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Alfonso Valencia

Publications and source records attributed to Alfonso Valencia.

At least 19 recordsLinked to original sources
PubMed2026

The Network of National COVID-19 Data Portals: public health equity through collaboration.

The network of the national COVID-19 Data Portals was developed and linked to the COVID-19 Data Portal (https://www.covid19dataportal.org/)inresponsetothe need for rapid data sharing and analysis during the 2020-2022 SARS-CoV-2 pandemic. Built on open-source code developed by the Swedish COVID-19 Data Portal (now the Swedish Pathogens Portal, www.pathogens.se) the network included 12 national portals addressing demand for local open data sharing and access, across data types and resources. It provides a robust case study of national initiatives for FAIR (Findable, Accessible, Interoperable and Reusable) resources and a foundation for future pandemic preparedness across pathogens globally. In this paper we outline the structure of the origins of the network of National COVID-19 Datal Portals, the technical aspects and code originating from the Swedish Portal and provide an overview of the services and tools offered by each Portal. The paper showcases the process and operation of four Portals: Sweden, Poland, Spain, Norway and The Netherlands. In this study, we observe that pandemic response greatly benefits from an established infrastructure that can be quickly mobilised, developed and extended. Collaborations and preparation built on solid foundations over several years, supported by investment in the form of national and international research grants, is key for sustainability, continuation and readiness to deploy such efforts.

COVID-19↗
PubMed2006

TSEMA: interactive prediction of protein pairings between interacting families.

An entire family of methodologies for predicting protein interactions is based on the observed fact that families of interacting proteins tend to have similar phylogenetic trees due to co-evolution. One application of this concept is the prediction of the mapping between the members of two interacting protein families (which protein within one family interacts with which protein within the other). The idea is that the real mapping would be the one maximizing the similarity between the trees. Since the exhaustive exploration of all possible mappings is not feasible for large families, current approaches use heuristic techniques which do not ensure the best solution to be found. This is why it is important to check the results proposed by heuristic techniques and to manually explore other solutions. Here we present TSEMA, the server for efficient mapping assessment. This system calculates an initial mapping between two families of proteins based on a Monte Carlo approach and allows the user to interactively modify it based on performance figures and/or specific biological knowledge. All the explored mappings are graphically shown over a representation of the phylogenetic trees. The system is freely available at http://pdg.cnb.uam.es/TSEMA. Standalone versions of the software behind the interface are available upon request from the authors.

Internet↗
PubMed2006

TreeDet: a web server to explore sequence space.

The TreeDet (Tree Determinant) Server is the first release of a system designed to integrate results from methods that predict functional sites in protein families. These methods take into account the relation between sequence conservation and evolutionary importance. TreeDet fully analyses the space of protein sequences in either user-uploaded or automatically generated multiple sequence alignments. The methods implemented in the server represent three main classes of methods for the detection of family-dependent conserved positions, a tree-based method, a correlation based method and a method that employs a principal component analyses coupled to a cluster algorithm. An additional method is provided to highlight the reliability of the position in the alignments. The server is available at http://www.pdg.cnb.uam.es/servers/treedet.

Amino Acid Sequence↗
PubMed2006

The three-dimensional structure of an eukaryotic glutamine synthetase: functional implications of its oligomeric structure.

The structure of the prokaryotic glutamine synthetases type I (GS-I), key enzymes in nitrogen metabolism, was determined several years ago by X-ray diffraction, and consists of a double hexameric ring. The structure of the eukaryotic GS from the plant Phaseolus vulgaris (Glutamine synthetase type II; GS-II) has now been determined at low-resolution using electron microscopy and image processing, and consists of an octamer composed of two tetramers placed back-to-back and rotated 90 degrees with respect to each other. The oligomeric structure possesses a twofold symmetry, very suggestive of each tetramer being composed of two dimers. This is reinforced by the fact that dimers are isolated as a stable albeit non-functional species during the purification procedure. Given the fact that the active site of all types of GS is formed by highly conserved residues located in the interface of two interacting monomers, the geometry of the reconstructed tetramer suggests that it only contains two functional active sites, i.e., an active site per dimer. This is supported by biochemical data, which reveal that while the octamer binds eight ATP molecules, it only binds four molecules of the transition state analogue and GS inhibitor methionine-(S)-sulfoximine-P (MetSox-P). All this suggests for the GS-II enzyme an oligomeric structure containing four active sites and four possible regulatory sites, which might point to a complex regulatory behavior.

