GRIB. Research Unit on Biomedical Informatics

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Supporting public-private partnerships in genomics and health

Over 80 participants from industry and academia gathered in Barcelona on 6-7 June for the ELIXIR Innovation and SME forum. Hosted by ELIXIR Spain, the two-day event showcased successful companies making use of public bioinformatics resources, and presented free bioinformatics resources available through ELIXIR and ELIXIR Spain.

The programme included presentations of ELIXIR and ELIXIR Spain resources as well as companies and SMEs active in bioinformatics, genomics and health research. Three keynotes each presented different aspects of working with open data in life science research: Roderic Guigo from the CRG gave a general overview of the Open data in genomics including the European Genome-phenome ArchiveFerran Sanz, director of GRIB (IMIMUPF) and participant of ELIXIR Spain, showed several examples of how bioinformatics can support pharmaceutical research; and David Henderson from Bayer AG  talked about reuse of biomedical data in pharmaceutical research. Colm Carroll from the Innovative Medicines Initiative (IMI) presented some of the future funding opportunities in this area

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Image provided by the authors, GRIB-UPF

Scientists reveal for the first time the details of protein association at the atomic level

The groups of Frank Noé at Freie Universität Berlin, and Gianni de Fabritiis, ICREA research professor and head of the Computational Biophysics group of GRIB ( IMIM- UPF), have now collaborated to produce what is the first atomic-detail computer simulation of the process of protein-protein association and dissociation. The results were published in the scientific journal Nature Chemistry and were validated with experimental data.

The main challenge was that atomic-detail molecular dynamics are incredible expensive to simulate. Key to the success of the Berlin-Barcelona team was the combination of several new technologies that enabled a "divide and conquer" approach to the problem. GPUGRID, a distributed network developed by De Fabritiis' group was employed to collect compute time on graphic processing units (GPUs) from Nvidia by volunteers around the globe. Thousands of short simulations were conducted that way, coordinated by a novel machine learning algorithm in such way that the overall protein association process could be simulated within one year instead of having to wait 10,000 years. Markov modeling, a method pioneered by Noé and colleagues, was used to combine the many short simulations to an overall dynamical model that describes protein association and dissociation in full detail. "This was clearly a risky but important proof of principle and we are happy that we managed to show that the simulations are able to capture associations between proteins", says De Fabritiis.

This achievement opens the door to understanding the details of viral infections, the inner workings of the immune system, and many other problems with biomedical or biotechnological relevance.

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Last project


The european project TransQSTTranslational Quantitative Systems Toxicology aims to develop novel computational approaches using the best available data from the public and private domains to address the problems of drug safety. Funded by IMI2 for the period 2017-2021.

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Last publications

  • Gea J, Pascual S, Casals F, Casadevall C, Castro-Acosta A, Cordoba R, Hernandez C, Marquez E, Monton C, Torralba Y, Seijo L, Alvarez-Martinez C, Barbera J, Cosio BG, Lopez-Campos J, Monso E, Peces-Barba G, Agusti A, Castelo R. Gene Expression Profile In The Blood Of COPD Patients. Am J Respir Crit Care Med, 2017; 195: A6936. PMID: .
  • Saadeh HA, Khasawneh MA, Samadi A, El-Haty IA, Satala G, Bojarski AJ, Ismaili L, Bautista-Aguilera OM, Yanez M, Mestres J, Marco-Contelles J. Design, Synthesis and Biological Evaluation of Potent Antioxidant 1-(2,5-Dimethoxybenzyl)-4-arylpiperazines and N-Azolyl Substituted 2-(4-Arylpiperazin-1-yl). CHEMISTRYSELECT, 2017; 2 (13): 3854-3859. PMID: . DOI: 10.1002/slct.201700397.
  • Jiménez J, Doerr S, Martínez-Rosell G, Rose AS, de Fabritiis G. DeepSite: Protein binding site predictor using 3D-convolutional neural networks. Bioinformatics, 2017. PMID: 28575181 . DOI: 10.1093/bioinformatics/btx350.
  • Plattner N, Doerr S, De Fabritiis G, Noé F. Complete protein-protein association kinetics in atomic detail revealed by molecular dynamics simulations and Markov modelling. Nat Chem, 2017 DOI: 10.1038/NCHEM.2785.

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