DINC 2.0: A New Protein–Peptide Docking Webserver Using an Incremental Approach

Abstract

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.

Design of an Epitope-Based Peptide Vaccine against the Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2): A Vaccine-informatics Approach

“…In current, we used DINC 2 . 0 (43) online server for docking of peptides. In our study, we have selected three CD8 + T-cell peptides ( LTDEMIAQY, WTAGAAAYY and WMESEFRVY) and five CD4 + T-cell peptides ( IRASANLAA, FGAISSVLN, VKQLSSNFG, FAMQMAYRF and FGAGAALQ ) for docking against the HLA molecule including HLA-B*53:01, HLA-B*44:03 and HLA-DRB1*01:01.…”

In-silico analysis of potential interaction of drugs and the SARS-CoV-2 spike protein

“…Protein-Protein interaction was performed by HDOCK/HADDOCK [24][25][26] . And the Binding Energy was calculated by the DINC 2.0 (http://dinc.kavrakilab.org)server which is a meta-docking method for the incremental docking based on AutoDock vina 27 .…”

“…We investigated the drug-target on the spike of SARS-CoV-2 protein by using the PROCHECK, PDBsum and DrugPort 8,16,17 by the protein sequence search which unveiled the drug molecules and their target proteins. We found the drugs targeted to the protein and performed molecular docking analysis by using the docking server [19][20][21][22][23][24][25][26][27] of those selected drugs to the spike protein of SARS-CoV-2. In each case, we demonstrated the Binding A nity by GalaxyWeb 12 ; Global energy by using PatchDock 20,21 and followed by FireDock 22,23 for the re nement; Binding Energy by using DINC.2 based on AutoDock vina 27 .…”

InterPep2: Global Peptide-Protein Docking with Structural Templates

“…Global peptide-docking prediction methods such as CABSdock, PIPER-FlexPepDock, pepATTRACT, and GalaxyPepDock have achieved high performances on individual benchmarks, but struggle to consistently produce reliable predictions (Kurcinski et al, 2015;Alam et al, 2017;Schindler et al, 2015;Lee et al, 2015). Local refinement predictions methods such as FlexPepDock, PEP-FOLD3, and DINC 2.0 have achieved high precision in the past, but require good starting positions (Raveh et al, 2010a;Lamiable et al, 2016;Antunes et al, 2017). FlexPepDock for example requires a starting position within 5.5ÅRMSD (root mean square deviation) of the correct structure to reliably produce near-native predictions (Raveh et al, 2010a).…”