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PlayMolecule CrypticScout: Predicting Protein Cryptic Sites Using Mixed-Solvent Molecular Simulations.
Martinez-Rosell, Gerard; Lovera, Silvia; Sands, Zara A; De Fabritiis, Gianni.
Afiliación
  • Martinez-Rosell G; Acellera Labs, C/Doctor Trueta 183, 08005 Barcelona, Spain.
  • Lovera S; CADD, UCB BioPharma, 1420 Braine-l'Alleud, Belgium.
  • Sands ZA; CADD, UCB BioPharma, 1420 Braine-l'Alleud, Belgium.
  • De Fabritiis G; Acellera Labs, C/Doctor Trueta 183, 08005 Barcelona, Spain.
J Chem Inf Model ; 60(4): 2314-2324, 2020 04 27.
Article en En | MEDLINE | ID: mdl-32175736
Cryptic pockets are protein cavities that remain hidden in resolved apo structures and generally require the presence of a co-crystallized ligand to become visible. Finding new cryptic pockets is crucial for structure-based drug discovery to identify new ways of modulating protein activity and thus expand the druggable space. We present here a new method and associated web application leveraging mixed-solvent molecular dynamics (MD) simulations using benzene as a hydrophobic probe to detect cryptic pockets. Our all-atom MD-based workflow was systematically tested on 18 different systems and 5 additional kinases and represents the largest validation study of this kind. CrypticScout identifies benzene probe binding hotspots on a protein surface by mapping probe occupancy, residence time, and the benzene occupancy reweighed by the residence time. The method is presented to the scientific community in a web application available via www.playmolecule.org using a distributed computing infrastructure to perform the simulations.
Asunto(s)

Texto completo: 1 Bases de datos: MEDLINE Asunto principal: Solventes / Simulación de Dinámica Molecular Tipo de estudio: Prognostic_studies / Risk_factors_studies Idioma: En Revista: J Chem Inf Model Asunto de la revista: INFORMATICA MEDICA / QUIMICA Año: 2020 Tipo del documento: Article País de afiliación: España

Texto completo: 1 Bases de datos: MEDLINE Asunto principal: Solventes / Simulación de Dinámica Molecular Tipo de estudio: Prognostic_studies / Risk_factors_studies Idioma: En Revista: J Chem Inf Model Asunto de la revista: INFORMATICA MEDICA / QUIMICA Año: 2020 Tipo del documento: Article País de afiliación: España