DINC 2.0: A New Protein-Peptide Docking Webserver Using an Incremental Approach.
DINC 2.0: A New Protein-Peptide Docking Webserver Using an Incremental Approach.
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DOI：
10.1158/0008-5472.can-17-0511
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发表时间：
2017-11-01
期刊：
影响因子：
11.2
通讯作者：
Kavraki LE
中科院分区：
文献类型：
--
作者：
Antunes DA;Moll M;Devaurs D;Jackson KR;Lizée G;Kavraki LE
分子对接是一种预测蛋白质 - 配体复合物结合模式的标准计算方法，它通过探索配体的不同取向和构象（即探索配体的柔性）来实现。对接工具在很大程度上用于对类药小分子进行虚拟筛选，但对于具有10个以上柔性键的配体，其准确性和效率会大幅下降。这阻碍了这些工具在对接更大的配体（如肽）方面的更广泛应用，而肽在癌症研究中是越来越受关注的分子。为了克服这一限制，我们小组先前提出了一种元对接策略，称为DINC，用于预测大配体的结合模式。通过逐步对接配体的重叠片段，DINC能够预测参与癌症的转录因子的肽类抑制剂的结合模式。在此我们介绍DINC 2.0，它是DINC网络服务器的改进版本，具有更强的功能和更友好的用户界面。DINC 2.0能够对接以前对DINC来说过于困难的配体，例如具有25个以上柔性键的肽。该网络服务器可通过http://dinc.kavrakilab.org免费访问，同时还提供额外的文档和视频教程。我们的团队将为该工具提供持续支持，并致力于将其应用范围扩展到其他具有挑战性的领域，例如癌症的个性化免疫治疗。
Molecular docking is a standard computational approach to predict binding modes of protein-ligand complexes, by exploring alternative orientations and conformations of the ligand (i.e., by exploring ligand flexibility). Docking tools are largely used for virtual screening of small drug-like molecules, but their accuracy and efficiency greatly decays for ligands with more than 10 flexible bonds. This prevents a broader use of these tools to dock larger ligands such as peptides, which are molecules of growing interest in cancer research. To overcome this limitation, our group has previously proposed a meta-docking strategy, called DINC, to predict binding modes of large ligands. By incrementally docking overlapping fragments of a ligand, DINC allowed predicting binding modes of peptide-based inhibitors of transcription factors involved in cancer. Here we describe DINC 2.0, a revamped version of the DINC webserver with enhanced capabilities and a more user-friendly interface. DINC 2.0 allows docking ligands that were previously too challenging for DINC, such as peptides with more than 25 flexible bonds. The webserver is freely accessible at http://dinc.kavrakilab.org, together with additional documentation and video tutorials. Our team will provide continuous support for this tool and is working on extending its applicability to other challenging fields, such as personalized immunotherapy against cancer.