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Current Medicinal Chemistry

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ISSN (Print): 0929-8673
ISSN (Online): 1875-533X

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Research Article

A Structure-based Data Set of Protein-peptide Affinities and its Nonredundant Benchmark: Potential Applications in Computational Peptidology

Author(s): Shaozhou Wang, Haiyang Ye, Shuyong Shang, Zilong Li, Yue Peng and Peng Zhou*

Volume 31, Issue 26, 2024

Published on: 06 November, 2023

Page: [4127 - 4137] Pages: 11

DOI: 10.2174/0929867331666230908102925

Price: $65

Abstract

Background: Peptides play crucial roles in diverse cellular functions and participate in many biological processes by interacting with a variety of proteins, which have also been exploited as a promising class of therapeutic agents to target druggable proteins over the past decades. Understanding the intrinsic association between the structure and affinity of protein-peptide interactions (PpIs) should be considerably valuable for the computational peptidology area, such as guiding protein-peptide docking calculations, developing protein-peptide affinity scoring functions, and designing peptide ligands for specific protein receptors.

Objective: We attempted to create a data source for relating PpI structure to affinity.

Methods: By exhaustively surveying the whole protein data bank (PDB) database as well as the ontologically enriched literature information, we manually curated a structure- based data set of protein-peptide affinities, PpI[S/A]DS, which assembled over 350 PpI complex samples with both the experimentally measured structure and affinity data. The data set was further reduced to a nonredundant benchmark consisting of 102 culled samples, PpI[S/A]BM, which only selected those of structurally reliable, functionally diverse and evolutionarily nonhomologous.

Results: The collected structures were resolved at a high-resolution level with either Xray crystallography or solution NMR, while the deposited affinities were characterized by dissociation constant, i.e. Kd value, which is a direct biophysical measure of the intermolecular interaction strength between protein and peptide, ranging from subnanomolar to millimolar levels. The PpI samples in the set/benchmark were arbitrarily classified into α-helix, partial α-helix, β-sheet formed through binding, β-strand formed through selffolding, mixed, and other irregular ones, totally resulting in six classes according to the secondary structure of their peptide ligands. In addition, we also categorized these PpIs in terms of their biological function and binding behavior.

Conclusion: The PpI[S/A]DS set and PpI[S/A]BM benchmark can be considered a valuable data source in the computational peptidology community, aiming to relate the affinity to structure for PpIs.

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