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BMC Bioinformatics. 2019 Feb 4;19(Suppl 13):426. doi: 10.1186/s12859-018-2449-y.

Benchmarking of different molecular docking methods for protein-peptide docking.

Author information

1
Center for Computation Biology, Indraprastha Institute of Information Technology, Okhla Phase III, New Delhi, 110020, India.
2
CSIR-Institute of Microbial Technology, Sector 39A, Chandigarh, India.
3
Center for Computation Biology, Indraprastha Institute of Information Technology, Okhla Phase III, New Delhi, 110020, India. raghava@iiitd.ac.in.
4
CSIR-Institute of Microbial Technology, Sector 39A, Chandigarh, India. raghava@iiitd.ac.in.

Abstract

BACKGROUND:

Molecular docking studies on protein-peptide interactions are a challenging and time-consuming task because peptides are generally more flexible than proteins and tend to adopt numerous conformations. There are several benchmarking studies on protein-protein, protein-ligand and nucleic acid-ligand docking interactions. However, a series of docking methods is not rigorously validated for protein-peptide complexes in the literature. Considering the importance and wide application of peptide docking, we describe benchmarking of 6 docking methods on 133 protein-peptide complexes having peptide length between 9 to 15 residues. The performance of docking methods was evaluated using CAPRI parameters like FNAT, I-RMSD, L-RMSD.

RESULT:

Firstly, we performed blind docking and evaluate the performance of the top docking pose of each method. It was observed that FRODOCK performed better than other methods with average L-RMSD of 12.46 Å. The performance of all methods improved significantly for their best docking pose and achieved highest average L-RMSD of 3.72 Å in case of FRODOCK. Similarly, we performed re-docking and evaluated the performance of the top and best docking pose of each method. We achieved the best performance in case of ZDOCK with average L-RMSD 8.60 Å and 2.88 Å for the top and best docking pose respectively. Methods were also evaluated on 40 protein-peptide complexes used in the previous benchmarking study, where peptide have length up to 5 residues. In case of best docking pose, we achieved the highest average L-RMSD of 4.45 Å and 2.09 Å for the blind docking using FRODOCK and re-docking using AutoDock Vina respectively.

CONCLUSION:

The study shows that FRODOCK performed best in case of blind docking and ZDOCK in case of re-docking. There is a need to improve the ranking of docking pose generated by different methods, as the present ranking scheme is not satisfactory. To facilitate the scientific community for calculating CAPRI parameters between native and docked complexes, we developed a web-based service named PPDbench ( http://webs.iiitd.edu.in/raghava/ppdbench/ ).

KEYWORDS:

ATTRACT; Benchmark; CAPRI; FRODOCK; Hex; PatchDock; Protein-peptide docking; ZDOCK; pepATTRACT

PMID:
30717654
DOI:
10.1186/s12859-018-2449-y
[Indexed for MEDLINE]
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