Anurag Sethi1, Divina Anunciado, Jianhui Tian, Dung M Vu, S Gnanakaran. 1. Theoretical Biology and Biophysics, Los Alamos National Laboratory, Los Alamos, NM, USA ; Center for Nonlinear Studies, Los Alamos National Laboratory, Los Alamos, NM, USA.
Abstract
As it remains practically impossible to generate ergodic ensembles for large intrinsically disordered proteins (IDP) with molecular dynamics (MD) simulations, it becomes critical to compare spectroscopic characteristics of the theoretically generated ensembles to corresponding measurements. We develop a Bayesian framework to infer the ensemble properties of an IDP using a combination of conformations generated by MD simulations and its measured infrared spectrum. We performed 100 different MD simulations totaling more than 10 µs to characterize the conformational ensemble of αsynuclein, a prototypical IDP, in water. These conformations are clustered based on solvent accessibility and helical content. We compute the amide-I band for these clusters and predict the thermodynamic weights of each cluster given the measured amide-I band. Bayesian analysis produces a reproducible and non-redundant set of thermodynamic weights for each cluster, which can then be used to calculate the ensemble properties. In a rigorous validation, these weights reproduce measured chemical shifts.
As it remains practically impossible to generate ergodic ensembles for large intrinsically n class="Disease">disordered proteins (IDP) with molecular dynamics (MD) simulations, it becomes critical to compare spectroscopic characteristics of the theoretically generated ensembles to corresponding measurements. We develop a Bayesian framework to infer the ensemble properties of an IDP using a combination of conformations generated by MD simulations and its measured infrared spectrum. We performed 100 different MD simulations totaling more than 10 µs to characterize the conformational ensemble of αsynuclein, a prototypical IDP, in water. These conformations are clustered based on solvent accessibility and helical content. We compute the amide-I band for these clusters and predict the thermodynamic weights of each cluster given the measured amide-I band. Bayesian analysis produces a reproducible and non-redundant set of thermodynamic weights for each cluster, which can then be used to calculate the ensemble properties. In a rigorous validation, these weights reproduce measured chemical shifts.
Authors: Paolo Moretti; Paolo Mariani; Maria Grazia Ortore; Nicoletta Plotegher; Luigi Bubacco; Mariano Beltramini; Francesco Spinozzi Journal: J Chem Inf Model Date: 2020-09-17 Impact factor: 4.956