Science

 

Acellera is actively involved in research and development. Since 2006 we support applied research for the development of biomedical simulation methods via agreements with academic institutions.

Furthermore, Acellera creates new software and hardware infrastructure for performing biomolecular simulations in high-throughput. Salient examples are Metrocubo, the Acellera GPU chassis, and the AceCloud interface. The latter two examples are currently patent pending.

We would be happy to consider scientific collaborations with interested parties to demonstrate the potentiality of biomolecular simulations. We are open to research projects (e.g. European projects) as well as other collaboration formats.

See Application Cases

Ongoing international and EU funded projects

CompBioMed: A Centre of Excellence in Computational Biomedicine. H2020-EINFRA-2015-1, Grant Agreement nr. 675451. 2016-2019

HTMD: Feasibility assessment of a cloud application platform for rational drug design using high-throughput. H2020 SME Instrument 2014, Grant Agreement nr. 674659. 2015

CCFBLD-CHEMO: Computer-Centric Fragment Based Ligand Discovery for the Development of candidate molecules targeting the chemkine system. Nuclis d’Innovació Tecnològica 2014. Acció, Generalitat de Catalunya. Nuclis Transnacionals Programa Bilateral Catalunya-Israel, Project nr. RDIS14-1-0002. 2014-2016

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Selected Publications

Concepts

J. Jiménez, S. Doerr, G. Martinez-Rosell, A.S. Rose, G. De Fabritiis DeepSite: Protein binding site predictor using 3D-convolutional neural networks in Structural Bioinformatics 2017

M. J. Harvey and G. De Fabritiis, High-throughput molecular dynamics: The powerful new tool for drug discovery, Drug Discovery Today, http://dx.doi.org/10.1016/j.drudis.2012.03.017 (2012).

G. Giupponi, M. Harvey and G. De Fabritiis, The impact of accelerator processors for high-throughput molecular modeling and simulation, Drug Discovery Today, 13, 1052 (2008). pdf

Applications

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
http://pubs.acs.org/doi/abs/10.1021/acs.jctc.7b00480

G. Martinez-Rosell, T. Giorgino, G. De Fabritiis PlayMolecule ProteinPrepare: a web application for protein preparation for molecular dynamics simulations in J. Chem. Inf. Model 2017, June 10.
http://pubs.acs.org/doi/abs/10.1021/acs.jcim.7b00190

N Plattner, S Doerr, G De Fabritiis, F Noé Complete protein-protein association kinetics in atomic details revealed by molecular dynamics simulations and Markov modelling in Nature Chemistry 2017 June 5. http://www.nature.com/nchem/journal/vaop/ncurrent/full/nchem.2785.html?foxtrotcallback=true

G Martinez-Rosell, T Giorgino, MJ Harvey, G De Fabritiis, Drug Discovery and Molecular Dynamics: Methods, Applications and Perspective Beyond the Second Timescale in Curr Top Med Chem. 2017 Apr 14. https://doi.org/10.2174/1568026617666170414142549

N Ferruz, G Tresadern, A Pineda-Lucena, G De Fabritiis, Multibody cofactor and substrate molecular recognition in the myo-inositol monophosphatase enzyme, in Sci Rep. 2016; 6: 30275.  http://www.nature.com/articles/srep30275

N Ferruz, G De Fabritiis, Binding Kinetics in Drug Discovery. Mol. Inf.2016 doi: 10.1002/minf.201501018

N Stanley, L Pardo, G De Fabritiis, The pathway of ligand entry from the membrane bilayer to a lipid G protein-coupled receptor, Sci Rep. 2016; 6: 22639.  http://www.nature.com/articles/srep22639

N Ferruz, M Harvey, J Mestres, G De Fabritiis, Insights from Fragment Hit Binding Assays by Molecular Simulations, J. Chem. Inf. Model., 2015, 55 (10), pp 2200–2205

M. Martí-Solano, A. Iglesias, G. de Fabritiis, F. Sanz, J. Brea, M. Loza, M. Pastor, J. Selent. Detection of new biased agonists for the serotonin 5-HT2A receptor: modeling and experimental validation. Mol Pharmacol. 2015 Apr;87(4):740-6.

E. Dainese, G. De Fabritiis, A. Sabatucci, S. Oddi, CB. Angelucci, C. Di Pancrazio, I. Giorgino, N. Stanley, M Del Carlo, B.F. Cravatt, M. Maccarrone. Membrane lipids are key modulators of the endocannabinoid-hydrolase FAAH. Biochem J. 2014 Feb 1;457(3):463-72.

N. Stanley, S. Esteban-Martín, G. De Fabritiis. Kinetic modulation of a disordered protein domain by phosphorylation. Nat Commun. 2014 Oct 28;5:5272.

