Exploring the quality of protein structural models from a Bayesian perspective

ARROYUELO, A.; VILA, JORGE A.; MARTÍN OSVALDO A.

JOURNAL OF COMPUTATIONAL CHEMISTRY

JOHN WILEY & SONS INC

Lugar: New York; Año: 2021

0192-8651

We explore how ideas and practices common in Bayesian modeling can be applied to help assess the quality of 3D protein structural models. The basic premise of our approach, is that the evaluation of a Bayesian statistical model?s fit may reveal aspectsof the quality of a structure, when the fitted data is related to protein structural properties. Therefore, we fit a Bayesian hierarchical linear regression model to experimental and theoretical 13 C α chemical shifts. Then, we propose two complementary approaches for the evaluation of such fitting: 1) in terms of the expected differences betweenexperimental and posterior predicted values; 2) in terms of the leave-one-out cross validation point-wise predictive accuracy. Finally, we present visualizations that can help interpret these evaluations. The analyses presented in this article are aimed to aid in detecting problematic residues in protein structures. The code developed for this work is available on: https://github.com/BIOS-IMASL/Hierarchical-Bayes-NMR-Validation.