Daniel L. Ensign

Accelerating molecular dynamic simulation on graphics processing units

We describe a complete implementation of all‐atom protein molecular dynamics running entirely on a graphics processing unit (GPU), including all standard force field terms, integration, constraints, and implicit solvent. We discuss the design of our algorithms and important optimizations needed to fully take advantage of a GPU. We evaluate its performance, and show that it can be more than 700 times faster than a conventional implementation running on a single CPU core.

Folding@home: Lessons from eight years of volunteer distributed computing

Accurate simulation of biophysical processes requires vast computing resources. Folding@home is a distributed computing system first released in 2000 to provide such resources needed to simulate protein folding and other biomolecular phenomena. Now operating in the range of 5 PetaFLOPS sustained, it provides more computing power than can typically be gathered and operated locally due to cost, physical space, and electrical/cooling load. This paper describes the architecture and operation of Folding@home, along with some lessons learned over the lifetime of the project.

Quantitative comparison of villin headpiece subdomain simulations and triplet–triplet energy transfer experiments

As the fastest folding protein, the villin headpiece (HP35) serves as an important bridge between simulation and experimental studies of protein folding. Despite the simplicity of this system, experiments continue to reveal a number of surprises, including structure in the unfolded state and complex equilibrium dynamics near the native state. Using 2.5 ms of molecular dynamics and Markov state models, we connect to current experimental results in three ways. First, we present and validate a novel method for the quantitative prediction of triplet–triplet energy transfer experiments. Second, we construct a many-state model for HP35 that is consistent with previous experiments. Finally, we predict contact-formation time traces for all 1,225 possible triplet–triplet energy transfer experiments on HP35.

The Fip35 WW Domain Folds with Structural and Mechanistic Heterogeneity in Molecular Dynamics Simulations

We describe molecular dynamics simulations resulting in the folding the Fip35 Hpin1 WW domain. The simulations were run on a distributed set of graphics processors, which are capable of providing up to two orders of magnitude faster computation than conventional processors. Using the Folding@home distributed computing system, we generated thousands of independent trajectories in an implicit solvent model, totaling over 2.73 ms of simulations. A small number of these trajectories folded; the folding proceeded along several distinct routes and the system folded into two distinct three-stranded beta-sheet conformations, showing that the folding mechanism of this system is distinctly heterogeneous.

Vibrational Stark Effect Spectroscopy at the Interface of Ras and Rap1A Bound to the Ras Binding Domain of RalGDS Reveals an Electrostatic Mechanism for Protein−Protein Interaction

Electrostatic fields at the interface of the Ras binding domain of the protein Ral guanine nucleotide dissociation stimulator (RalGDS) with the structurally analogous GTPases Ras and Rap1A were measured with vibrational Stark effect (VSE) spectroscopy. Eleven residues on the surface of RalGDS that participate in this protein-protein interaction were systematically mutated to cysteine and subsequently converted to cyanocysteine in order to introduce a nitrile VSE probe in the form of the thiocyanate (SCN) functional group. The measured SCN absorption energy on the monomeric protein was compared with solvent-accessible surface area (SASA) calculations and solutions to the Poisson-Boltzmann equation using Boltzmann-weighted structural snapshots from molecular dynamics simulations. We found a weak negative correlation between SASA and measured absorption energy, indicating that water exposure of protein surface amino acids can be estimated from experimental measurement of the magnitude of the thiocyanate absorption energy. We found no correlation between calculated field and measured absorption energy. These results highlight the complex structural and electrostatic nature of the protein-water interface. The SCN-labeled RalGDS was incubated with either wild-type Ras or wild-type Rap1A, and the formation of the docked complex was confirmed by measurement of the dissociation constant of the interaction. The change in absorption energy of the thiocyanate functional group due to complex formation was related to the change in electrostatic field experienced by the nitrile functional group when the protein-protein interface forms. At some locations, the nitrile experiences the same shift in field when bound to Ras and Rap1A, but at others, the change in field is dramatically different. These differences identify residues on the surface of RalGDS that direct the specificity of RalGDS binding to its in vivo binding partner, Rap1A, through an electrostatic mechanism.

Bayesian Detection of Intensity Changes in Single Molecule and Molecular Dynamics Trajectories

Single molecule spectroscopy experiments and molecular dynamics simulations have several profound features in common, chief among which is that both follow the dynamics of some degrees of freedom of a single molecule over time. The analysis is essentially the same: one investigates the changes in the degrees of freedom followed. For instance, in a single molecule fluorescence experiment, the degree of freedom is often the number of photons detected in some time period. In this article, we introduce a straightforward Bayesian method for detecting if and when changes occurred. In contrast to methods based upon maximum likelihood estimates, a Bayesian approach allows for a more systematic means not only to change point detection but also to cluster the data into states. Most importantly, the Bayesian method supplies a simpler hypothesis testing framework. Although we focus on Poisson-distributed data, the Bayesian methods outlined here can in principle be applied to data sampled from any distribution.

Accelerating molecular dynamic simulation on the cell processor and Playstation 3

Implementation of molecular dynamics (MD) calculations on novel architectures will vastly increase its power to calculate the physical properties of complex systems. Herein, we detail algorithmic advances developed to accelerate MD simulations on the Cell processor, a commodity processor found in PlayStation 3 (PS3). In particular, we discuss issues regarding memory access versus computation and the types of calculations which are best suited for streaming processors such as the Cell, focusing on implicit solvation models. We conclude with a comparison of improved performance on the PS3s Cell processor over more traditional processors.

Combining molecular dynamics with bayesian analysis to predict and evaluate ligand-binding mutations in influenza hemagglutinin.

Influenza virus attaches to and infects target cells via binding of cell-surface glycans by the viral hemagglutinin. This binding specificity is considered a major reason why avian influenza is typically poorly transmitted between humans, while swine influenza is better transmitted due to glycan similarity between the human and swine upper respiratory tract. Predicting mutations that control glycan binding is thus important to continued surveillance against new pandemic influenza strains. We have designed a molecular-dynamics approach for scoring potential mutants with predictive power for both receptor-binding-domain and allosteric mutations similar to those identified from clinical isolates of avian influenza. We have performed thousands of simulations of 17 different hemagglutinin mutants totaling >1 ms in length and employ a bayesian model to rank mutations that disrupt the stability of the hemagglutinin-ligand complex. Based on our simulations, we predict a significantly increased k(off) for seven of these mutants. This means of using molecular dynamics analysis to make experimentally verifiable predictions offers a potentially general method to identify ligand-binding mutants, particularly allosteric ones. Our analysis of ligand dissociation provides a means to evaluate mutants prior to experimental mutagenesis and testing and constitutes an important step toward understanding the determinants of ligand binding by H5N1 influenza.

Bayesian single-exponential kinetics in single-molecule experiments and simulations.

In this work, we develop a fully Bayesian method for the calculation of probability distributions of single-exponential rates for any single-molecule process. These distributions can even be derived when no transitions from one state to another have been observed, since in that case the data can be used to estimate a lower bound on the rate. Using a Bayesian hypothesis test, one can easily test whether a transition occurs at the same rate or at different rates in two data sets. We illustrate these methods with molecular dynamics simulations of the folding of a beta-sheet protein. However, the theory presented here can be used on any data from simulation or experiment for which a two-state description is appropriate.