Structural Computational Biology Group

Madrid, Spain

Structural Computational Biology Group

Madrid, Spain
Time filter
Source Type

Guillou E.,DNA Replication Group | Guillou E.,Laboratoire Of Biologie Moleculaire Eucaryote | Ibarra A.,DNA Replication Group | Coulon V.,Montpellier University | And 8 more authors.
Genes and Development | Year: 2010

Genomic DNA is packed in chromatin fibers organized in higher-order structures within the interphase nucleus. One level of organization involves the formation of chromatin loops that may provide a favorable environment to processes such as DNA replication, transcription, and repair. However, little is known about the mechanistic basis of this structuration. Here we demonstrate that cohesin participates in the spatial organization of DNA replication factories in human cells. Cohesin is enriched at replication origins and interacts with prereplication complex proteins. Down-regulation of cohesin slows down S-phase progression by limiting the number of active origins and increasing the length of chromatin loops that correspond with replicon units. These results give a new dimension to the role of cohesin in the architectural organization of interphase chromatin, by showing its participation in DNA replication. © 2010 by Cold Spring Harbor Laboratory Press.

The epigenomic communication system of embryonic stem cells can be explored interactively on the following website: Credit: CNIO One of the big questions for which there is still no clear answer in biology is how, based on the four universal letters that make up DNA, it is possible to generate such different organisms as a fly or a human, or the different organs and tissues they comprise. In recent years, researchers have discovered that the system is much more complicated than was originally thought. The letters are important, but histones and nucleotide chemical modifications can make up genetic instructions to reinterpret the information contained in the DNA. Reinterpretation of genetic instructions can lead to the development, for example, of an eye or the pancreas in the embryo. Alterations of this make up of the DNA code can also be linked to pathological processes and to the appearance of diseases, such as cancer. The team of the Structural Computational Biology Group of the Spanish National Cancer Research Centre (CNIO), headed by Alfonso Valencia, the Centre's Vice-Director of Basic Research, has used network theory to build and study the first communication network between the components that constitute this genomic make up, known as epigenome. The conclusions were published today in the journal Cell Reports. Epigenomics: Turning Genes On Or Off Epigenomic marks can be thought of as a switch panel that determines which parts of the genome are turned on and will be visible to the cell. Consequently, the same genetic information contained in all the cells of a living being can generate hundreds of different cell types, using certain genes and disregarding others. In order to study this aspect in greater depth, the researchers collected data from literature that include 3 chemical modifications in cytosine (letter "C" of DNA), 13 modifications of histones (the proteins around which DNA is wrapped) and 61 DNA associated proteins, from mouse embryonic stem cells. The authors apply mathematical algorithms used to measure the popularity and influence of websites (such as Wikipedia or Facebook) to the network of epigenomic communication. They reached the conclusion that the 5hmC mark (chemical modification of the cytosine with a hydroxymethyl group in position 5') is the most influential component of this network in stem cells. "We have approached systems biology by studying chromatin signals [the DNA with chemical modifications and proteins that bind to it] as a comprehensive system and from this we have built the first communication network between these signals," says Daniel Rico, the CNIO researcher who has directed the study together with Valencia. "In this case we are speaking of an internal communication system inside each cell,, more specifically within the nucleus." As described in the paper, 5hmC acts as a key signal that connects with complexes that modify cytosines and histones to regulate gene expression. Through these connections, 5hmC regulates changes in the compaction of the chromatin, cellular differentiation processes and the energy metabolism in embryonic stem cells. At the same time, phylogenetic analyses conducted for the proteins in the network also point to 5hmC as the center of the coordinated evolution, or co-evolution, of these chromatin related proteins. The next step is to establish whether the results can also be assigned to other cell types. "We knew that 5hmC was extremely abundant in embryonic stem cells, but now we also know that this is true for other cell types, such as neurons or certain tumours," assert the authors of the paper. And they add: "Cancer cells have stem cell features; therefore it seems appropriate to investigate whether these results can also be transferred to cancer epigenomes, which would provide new outlooks on how they are regulated." More information: Epigenomic Co-localization and Co-evolution Reveal a Key Role for 5hmC as a Communication Hub in the Chromatin Network of ESCs. David Juan, Juliane Perner, Enrique Carrillo de Santa Pau, Simone Marsili, David Ochoa, Ho-Ryun Chung, Martin Vingron, Daniel Rico, Alfonso Valencia. Cell Reports (2016). DOI: 10.1016/j.celrep.2016.01.008

