Acellera's Blog

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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Complete protein–protein kinetics by molecular dynamics

From Stefan Doerr

Proteins are essential in the regulation of most of the processes in a cell. They do so by interacting with other molecules, including other proteins.
Protein-protein interactions have been the subject of study for a long time using both experimental and computational methods, however they have eluded the atomic-level analysis and accuracy that unbiased molecular dynamics simulations can provide.

Franck ChevalierComplete protein–protein kinetics by molecular dynamics
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A Decade of GPUs based MD

By Gerard Martinez-Rosell

ACEMD was introduced in the field of Molecular Dynamics (MD) back in 2008. Its release, together with other MD engines, revolutionized the simulation throughput by leveraging the intrinsic parallelism present in a specific computer processor termed GPU (Graphic Processor Unit). Using a cluster of GPUs or a distributed network like GPUGRID one is able to reach performances of HPC-class computers using commodity software, yielding a significant (x10) reduction in the Euro/simulated time cost. Furthermore, the evolution of GPUs performance, which is mainly driven by a market demanding more graphic processing power (e.g. in videogames), has doubled approximately every 3 years and with it, since 2008, ACEMD performance has increased 2.1x. To illustrate this fact, we have compiled the maximum simulated time per year published by prof. Gianni de Fabritiis using ACEMD (which yields the oldest historic data) and we extrapolate that the MD field will reach the second aggregated time timescale by 2022.

Franck ChevalierA Decade of GPUs based MD
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Helical unwinding and side-chain unlocking unravel the outward open conformation of the melibiose transporter

By Alex Perálvarez-Marín


In Wang et al. we have studied and described an intermediate step of the molecular mechanism of sugar transport thanks to the possibility to run several MD simulation replicates in the 100 ns range using a single GPU-based desktop running ACEMD. Our solvated membrane protein system embedded in the lipid bilayer consisted of ca. 100,000 atoms, and the combination between ACEMD and a single GTX-780 helped us to understand discrete events happening between two metastable intermediates. The combination of MD simulations and experimental data was key to achieve our goal to understand the conformational changes to allow the entrance of the substrates within the transporter. For the full story, refer to Wang et al Sci Rep 2016(

For the full story, refer to Wang et al Sci Rep 2016(

Franck ChevalierHelical unwinding and side-chain unlocking unravel the outward open conformation of the melibiose transporter
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Acellera, celebrating the 10th Anniversary

What has been achieved in Molecular Dynamics and Acellera contribution.

Acellera celebrates its 10th anniversary. A decade focusing on molecular dynamics (MD) and dedicated to improve this technique as a strong partner of biophysical approaches. Objectively, molecular dynamics is the only technique allowing to study conformational changes and interaction at atomistic level, to quantify binding energies, binding kinetics for any kind of molecules since a force field has been defined for these. It represents an adequate combination with NMR, Crystallography, ITC, Surface plasmon resonance, Thermal shift that are already widely used in Drug Discovery.

Franck ChevalierAcellera, celebrating the 10th Anniversary
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By Franck Chevalier

Introduction on kinetics

Quest for new drug discovery usually involves a clear understanding of ligand binding to its receptor. Free binding energy helps to rank compounds and allows to select those based on their target affinity. Drug discovery programs focused on optimization of target affinity as a proxy of in-vivo efficacy. Within the last years, researchers have increasingly become interested in measuring and understanding drugs’ binding kinetics. Namely, the time in which drug and its target associate and dissociate.

Until recently, few methods offered the possibility to determine kinetics parameters and almost none at atomistic resolution. Amazing recent emergence of computational methods provided powerful tools in measuring and understanding binding event. It offers interesting and accurate methods to determine qualitative (binding mode) and quantitative (binding energy, Kon, Koff) parameters in a single atomistic approach.

Determination of these parameters with HTMD has been reviewed in previous posts:
such as this one . Recent works that highlight the importance of binding, molecular recognition and molecular determinants for rational optimization have been reviewed here.

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