Peptide and Protein Interaction Prediction and Intervention with Computational Methods

Newsha Fallah, Saeed Siavashy, Nasim Ghaemian, Farshad Bahramian, Fatemeh Ghorbani-Bidkorbeh



Proteins are the most fascinating multifaceted biomacromolecules in living systems and play various important roles such as structural, sensory, catalytic, and regulatory function. Protein and peptide interactions have emerged as an important and challenging topic in
biochemistry and medicinal chemistry. Computational methods as promising tools have been utilized to predict protein and peptide interactions in order to intervene in the biochemical processes and facilitate pharmaceutical peptide design and clarify the complications. This review will introduce the computational methods which are applicable in protein and peptide interaction prediction and summarizes the most successful examples of computational methods described in the literature.

•Highlights the importance of peptides and proteins interactions.
•Summarizes the computational methods which are applicable in peptide and protein interaction prediction.
•Highlights the applications of computational methods in peptides and proteins interactions.


Peptides and Proteins; Computational Methods; Simulation; Interaction Prediction

Full Text:



Banerjee, V., Oren, O., Ben-Zeev, E., Taube, R., Engel, S. and N. Papo, (2017). "A computational combinatorial approach identifies a protein inhibitor of superoxide dismutase 1 misfolding, aggregation, and cytotoxicity." Journal of Biological Chemistry, 292(38): 15777-15788.

Bardwell, V. J. and R. Treisman, (1994). "The POZ domain: a conserved protein-protein interaction motif." Genes & development, 8(14): 1664-1677.

Bhattacherjee, A. and S. Wallin, (2013). "Exploring protein-peptide binding specificity through computational peptide screening." PLoS Computational Biology, 9(10): e1003277.

Blaszczyk, M., Jamroz, M., Kmiecik, S. and A. Kolinski, (2013). "CABS-fold: server for the de novo and consensus-based prediction of protein structure." Nucleic Acids Research, 41(W1), pp.W406-W411.

Blaszczyk, M., Kurcinski, M., Kouza, M., Wieteska, L., Debinski, A., Kolinski, A. and S. Kmiecik, (2016). "Modeling of protein–peptide interactions using the CABS-dock web server for binding site search and flexible docking." Methods, 93: 72-83.

Boyce, S. E., Mobley, D. L., Rocklin, G. J., Graves, A. P., Dill, K. A. and B. K. Shoichet, (2009). "Predicting ligand binding affinity with alchemical free energy methods in a polar model binding site." Journal of Molecular Biology, 394(4): 747-763.

Chen, T. S. and A. E. Keating, (2012). "Designing specific protein–protein interactions using computation, experimental library screening, or integrated methods." Protein Science, 21(7): 949-963.

Chen, W., Gilson, M. K., Webb, S. P. and M. J. Potter, (2010). "Modeling protein− ligand binding by mining minima." Journal of Chemical Theory and Computation, 6(11): 3540-3557.

Chia-en, A. C., Chen, W. and M. K. Gilson, (2007). "Ligand configurational entropy and protein binding." Proceedings of the National Academy of Sciences, 104(5): 1534-1539.

Cho, K. H., Shin, S. Y., Kolch, W. and O. Wolkenhauer, (2003). "Experimental design in systems biology, based on parameter sensitivity analysis using a monte carlo method: A case study for the tnfα-mediated nf-κ b signal transduction pathway." Simulation, 79(12): 726-739.

Das, A. A., Sharma, O. P., Kumar, M. S., Krishna, R. and P. P. Mathur, (2013). "PepBind: a comprehensive database and computational tool for analysis of protein–peptide interactions." Genomics, Proteomics & Bioinformatics, 11(4): 241-246.

de Ruiter, A. and C. Oostenbrink, (2011). "Free energy calculations of protein–ligand interactions." Current Opinion in Chemical Biology, 15(4): 547-552.

de Vries, S. J., Rey, J., Schindler, C. E., Zacharias, M. and P. Tuffery, (2017). ″The pepATTRACT web server for blind, large-scale peptide–protein docking.″ Nucleic Acids Research, 2017.

de Vries, S. J., Schindler, C. E., de Beauchêne, I. C. and M. Zacharias, (2015). "A web interface for easy flexible protein-protein docking with ATTRACT." Biophysical Journal, 108(3): 462-465.

Deng, Y. and B. Roux, (2009). "Computations of standard binding free energies with molecular dynamics simulations." The Journal of Physical Chemistry B, 113(8): 2234-2246.

Ding, F., Yin, S. and N. V. Dokholyan, (2010). "Rapid flexible docking using a stochastic rotamer library of ligands." Journal of Chemical Information and Modeling, 50(9): 1623-1632.

Dominguez, C., Boelens, R. and A. M. Bonvin, (2003). "HADDOCK: a protein− protein docking approach based on biochemical or biophysical information." Journal of the American Chemical Society, 125(7): 1731-1737.

Gallicchio, E. and R. M. Levy, (2011). "Recent theoretical and computational advances for modeling protein-ligand binding affinities." Advances in Protein Chemistry and Structural Biology, 85: 27.

Genheden, S. and U. Ryde, (2015). "The MM/PBSA and MM/GBSA methods to estimate ligand-binding affinities." Expert Opinion on Drug Discovery, 10(5): 449-461.

Gilson, M. K., Given, J. A., Bush, B. L. and J. A. McCammon, (1997). "The statistical-thermodynamic basis for computation of binding affinities: a critical review." Biophysical Journal, 72(3): 1047-1069.

