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Florencio Pazos

Publications and source records attributed to Florencio Pazos.

18 recordsLinked to original sources
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 proteome: structure, function and evolution.

This paper reports two studies to model the inter-relationships between protein sequence, structure and function. First, an automated pipeline to provide a structural annotation of proteomes in the major genomes is described. The results are stored in a database at Imperial College, London (3D-GENOMICS) that can be accessed at www.sbg.bio.ic.ac.uk. Analysis of the assignments to structural superfamilies provides evolutionary insights. 3D-GENOMICS is being integrated with related proteome annotation data at University College London and the European Bioinformatics Institute in a project known as e-protein (http://www.e-protein.org/). The second topic is motivated by the developments in structural genomics projects in which the structure of a protein is determined prior to knowledge of its function. We have developed a new approach PHUNCTIONER that uses the gene ontology (GO) classification to supervise the extraction of the sequence signal responsible for protein function from a structure-based sequence alignment. Using GO we can obtain profiles for a range of specificities described in the ontology. In the region of low sequence similarity (around 15%), our method is more accurate than assignment from the closest structural homologue. The method is also able to identify the specific residues associated with the function of the protein family.

Computational Biology↗
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↗
PubMed2005

Assessing protein co-evolution in the context of the tree of life assists in the prediction of the interactome.

The identification of the whole set of protein interactions taking place in an organism is one of the main tasks in genomics, proteomics and systems biology. One of the computational techniques used by many investigators for studying and predicting protein interactions is the comparison of evolutionary histories (phylogenetic trees), under the hypothesis that interacting proteins would be subject to a similar evolutionary pressure resulting in a similar topology of the corresponding trees. Here, we present a new approach to predict protein interactions from phylogenetic trees, which incorporates information on the overall evolutionary histories of the species (i.e. the canonical "tree of life") in order to correct by the expected background similarity due to the underlying speciation events. We test the new approach in the largest set of annotated interacting proteins for Escherichia coli. This assessment of co-evolution in the context of the tree of life leads to a highly significant improvement (P(N) by sign test approximately 10E-6) in predicting interaction partners with respect to the previous technique, which does not incorporate information on the overall speciation tree. For half of the proteins we found a real interactor among the 6.4% top scores, compared with the 16.5% by the previous method. We applied the new method to the whole E.coli proteome and propose functions for some hypothetical proteins based on their predicted interactors. The new approach allows us also to detect non-canonical evolutionary events, in particular horizontal gene transfers. We also show that taking into account these non-canonical evolutionary events when assessing the similarity between evolutionary trees improves the performance of the method predicting interactions.

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

MetaRouter: bioinformatics for bioremediation.

Bioremediation, the exploitation of biological catalysts (mostly microorganisms) for removing pollutants from the environment, requires the integration of huge amounts of data from different sources. We have developed MetaRouter, a system for maintaining heterogeneous information related to bioremediation in a framework that allows its query, administration and mining (application of methods for extracting new knowledge). MetaRouter is an application intended for laboratories working in biodegradation and bioremediation, which need to maintain and consult public and private data, linked internally and with external databases, and to extract new information from it. Among the data-mining features is a program included for locating biodegradative pathways for chemical compounds according to a given set of constraints and requirements. The integration of biodegradation information with the corresponding protein and genome data provides a suitable framework for studying the global properties of the bioremediation network. The system can be accessed and administrated through a web interface. The full-featured system (except administration facilities) is freely available at http://pdg.cnb.uam.es/MetaRouter. Additional material: http://www.pdg.cnb.uam.es/biodeg_net/MetaRouter.

Bacteria↗
PubMed2004

Automated prediction of protein function and detection of functional sites from structure.

Current structural genomics projects are yielding structures for proteins whose functions are unknown. Accordingly, there is a pressing requirement for computational methods for function prediction. Here we present PHUNCTIONER, an automatic method for structure-based function prediction using automatically extracted functional sites (residues associated to functions). The method relates proteins with the same function through structural alignments and extracts 3D profiles of conserved residues. Functional features to train the method are extracted from the Gene Ontology (GO) database. The method extracts these features from the entire GO hierarchy and hence is applicable across the whole range of function specificity. 3D profiles associated with 121 GO annotations were extracted. We tested the power of the method both for the prediction of function and for the extraction of functional sites. The success of function prediction by our method was compared with the standard homology-based method. In the zone of low sequence similarity (approximately 15%), our method assigns the correct GO annotation in 90% of the protein structures considered, approximately 20% higher than inheritance of function from the closest homologue.

