Biology subjects

Frank, A. T.

Publications and source records attributed to Frank, A. T..

2 recordsLinked to original sources

bioRxiv2019

Conditional Prediction of RNA Secondary Structure Using NMR Chemical Shifts

Inspired by methods that utilize chemical-mapping data to guide secondary structure prediction, we sought to develop a framework for using assigned chemical shift data to guide RNA secondary structure prediction. We first used machine learning to develop classifiers which predict the base-pairing status of individual residues in an RNA based on their assigned chemical shifts. Then, we used these base-pairing status predictions as restraints to guide RNA folding algorithms. Our results showed that we could recover the correct secondary folds for nearly all of the 108 RNAs in our dataset with remarkable accuracy. Finally, we assessed whether we could conditionally predict the structure of the model RNA, microRNA-20b (miR-20b), by folding it using folding restraints derived from chemical shifts associated with two distinct conformational states, one a free (apo) state and the other a protein-bound (holo) state. For this test, we found that by using folding restraints derived from chemical shifts, we could recover the two distinct structures of the miR-20b, confirming our ability to conditionally predict its secondary structure. A command-line tool for Chemical Shifts to Base-Pairing Status (CS2BPS) predictions in RNA has been incorporated into our CS2Structure Git repository and can be accessed via: https://github.com/atfrank/CS2Structure.

biophysics↗

bioRxiv2019

Accelerating Dissociative Events in Molecular Dynamics Simulations by Selective Potential Scaling

Molecular dynamics (or MD) simulations can be a powerful tool for modeling complex dissociative processes such as ligand unbinding. However, many biologically relevant dissociative processes occur on timescales that far exceed the timescales of typical MD simulations. Here, we implement and apply an enhanced sampling method in which specific energy terms in the potential energy function are selectively "scaled" to accelerate dissociative events during simulations. Using ligand unbinding as an example of a complex dissociative process, we selectively scaled-up ligand-water interactions in an attempt to increase the rate of ligand unbinding. By applying our selectively scaled MD (or ssMD) approach to three cyclin-dependent kinase 2 (CDK2)-inhibitor complexes, we were able to significantly accelerate ligand unbinding thereby allowing, in some cases, unbinding events to occur within as little as 2 ns. Moreover, we found that we could make realistic estimates of the unbinding [Formula] as well as the binding free energies ({triangleup}Gsim) of the three inhibitors from our ssMD simulation data. To accomplish this, we employed a previously described Kramers-based rate extrapolation (KRE) method and a newly described free energy extrapolation (FEE) method. Because our ssMD approach is general, it should find utility as an easy-to-deploy, enhanced sampling method for modeling other dissociative processes.

biophysics↗