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Acellera HTMD: A complete software workspace for simulation-guided drug design

Gianni De Fabritiis’ talk at Boston ACS on 18th August, at 8.30AM:

HTMD: A complete software workspace for simulation-guided drug design

Abstract: Performing computational experiments using molecular dynamics simulations is still too difficult and the recent capability of running thousands of simulations has further exacerbated the problem. HTMD is a Matlab-like programmable workspace that provides the power of system preparation, molecular dynamics, Markov state model analysis and visualization at once. As a result, a single short script can lead from the PDB structure to useful quantities such as relaxation timescales, equilibrium populations, conformations and kinetic rates. This facilitates scientists with minimal background to easily integrate MD into their discovery workflow, drastically reduce errors and improves reproducibility.

COMP: Division of Computers in Chemistry
Room 156A – Boston Convention & Exhibition Center
Publication Number: 153

Franck ChevalierAcellera HTMD: A complete software workspace for simulation-guided drug design
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AceCloud – Simplified, Limitless Molecular Dynamics Simulations on the Cloud

by Matt Harvey, CTO

Physics-based computer simulation offers a tremendously powerful way to gain insight into the behavior of a wide variety of complex systems. At its most successful, simulation has become a ‘third way’ for scientists and engineers, complementing analytical and experimental methods. In engineering in particular, computational fluid dynamics simulation and finite element analysis are now an integral part of any design effort.

Similarly, we believe that physics-based simulation, in the form of molecular dynamics simulation, has great potential to become one of the basic tools in biochemical, and biophysical R&D, and establish itself as a robust tool in the drug discovery pipeline. Recent work, for example, has demonstrated the ability of MD simulation to produce quantitative estimates of small molecule binding kinetics (PNAS) and of conformational selection in selection in proteins (Nat. Comm., and blog).

Why perform molecular dynamics simulations on the cloud?

Currently, the amount of MD simulation required to undertake these types of studies is often beyond the reach of anyone without access to dedicated high-performance computing resources. To help overcome this critical limiting factor and bring MD simulation to a wider audience, we are pleased to introduce our new product AceCloud.

AceCloud is designed to free you from the constraints of your workstation and – though the use of cloud computing technology – allow you to run hundreds of simulations with the same ease as running one without the need of any additional setup.


Performing cloud molecular dynamics simulations with AceCloud

Accessing AceCloud is a simple matter of using three new commands built into the ACEMD molecular dynamics software package: for running simulations, retrieving results, and monitoring progress. No additional knowledge is required – our software takes care of all of the interaction with the Cloud. Here’s a video of AceCloud in action:


No changes are required to your existing ACEMD simulations, and all features are supported, including extensions and plugins such as PLUMED Metadynamics. As a bonus, users who are already familiar with Gromacs may run their existing simulation inputs on AceCloud, without the need to make any conversions.

The compute resources behind AceCloud are GPU-based and allow simulation rates over 100ns/day for the DHFR benchmark, making them around 40% as fast as the very latest GTX980s in our Metrocubo workstation, but still offering a compelling, cost-effective, level of performance.

AceCloud Costs

AceCloud uses Amazon Web Services (AWS) technology to dynamically provision compute hardware as and when you require it. The only charges for AceCloud are for the time used, from less than 0.40 US$ per hour. There are no minimum or on-going fees. There is no setup required and you can start using it at your convenience in just a few steps. Billing is hassle free and it is done through your own existing Amazon account.

You can calculate representative costs using our AceCloud Cost Estimator in the AceCloud page (this estimate already includes data transfer costs).

Start using AceCloud Today!

AceCloud is available right now. Visit our website for details on getting started.

mattAceCloud – Simplified, Limitless Molecular Dynamics Simulations on the Cloud
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Maxwell GPU review: MD simulations with GeForce GTX980

by Matt Harvey

After a long wait, the greatly-anticipated Maxwell GPU from NVIDIA has finally arrived, in the form of the Geforce GTX980, to rave reviews from the gaming world where it has been acclaimed as the new king of the performance hill.

At Acellera, we’re always tracking the cutting edge of technology to deliver the best systems for molecular dynamics simulation, we’ve been hard at work putting these new cards through their paces. Before we see how they perform, let’s have a quick look at what’s changed.

What’s new? Maxwell GPUs: the new state of the art for molecular dynamics simulations

NVIDIA’s previous generation GPU, named Kepler, has been our workhorse for almost three years now, first in the form of the GK104 silicon, and then its big brother the GK110. These two devices both have a similar design, based on a 192-processing element block, called a “Streaming Multiprocessor“, or SM for short.
Manufactured on a 28nm process, the GK110 had 15 SMs, although it’s only with the very latest Tesla K40 and Geforce GTX780Ti that we have seen products with all of those cores enabled – the GTX780 systems we have been shipping to this past year have had only 12 SMs activated.

Maxwell GPU vs Kepler: Main differences

Maxwell’s SM is a refinement of that of Kepler, reducing the number of processing cores by a third to 128 but incorporating additional design improvements. NVIDIA claims that the real-world performance is reduced by only 10% relative to Kepler. The new Maxwell processor, GM204, is still manufactured on the same 28nm process as GK110 rather than the anticipated 20nm process. Nevertheless, the smaller SM, and the intervening refinements to the manufacturing process mean that GM204 can run at higher clock frequencies and contain more SMs than Kepler (16 versus 15) on a die about 40% smaller.

Maxwell GPU Performance in MD Simulation: Faster, stable and more efficient

So how does the Maxwell-based GTX980 fare when running ACEMD, our flagship molecular dynamics code?