Amino Acid Sequence↗
PubMed2006

The success (or not) of HUGO nomenclature.

Current usage of gene nomenclature is ambiguous and impairs the efficient handling of scientific information. Therefore it is important to propose guidelines to deal with this problem. This study attempts to evaluate the success of HUGO nomenclature for human genes. The results indicate that HUGO guidelines are not supported by the scientific community.

Genes↗
PubMed2006

An analysis of the Sargasso Sea resource and the consequences for database composition.

BACKGROUND: The environmental sequencing of the Sargasso Sea has introduced a huge new resource of genomic information. Unlike the protein sequences held in the current searchable databases, the Sargasso Sea sequences originate from a single marine environment and have been sequenced from species that are not easily obtainable by laboratory cultivation. The resource also contains very many fragments of whole protein sequences, a side effect of the shotgun sequencing method.These sequences form a significant addendum to the current searchable databases but also present us with some intrinsic difficulties. While it is important to know whether it is possible to assign function to these sequences with the current methods and whether they will increase our capacity to explore sequence space, it is also interesting to know how current bioinformatics techniques will deal with the new sequences in the resource. RESULTS: The Sargasso Sea sequences seem to introduce a bias that decreases the potential of current methods to propose structure and function for new proteins. In particular the high proportion of sequence fragments in the resource seems to result in poor quality multiple alignments. CONCLUSION: These observations suggest that the new sequences should be used with care, especially if the information is to be used in large scale analyses. On a positive note, the results may just spark improvements in computational and experimental methods to take into account the fragments generated by environmental sequencing techniques.

Amino Acid Sequence↗
PubMed2006

Phylogeny-independent detection of functional residues.

MOTIVATION: Current projects for the massive characterization of proteomes are generating protein sequences and structures with unknown function. The difficulty of experimentally determining functionally important sites calls for the development of computational methods. The first techniques, based on the search for fully conserved positions in multiple sequence alignments (MSAs), were followed by methods for locating family-dependent conserved positions. These rely on the functional classification implicit in the alignment for locating these positions related with functional specificity. The next obvious step, still scarcely explored, is to detect these positions using a functional classification different from the one implicit in the sequence relationships between the proteins. Here, we present two new methods for locating functional positions which can incorporate an arbitrary external functional classification which may or may not coincide with the one implicit in the MSA. The Xdet method is able to use a functional classification with an associated hierarchy or similarity between functions to locate positions related to that classification. The MCdet method uses multivariate statistical analysis to locate positions responsible for each one of the functions within a multifunctional family. RESULTS: We applied the methods to different cases, illustrating scenarios where there is a disagreement between the functional and the phylogenetic relationships, and demonstrated their usefulness for the phylogeny-independent prediction of functional positions.

Amino Acid Sequence↗
PubMed2006

Gas1 is related to the glial cell-derived neurotrophic factor family receptors alpha and regulates Ret signaling.

The growth arrest-specific gene 1 (Gas1) protein has been proposed to function during development as an inhibitor of growth and a mediator of cell death and is also re-expressed in adult neurons during excitotoxic insult. Here we have demonstrated that the Gas1 protein shows high structural similarity to the glial cell-derived neurotrophic factor (GDNF) family receptors alpha, which mediate GDNF responses through the receptor tyrosine kinase Ret. We found that Gas1 binds Ret in a ligand-independent manner and sequesters Ret in lipid rafts. Signaling downstream of Ret is thus modified through a mechanism that involves the adaptor protein Shc as well as ERK, eventually blocking Akt activation. Consequently, when Gas1 is induced, Ret-mediated GDNF-dependent survival effects are compromised.

Amino Acid Sequence↗
PubMed2005

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Algorithms↗
PubMed2005

A framework for computational and experimental methods: identifying dimerization residues in CCR chemokine receptors.