Amino Acid Sequence↗
PubMed2003

The organization of the microbial biodegradation network from a systems-biology perspective.

Microbial biodegradation of environmental pollutants is a field of growing importance because of its potential use in bioremediation and biocatalysis. We have studied the characteristics of the global biodegradation network that is brought about by all the known chemical reactions that are implicated in this process, regardless of their microbial hosts. This combination produces an efficient and integrated suprametabolism, with properties similar to those that define metabolic networks in single organisms. The characteristics of this network support an evolutionary scenario in which the reactions evolved outwards from the central metabolism. The properties of the global biodegradation network have implications for predicting the fate of current and future environmental pollutants.

Bacteria↗
PubMed2003

EVA: Evaluation of protein structure prediction servers.

EVA (http://cubic.bioc.columbia.edu/eva/) is a web server for evaluation of the accuracy of automated protein structure prediction methods. The evaluation is updated automatically each week, to cope with the large number of existing prediction servers and the constant changes in the prediction methods. EVA currently assesses servers for secondary structure prediction, contact prediction, comparative protein structure modelling and threading/fold recognition. Every day, sequences of newly available protein structures in the Protein Data Bank (PDB) are sent to the servers and their predictions are collected. The predictions are then compared to the experimental structures once a week; the results are published on the EVA web pages. Over time, EVA has accumulated prediction results for a large number of proteins, ranging from hundreds to thousands, depending on the prediction method. This large sample assures that methods are compared reliably. As a result, EVA provides useful information to developers as well as users of prediction methods.

Automation↗
PubMed2003

Automatic methods for predicting functionally important residues.

Sequence analysis is often the first guide for the prediction of residues in a protein family that may have functional significance. A few methods have been proposed which use the division of protein families into subfamilies in the search for those positions that could have some functional significance for the whole family, but at the same time which exhibit the specificity of each subfamily ("Tree-determinant residues"). However, there are still many unsolved questions like the best division of a protein family into subfamilies, or the accurate detection of sequence variation patterns characteristic of different subfamilies. Here we present a systematic study in a significant number of protein families, testing the statistical meaning of the Tree-determinant residues predicted by three different methods that represent the range of available approaches. The first method takes as a starting point a phylogenetic representation of a protein family and, following the principle of Relative Entropy from Information Theory, automatically searches for the optimal division of the family into subfamilies. The second method looks for positions whose mutational behavior is reminiscent of the mutational behavior of the full-length proteins, by directly comparing the corresponding distance matrices. The third method is an automation of the analysis of distribution of sequences and amino acid positions in the corresponding multidimensional spaces using a vector-based principal component analysis. These three methods have been tested on two non-redundant lists of protein families: one composed by proteins that bind a variety of ligand groups, and the other composed by proteins with annotated functionally relevant sites. In most cases, the residues predicted by the three methods show a clear tendency to be close to bound ligands of biological relevance and to those amino acids described as participants in key aspects of protein function. These three automatic methods provide a wide range of possibilities for biologists to analyze their families of interest, in a similar way to the one presented here for the family of proteins related with ras-p21.

Algorithms↗
PubMed2003

CAFASP3 in the spotlight of EVA.

We have analysed fold recognition, secondary structure and contact prediction servers from CAFASP3. This assessment was carried out in the framework of the fully automated, web-based evaluation server EVA. Detailed results are available at http://cubic.bioc.columbia.edu/eva/cafasp3/. We observed that the sequence-unique targets from CAFASP3/CASP5 were not fully representative for evaluating performance. For all three categories, we showed how careless ranking might be misleading. We compared methods from all categories to experts in secondary structure and contact prediction and homology modellers to fold recognisers. While the secondary structure experts clearly outperformed all others, the contact experts appeared to outperform only novel fold methods. Automatic evaluation servers are good at getting statistics right and at using these to discard misleading ranking schemes. We challenge that to let machines rule where they are best might be the best way for the community to enjoy the tremendous benefit of CASP as a unique opportunity for brainstorming.

Algorithms↗
PubMed2003

The single tryptophan of the PsbQ protein of photosystem II is at the end of a 4-alpha-helical bundle domain.

