Biology subjects

Zuckerman, D. M.

Publications and source records attributed to Zuckerman, D. M..

3 recordsLinked to original sources

bioRxiv2018

Computational estimation of ms-sec atomistic folding times

Despite the development of massively parallel computing hardware including inexpensive graphics processing units (GPUs), it has remained infeasible to simulate the folding of atomistic proteins at room temperature using conventional molecular dynamics (MD) beyond the s scale. Here we report the folding of atomistic, implicitly solvated protein systems with folding times {tau}f ranging from ~100 s to ~10s using the weighted ensemble (WE) strategy in combination with GPU computing. Starting from an initial structure or set of structures, WE organizes an ensemble of GPU-accelerated MD trajectory segments via intermittent pruning and replication events to generate statistically unbiased estimates of rate constants for rare events such as folding; no biasing forces are used. Although the variance among atomistic WE folding runs is significant, multiple independent runs are used to reduce and quantify statistical uncertainty. Three systems were examined: NTL9 at low solvent viscosity (yielding {tau}f ~ 5s), NTL9 at water-like viscosity ({tau}f ~ 40s), and Protein G at low viscosity ({tau}f ~ 10s). In all cases the folding time, uncertainty, and ensemble properties could be estimated from WE simulation; for protein G, this characterization required significantly less overall computing than would be required to observe a single folding event with conventional MD simulations. Our results suggest discrepancies with experimental folding times that should enable improvement of force fields and solvent models.

biophysics↗

bioRxiv2018

Middle-way flexible docking: Pose prediction using mixed-resolution Monte Carlo in estrogen receptor α

There is a vast gulf between the two primary strategies for simulating protein-ligand interactions. Docking methods significantly limit or eliminate protein flexibility to gain great speed at the price of uncontrolled inaccuracy, whereas fully flexible atomistic molecular dynamics simulations are expensive and often suffer from limited sampling. We have developed a flexible docking approach geared especially for highly flexible or poorly resolved targets based on mixed-resolution Monte Carlo (MRMC), which is intended to offer a balance among speed, protein flexibility, and sampling power. The binding region of the protein is treated with a standard atomistic force field, while the remainder of the protein is modeled at the residue level with a G[o] model that permits protein flexibility while saving computational cost. Implicit solvation is used. Here we assess three facets of the MRMC approach with implications for other docking studies: (i) the role of receptor flexibility in cross-docking pose prediction; (ii) the use of non-equilibrium candidate Monte Carlo (NCMC) and (iii) the use of pose-clustering in scoring. We examine 61 co-crystallized ligands of estrogen receptor , an important cancer target known for its flexibility. We also compare the performance of the MRMC approach with Autodock smina, a docking program. [1] Adding protein flexibility, not surprisingly, leads to significantly lower total energies and stronger interactions between protein and ligand, but notably we document the important role of backbone flexibility in the improvement. The improved backbone flexibility also leads to improved performance relative to smina. Somewhat unexpectedly, our implementation of NCMC leads to only modestly improved sampling of ligand poses. Overall, the addition of protein flexibility improves the performance of docking, as measured by energy-ranked poses, but we do not find significant improvements based on cluster information or the use of NCMC.

biophysics↗

bioRxiv2018

Stochastic Simulation to Visualize Gene Expression and Error Correction in Living Cells

Stochastic simulation can make the molecular processes of cellular control more vivid than the traditional differential-equation approach by generating typical system histories instead of just statistical measures such as the mean and variance of a population. Simple simulations are now easy for students to construct from scratch, that is, without recourse to black-box packages. In some cases, their results can also be compared directly to single-molecule experimental data. After introducing the stochastic simulation algorithm, this article gives two case studies, involving gene expression and error correction, respectively. Code samples and resulting animations showing results are given in the online supplement.

biophysics↗