Solving relevant biological problems requires answering complex questions. Addressing such questions traditionally implied the design of time-consuming experimental procedures which most of the time are not accessible to average-sized laboratories. The current trend is to move towards a multidisciplinary approach integrating both theoretical knowledge and experimental work. This combination creates a powerful tool for shedding light on biological problems. To illustrate this concept, we present here a descriptive example of where computational methods were shown to be a key aspect in detecting crucial players in an important biological problem: the dimerization of chemokine receptors. Using evolutionary based sequence analysis in combination with structural predictions two CCR5 residues were selected as important for dimerization and further validated experimentally. The experimental validation of computational procedures demonstrated here provides a wealth of valuable information not obtainable by any of the individual approaches alone.

Amino Acid Sequence↗
PubMed2005

Implementing the iHOP concept for navigation of biomedical literature.

MOTIVATION: The World Wide Web has profoundly changed the way in which we access information. Searching the internet is easy and fast, but more importantly, the interconnection of related contents makes it intuitive and closer to the associative organization of human memory. However, the information retrieval tools currently available to researchers in biology and medicine lag far behind the possibilities that the layman has come to expect from the internet. RESULTS: By using genes and proteins as hyperlinks between sentences and abstracts, the information in PubMed can be converted into one navigable resource. iHOP (Information Hyperlinked over Proteins) is an online service that provides this gene-guided network as a natural way of accessing millions of PubMed abstracts and brings all the advantages of the internet to scientific literature research. Navigating across interrelated sentences within this network is closer to human intuition than the use of conventional keyword searches and allows for stepwise and controlled acquisition of information. Moreover, this literature network can be superimposed upon experimental interaction data to facilitate the simultaneous analysis of novel and existing knowledge. The network presented in iHOP currently contains5 million sentences and 40 000 genes from Homo sapiens, Mus musculus, Drosophila melanogaster, Caenorhabditis elegans, Danio rerio, Arabidopsis thaliana, Saccharomyces cerevisiae and Escherichia coli. AVAILABILITY: iHOP is freely accessible at http://www.pdg.cnb.uam.es/UniPub/iHOP/

Biomedical Engineering↗
PubMed2005

Scoring docking models with evolutionary information.

We have developed methods for the extraction of evolutionary information from multiple sequence alignments for use in the study of the evolution of protein interaction networks and in the prediction of protein interaction. For Rounds 3, 4, and 5 of the CAPRI experiment, we used scores derived from the analysis of multiple sequence alignments to submit predictions for 7 of the 12 targets. Our docking models were generated with Hex and GRAMM, but all our predictions were selected using methods based on multiple sequence alignments and on the available experimental evidence. With this approach, we were able to predict acceptable level models for 4 of the targets, and for a fifth target, we located the residues involved in the binding surface. Here we detail our successes and highlight several of the limitations and problems that we faced while dealing with particular docking cases.

Algorithms↗
PubMed2005

EVAcon: a protein contact prediction evaluation service.

Here we introduce EVAcon, an automated web service that evaluates the performance of contact prediction servers. Currently, EVAcon is monitoring nine servers, four of which are specialized in contact prediction and five are general structure prediction servers. Results are compared for all newly determined experimental structures deposited into PDB ( approximately 5-50 per week). EVAcon allows for a precise comparison of the results based on a system of common protein subsets and the commonly accepted evaluation criteria that are also used in the corresponding category of the CASP assessment. EVAcon is a new service added to the functionality of the EVA system for the continuous evaluation of protein structure prediction servers. The new service is accesible from any of the three EVA mirrors: PDG (CNB-CSIC, Madrid) (http://www.pdg.cnb.uam.es/eva/con/index.html); CUBIC (Columbia University, NYC) (http://cubic.bioc.columbia.edu/eva/con/index.html); and Sali Lab (UCSF, San Francisco) (http://eva.compbio.ucsf.edu/~eva/con/index.html).

Amino Acids↗
PubMed2005

Text-mining and information-retrieval services for molecular biology.

Text-mining in molecular biology -- defined as the automatic extraction of information about genes, proteins and their functional relationships from text documents -- has emerged as a hybrid discipline on the edges of the fields of information science, bioinformatics and computational linguistics. A range of text-mining applications have been developed recently that will improve access to knowledge for biologists and database annotators.

Computational Biology↗