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1.
J Theor Biol ; 340: 30-7, 2014 Jan 07.
Artigo em Inglês | MEDLINE | ID: mdl-24021866

RESUMO

Many important protein-protein interactions in eukaryotic signaling networks are mediated by peptide recognition domains (PRDs), which bind short linear sequence motifs in other proteins. However, high ligand cross-reactivity is observed within most PRD families, rendering a broad specificity for the family members. In the present study, we attempt to explore the molecular mechanism and physicochemical origin of PRD cross-reactivity. In the procedure, a structure-based method called atomic cross-nonbonded interaction analysis (ACNIA) is described to extract atomic-level nonbonded interaction information at domain-peptide interface and to correlate the information with peptide affinity based on a set of structure-solved, affinity-known protein-peptide complex samples compiled from numerous literatures and databases. The ACNIA-derived affinity predictor is tested rigorously with statistical validation approach, which is also demonstrated to be capable of perceiving slight structural change in the interface using three distinct panels of SH3-binding peptide data. Subsequently, with help of the affinity predictor we adopt the human c-Src SH3 domain, one of the most sophisticated PRDs, as a paradigm to investigate the ligand cross-reactivity within SH3 family. It is found that most of the family members have only few non-essential residue differences in their peptide-binding pockets, and thus exhibit a similar peptide recognition profile and high cross-reactivity. The cross-reactivity is even shared by different subclasses of SH3 domains. The findings suggest that inherent binding specificity is not the only factor to select appropriate binders for specific SH3 domains, and other aspects such as cellular context and the rest of the SH3-containing proteins may play important roles in reducing their ligand cross-reactivity.


Assuntos
Ligantes , Peptídeos/química , Proteínas Proto-Oncogênicas pp60(c-src)/química , Algoritmos , Sequência de Aminoácidos , Aminoácidos/química , Sítios de Ligação , Humanos , Interações Hidrofóbicas e Hidrofílicas , Dados de Sequência Molecular , Mutação Puntual , Ligação Proteica , Estrutura Terciária de Proteína , Alinhamento de Sequência , Homologia de Sequência de Aminoácidos , Transdução de Sinais , Eletricidade Estática , Domínios de Homologia de src
2.
Amino Acids ; 38(4): 1209-18, 2010 Apr.
Artigo em Inglês | MEDLINE | ID: mdl-19669081

RESUMO

A new structure-based approach was proposed to quantitatively characterize the binding profile of human amphiphysin-1 (hAmph1) SH3 domain-peptide complexes. In this protocol, the protein/peptide atoms were classified into 16 types in terms of their physicochemical meaning and biological function, and then a 16 x 16 atom-pair interaction matrix was constructed to describe 256 atom-pair types between the SH3 domain and the peptide ligand, with atoms from peptide and SH3 domain served as the matrix columns and rows, respectively. Three non-covalent effects dominating SH3 domain-peptide binding as electrostatic, van der Waals (steric) and hydrophobic interactions were separately calculated for the 256 atom-pair types. As a result, 768 descriptors coding detailed information about SH3 domain-peptide interactions were yielded for further statistical modeling and analysis. Based on a culled data set consisting of 592 samples with known affinities, we employed this approach, coupled with partial least square (PLS) regression and genetic algorithm (GA), to predict and to interpret the peptide-binding behavior to SH3 domain. In comparison with the previous works, our method is more capable of capturing important factors in the SH3 domain-peptide binding, thus, yielding models with better statistical performance. Furthermore, the optimal GA/PLS model indicates that the electrostatic effect plays a crucial role in SH3 domain-peptide complexes, and steric contact and hydrophobic force also contribute significantly to the binding.


Assuntos
Modelos Moleculares , Proteínas do Tecido Nervoso/química , Proteínas do Tecido Nervoso/metabolismo , Peptídeos/química , Peptídeos/metabolismo , Domínios de Homologia de src , Algoritmos , Aminoácidos/química , Aminoácidos/classificação , Aminoácidos/metabolismo , Inteligência Artificial , Fenômenos Químicos , Biologia Computacional , Sistemas Inteligentes , Humanos , Ligação de Hidrogênio , Interações Hidrofóbicas e Hidrofílicas , Ligantes , Peptídeos/classificação , Ligação Proteica , Conformação Proteica , Isoformas de Proteínas/química , Isoformas de Proteínas/metabolismo , Relação Quantitativa Estrutura-Atividade , Estatística como Assunto , Propriedades de Superfície
4.
Biopolymers ; 96(3): 288-301, 2011.
Artigo em Inglês | MEDLINE | ID: mdl-20690173

RESUMO

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.


Assuntos
Aminoácidos/química , Modelos Moleculares , Peptídeos/química , Domínios de Homologia de src , Animais , Humanos
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