We examined the microenvironment of the single tryptophan and the tyrosine residues of PsbQ, one of the three main extrinsic proteins of green algal and higher plant photosystem II. On the basis of this information and the previous data on secondary structure [Balsera, M., Arellano, J.B., Gutiérrez, J.R., Heredia, P., Revuelta, J.L. & De Las Rivas, J. (2003) Biochemistry42, 1000-1007], we screened structural models derived by combining various threading approaches. Experimental results showed that the tryptophan residue is partially buried in the core of the protein but still in a polar environment, according to the intrinsic fluorescence emission of PsbQ and the fact that fluorescence quenching by iodide was weaker than that by acrylamide. Furthermore, quenching by cesium suggested that a positively charged barrier shields the tryptophan microenvironment. Comparison of the absorption spectra in native and denaturing conditions indicated that one or two out of six tyrosines of PsbQ are buried in the core of the structure. Using threading methods, a 3D structural model was built for the C-terminal domain of the PsbQ protein family (residues 46-149), while the N-terminal domain is predicted to have a flexible structure. The model for the C-terminal domain is based on the 3D structure of cytochrome b562, a mainly alpha-protein with a helical up/down bundle folding. Despite the large sequence differences between the template and PsbQ, the structural and energetic parameters for the explicit model are acceptable, as judged by the corresponding tools. This 3D model is compatible with the experimentally determined environment of the tryptophan residue and with published structural information. The future experimental determination of the 3D structure of the protein will offer a good validation point for our model and the technology used. Until then, the model can provide a starting point for further studies on the function of PsbQ.

Acrylamide↗
PubMed2003

The oligomerization domain of VP3, the scaffolding protein of infectious bursal disease virus, plays a critical role in capsid assembly.

Infectious bursal disease virus (IBDV) capsids are formed by a single protein layer containing three polypeptides, pVP2, VP2, and VP3. Here, we show that the VP3 protein synthesized in insect cells, either after expression of the complete polyprotein or from a VP3 gene construct, is proteolytically degraded, leading to the accumulation of product lacking the 13 C-terminal residues. This finding led to identification of the VP3 oligomerization domain within a 24-amino-acid stretch near the C-terminal end of the polypeptide, partially overlapping the VP1 binding domain. Inactivation of the VP3 oligomerization domain, by either proteolysis or deletion of the polyprotein gene, abolishes viruslike particle formation. Formation of VP3-VP1 complexes in cells infected with a dual recombinant baculovirus simultaneously expressing the polyprotein and VP1 prevented VP3 proteolysis and led to efficient virus-like particle formation in insect cells.

Amino Acid Sequence↗
PubMed2002

In silico two-hybrid system for the selection of physically interacting protein pairs.

Deciphering the interaction links between proteins has become one of the main tasks of experimental and bioinformatic methodologies. Reconstruction of complex networks of interactions in simple cellular systems by integrating predicted interaction networks with available experimental data is becoming one of the most demanding needs in the postgenomic era. On the basis of the study of correlated mutations in multiple sequence alignments, we propose a new method (in silico two-hybrid, i2h) that directly addresses the detection of physically interacting protein pairs and identifies the most likely sequence regions involved in the interactions. We have applied the system to several test sets, showing that it can discriminate between true and false interactions in a significant number of cases. We have also analyzed a large collection of E. coli protein pairs as a first step toward the virtual reconstruction of its complete interaction network.

Animals↗
PubMed2002

Computational methods for the prediction of protein interactions.

Establishing protein interaction networks is crucial for understanding cellular operations. Detailed knowledge of the 'interactome', the full network of protein-protein interactions, in model cellular systems should provide new insights into the structure and properties of these systems. Parallel to the first massive application of experimental techniques to the determination of protein interaction networks and protein complexes, the first computational methods, based on sequence and genomic information, have emerged.

Artificial Gene Fusion↗
PubMed2002

Prediction of protein--protein interaction sites in heterocomplexes with neural networks.

In this paper we address the problem of extracting features relevant for predicting protein--protein interaction sites from the three-dimensional structures of protein complexes. Our approach is based on information about evolutionary conservation and surface disposition. We implement a neural network based system, which uses a cross validation procedure and allows the correct detection of 73% of the residues involved in protein interactions in a selected database comprising 226 heterodimers. Our analysis confirms that the chemico-physical properties of interacting surfaces are difficult to distinguish from those of the whole protein surface. However neural networks trained with a reduced representation of the interacting patch and sequence profile are sufficient to generalize over the different features of the contact patches and to predict whether a residue in the protein surface is or is not in contact. By using a blind test, we report the prediction of the surface interacting sites of three structural components of the Dnak molecular chaperone system, and find close agreement with previously published experimental results. We propose that the predictor can significantly complement results from structural and functional proteomics.

Animals↗