Abstract
The cPEPmatch approach is a rapid computational methodology for the rational design of cyclic peptides to target desired regions of protein–protein interfaces. The method selects cyclic peptides that structurally match backbone structures of short segments at a protein–protein interface. In a second step, the cyclic peptides act as templates for designed binders by adapting the amino acid side chains to the side chains found in the target complex. A link to access the different tools that comprise the cPEPmatch method and a detailed step-by-step guide is provided. We outline the protocol by following the application to a trypsin protease in complex with the bovine inhibitor protein (BPTI). An extension of our original approach is also presented, where we give a detailed description of the usage of the cPEPmatch methodology focusing on identifying hot regions of protein–protein interfaces prior to the matching. This extension allows one to reduce the amount of evaluated putative cyclic peptides and to specifically design only those that compete with the strongest protein–protein binding regions. It is illustrated by an application to an MHC class I protein complex.
Key words
- Protein–protein interactions
- Protein interaction inhibition
- Protein binding modulation
- Peptidomimetics
- Cyclic peptide design
- Drug design with cyclic peptides
- Rational cyclic peptide binders
This is a preview of subscription content, access via your institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsSpringer Nature is developing a new tool to find and evaluate Protocols. Learn more
References
Fontaine F, Overman J, François M (2015) Pharmacological manipulation of transcription factor protein-protein interactions: opportunities and obstacles. Cell Regen 4:2
Bahadur RP, Zacharias M (2018) The interface of protein-protein complexes: analysis of contacts and prediction of interactions. Cell Mol Life Sci 65:1059–1072. https://doi.org/10.1007/s00018-007-7451-x
Murray JK, Gellman SH (2007) Targeting protein–protein interactions: lessons from p53/MDM2. Biopolymers 88:657–686. https://doi.org/10.1002/bip.20741
Corbi-Verge C, Kim PM (2016) Motif mediated protein-protein interactions as drug targets. Cell Commun Signal 14:8
Conte LL, Chothia C, Janin J (1999) The atomic structure of protein-protein recognition sites. J Mol Biol 285:2177–2198. https://doi.org/10.1006/jmbi.1998.2439
Keskin O, Ma B, Nussinov R (2005) Hot regions in protein-protein interactions: the organization and contribution of structurally conserved hot spot residues. J Mol Biol 345:1281–1294. https://doi.org/10.1016/j.jmb.2004.10.077
Metz A, Pfleger C, Kopitz H et al (2012) Hot spots and transient pockets: predicting the determinants of small-molecule binding to a protein-protein interface. J Chem Inf Model 52:120–133. https://doi.org/10.1021/ci200322s
Wells JA, McClendon CL (2007) Reaching for high-hanging fruit in drug discovery at protein-protein interfaces. Nature 450:1001–1009. https://doi.org/10.1038/nature06526
Arkin MR, Tang Y, Wells JA (2014) Small-molecule inhibitors of protein-protein interactions: progressing toward the reality. Chem Biol 21:1102–1114. https://doi.org/10.1016/j.chembiol.2014.09.001
Qiu Y, Li X, He X et al (2020) Computational methods-guided design of modulators targeting protein-protein interactions (PPIs). Eur J Med Chem 207:112764. https://doi.org/10.1016/j.ejmech.2020.112764
Scott DE, Bayly AR, Abell C, Skidmore J (2016) Small molecules, big targets: drug discovery faces the protein-protein interaction challenge. Nat Rev Drug Discov 15:533–550. https://doi.org/10.1038/nrd.2016.29
Andrei SA, de Vink P, Sijbesma E et al (2018) Rationally designed semisynthetic natural product analogues for stabilization of 14-3-3 protein-protein interactions. Angew Chemie 130:13658–13662. https://doi.org/10.1002/ange.201806584
Santini BL, Zacharias M (2020) Rapid in silico design of potential cyclic peptide binders targeting protein-protein interfaces. Front Chem 8:2134. https://doi.org/10.3389/fchem.2020.573259
Duffy FJ, Devocelle M, DCS (2015) Computational approaches to developing short cyclic peptide modulators of protein–protein interactions. Methods Mol Biol 1268:241–271. https://doi.org/10.1007/978-1-4939-2285-7_11
Case DA, Belfon K, Ben-Shalom IY, Brozell SR, Cerutti DS, Cheatham TE III, Cruzeiro VWD, Darden TA, Duke RE, Giambasu G, Gilson MK, Gohlke H, Goetz AW, Harris R, Izadi S, Izmailov SA, Kasavajhala K, Kovalenko A, Krasny R, York DM, Kollman PA (2018) AMBER 2018. University of California, San Francisco
Maier JA, Martinez C, Kasavajhala K et al (2015) ff14SB: improving the accuracy of protein side chain and backbone parameters from ff99SB. J Chem Theory Comput 11:3696–3713. https://doi.org/10.1021/acs.jctc.5b00255
Jorgensen WL, Chandrasekhar J, Madura JD et al (1983) Comparison of simple potential functions for simulating liquid water. J Chem Phys 79:926–935. https://doi.org/10.1063/1.445869
Ryckaert JP, Ciccotti G, Berendsen HJC (1977) Numerical integration of the cartesian equations of motion of a system with constraints: molecular dynamics of n-alkanes. J Comput Phys 23:327–341. https://doi.org/10.1016/0021-9991(77)90098-5
Wang C, Greene D, Xiao L et al (2018) Recent developments and applications of the MMPBSA method. Front Mol Biosci 4:201–215
Maenaka K, Jones Y (1999) MHC superfamily structure and the immune system. Curr Opin Struct Biol 9:745–753
Montealegre S, Venugopalan V, Fritzsche S et al (2015) Dissociation of β2-microglobulin determines the surface quality control of major histocompatibility complex class I molecules. FASEB J 29:2780–2788. https://doi.org/10.1096/fj.14-268094
Gohlke H, Kiel C, Case DA (2003) Insights into protein-protein binding by binding free energy calculation and free energy decomposition for the Ras-Raf and Ras-RalGDS complexes. J Mol Biol 330:891–913. https://doi.org/10.1016/S0022-2836(03)00610-7
Acknowledgments
This research was conducted within the Max Planck School Matter to Life supported by the German Federal Ministry of Education and Research (BMBF) in collaboration with the Max Planck Society. We acknowledge also support by the Leibniz super computer (LRZ) center for providing supercomputer support by grant pr27za.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature
About this protocol
Cite this protocol
Santini, B.L., Zacharias, M. (2022). Rapid Rational Design of Cyclic Peptides Mimicking Protein–Protein Interfaces. In: Simonson, T. (eds) Computational Peptide Science. Methods in Molecular Biology, vol 2405. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-1855-4_12
Download citation
DOI: https://doi.org/10.1007/978-1-0716-1855-4_12
Published:
Publisher Name: Humana, New York, NY
Print ISBN: 978-1-0716-1854-7
Online ISBN: 978-1-0716-1855-4
eBook Packages: Springer Protocols