Determining and understanding the intrinsic conformational manifolds of unstructured<br/>peptides is not only a prerequisite to elucidate the structure-function-dynamics relationship of functional peptides, but also essential for constructing accurate models of intrinsically disordered proteins, elucidating the principles underlying protein folding, and designing novel peptides to modulate biological processes. Despite intensive efforts, quantitatively defining the unfolded ensemble remains a key fundamental challenge in protein biophysics. In this talk, I will present an integrated computational experimental- Bayesian approach to address this fundamental challenge. In our approach, peptide conformational sub-states are first defined objectively by clustering molecular dynamics snapshots based on both structural and dynamic information. Next, a set of spectroscopic data for each conformational sub-state is computed. Finally, a Bayesian statistical analysis of both experimentally measured spectroscopic data and computational results is carried out to generate a current best estimate of the sub-state population ensemble together with corresponding confidence intervals. This distribution of sub-states can be further systematically refined with additional high-quality experimental data and more accurate computational modeling. Finally, I will briefly describe an  efficient clustering algorithm  for defining conformation sub-states and identifying representative structures.<br/>

Integrated computational-experimental-Bayesian approach  to quantify conformation ensembles of unstructured peptides

251st National Meeting (2016)

The official archive and repository for all ACS Meetings & Expositions abstracts and select posters or presentations.