSITEcomb2.0，FRAGSITE将VLS精确度和召回率分别提高了14.3%和18.5%。此外，平均前1%富集因子从25.2增加到30.2。平均而言，这两种方法的性能都优于最先进的基于深度学习的方法，如AtomNet。在更具挑战性的无偏集LIT-PCBA上，FRAGSITE也显示出比基于配体相似性和对接方法（如二维ECFP 4和Surflex-Dock v.3066）更好的性能。在DEKOIS 2.0的23个目标的子集上，FRAGSITE显示出比基于提升树回归的vScreenML评分函数更好的性能。对FRAGSITE预测的实验测试表明，它比FINDSITEcomb2.0有更多的命中率，覆盖了更多样化的化学空间区域。对于实验测试的两种蛋白质，DHFR，一种催化二氢叶酸转化为四氢叶酸的研究充分的蛋白质，以及激酶ACVR 1，FRAGSITE鉴定了新的小分子纳摩尔结合剂。有趣的是，一种新的DHFR结合剂是一种激酶抑制剂，预计将结合在一个新的亚口袋中。对于ACVR 1，FRAGSITE鉴定了具有不同支架的新分子，并估计了纳摩尔到微摩尔的亲和力。因此，FRAGSITE显示出优于现有技术配体虚拟筛选方法的显著改进。学术用户可以免费使用网络服务器：http://sites.gatech.edu/cssb/FRAGSITE。
To reduce time and cost, virtual ligand screening (VLS) often precedes experimental ligand screening in modern drug discovery. Traditionally, high-resolution structure-based docking approaches rely on experimental structures, while ligand-based approaches need known binders to the target protein and only explore their nearby chemical space. In contrast, our structure-based FINDSITEcomb2.0 approach takes advantage of predicted, low-resolution structures and information from ligands that bind distantly related proteins whose binding sites are similar to the target protein. Using a boosted tree regression machine learning framework, we significantly improved FINDSITEcomb2.0 by integrating ligand fragment scores as encoded by molecular fingerprints with the global ligand similarity scores of FINDSITEcomb2.0. The new approach, FRAGSITE, exploits our observation that ligand fragments, e.g., rings, tend to interact with stereochemically conserved protein subpockets that also occur in evolutionarily unrelated proteins. FRAGSITE was benchmarked on the 102 protein DUD-E set, where any template protein whose sequence identify >30% to the target was excluded. Within the top 100 ranked molecules, FRAGSITE improves VLS precision and recall by 14.3 and 18.5%, respectively, relative to FINDSITEcomb2.0. Moreover, the mean top 1% enrichment factor increases from 25.2 to 30.2. On average, both outperform state-of-the-art deep learning-based methods such as AtomNet. On the more challenging unbiased set LIT-PCBA, FRAGSITE also shows better performance than ligand similarity-based and docking approaches such as two-dimensional ECFP4 and Surflex-Dock v.3066. On a subset of 23 targets from DEKOIS 2.0, FRAGSITE shows much better performance than the boosted tree regression-based, vScreenML scoring function. Experimental testing of FRAGSITE’s predictions shows that it has more hits and covers a more diverse region of chemical space than FINDSITEcomb2.0. For the two proteins that were experimentally tested, DHFR, a well-studied protein that catalyzes the conversion of dihydrofolate to tetrahydrofolate, and the kinase ACVR1, FRAGSITE identified new small-molecule nanomolar binders. Interestingly, one new binder of DHFR is a kinase inhibitor predicted to bind in a new subpocket. For ACVR1, FRAGSITE identified new molecules that have diverse scaffolds and estimated nanomolar to micromolar affinities. Thus, FRAGSITE shows significant improvement over prior state-of-the-art ligand virtual screening approaches. A web server is freely available for academic users at http://sites.gatech.edu/cssb/FRAGSITE.
登录
查看更多内容
影响因子：
4.3
作者：
Gao M;Skolnick J
通讯作者：
Skolnick J
影响因子：
5.9
作者：
Cao, Hongnan;Gao, Mu;Skolnick, Jeffrey
通讯作者：
Skolnick, Jeffrey
影响因子：
56.9
作者：
Boehr, David D.;McElheny, Dan;Wright, Peter E.
通讯作者：
Wright, Peter E.
DOI：
10.1021/ci970437z
发表时间：
1998-05-01
期刊：
JOURNAL OF CHEMICAL INFORMATION AND COMPUTER SCIENCES
影响因子：
--
作者：
Flower, DR
通讯作者：
Flower, DR
影响因子：
4.5
作者：
Friedman, JH
通讯作者：
Friedman, JH