Your browser doesn't support javascript.
loading
DCA-MOL: A PyMOL Plugin To Analyze Direct Evolutionary Couplings.
Jarmolinska, Aleksandra I; Zhou, Qin; Sulkowska, Joanna I; Morcos, Faruck.
Afiliação
  • Jarmolinska AI; Centre of New Technologies , University of Warsaw , Banacha 2c , 02-097 , Warsaw , Poland.
  • Zhou Q; College of Inter-Faculty Individual Studies in Mathematics and Natural Sciences , Banacha 2c , 02-097 Warsaw , Poland.
  • Sulkowska JI; Department of Biological Sciences , University of Texas at Dallas , Richardson , Texas 75080 , United States.
  • Morcos F; Centre of New Technologies , University of Warsaw , Banacha 2c , 02-097 , Warsaw , Poland.
J Chem Inf Model ; 59(2): 625-629, 2019 02 25.
Article em En | MEDLINE | ID: mdl-30632747
Direct coupling analysis (DCA) is a statistical modeling framework designed to uncover relevant molecular evolutionary relationships from biological sequences. Although DCA has been successfully used in several applications, mapping and visualizing of evolutionary couplings and direct information to a particular set of molecules requires multiple steps and could be prone to errors. DCA-MOL extends PyMOL functionality to allow users to interactively analyze and visualize coevolutionary residue-residue interactions between contact maps and structures. True positive rates for the top N pairs can be computed and visualized in real-time to evaluate the quality of residue-residue contact predictions. Different types of interactions in monomeric proteins, RNA, molecular interfaces, and protein conformational dynamics as well as multiple protein complexes can be studied efficiently within one application. DCA-MOL is available for download from http://dca-mol.cent.uw.edu.pl.
Assuntos

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Evolução Molecular / Biologia Computacional Tipo de estudo: Prognostic_studies Idioma: En Ano de publicação: 2019 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Evolução Molecular / Biologia Computacional Tipo de estudo: Prognostic_studies Idioma: En Ano de publicação: 2019 Tipo de documento: Article