QUIROGA RODRIGO
Congresos y reuniones científicas
Título:
A new scoring function for molecular docking
Autor/es:
VILLARREAL, MARCOS; QUIROGA, RODRIGO
Lugar:
San Luis
Reunión:
Congreso; XLVIII Reunión Anual de la Sociedad Argentina de Biofísica; 2019
Institución organizadora:
Sociedad Argentina de Biofísica
Resumen:
Molecular Docking is a computational method to predict the strength, position and
orientation of the ligand binding to a protein, and is a key tool in structure-based drug
design. In essence, the method consists of the search for the global maximum of a
mathematical function which represents the affinity of ligand at a given position in the
protein surface. This mathematical function, termed scoring function, is developed based
on different approaches. Among them, there are semi empirical, fully empirical,
knowledge and artificial intelligence based methods. In our lab we started to develop an
empirical scoring function based on the well-known Vina scoring function. In this work we
present the newest version of our scoring function which includes several improvements.
In particular we focused in a better atom-typing, hydrogen bond interaction, and
solvation terms. The philosophy behind our development was to come up with coarse
grained interactions, where the atomic parameters that define the interactions are
picked from a predefined and scalonated set of values.
The performance of the scoring function was evaluated in rescoring, redocking and
virtual screening tasks. The results show that the new function performs better at the
three tasks, when compared with our previous version and also with the original Vina
function
orientation of the ligand binding to a protein, and is a key tool in structure-based drug
design. In essence, the method consists of the search for the global maximum of a
mathematical function which represents the affinity of ligand at a given position in the
protein surface. This mathematical function, termed scoring function, is developed based
on different approaches. Among them, there are semi empirical, fully empirical,
knowledge and artificial intelligence based methods. In our lab we started to develop an
empirical scoring function based on the well-known Vina scoring function. In this work we
present the newest version of our scoring function which includes several improvements.
In particular we focused in a better atom-typing, hydrogen bond interaction, and
solvation terms. The philosophy behind our development was to come up with coarse
grained interactions, where the atomic parameters that define the interactions are
picked from a predefined and scalonated set of values.
The performance of the scoring function was evaluated in rescoring, redocking and
virtual screening tasks. The results show that the new function performs better at the
three tasks, when compared with our previous version and also with the original Vina
function
