Advances in the Prediction of Protein–Peptide Binding Affinities: Implications for Peptide‐Based Drug Discovery

Abstract

Peptides hold great promise as novel medicinal and biologic agents, and computational methods can help unlock that promise. In particular, structure-based peptide design can be used to identify and optimize peptide ligands. Successful structure-based design, in turn, requires accurate and fast methods for predicting protein-peptide binding affinities. Here, we review the development of such methods, emphasizing structure-based methods that assume rigid-body association and the single-structure approximation. We also briefly review recent applications of computational free energy prediction methods to enable and guide novel peptide drug and biomarker discovery. We close the review with a brief perspective on the future of computational, structure-based protein-peptide binding affinity prediction.

Development of QSAR‐Improved Statistical Potential for the Structure‐Based Analysis of ProteinPeptide Binding Affinities

“…With the rapid increase in proteinpeptide complex structures available in the PDB and the advent of high‐performance molecular modeling and computational tools offer an effective and practical route to characterizing proteinpeptide interactions at atomic‐resolution level 69. Clearly, reliable and fast prediction of peptide binding affinity is crucial for fulfilling such characterizations 12. In this study, we presented a new approach that integrated unsupervised statistical potential and supervised QSAR modeling to develop a structure‐based, general‐purpose method called QSAR‐improved PPRCP for fast and reliably predicting proteinpeptide binding affinities.…”

“…Despite these successful stories, rational design of peptide ligands that can stably and specifically bind their cognate targets still remains as a great challenge in the drug development community owing to the significant flexibility and bulky size of peptide entities. Clearly, computational peptide design, as well as many molecular modeling protocols such as peptide docking and virtual screening, [11] would benefit from the use of fast and accurate methods for predicting proteinpeptide binding affinities; a detailed review covering this area has been published recently 12. Nonetheless, currently available methods have some drawbacks that significantly limit their applications in the peptide design area:…”

The future of crystallography in drug discovery

“…Emerging technologies in CADD tend to emphasize the small molecule aspects [2], including virtual screening [15], ‘click chemistry’ (the use of small modular building blocks to generate new compounds) [16], cheminformatics [17] and peptide-based drug discovery [18]. However, accurate understanding of the target macromolecule deserves and requires no less attention and has been the subject of considerable discussion in CADD in the past decade [19].…”

Beyond the Hundred Dollar Genome – Drug Discovery Futures

“…The ability to incorporate a single mutation into the calculation of a drug’s affinity will be necessary for determining the likely effectiveness of drugs in a patient‐specific manner. Audie and Swanson (7) describe advances in the prediction of protein‐peptide affinities and the relevance to peptide‐based drug discovery. Given the importance of peptides in addressing some of the more difficult PPI targets, this intrinsically more challenging aspect of virtual screening still seems of great importance.…”