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SAMPL6 challenge: How well can we predict log P?

By Boris Sattarov

The Computational Science Lab, a research group at Universitat Pompeu Fabra, lead by Gianni De Fabritiis (Acellera’s CEO) recently participated in the 2019 D3R SAMPL6 challenge, comprised of predicting octanol / water partition coefficients (logP) of chemical compounds. logP measures the difference in solubility of a compound in two different unmixable phases. If these two phases are water and a highly hydrophobic solvent (octanol in this case), logP will tell us how hydrophobic or hydrophilic that compound is, which is one of the several properties that can determine the viability of a drug candidate. Hence, predicting accurately this property will help medicinal chemists to take better decisions.

alejandroSAMPL6 challenge: How well can we predict log P?
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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 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.

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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