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mkgridXf: Consistent Identification of Plausible Binding Sites Despite the Elusive Nature of Cavities and Grooves in Protein Dynamics.
Monet, Damien; Desdouits, Nathan; Nilges, Michael; Blondel, Arnaud.
Afiliação
  • Monet D; Unité de Bioinformatique Structurale, Département de Biologie Structurale et Chimie, CNRS-UMR 3528, CNRS-USR 3756 , Institut Pasteur , 28 rue du Dr. Roux , 75015 Paris , France.
  • Desdouits N; Sorbonne Université, Collège doctoral , ED515 - Complexité du Vivant , 75005 Paris , France.
  • Nilges M; Unité de Bioinformatique Structurale, Département de Biologie Structurale et Chimie, CNRS-UMR 3528, CNRS-USR 3756 , Institut Pasteur , 28 rue du Dr. Roux , 75015 Paris , France.
  • Blondel A; Sorbonne Université, Collège doctoral , ED515 - Complexité du Vivant , 75005 Paris , France.
J Chem Inf Model ; 59(8): 3506-3518, 2019 08 26.
Article em En | MEDLINE | ID: mdl-31287306
We describe here a method to identify potential binding sites in ensembles of protein structures as obtained by molecular dynamics simulations. This is a highly important task in the context of structure-based drug discovery, and many methods exist for the much simpler case of static structures. However, during molecular dynamics, the cavities and grooves that are used to define binding sites merge, split, appear, and disappear, and cover a large volume. Combined with the large number of sites (∼105 and more), these characteristics hamper a consistent and comprehensive definition of binding sites. Our method is based on the calculation of instantaneous cavities and of the pockets delineating them. Classification of the pockets over the structure ensemble generates consensus pockets, which define sites. Sites are reported as lists of atoms or residues. This avoids the pitfalls of the classification of cavities by spatial overlap, used in most existing methods, which is bound to fail on nonordered or unaligned ensembles or as soon as significant molecular motions are involved. To achieve a robust and consistent classification, we thoroughly optimized and benchmarked the method. For this, we assembled from the literature a set of reference sites on systems involving significant functional molecular motions. We tested different descriptors, metrics, and clustering methods. The resulting method is able to perform a global analysis of potential sites efficiently. Tests on examples show that our approach can make predictions of potential sites on the whole surface of a protein and identify novel sites absent from static structures.
Assuntos

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Proteínas / Modelos Moleculares Tipo de estudo: Diagnostic_studies / Prognostic_studies Idioma: En Revista: J Chem Inf Model Assunto da revista: INFORMATICA MEDICA / QUIMICA Ano de publicação: 2019 Tipo de documento: Article País de afiliação: França

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Proteínas / Modelos Moleculares Tipo de estudo: Diagnostic_studies / Prognostic_studies Idioma: En Revista: J Chem Inf Model Assunto da revista: INFORMATICA MEDICA / QUIMICA Ano de publicação: 2019 Tipo de documento: Article País de afiliação: França