Grigoryan, G., Reinke, A. W. and A. E. Keating, (2009). "Design of protein-interaction specificity gives selective bZIP-binding peptides." Nature, 458(7240): 859-864.

Guvench, O. and A. D. MacKerell Jr, (2009). "Computational fragment-based binding site identification by ligand competitive saturation." PLoS Computational Biology, 5(7): e1000435.

Hou, T., Chen, K., McLaughlin, W. A., Lu, B. and W. Wang, (2006). "Computational analysis and prediction of the binding motif and protein interacting partners of the Abl SH3 domain." PLoS Computational Biology, 2(1): e1.

Jamroz, M., Kolinski, A. and S. Kmiecik, (2013). "CABS-flex: server for fast simulation of protein structure fluctuations." Nucleic Acids Research, 41(W1): W427-W431.

Kilburg, D. and E. Gallicchio, (2016). "Chapter Two-Recent Advances in Computational Models for the Study of Protein–Peptide Interactions." Advances in Protein Chemistry and Structural Biology, 105: 27-57.

Kmiecik, S., Gront, D., Kolinski, M., Wieteska, L., Dawid, A. E. and A. Kolinski, (2016). "Coarse-grained protein models and their applications." Chemical Reviews, 116(14): 7898-7936.

Kuczera, K. (2011) "Molecular Modeling in Peptide and Protein Analysis." In: Encyclopedia of Analytical Chemistry, John Wiley & Sons, Ltd.

Kurcinski, M., Jamroz, M., Blaszczyk, M., Kolinski, A. and S. Kmiecik, (2015). "CABS-dock web server for the flexible docking of peptides to proteins without prior knowledge of the binding site." Nucleic Acids Research, 43(W1): W419-W424.

Lee, H., Heo, L., Lee, M. S. and C. Seok, (2015). "GalaxyPepDock: a protein–peptide docking tool based on interaction similarity and energy optimization." Nucleic Acids Research, 43(W1): W431-W435.

Levitt, M. (1983). "Molecular dynamics of native protein: I. computer simulation of trajectories." Journal of Molecular Biology, 168(3): 595-617.

MacKerell Jr, A. D., Bashford, D., Bellott, M. L. D. R., Dunbrack Jr, R. L., Evanseck, J. D., Field, M. J., Fischer, S., Gao, J., Guo, H., Ha, S. and D. Joseph-McCarthy, (1998). "All-atom empirical potential for molecular modeling and dynamics studies of proteins." The Journal of Physical Chemistry B, 102(18): 3586-3616.

May, A. and M. Zacharias, (2008). "Protein− ligand docking accounting for receptor side chain and global flexibility in normal modes: Evaluation on kinase inhibitor cross docking." Journal of Medicinal Chemistry, 51(12): 3499-3506.

Meng, X. Y., Zhang, H. X., Mezei, M. and M. Cui, (2011). "Molecular docking: a powerful approach for structure-based drug discovery. " Current Computer-Aided Drug Design, 7(2): 146-157.

Neumaier, A. (1997). "Molecular modeling of proteins and mathematical prediction of protein structure." SIAM Review, 39(3): 407-460.

Nevola, L. and E. Giralt, (2015). "Modulating protein–protein interactions: the potential of peptides." Chemical Communications, 51(16): 3302-3315.

Paladino, A., Marchetti, F., Rinaldi, S. and G. Colombo, (2017). "Protein design: from computer models to artificial intelligence." Wiley Interdisciplinary Reviews: Computational Molecular Science, 2017.

Palmer, A. E., Giacomello, M., Kortemme, T., Hires, S. A., Lev-Ram, V., Baker, D. and R. Y. Tsien, (2006). "Ca2+ indicators based on computationally redesigned calmodulin-peptide pairs." Chemistry & Biology, 13(5): 521-530.

Radhika, V. and V. S. H. Rao, (2015). "Computational approaches for the classification of seed storage proteins." Journal of Food Science and Technology, 52(7): 4246-4255.

Rodrigues, J. P. and A. M. Bonvin, (2014). "Integrative computational modeling of protein interactions." The FEBS Journal, 281(8): 1988-2003.

Rose, P. W., Beran, B., Bi, C., Bluhm, W. F., Dimitropoulos, D., Goodsell, D. S., Prlić, A., Quesada, M., Quinn, G. B., Westbrook, J. D. and J. Young, (2010). "The RCSB Protein Data Bank: redesigned web site and web services. " Nucleic Acids Research, 39(suppl_1): D392-D401.

Sarkar, D., Patra, P., Ghosh, A. and S. Saha, (2016). "Computational framework for prediction of peptide sequences that may mediate multiple protein interactions in cancer-associated hub proteins." PLoS One, 11(5): e0155911.

Tiwari, M. K., Singh, R., Singh, R. K., Kim, I. W. and J. K. Lee, (2012). "Computational approaches for rational design of proteins with novel functionalities." Computational and Structural Biotechnology Journal, 2(3): 1-13.

Valleau J. P. and G. M. Torrie, (1977). ″A Guide to Monte Carlo for Statistical Mechanics: 2. Byways.″ In: Berne B. J. (Eds), Statistical Mechanics. Modern Theoretical Chemistry, vol 5. Springer, Boston, MA.

Woo, H.J. and B. Roux, (2005). "Calculation of absolute protein–ligand binding free energy from computer simulations. " Proceedings of the National Academy of Sciences of the United States of America, 102(19): 6825-6830.


  • There are currently no refbacks.