Wass M.N.,Imperial College London | Wass M.N.,Structural Computational Biology Group | Barton G.,Imperial College London | Sternberg M.J.E.,Imperial College London
Nucleic Acids Research | Year: 2012

Only a small fraction of known proteins have been functionally characterized, making protein function prediction essential to propose annotations for uncharacterized proteins. In recent years many function prediction methods have been developed using various sources of biological data from protein sequence and structure to gene expression data. Here we present the CombFunc web server, which makes Gene Ontology (GO)-based protein function predictions. CombFunc incorporates ConFunc, our existing function prediction method, with other approaches for function prediction that use protein sequence, gene expression and protein-protein interaction data. In benchmarking on a set of 1686 proteins CombFunc obtains precision and recall of 0.71 and 0.64 respectively for gene ontology molecular function terms. For biological process GO terms precision of 0.74 and recall of 0.41 is obtained. CombFunc is available at © 2012 The Author(s).

Rubio-Camarillo M.,Structural Computational Biology Group | Gomez-Lopez G.,Bioinformatics Unit UBio | Fernandez J.M.,Spanish National Bioinformatics Institute INB | Valencia A.,Structural Computational Biology Group | And 2 more authors.
Bioinformatics | Year: 2013

Motivation: RUbioSeq has been developed to facilitate the primary and secondary analysis of re-sequencing projects by providing an integrated software suite of parallelized pipelines to detect exome variants (single-nucleotide variants and copy number variations) and to perform bisulfite-seq analyses automatically. RUbioSeq's variant analysis results have been already validated and published. © The Author 2013.

Carro A.,Bioinformatics Unit | Rico D.,Structural Computational Biology Group | Rueda O.M.,Cancer Research UK | Diaz-Uriarte R.,Structural Computational Biology Group | Pisano D.G.,Bioinformatics Unit
Nucleic Acids Research | Year: 2010

waviCGH is a versatile web server for the analysis and comparison of genomic copy number alterations in multiple samples from any species. waviCGH processes data generated by high density SNP-arrays, array-CGH or copy-number calls generated by any technique. waviCGH includes methods for pre-processing of the data, segmentation, calling of gains and losses, and minimal common regions determination over a set of experiments. The server is a user-friendly interface to the analytical methods, with emphasis on results visualization in a genomic context. Analysis tools are introduced to the user as the different steps to follow in an experimental protocol. All the analysis steps generate high quality images and tables ready to be imported into spreadsheet programs. Additionally, for human, mouse and rat, altered regions are represented in a biological context by mapping them into chromosomes in an integrated cytogenetic browser. waviCGH is available at © The Author(s) 2010. Published by Oxford University Press.

Irisarri I.,CSIC - National Museum of Natural Sciences | Mauro D.S.,University of Barcelona | Abascal F.,CSIC - National Museum of Natural Sciences | Abascal F.,Structural Computational Biology Group | And 3 more authors.
BMC Genomics | Year: 2012

