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Adaptive Molecular Dynamics on the Cloud with HTMD and AceCloud

From João M. Damas

Researchers have been looking beyond traditional Molecular Dynamics (MD) for a long time now. While many have been using many forms of biased MD (for example, metadynamics), we have alternatively proposed Adaptive Molecular Dynamics 1,2 as a way to unbiasedly enhance the exploration of the phase space through on-the-fly learning of the explored space.

Since the beginning, HTMD 2 has provided an Adaptive MD protocol, which automatically manages all the process of analyzing the explored space and spawning of new MD simulations. An explanation of how Adaptive MD works and examples on how to use it can be found in the cited papers and in the online documentation of HTMD. One of the limitations of Adaptive MD has been the access to computing resources to take advantage of its high-throughput nature.

However, with the advent of cloud computing, resources are more readily available in an elastic manner that suits different needs. We have developed AceCloud 3 to run MD simulations on AWS and, together with HTMD, it can be used to run Adaptive MD simulations on the cloud. By using a system example for the generators (initial simulations), one can use the following script to quickly run an Adaptive MD job using HTMD and AceCloud:


References:
1. S. Doerr and G. De Fabritiis. On-the-fly learning and sampling of ligand binding by high-throughput molecular simulations. Journal of Chemical Theory and Computation, 10(5):2064–2069, 2014.
2. S. Doerr, M. J. Harvey, Frank Noé, and G. De Fabritiis. HTMD: High-throughput molecular dynamics for molecular discovery. Journal of Chemical Theory and Computation, 12(4):1845–1852, 2016.
3. M. J. Harvey and G. De Fabritiis. AceCloud: Molecular dynamics simulations in the cloud. Journal of Chemical Information and Modeling, 55(5):909–914,2015.

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Acellera Flows

“A command-line interface designed to perform complex workflows with few inputs”

By Alberto Cuzzolin

Nowadays, Molecular Dynamics (MD) simulation is known to be an important tool in drug discovery process. Although its potential is clearly proved, it is still not trivial to handle all the steps necessary to retrieve results. To handle all these aspects, we developed HTMD, a python framework that manages all these aspects and allows an easily scale up to an high-throughput manner.

Despite HTMD provides all the necessary functionalities, we decided to develop automated workflows, called “protocols or flows” that will ease and standardize the use of HTMD features. A command-line interface was designed to perform complex workflows with few inputs, allowing the user to customize its own protocol.

Several protocols are available with their specific options that the user can play with.
While the interface is simple and fixed (few options are available), Acellera Flows provide enhanced flexibility through template generation, meaning that the entire workflow is saved in pure python script.

At the moment, 4 protocols are available:

  • Build Flow: A protocol to build systems with amber and charmm.
  • SimpleRun Flow: A protocol to equilibrate and run MD simulation
  • RunAdaptLig Flow: A protocol to run adaptive sampling for ligand-protein recognition
  • CheckAdaptLig FLow: A protocol to analyze the ligand-protein adaptive simulation

Coming soon:

  • RunAdaptProt Flow: A protocol to run adaptive sampling for protein conformation
  • MembraneBuilder Flow: A protocol to prepare phospholipid bilayer
  •  CheckAdaptProtFlow: A protocol to analyze the protein adaptive simulations

For more details, please visit our software website.

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Acellera’s new website

As you might have noticed, Acellera’s website has experienced a few changes: Our consultancy services have been included inside the products section so it is easier to find out what fits your needs best; we have also simplified the Science section, classifying our articles in categories in order to make it easier for you to find them. At the bottom of every page, you can see the latest posts in the blog, our recent activity on Twitter -we invite you to follow us 😉 – and the subscription form to our products’ newsletters.

All our pages follow a similar architecture so that it is easier to navigate trough our contents and find what you are looking for. We have also included some videos to better illustrate some possible uses of our products and/or software. Finally, the new Partnership page includes information on how Acellera manages relationships with companies and laboratories which want to work with us.

We hope you like the new website and we will be glad to hear your feedback in the comments section.

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Automated Preparation and Simulation of Membrane Proteins with HTMD

By Stefan Doerr

Molecular dynamics has matured to the point where users can simulate multiple protein systems over timescales as large as milliseconds (see reference of HTMD and ACEMD at https://www.acellera.com/science/).
However, the preparation of the protein systems remains a complex step.
Tools already exist which allow the preparation of an MD system using visual GUIs or webservers.
However few, like HTMD, are built allowing scriptable system preparation for multiple hundreds of such systems.

Adapted with permission from J. Chem. Theory Comput., 2017, 13 (9), pp 4003–4011. Copyright 2017 American Chemical Society.

