Consensus set of amino acid residues derived from cavities detection and cut-off distances-to-ligand for the analysis of enzyme binding sites
Geometric detection of protein cavities (also referred to as pockets) and protein-ligand cut-off distances represent two different classes of methods that have been largely used as criteria to determine which amino acids in a protein constitute the ligand-binding site. Therefore, they all play a cri...
- Autores:
-
Vásquez Jiménez, Andrés Felipe
- Tipo de recurso:
- Fecha de publicación:
- 2019
- Institución:
- Universidad de los Andes
- Repositorio:
- Séneca: repositorio Uniandes
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.uniandes.edu.co:1992/48414
- Acceso en línea:
- http://hdl.handle.net/1992/48414
- Palabra clave:
- Aminoácidos
Proteínas
Enzimas
Ligandos
Biología
- Rights
- openAccess
- License
- http://creativecommons.org/licenses/by-nc-sa/4.0/
| Summary: | Geometric detection of protein cavities (also referred to as pockets) and protein-ligand cut-off distances represent two different classes of methods that have been largely used as criteria to determine which amino acids in a protein constitute the ligand-binding site. Therefore, they all play a critical role in both the visualization and the analysis of tridimensional structures, the development of molecular docking protocols and the identification of similar binding sites in other proteins. However, in many cases the individual use of these methods may not be sufficient to recognize the amino acids with higher conservation or greater protein-ligand interaction potential, which are all characteristic properties in binding sites. Here we propose a combined strategy intended to identify a consolidated, ?elite? set of amino acids comprising a particular binding site in a protein. To achieve this goal, we will use the crystallographic structures of a collection of human enzymes that will be evaluated by three different methods within each category herein mentioned. Next, a consensus set of amino acids derived from the combination of the best performance pair of methods out of these two classes will be elucidated and compared with the sets obtained by its component approaches. In this study, we aimed to identify a rapid and intuitive approach for scientists working on molecular modeling and structure-based drug discovery, by facilitating the selection of amino acids that share a profile of features strongly associated with their occurrence in binding sites and that, according to our hypothesis, more correctly circumscribe these functional regions |
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