S. K. Sadiq, F. Noé, G. De Fabritiis, Kinetic characterization of the critical step in HIV-1 protease maturation, Proc. Natl. Acad. Sci. USA. 2012, 109 (50), 20449-20454. http://www.pnas.org/content/109/50/20449.full.pdf+html

D. W. Wright, S. K. Sadiq, G. De Fabritiis, P. V. Coveney, Thumbs Down for HIV: Domain Level Rearrangements Do Occur in the NNRTI-Bound HIV-1 Reverse Transcriptase, J. Am. Chem. Soc., 2012, 134 (31), 12885–12888. http://pubs.acs.org/doi/abs/10.1021/ja301565k

B. M. Satelle, B. Bose-Basu, M. Tessier, R. J. Woods, A. S. Serianni, A. Almond, Dependence of Pyranose Ring Puckering on Anomeric Configuration: Methyl Idopyranosides, J. Phys. Chem. B., 2012, 116, 6380-6386. http://pubs.acs.org/doi/abs/10.1021/jp303183y

T. Giorgino, I. Buch and G. De Fabritiis, Visualizing the induced binding of SH2-phosphopeptide, J. Chem. Theory Comput., 8, 1171–1175 (2012) http://pubs.acs.org/doi/pdfplus/10.1021/ct300003f

I. Buch, T. Giorgino and G. De Fabritiis, Complete reconstruction of an enzyme-inhibitor binding process by molecular dynamics simulations, PNAS, 108, 10184-10189, (2011). pdf

I. Buch, K. Sadiq and G. De Fabritiis, Optimized potential of mean force calculations of standard binding free energy, J. Chem. Theory Comput., 7, 1765–1772 (2011). pdf

T. Giorgino and G. De Fabritiis, A high-throughput steered molecular dynamics study on the free energy profile of ion permeation through gramicidin A, J. Chem. Theory Comput., 7 , 1943–1950 (2011). pdf

J. Selent, F. Sanz, M. Pastor and G. De Fabritiis, Induced Effects of Sodium Ions on Dopaminergic G-Protein Coupled Receptors, PLoS Comput Biol. Aug 12;6(8). pii: e1000884, (2010).

I. Buch, M. J. Harvey, T. Giorgino, D. P. Anderson and G. De Fabritiis. High-throughput all-atom molecular dynamics simulations using distributed computing. J. Chem. Inf. and Mod. 50, 397 (2010).

K. Sadiq and G. De Fabritiis, Explicit solvent dynamics and energetics of HIV-1 protease flap-opening and closing, Proteins, 78, 2873 (2010).

J. Fidelak, J. Juraszek, D. Branduardi, M. Bianciotto and F.L. Gervasio. Free-Energy-Based Methods for Binding Profile Determination in a Congeneric Series of CDK2 Inhibitors, The Journal of Physical Chemistry B2010 114 (29), 9516-9524 (2010).

Theory and Description of the Code

M. J. Harvey and G. De Fabritiis, An implementation of the smooth particle-mesh Ewald (PME) method on GPU hardware, J. Chem. Theory Comput., 5, 2371–2377 (2009). pdf

M. Harvey, G. Giupponi and G. De Fabritiis, ACEMD: Accelerated molecular dynamics simulations in the microseconds timescale, J. Chem. Theory and Comput. 5, 1632 (2009). pdf

Papers Related to ACEMD Features

M. J. Harvey and G. De Fabritiis. AceCloud: Molecular Dynamics Simulations in the Cloud. J. Chem. Inf. Model. (2015) 55 (5), pp 909–914.

S. Piana, K. Lindorff-Larsen, R. M. Dirks, J. K. Salmon, R. O. Dror, and D. E. Shaw, Evaluating the Effects of Cutoffs and Treatment of Long-range Electrostatics in Protein Folding Simulations, PLoS ONE (2012) vol. 7, no. 6, pp. e39918.

M. Bonomi, D. Branduardi, G. Bussi, C. Camilloni, D. Provasi, P. Raiteri, D. Donadio, F. Marinelli, F. Pietrucci, R.A. Broglia and M. Parrinello, PLUMED: a portable plugin for free energy calculations with molecular dynamics, Comp. Phys. Comm. (2009) 180, 1961.

Papers Related to HTMD Features

S. Doerr, MJ. Harvey, F. Noé and G. De Fabritiis. HTMD: High-Throughput Molecular Dynamics for Molecular Discovery.  J. Chem. Theory Comput. (2016) 12 (4), pp 1845-1852. http://pubs.acs.org/doi/10.1021/acs.jctc.6b00049

S. Doerr and G. De Fabritiis. On-the-Fly Learning and Sampling of Ligand Binding by High-Throughput Molecular Simulations. J. Chem. Theory Comput. (2014) 10 (5), pp 2064–2069.

G. Pérez-Hernández, F. Paul, T. Giorgino, G. De Fabritiis, F. Noé. Identification of slow molecular order parameters for Markov model construction., J Chem Phys (2013) Jul 7;139(1):015102.

Citing ACEMD in Google Scholar
Comprehensive list of publications with pdfs published by the De Fabritiis group.