title: Analyzing Ensembles of Amyloid Proteins Using Bayesian Statistics.
abstract: Intrinsically disordered proteins (IDPs) are notoriously difficult to study experimentally because they rapidly interconvert between many dissimilar conformations during their biological lifetime, and therefore cannot be described by a single structure. The importance of studying these systems, however, is underscored by the fact that they form toxic aggregates that play a role in the pathogenesis of many disorders. The first step towards a comprehensive understanding of the aggregation mechanism of these proteins involves a description of their thermally accessible states under physiologic conditions. The resulting conformational ensembles correspond to coarse-grained descriptions of their energy landscapes, where the number of structures in the ensemble is related to the resolution in which one views the free energy surface. Here, we provide step-by-step instructions on how to use experimental data to construct a conformational ensemble for an IDP using a Variational Bayesian Weighting (VBW) algorithm. We further discuss how to leverage this Bayesian approach to identify statistically significant ensemble-wide observations that can form the basis of further experimental studies.
authors.affiliation: Computational and Systems Biology Initiative, Massachusetts Institute of Technology, Cambridge, MA, 02139-4307, USA.|Research Laboratory of Electronics, Massachusetts Institute of Technology, Cambridge, MA, 02139-4307, USA.
authors.forename: Thomas
authors.initials: T
authors.lastname: Gurry
authors.affiliation: Physics Department, Boston University, Boston, MA, 02215, USA.
authors.forename: Charles K
authors.initials: CK
authors.lastname: Fisher
authors.affiliation: Research Laboratory of Electronics, Massachusetts Institute of Technology, Cambridge, MA, 02139-4307, USA.|Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, 02139-4307, USA.
authors.forename: Molly
authors.initials: M
authors.lastname: Schmidt
authors.affiliation: Computational and Systems Biology Initiative, Massachusetts Institute of Technology, Cambridge, MA, 02139-4307, USA. cmstultz@mit.edu.|Research Laboratory of Electronics, Massachusetts Institute of Technology, Cambridge, MA, 02139-4307, USA. cmstultz@mit.edu.|Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, 02139-4307, USA. cmstultz@mit.edu.|The Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA, 02139-4307, USA. cmstultz@mit.edu.
authors.forename: Collin M
authors.initials: CM
authors.lastname: Stultz
mesh_terms: D058227:Amyloidogenic Proteins / Q000737:chemistry* / Q000235:genetics; D001499:Bayes Theorem; D006801:Humans; D064267:Intrinsically Disordered Proteins / Q000737:chemistry* / Q000235:genetics; D008958:Models, Molecular; D008967:Molecular Biology / Q000379:methods*; D066263:Protein Aggregation, Pathological / Q000235:genetics*; D011487:Protein Conformation
chemical_list: D058227:Amyloidogenic Proteins; D064267:Intrinsically Disordered Proteins
keywords: Bayesian weighting; Conformational ensemble; Degeneracy; Intrinsically disordered proteins; Variational Bayesian Method