Bayesian multilevel models for assessing the quality of NMR resolved protein structures

AGUSTINA ARROYUELO; OSVALDO MARTIN; OSVALDO MARTIN; AGUSTINA ARROYUELO

Montevideo

Congreso; KHIPU Latin American Meeting in Artificial Intelligence; 2019

On the present work we exploit the benefits of multilevel Bayesian models trained with protein structural observables, with the purpose of protein structure validation. The Bayesian models trained in this work allow us to estimate a reference for the observables that is unique for each protein structure thus revising the computation of these values. Through a data set of high quality protein structures we obtain reference curves for the expected differences between observed and corrected magnitudes, and benchmark NMR resolved structures against these curves. We also present a computational tool for graphic validation of NMR resolved protein structures based on the presented Bayesian framework.