#101: Slow unfolded-state structuring in Acyl-CoA binding protein folding revealed by simulation and experiment.
To gain further insight into the folding mechanism, we independently perform a large-scale molecular simulation study of ACBP folding. Previously, systems such as ACBP have been too large and slow for simulation studies, which have been limited to nanosecond to microsecond time scales for proteins with less than 40 residues. Today, recent advances in simulation methodology(8-10) and network models called Markov state models (MSMs), in which conformational dynamics is modeled as transitions between kinetically metastable states, make it possible to model folding on the millisecond time scale. Here, we use over 30 ms of trajectory data to construct a MSM of ACBP folding, which predicts residual unfolded-state structure and kinetics consistent with experiment.
#108: OpenMM 4: A Reusable, Extensible, Hardware Independent Library for High Performance Molecular SimulationEven the most sophisticated single-molecule experiments, however, cannot resolve the entire microscopic complexity of folding due to the limited number of photons that can be detected on the microsecond time scale. It is therefore likely that ensemble and single-molecule fast kinetic observables cannot capture the full complexity of folding, and instead we must turn to computer simulation. We expect Markov state model approaches to be increasingly useful in this regard, as direct comparisons to experiment can made by projecting predicted microscopic dynamics onto macroscopic observables.
Edit: OpenMM is one of the key pieces of software behind the FAH GPU cores. Proteneer informed me that OpenMM 5 powers the upcoming FahCore 17 (Zeta) and that all of those numbers rely on OpenMM 4. OpenMM 5.1, not currently released, should introduce a lot of exciting scientific features.The CUDA simulations were run on a single Nvidia GTX 580 GPU. For OpenCL, simulations were run on both the GTX 580 and on an AMD Radeon HD 7970. The OpenCL platform can parallelize an explicit solvent simulation across multiple GPUs. We therefore repeated each of the above benchmarks using one to four Nvidia C2070 GPUs in parallel. The results are shown in Table 5.The scaling with the number of GPUs is much less than linear. Using up to three GPUs produces a significant speedup, but there is little benefit beyond that. For PME, using four GPUs is actually slightly slower than using three. This is primarily due to the cost of transferring data over the high latency PCIe bus.Code: Select all
1 GPU 2 GPUs 3 GPUs 4 GPUs Explicit-RF, H-bonds 25.9 (1.0) 40.2 (1.55) 48.5 (1.87) 52.3 (2.02) Explicit-RF, H-angles 47.6 (1.0) 69.4 (1.46) 80.8 (1.70) 87.9 (1.85) Explicit-PME, H-bonds 16.5 (1.0) 27.1 (1.64) 30.1 (1.83) 29.8 (1.81) Explicit-PME, H-angles 30.9 (1.0) 49.7 (1.61) 55.5 (1.80) 54.9 (1.78) all results are in ns/day. The value in parentheses is the speedup relative to a single GPU.
#109: To milliseconds and beyond: challenges in the simulation of protein folding
These papers have some really neat abstracts on the site as well. I'm pretty impressed by these new papers and thought I'd share. In particular, the information from #108 helps answer a question that has been asked a number of times on this forum.The list of potential questions the folding field might hope to address through simulation is long. A few of the most exciting includeMuch effort has been poured into advancing molecular simulation, and in this decade the fruits of that effort are coming to bear. Hopefully with continued progress in sampling and forcefields, combined with powerful analysis techniques, simulation can play a key role — alongside experiment and theory — in discovering how proteins fold.
- Can we build models allowing for the detailed comparison of simulations to experiment in order to both test simulations and aid in the interpretation of experiments [81]? Further, simulations might be able to direct the design of future experiments, suggesting those with the greatest impact.
- With a detailed comparison of experiment in hand as tests of simulation accuracy, can we answer how do particular proteins fold? Why do so many proteins appear to fold in a two-state manner? What is the nature of ‘downhill’ folding? Can we describe these in microscopic, physical terms?
- With the knowledge the mechanism of how particular proteins fold, we can learn how this mechanism is encoded in the inherent physical interaction of the amino acids in a given protein sequence?
- With the knowledge of how many individual proteins fold, can simulations help reveal general features of protein folding amongst broad groups of proteins (or ideally some general properties for all proteins)?