Efficient Sampling and Characterization of Free Energy Landscapes of Ion-Peptide Systems
2018-11-13, Lemke, Tobias, Peter, Christine, Kukharenko, Oleksandra
Proteins that influence nucleation, growth, or polymorph selection during biomineralization processes are often rich in glutamic- or aspartic acid. Here, the interactions between carboxylate side chains and ions lead to an interplay of peptide conformations and ion structuring in solution. Molecular dynamics simulations are an ideal tool to mechanistically investigate these processes. Unfortunately, the formation of strong ion-peptide contacts and ion bridges drastically impedes structural reorganization of ionic bonds and conformational transitions of the polymers. Thus, to obtain a complete thermodynamical picture of such systems, enhanced sampling techniques become necessary as well as the methods to characterize the conformational states of these partially disordered polymer-ion systems. Here, we propose a new set of Hamiltonian replica exchange (HRE) parameters for efficient simulations of peptide-ion systems, with an aspartic acid trimer in the presence of Ca2+ and Cl- ions as a test system. We introduce dimensionality reduction and clustering strategies to characterize the states of such a multicomponent system and to analyze the outcome of the proposed HRE with different reweighting methods.
Using Dimensionality Reduction to Systematically Expand Conformational Sampling of Intrinsically Disordered Peptides
2016-10-11, Kukharenko, Oleksandra, Sawade, Kevin, Steuer, Jakob, Peter, Christine
One of the approaches to improve our ability to characterize biologically important processes and to map out an underlying free energy landscape is to direct MD simulations to explore molecular conformational phase space faster. Intrinsically disordered systems with shallow free energy landscapes of a huge number of metastable minima pose a particular challenge in this regard. Both characterization of the often ill-defined conformational states as well as the assessment of the degree of convergence of phase space exploration are problematic. We have used a multidimensional scaling-like embedding (sketch-map) to describe the energetically accessible regions of phase space for a peptide fragment of the intrinsically disordered protein α-synuclein. Using sketch-map coordinates from a short initial simulation, we guided additional MD simulations to efficiently expand sampling of the conformational space. The sketch-map projections are very well suited to detect rare but possibly functionally relevant events, metastable intermediates, and transition states in the vast amount of data.