 

The purpose of HTMD is to provide all tools necessary for an integrated Molecular Dynamics simulation based discovery pipeline.
Therefore, in our most recent published work (S. Doerr, T. Giorgino, G. Martinez-Rosell, J.M. Damas, G. de Fabritiis High-Throughput Automated Preparation and Simulation of Membrane Proteins with HTMD in J. Chem. Theory Comput. 2017,) we present the system building and preparation tools of HTMD and it’s application on a complex use case which is the preparation of protein-membrane systems.
We apply a single building and equilibration protocol on all eukaryotic membrane proteins of the OPM database (Orientations of Proteins in Membranes)
and perform a short equilibration runs to test for the stability of the systems.
All data from the building and equilibration runs of the OPM systems is provided to the users through a webservice which also allows direct view of the trajectories, various plots and download options for further inspection.

We believe that this and upcoming advances of HTMD will help simplifying the process of performing MD-based experiments and will further broaden the user-base and popularity of Molecular Dynamics simulations.

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Preparing a Molecular System for MD with PlayMolecule

By Gerard Martínez-Rosell

PlayMolecule Introduction

One step of the simulation workflow has typically remained relatively under addressed, namely, the preparation steps before building a molecular system. These preparation steps aim to make a protein structure, usually extracted from the PDB database, ready for system building. In particular, there’s two main points that need to be addressed: (a) titration of the residues and (b) optimization of hydrogen bond (H-bond) network. Resolved protein structures usually don’t contain hydrogen atoms and therefore need to be added by the researcher before running a molecular dynamics (MD) simulation. While most hydrogens can be easily guessed, some protein residues prove to be more challenging as they co-exist in different protonation states. While in a constant-pH simulation the residues would be free to switch among the different protonation states, in a classic MD simulation protonation states are fixed and therefore must be decided beforehand. These protonation states greatly depend on the local environment of the residue and the simulation pH.

PlayMolecule ProteinPrepare

For this reason, we devised ProteinPrepare, a protein preparation web application that allows the estimation and visualization of charge states, optimization of the hydrogen bonding network of protonated structures, thorough visual inspection of the results, and rapid iteration of changes. The application provides an evaluation of the titration states of a target protein’s residues on the basis of their local environment and the optimization of its hydrogen-bonding network through the placement of missing hydrogen atoms and flipping of side chains. Special consideration is given to residues whose pKa is close to the solvent pH because they are more prone to be misclassified by the customary binary (protonated vs unprotonated) assignments; the user can interact with the results, force their chosen titration states, and have the application reoptimize the structure. The computation is executed on the server taking advantage of High-Throughput Molecular Dynamics (HTMD) a Python framework for simple molecular-simulation-based discovery. In particular, we used the proteinPrepare() functionality of HTMD, currently based on PROPKA 3.1 and PDB2PQR 2.1. As such, in contrast to most other graphical tools, this web application also provides the short HTMD Python code required to perform the same task offline for the specific structure. The web application is publicly available at www. playmolecule.org for use on the web as part of the PlayMolecule web platform.

Conclusion

The protein preparation tool presented offers three main features: (1) help in deciding the protonation states and charges of residues while still retaining the capability to change them, (2) optimization of the hydrogen-bonding network as it might have been approximative from the crystal structure, and (3) both an interactive and scriptable way to perform these tasks. Deciding on protonation states in classical molecular dynamics is critical because a change in charge can have drastic effects and invalidate all of the results. The hydrogen-bonding network optimization is important to help the protein retain the original state of the crystal structure, especially when losing an active or inactive state can take milliseconds or more to recover. The third aspect is purely one of convenience: the use of the HTMD framework allows the reproduction of any result obtained through the ProteinPrepare web interface on local computing resources and the possibility to automate the preparation steps, e.g., repeating them for a large set of structures in a high-throughput context. Coding of HTMD based analysis on local resources takes place through the Python language and can thus take advantage of any of its numerous libraries and facilities for reproducible research.

References

Martínez-Rosell, G.; Giorgino, T.; De Fabritiis, G.; J. Chem. Inf. Model., 2017, 57 (7), pp 1511–1516
Doerr, S.; Harvey, M. J.; Noe, F.; De Fabritiis, G.; J. Chem. Theory Comput. 2016 , 12, 1845 −1852.
Søndergaard, C. R.; Olsson, M. H. M.; Rostkowski, M.; Jensen, J. H. J. Chem. Theory Comput. 2011 , 7, 2284 −2295.
Dolinsky, T. J.; Czodrowski, P.; Li, H.; Nielsen, J. E.; Jensen, J. H.; Klebe, G.; Baker, N. A. Nucleic Acids Res. 2007 , 35, W522 −W525.

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