IMASL   20939
INSTITUTO DE MATEMATICA APLICADA DE SAN LUIS "PROF. EZIO MARCHI"
Unidad Ejecutora - UE
congresos y reuniones científicas
Título:
Gaussian Process Density Estimation to Obtain Ramachandran Probability Distribution
Autor/es:
AGUSTINA ARROYUELO; JORGE VILA; OSVALDO MARTIN
Lugar:
San Luis
Reunión:
Conferencia; PyData San Luis 2017; 2017
Institución organizadora:
Universidad Nacional de San Luis
Resumen:
Protein dihedral degrees of freedom play a central role in simulation and structural analysis of these biomolecules with φ and angles as the main torsionals influencing protein 3D structure. The Ramachandran map displays the sterically allowed regions and forbidden ones for the torsional angle combination (φ, ) [1]. Therefore featuring enlightenment about the conformation that every possible aminoacid in a protein structure can present. Sampling of protein conformational space using a bayesian machinery requires the inclussion of a prior for the torsional angles (φ, ). Taking these torsionals from a uniform distribution is a very poor prior for this task. In the present work we explore the implementation of a non parametric model, such as Gaussian Process to learn a Ramachandran Probability Distribution for its use in Bayesian Inference. Our implementation uses Python?s probabilistic programming module, PyMC3 [2].