Exploring the activity space of peptides binding to diverse SH3 domains using principal property descriptors derived from amino acid rotamers.

Although there were intensive works addressed on multivariate extraction of the informative components from numerous physicochemical parameters of amino acids in isolated state, the various conformational behaviors of amino acids in complicated biological context have long been underappreciated in the field of quantitative structure-activity relationship (QSAR). In this work, the amino acid rotamers, which were derived from statistical survey of protein crystal structures, were used to reproduce the conformational variety of amino acid side-chains in real condition. In this procedure, these rotamers were superposed into a nx x ny x nz lattice and an artificial probe was employed to detect four kinds of nonbonding field potentials (i.e., electrostatic, steric, hydrophobic, and hydrogen bonds) at each lattice point using a Gaussian-type potential function; the generated massive data were then subjected to a principal component analysis (PCA) treatment to obtain a set of few, informative amino acid descriptors. We used this set of descriptors, that we named principal property descriptors derived from amino acid rotamers (PDAR), to characterize over 13,000 peptides with known binding affinities to 10 types of SH3 domains. Genetic algorithm/ partial least square regression (GA/PLS) modeling and Monte Carlo cross-validation (MCCV) demonstrated that the correlation between the PDAR descriptors and the binding affinities of peptides are comparable with or even better than previously published models. Furthermore, from the PDAR-based QSAR models we concluded that the core motif of peptides, particularly the electrostatic property, hydrophobicity, and hydrogen bond at residue positions P3, P2, and/or P0, contribute significantly to the hAmph SH3 domain-peptide binding, whereas two ends of the peptides, such as P6, P4, P-4, and P5, only play a secondary role in the binding.