Background: Understanding the causes underlying heterogeneity of molecular evolutionary rates among lineages is a long-standing and central question in evolutionary biology. Although several earlier studies showed that modern frogs (Neobatrachia) experienced an acceleration of mitochondrial gene substitution rates compared to non-neobatrachian relatives, no further characterization of this phenomenon was attempted. To gain new insights on this topic, we sequenced the complete mitochondrial genomes and nine nuclear loci of one pelobatoid (Pelodytes punctatus) and five neobatrachians, Heleophryne regis (Heleophrynidae), Lechriodus melanopyga (Limnodynastidae), Calyptocephalella gayi (Calyptocephalellidae), Telmatobius bolivianus (Ceratophryidae), and Sooglossus thomasseti (Sooglossidae). These represent major clades not included in previous mitogenomic analyses, and most of them are remarkably species-poor compared to other neobatrachians.Results: We reconstructed a fully resolved and robust phylogeny of extant frogs based on the new mitochondrial and nuclear sequence data, and dated major cladogenetic events. The reconstructed tree recovered Heleophryne as sister group to all other neobatrachians, the Australasian Lechriodus and the South American Calyptocephalella formed a clade that was the sister group to Nobleobatrachia, and the Seychellois Sooglossus was recovered as the sister group of Ranoides. We used relative-rate tests and direct comparison of branch lengths from mitochondrial and nuclear-based trees to demonstrate that both mitochondrial and nuclear evolutionary rates are significantly higher in all neobatrachians compared to their non-neobatrachian relatives, and that such rate acceleration started at the origin of Neobatrachia.Conclusions: Through the analysis of the selection coefficient (ω) in different branches of the tree, we found compelling evidence of relaxation of purifying selection in neobatrachians, which could (at least in part) explain the observed higher mitochondrial and nuclear substitution rates in this clade. Our analyses allowed us to discard that changes in substitution rates could be correlated with increased mitochondrial genome rearrangement or diversification rates observed in different lineages of neobatrachians. © 2012 Irisarri et al.; licensee BioMed Central Ltd.

Spiga F.M.,Ecole Polytechnique Federale de Lausanne | Maietta P.,Structural Computational Biology Group | Guiducci C.,Ecole Polytechnique Federale de Lausanne
ACS Combinatorial Science | Year: 2015

To address limitations in the production of DNA aptamers against small molecules, we introduce a DNA-based capture-SELEX (systematic evolution of ligands by exponential enrichment) protocol with long and continuous randomized library for more flexibility, coupled with in-stream direct-specificity monitoring via SPR and high throughput sequencing (HTS). Applying this capture-SELEX on tobramycin shows that target-specificity arises at cycle number 8, which is confirmed by sequence convergence in HTS analysis. Interestingly, HTS also shows that the most enriched sequences are already visible after only two capture-SELEX cycles. The best aptamers displayed KD of approximately 200 nM, similar to RNA and DNA-based aptamers previously selected for tobramycin. The lowest concentration of tobramycin detected on label-free SPR experiments with the selected aptamers is 20-fold smaller than the clinical range limit, demonstrating suitability for small-drug biosensing. © 2015 American Chemical Society.

Malinverni D.,Ecole Polytechnique Federale de Lausanne | Marsili S.,Structural Computational Biology Group | Barducci A.,Ecole Polytechnique Federale de Lausanne | de Los Rios P.,Ecole Polytechnique Federale de Lausanne
PLoS Computational Biology | Year: 2015

Hsp70s are a class of ubiquitous and highly conserved molecular chaperones playing a central role in the regulation of proteostasis in the cell. Hsp70s assist a myriad of cellular processes by binding unfolded or misfolded substrates during a complex biochemical cycle involving large-scale structural rearrangements. Here we show that an analysis of coevolution at the residue level fully captures the characteristic large-scale conformational transitions of this protein family, and predicts an evolutionary conserved–and thus functional–homo-dimeric arrangement. Furthermore, we highlight that the features encoding the Hsp70 dimer are more conserved in bacterial than in eukaryotic sequences, suggesting that the known Hsp70/Hsp110 hetero-dimer is a eukaryotic specialization built on a pre-existing template. © 2015 Malinverni et al.

