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pyScoMotif: discovery of similar 3D structural motifs across proteins.
Cia, Gabriel; Kwasigroch, Jean; Stamatopoulos, Basile; Rooman, Marianne; Pucci, Fabrizio.
Afiliación
  • Cia G; Computational Biology and Bioinformatics, Université Libre de Bruxelles, Brussels, 1050, Belgium.
  • Kwasigroch J; Interuniversity Institute of Bioinformatics in Brussels, Triomflaan, Brussels,1050, Belgium.
  • Stamatopoulos B; Computational Biology and Bioinformatics, Université Libre de Bruxelles, Brussels, 1050, Belgium.
  • Rooman M; Laboratory of Clinical Cell Therapy, Jules Bordet Institute, Université Libre de Bruxelles, Brussels, 1070, Belgium.
  • Pucci F; Computational Biology and Bioinformatics, Université Libre de Bruxelles, Brussels, 1050, Belgium.
Bioinform Adv ; 3(1): vbad158, 2023.
Article en En | MEDLINE | ID: mdl-38023327
Motivation: The fast and accurate detection of similar geometrical arrangements of protein residues, known as 3D structural motifs, is highly relevant for many applications such as binding region and catalytic site detection, drug discovery and structure conservation analyses. With the recent publication of new protein structure prediction methods, the number of available protein structures is exploding, which makes efficient and easy-to-use tools for identifying 3D structural motifs essential. Results: We present an open-source Python package that enables the search for both exact and mutated motifs with position-specific residue substitutions. The tool is efficient, flexible, accurate, and suitable to run both on computer clusters and personal laptops. Two successful applications of pyScoMotif for catalytic site identification are showcased. Availability and implementation: The pyScoMotif package can be installed from the PyPI repository and is also available at https://github.com/3BioCompBio/pyScoMotif. It is free to use for non-commercial purposes.

Texto completo: 1 Base de datos: MEDLINE Idioma: En Revista: Bioinform Adv Año: 2023 Tipo del documento: Article

Texto completo: 1 Base de datos: MEDLINE Idioma: En Revista: Bioinform Adv Año: 2023 Tipo del documento: Article