Abstract
Binding recognition is in the core of how nature controls processes in living cells, how enzyme–substrate binding leads to catalysis and how drugs modulate enzymes and receptors to convey a desirable physiological response. Thus, understanding binding recognition in a systematic manner is paramount, not only to understand biological processes but also to be able to design and discover new bioactive compounds. One such way to analyze binding interactions is through the development of binding interaction fingerprints. Here, we present the methodology to develop interaction fingerprints with three different software platforms along with two representative examples.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Medina-Franco JL, Méndez-Lucio O, Martinez-Mayorga K (2014) Chapter one: the interplay between molecular modeling and chemoinformatics to characterize protein–ligand and protein–protein interactions landscapes for drug discovery. In: Tatyana K-C (ed) Advances in protein chemistry and structural biology, vol. 96. pp. 1–37. Academic Press
Deng Z, Chuaqui C, Singh J (2003) Structural interaction fingerprint (SIFt): a novel method for analyzing three-dimensional protein–ligand binding interactions. J Med Chem 47:337–344
Brewerton SC (2008) The use of protein–ligand interaction fingerprints in docking. Curr Opin Drug Discov Dev 11(3):356–364
Desaphy J, Raimbaud E, Ducrot P, Rognan D (2013) Encoding protein–ligand interaction patterns in fingerprints and graphs. J Chem Inf Model 53(3):623–637
Seebeck B, Wagener M, Rarey M (2011) From activity cliffs to target-specific scoring models and pharmacophore hypotheses. ChemMedChem 6(9):1630–1639
Méndez-Lucio O, Kooistra AJ, Graaf CD, Bender A, Medina-Franco JL (2015) Analysing multitarget activity landscapes using protein–ligand interaction fingerprints: interaction cliffs. J Chem Inf Mod 55:251–262
Berman HM, Westbrook J, Feng Z, Gilliland G, Bhat TN, Weissig H, Shindyalov IN, Bourne PE (2000) The protein data bank. Nucleic Acids Res 28(1):235–242
MOE: Molecular Operating Environment (MOE), version 2013.08, Chemical Computing Group Inc., Montreal, Quebec, Canada. http://www.chemcomp.com. In: Molecular Operating Environment (MOE), version 2013.2008, Chemical Computing Group Inc., Montreal, Quebec, Canada. http://www.chemcomp.com
Maestro, version 9.3 (2012) Schrödinger, LLC: New York, NY
Martinez-Mayorga K, Byler KG, Ramirez-Hernandez AI, Terrazas-Alvares DE (2015) Cruzain inhibitors: efforts made, current leads and a structural outlook of new hits. Drug Discov Today. doi: 10.1016/j.drudis.2015.02.004 (in press)
Acknowledgments
KMM acknowledge Institute of Chemistry and PAPIIT IA200513-2 for financial support. JLMF thanks the Department of Pharmacy, School of Chemistry at UNAM for support. This work was partially supported by the State of Florida, Executive Officer of the Governor’s Department of Economic Development.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer Science+Business Media New York
About this protocol
Cite this protocol
Marmolejo, A.F., Medina-Franco, J.L., Giulianotti, M., Martinez-Mayorga, K. (2015). Interaction Fingerprints and Their Applications to Identify Hot Spots. In: Filizola, M. (eds) G Protein-Coupled Receptors in Drug Discovery. Methods in Molecular Biology, vol 1335. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-2914-6_20
Download citation
DOI: https://doi.org/10.1007/978-1-4939-2914-6_20
Publisher Name: Humana Press, New York, NY
Print ISBN: 978-1-4939-2913-9
Online ISBN: 978-1-4939-2914-6
eBook Packages: Springer Protocols