Lopez G.,Structural Computational Biology Group | Lopez G.,Columbia University | Maietta P.,Structural Computational Biology Group | Rodriguez J.M.,Structural Computational Biology Group | Valencia A.,Structural Computational Biology Group
Nucleic Acids Research | Year: 2011

firestar is a server for predicting catalytic and ligand-binding residues in protein sequences. Here, we present the important developments since the first release of firestar. Previous versions of the server required human interpretation of the results; the server is now fully automatized. firestar has been implemented as a web service and can now be run in high-throughput mode. Prediction coverage has been greatly improved with the extension of the FireDB database and the addition of alignments generated by HHsearch. Ligands in FireDB are now classified for biological relevance. Many of the changes have been motivated by the critical assessment of techniques for protein structure prediction (CASP) ligand-binding prediction experiment, which provided us with a framework to test the performance of firestar. URL: http://firedb.bioinfo.cnio. es/Php/FireStar.php. © 2011 The Author(s).

News Article | December 28, 2016

The study of the evolution of thousands of bacterial proteins allows deciphering many interactions between human proteins. The results will help to clarify the molecular details of thousands of interactions potentially involved in diseases such as cancer. Cells operate like an incredibly well-synchronized orchestra of molecular interactions among proteins. Understanding this molecular network is essential not only to understand how an organism works but also to determine the molecular mechanisms responsible for a multitude of diseases. In fact, it has been observed that protein interacting regions are preferentially mutated in tumours. The investigation of many of these interactions is challenging. However, a study coordinated by Simone Marsili and David Juan, from Alfonso Valencia's team at the CNIO, will advance our knowledge on thousands of them. The work, published in the journal Proceedings of the National Academy of Sciences (PNAS), demonstrates that it is possible to understand a significant number of interactions among human proteins from the evolution of their counterparts in simpler cells, such as bacteria cells. According to Juan Rodríguez, from the Structural Computational Biology Group at the CNIO and first author of the paper, "the complexity of human beings does not only result from the number of proteins that we have, but primarily from how they interact with each other. However, out of 200,000 protein-protein interactions estimated, only a few thousand have been characterised at the molecular level". It is very difficult to study the molecular properties of many important interactions without reliable structural information. It is this "twilight zone" that, for the first time, CNIO researchers have managed to explore. FROM BACTERIA TO HUMANS TO UNDERSTAND DISEASES Although more than 3,000 million years of evolution separate bacteria and humans, the CNIO team has utilized the information accumulated over thousands of bacterial sequences to predict interactions between proteins in humans. "We have used the protein coevolution phenomenon: proteins that interact tend to experience coordinated evolutionary changes that maintain the interaction despite the accumulation of mutations over time," says David Juan. "We have demonstrated that we can use this phenomenon to detect molecular details of interactions in humans that we share with very distant species. What is most interesting is that this allows us to transfer information from bacteria in order to study interactions in humans that we knew almost nothing about," adds Simone Marsili. These new results may lead to important implications for future research. "A deeper understanding of these interactions opens the door to the modeling of three-dimensional structures that may help us to design drugs targeting important interactions in various types of cancer," explains David Juan. "This knowledge can also improve our predictions of the effects of various mutations linked to tumour development," says Rodríguez. The laboratory of Alfonso Valencia, head of the Structural Biology and Biocomputing Programme, has been working in the field of protein coevolution since the 1990s. This field has significantly advanced in recent years. "Thanks to the amount of biological data that is being generated today, we can use new computational methods that take into account a greater number of factors," explains Valencia. According to the researchers, the pace of innovation in massive experimental techniques is providing additional data, making it possible to design more complex statistical models that provide an ever more complete view of the biological systems, "something particularly important in multifactorial diseases, such as cancer." This work was supported by the Spanish Ministry of Economy and Competitiveness and the European Regional Development Fund.

Loading Structural Computational Biology Group collaborators
Loading Structural Computational Biology Group collaborators