Biology subjects

Harrigan, M. P.

Publications and source records attributed to Harrigan, M. P..

4 recordsLinked to original sources

bioRxiv2017

Landmark Kernel tICA For Conformational Dynamics

Molecular dynamics simulations of biomolecules produce a very high dimensional time-series dataset. Performing analysis necessarily involves projection onto a lower dimensional space. A priori selection of projection coordinates requires (perhaps unavailable) prior information or intuition about the system. At best, such a projection can only confirm the intuition. At worst, a poor projection can obscure new features of the system absent from the intuition. Previous statistical methods such a time-structure based independent component analysis (tICA) and Markov state modeling (MSMs) have offered relatively unbiased means of projecting conformations onto coordinates or state labels, respectively. These analyses are underpinned by the propagator formalism and the assumption that slow dynamics are biologically interesting. Although arising from the same mathematics, tICA and MSMs have different strengths and weaknesses. We introduce a unifying method which we term \"landmark kernel tICA\" (lktICA) which uses a variant of the Nystrom kernel approximation to permit approximate non-linear solutions to the tICA problem. We show that lktICA is equivalent to MSMs with \"soft\" states. We demonstrate the advantages of this united method by finding improved projections of (a) a 1D potential surface (b) a peptide folding trajectory and (c) an ion channel conformational change.

biophysics↗

bioRxiv2016

Markov modeling reveals novel intracellular modulation of the human TREK-2 selectivity filter

Two-pore domain potassium (K2P) channel ion conductance is regulated by diverse stimuli that directly or indirectly gate the channel selectivity filter (SF). Recent crystal structures for the TREK-2 member of the K2P family reveal distinct \"up\" and \"down\" states assumed during activation via mechanical stretch. We performed 195 ps of all-atom, unbiased molecular dynamics simulations of the TREK-2 channel to probe how membrane stretch regulates the SF gate. Markov modeling reveals a novel \"pinched\" SF configuration that stretch activation rapidly destabilizes. Free-energy barrier heights calculated for critical steps in the conduction pathway indicate that this pinched state impairs ion conduction. Our simulations predict that this low-conductance state is accessed exclusively in the compressed, \"down\" conformation in which the intracellular helix arrangement allosterically pinches the SF. By explicitly relating structure to function, we contribute a critical piece of understanding to the evolving K2P puzzle.

biophysics↗

bioRxiv2016

OpenMM 7: Rapid Development of High Performance Algorithms for Molecular Dynamics

OpenMM is a molecular dynamics simulation toolkit with a unique focus on extensibility. It allows users to easily add new features, including forces with novel functional forms, new integration algorithms, and new simulation protocols. Those features automatically work on all supported hardware types (including both CPUs and GPUs) and perform well on all of them. In many cases they require minimal coding, just a mathematical description of the desired function. They also require no modification to OpenMM itself and can be distributed independently of OpenMM. This makes it an ideal tool for researchers developing new simulation methods, and also allows those new methods to be immediately available to the larger community.

biophysics↗

bioRxiv2016

MSMBuilder: Statistical Models for Biomolecular Dynamics

MSMBuilder is a software package for building statistical models of high-dimensional time-series data. It is designed with a particular focus on the analysis of atomistic simulations of biomolecular dynamics such as protein folding and conformational change. MSMBuilder is named for its ability to construct Markov State Models (MSMs), a class of models that has gained favor among computational biophysicists. In addition to both well-established and newer MSM methods, the package includes complementary algorithms for understanding time-series data such as hidden Markov models (HMMs) and time-structure based independent component analysis (tICA). MSMBuilder boasts an easy to use command-line interface, as well as clear and consistent abstractions through its Python API (application programming interface). MSMBuilder is developed with careful consideration for compatibility with the broader machine-learning community by following the design of scikit-learn. The package is used primarily by practitioners of molecular dynamics but is just as applicable to other computational or experimental time-series measurements. http://msmbuilder.org

biophysics↗