First CompBioMed Containerisation Meeting


By João M. Damas

Last month in Amsterdam, the first CompBioMed Containerisation Meeting gathered together some of the world-leading parties in the field of container technologies to discuss the present and the future of these technologies in Cloud and High Performance Computing research and commercial applications. The organizing committee invited me to talk in the meeting, where I shared the recent developments that have been happening in Acellera related with containerisation for reproducible deployment of biomedical applications and workflows in diverse computing infrastructures.

alejandroFirst CompBioMed Containerisation Meeting
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Nanobody interaction unveils structure, dynamics and proteotoxicity of the Finnish-type amyloidogenic gelsolin variant

By Toni Giorgino

Agel amyloidosis (also known as Finnish-type) is a rare hereditary disease caused by the abnormal aggregation and accumulation of fragments of the gelsolin protein. The fragments form fibrillar aggregates and affect the cornea, facial nerves, skin, and kidneys. A number of mutations in the G2 domain of the protein have been so far identified as disease-causing. While several mutated forms of G2 have been crystallized in the past, providing insights on the molecular etiology of fibrillation and possible therapeutic pathways, the D187N has long remained elusive, probably due to a shifted order-disorder equilibrium.

alejandroNanobody interaction unveils structure, dynamics and proteotoxicity of the Finnish-type amyloidogenic gelsolin variant
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How we did on D3R Grand Challenge 4

By Alejandro Varela, Davide Sabbadin and Gianni De Fabritiis

Results for BACE free energy prediction challenge (Stage 2)

It was late September in Barcelona and, at Acellera, we had only a few days left to make the first submission for the D3R challenge.

alejandroHow we did on D3R Grand Challenge 4
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LigVoxel: A deep learning pharmacophore-field predictor

By Alejandro Varela

Our current understanding of drug design is fundamentally structure based. The process works as follows: once the structure of the target protein is known, and some interesting pockets have been identified on it, medicinal chemists can study these spaces and suggest small molecules which can create strong interactions with that protein environment, hopefully leading to a conformational change in the protein which will modify its behavior.

alejandroLigVoxel: A deep learning pharmacophore-field predictor
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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.

alejandroAdaptive Molecular Dynamics on the Cloud with HTMD and AceCloud
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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.


alejandroAcellera Flows
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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.

alejandroAcellera’s new website
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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
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.

alejandroAutomated Preparation and Simulation of Membrane Proteins with HTMD
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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

alejandroPreparing a Molecular System for MD with PlayMolecule
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Crucial step in AIDS virus maturation simulated for first time

Source: IMIM

Bioinformaticians at IMIM (Hospital del Mar Medical Research Institute) and UPF (Pompeu Fabra University) have used molecular simulation techniques to explain a specific step in the maturation of the HIV virions, i.e., how newly formed inert virus particles become infectious, which is essential in understanding how the virus replicates. These results, which have been published in the latest edition of PNAS, could be crucial to the design of future antiretrovirals.

HIV virions mature and become infectious as a result of the action of a protein called HIV protease. This protein acts like a pair of scissors, cutting the long chain of connected proteins that form HIV into individual proteins that will form the infectious structure of new virions. According to the researchers of the IMIM-UPF computational biophysics group, “One of the most intriguing aspects of the whole HIV maturation process is how free HIV protease, i.e. the ‘scissors protein,’ appears for the first time, since it is also initially part of the long poly-protein chains that make up new HIV virions.

Using ACEMD a software for molecular simulations and a technology known as, Gianni De Fabritiis’ group has demonstrated that the first “scissors proteins” can cut themselves out from within the middle of these poly-protein chains. They do this by binding one of their connected ends (the N-terminus) to their own active site and then cutting the chemical bond that connects them to the rest of the chain. This is the initial step of the whole HIV maturation process. If the HIV protease can be stopped during the maturation process, it will prevent viral particles, or virions, from reaching maturity and, therefore, from becoming infectious.

This work was performed using, a voluntary distributed computing platform that harnesses the processing power of thousands of NVIDIA GPU accelerators from household computers made available by the public for research purposes. It’s akin to accessing a virtual supercomputer. One of the benefits of GPU acceleration is that it provides computing power that is around 10 times higher than that generated by computers based on CPUs alone. It reduces research costs accordingly by providing a level computational power that previously was only available on dedicated, multi-million dollar supercomputers.

Researchers use this computing power to process large numbers of data and generate highly complex molecular simulations. In this specific case, thousands of computer simulations have been carried out, each for hundreds of nanoseconds (billionths of a second) for a total of almost a millisecond.

According to researchers, this discovery in the HIV maturation process provides an alternative approach in the design of future pharmaceutical products based on the use of these new molecular mechanisms. For now, this work provides a greater understanding of a crucial step in the life cycle of HIV, a virus that directly attacks and weakens the human immune system, making it vulnerable to a wide range of infections, and which affects millions of people around the world.


Kinetic characterization of the critical step in HIV-1 protease maturation”. S Kashif Sadiq, Frank Noe and Gianni De Fabritiis. PNAS. DOI:10.1073/pnas.1210983109.

ACEMD Simulation details.

alejandroCrucial step in AIDS virus maturation simulated for first time
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