Protein-protein interactions are considered necessary in the interactome analysis as they play an important in various biological processes such as post-translational modifications and signal transduction, and short peptides mediate around 40% of protein-protein interactions[1]. They are also found involved in some kind of infections and critical human diseases such as cancer [2,3].

Given the importance of protein-peptide interactions, it is necessary to determine their structure complexes to understand the involved mechanisms and then study further for their applications in the development of personalized drugs [4]. Protein-peptide docking methods are categorized into three: local docking, global docking, and template-based docking (for reading in detail, click here). 

This article explains some of the widely used web-based tools for protein-peptide docking.

1. CABS-dock [5]

It is a global protein-peptide docking webserver without a priori information about the binding sites. It searches for the binding sites with random conformations of the protein and positions of the peptide [5]. It follows four major steps: first, it generates random structures of the peptide, second, it simulates the binding and docking models using Replica Exchange Monte Carlo dynamics producing 10 trajectories for 10 replicas with each consisting of 1000 time-stamped simulation snapshots; in the third step, final models are selected on the basis of initial-filtering during which all unbound models are discarded and 100 models showing lowest energy are selected, followed by k-medoids clustering performed 100 times giving 10 models; finally, these 10 models are reconstructed and optimized using Modeller [6].

The web server (http://biocomp.chem.uw.edu.pl/CABSdock/) offers various options for users. They can either upload the PDB structure of the protein or provide the PDB code along with the peptide sequence or structure and set the simulation cycles.

2. Rosetta FlexPepDock [7]

It is a local protein-peptide docking-based tool that refines protein-peptide complex structures using Monte-Carlo minimization approach. The input structures of both the protein and the peptide undergo optimization during which side-chain conformers are optimized to reduce internal clashes of the structures. Later, a refinement step is followed to provide one single model, during which reduced repulsive Van der Waals term and increased attractive Van der Waals terms are optimized in 10 cycles.

The web server (http://flexpepdock.furmanlab.cs.huji.ac.il/cite.php) offers various advanced options such as constraint file upload, reference structure, and so forth [7].

3. GalaxyPepDock [8]

This is a template-based docking method that uses known structures as the templates to generate a protein-peptide complex. It involves two major steps: template selection and model building. In the first step, the templates are searched in the PepBind database [9] on the basis of interaction and structure similarity calculating a S_complex score for each complex. In the second step, 50 complex models are built using GalaxyTBM [10], which undergo optimization, and ultimately, 10 complex structures are selected depending on the best energy values and are further processed using the GALAXY refinement method [11] which adjusts the side-chains and backbone using molecular dynamics.

The web server (http://galaxy.seoklab.org/cgi-bin/submit.cgi?type=PEPDOCK) is also user-friendly and also provides a stand-alone version.

4. pepATTRACT [12]

This is a completely global protein-peptide docking method for blind and large-scale docking. It follows two major steps: protein-peptide docking; and interface analysis and prediction. During the first step, three model structures are generated using PeptideBuilder [13] followed by filling missing atoms using PDB2PQR [14]. These structures are later converted into ATTRACT coarse-grained atom type representation [15] followed by docking using ATTRACT [15]. The  50 docked models are selected for each of which interface propensity is calculated and finally presented to the user.

The web server (http://bioserv.rpbs.univ-paris-diderot.fr/services/pepATTRACT/) requires the simple protein and peptide structures/sequences as the input.

References

1. Petsalaki, E., & Russell, R. B. (2008). Peptide-mediated interactions in biological systems: new discoveries and applications. Current opinion in biotechnology, 19(4), 344-350.
2. Soni, V., Cahir-McFarland, E., & Kieff, E. (2007). LMP1 TRAFficking activates growth and survival pathways. In TNF Receptor Associated Factors (TRAFs) (pp. 173-187). Springer, New York, NY.
3. MacLaine, N. J., & Hupp, T. R. (2011). How phosphorylation controls p53. Cell Cycle, 10(6), 916-921.
4. Craik, D. J., Fairlie, D. P., Liras, S., & Price, D. (2013). The future of peptide‐based drugs. Chemical biology & drug design, 81(1), 136-147.
5. Eswar, N., Webb, B., Marti‐Renom, M. A., Madhusudhan, M. S., Eramian, D., Shen, M. Y., … & Sali, A. (2006). Comparative protein structure modeling using Modeller. Current protocols in bioinformatics, 15(1), 5-6.
6. Eswar, N., Webb, B., Marti‐Renom, M. A., Madhusudhan, M. S., Eramian, D., Shen, M. Y., … & Sali, A. (2006). Comparative protein structure modeling using Modeller. Current protocols in bioinformatics, 15(1), 5-6.
7. London, N., Raveh, B., Cohen, E., Fathi, G., & Schueler-Furman, O. (2011). Rosetta FlexPepDock web server—high resolution modeling of peptide–protein interactions. Nucleic acids research, 39(suppl_2), W249-W253.
8. Lee, H., Heo, L., Lee, M. S., & Seok, C. (2015). GalaxyPepDock: a protein–peptide docking tool based on interaction similarity and energy optimization. Nucleic acids research, 43(W1), W431-W435.
9. Das, A. A., Sharma, O. P., Kumar, M. S., Krishna, R., & Mathur, P. P. (2013). PepBind: a comprehensive database and computational tool for analysis of protein–peptide interactions. Genomics, proteomics & bioinformatics, 11(4), 241-246.
10. Ko, J., Park, H., & Seok, C. (2012). GalaxyTBM: template-based modeling by building a reliable core and refining unreliable local regions. BMC bioinformatics, 13(1), 198.
11. Heo, L., Park, H., & Seok, C. (2013). GalaxyRefine: protein structure refinement driven by side-chain repacking. Nucleic acids research, 41(W1), W384-W388.
12. de Vries, S. J., Rey, J., Schindler, C. E., Zacharias, M., & Tuffery, P. (2017). The pepATTRACT web server for blind, large-scale peptide–protein docking. Nucleic acids research, 45(W1), W361-W364.
13. Tien, M. Z., Sydykova, D. K., Meyer, A. G., & Wilke, C. O. (2013). PeptideBuilder: A simple Python library to generate model peptides. PeerJ, 1, e80.
14. Dolinsky, T. J., Nielsen, J. E., McCammon, J. A., & Baker, N. A. (2004). PDB2PQR: an automated pipeline for the setup of Poisson–Boltzmann electrostatics calculations. Nucleic acids research, 32(suppl_2), W665-W667.
15. Zacharias, M. (2003). Protein-protein docking with a reduced protein model accounting for side‐chain flexibility. Protein Science, 12(6), 1271-1282.