Over the last year, we have been selling GTX780-based systems. On the popular dihydrofolate reductase benchmark system of 23,500 atoms, we saw single-GPU performance of around 210ns/day.

Performance

Running the same test on a GTX980, with no other performance optimisations, yields an impressive rate of 280ns/day, around 30% faster. On a Metrocubo equipped with 4 GTX980s, that’s over 1 microsecond per day of MD sampling. If you prefer maximum performance over throughput, a two-GPU run can achieve 380ns/day, a new performance record!

Benchmarking conditions: ECC off. X79 chipset. CUDA4.2 and ACEMD ver 2500 or greater. Periodic boundary conditions, 9 A cutoff, PME long range electrostatic grid size 1, hydrogen mass repartitioning, rigid bonds, Langevin thermostat, time step 4 fs. Note: Other codes make benchmarks with smaller cutoff or less atoms. Performace as of October 8th 2014. See ACEMD page for latest results, and to run a benchmark simulation with your system.

Efficiency

And that’s not all: compared to Kepler, the new GPU is much more power-efficient – an at the wall measurement of a 4-GPU E3-based Metrocubo system running at full tilt draws almost 200W less, making it even quieter and cooler than before.

It’s quite remarkable that such an improvement has been made without moving to a newer manufacturing node, and makes the future 20nm parts even more tantalising.

GPU hardware for MD simulations available now with Maxwell GPUs

All in all, the new Maxwell has passed its tests with flying colours and we’re very pleased to announce that we are shipping them to customers now.

As usual feel free to request a test drive. We will be more than happy to make some time available in one of our machines. Maxwell is already available for testing.

Also, feel free to request a quote.

gianniMaxwell GPU review: MD simulations with GeForce GTX980
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Adaptive Sampling for MD Simulations Talk at GTCBio

by David Soriano

A few of weeks ago we were invited to present at GTCBio Drug Design and Medicinal Chemistry 2014 Conference. The conference was very interesting and very well organized, and we will make sure to keep it our agenda for future years. The meeting also happened to be in Berlin, which instantly became one of my favorite cities. Our talk was titled “Machine Learning in FBLD” and discussed our new adaptive sampling method for probing protein ligand interactions using atomistic molecular dynamics simulations that was developed in collaboration with the De Fabritiis lab. So how did we get there?

High-throughput molecular dynamics can be used for profiling protein-ligand interactions

In 2011 we reported our seminal work towards a practical approach for profiling protein ligand interactions in silico with all atom resolution. In this proof-of-principle study we showed that with enough sampling we could use atomistic high-throughput molecular dynamics simulations to reconstruct the binding of benzamidine to trypsin, and not only obtain accurate energies, kinetics, poses but also resolve a ligand binding pathway. We were also able to predict several metastable states later observed experimentally.

Application of molecular dynamics simulations in fragment based lead discovery (FBLD)

We next focused on expanding the library size, and on adapting the methodology to fragment based ligand design. Benzamidine is a small molecule of micromolar affinity for trypsin, and therefore extending this method to FBLD was a natural progression. For the fragment study we used a 2003 STD NMR study that targeted Factor Xa, a target related to trypsin, and a set of forty fragments. Once more our MD experiments were able to recapitulate the binding of the library to the target, and also give accurate representations of binding energies, kinetics, poses and binding pathways. Importantly, we were also able to rank the compounds in order of increasing binding affinity accurately. As exciting as the results were, we needed to collect 2milliseconds of aggregate biological timescale trajectory data, a challenge we could only meet because of our access to GPUGrid through our collaboration with the De Fabritiis group.

Development of an adaptive sampling protocol for MD

One limitation of our HTMD based in-silico binding method at that time involved the amount of sampling needed for system convergence, which was in the order of 50×10^-6 s per compound for Factor Xa. Clearly, in order to make this high-throughput molecular dynamics methodology a practical complement to current experimental FBLD screens, we needed to improve the efficiency of our simulation methods. To this end, we developed a fully automated adaptive sampling method that can deliver system convergence about an order of magnitude faster than the brute-force HTMD approach.

As opposed to HTMD, where we run hundreds of simulations at once, our adaptive sampling protocol begins with only ten trajectories each starting from random initial states and each a few tens of nanoseconds long. We then analyze these trajectories using Markov State modelling, and use residence time information obtained from clustering to select the starting conformations of future runs. This run-analyze-respawn cycle is defined as an epoch, and each adaptive experiment is composed of ten epochs. Note that all of this is automated and that we only need to setup the initial trajectories before working up the results after the last epoch. For each protein-ligand system we run ten replicate adaptive experiments.

For our proof-of-concept we tested this adaptive sampling method on trypsin-benzamidine, a system for we which we have a very large amount of data and that over the years has become our benchmark. As opposed to regular HTMD where we needed to collect about 50×10^-6 s of data for convergence, only 5×10^-6 s were needed in our adaptive experiment. In each experiment, learning was evident after the 3rd epoch, and the energy and pose matched those of our control HTMD experiment after the 5th epoch, in about 80% of the experiments.

What next?

We are very excited about these results as in principle — and assuming there were no other bottlenecks — we should be able to screen 400 compounds in the same amount of time previously spent with 40. This would make this technique fast enough for medicinal chemists to start looking at it as a practical and reliable complement to current FBLD screens. We hope to be there soon but we still need to test the universality of the method thoroughly as well as work out other kinks such as the issue of ligand parametrization.

gianniAdaptive Sampling for MD Simulations Talk at GTCBio
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