Florencio Pazos

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. Proteins 2002;47:219–227.

EVA: continuous automatic evaluation of protein structure prediction servers

UNLABELLED Evaluation of protein structure prediction methods is difficult and time-consuming. Here, we describe EVA, a web server for assessing protein structure prediction methods, in an automated, continuous and large-scale fashion. Currently, EVA evaluates the performance of a variety of prediction methods available through the internet. Every week, the sequences of the latest experimentally determined protein structures are sent to prediction servers, results are collected, performance is evaluated, and a summary is published on the web. EVA has so far collected data for more than 3000 protein chains. These results may provide valuable insight to both developers and users of prediction methods. AVAILABILITY http://cubic.bioc.columbia.edu/eva. CONTACT eva@cubic.bioc.columbia.edu

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.

High-confidence prediction of global interactomes based on genome-wide coevolutionary networks

Interacting or functionally related protein families tend to have similar phylogenetic trees. Based on this observation, techniques have been developed to predict interaction partners. The observed degree of similarity between the phylogenetic trees of two proteins is the result of many different factors besides the actual interaction or functional relationship between them. Such factors influence the performance of interaction predictions. One aspect that can influence this similarity is related to the fact that a given protein interacts with many others, and hence it must adapt to all of them. Accordingly, the interaction or coadaptation signal within its tree is a composite of the influence of all of the interactors. Here, we introduce a new estimator of coevolution to overcome this and other problems. Instead of relying on the individual value of tree similarity between two proteins, we use the whole network of similarities between all of the pairs of proteins within a genome to reassess the similarity of that pair, thereby taking into account its coevolutionary context. We show that this approach offers a substantial improvement in interaction prediction performance, providing a degree of accuracy/coverage comparable with, or in some cases better than, that of experimental techniques. Moreover, important information on the structure, function, and evolution of macromolecular complexes can be inferred with this methodology.

Protein co-evolution, co-adaptation and interactions.

Co‐evolution has an important function in the evolution of species and it is clearly manifested in certain scenarios such as host–parasite and predator–prey interactions, symbiosis and mutualism. The extrapolation of the concepts and methodologies developed for the study of species co‐evolution at the molecular level has prompted the development of a variety of computational methods able to predict protein interactions through the characteristics of co‐evolution. Particularly successful have been those methods that predict interactions at the genomic level based on the detection of pairs of protein families with similar evolutionary histories (similarity of phylogenetic trees: mirrortree). Future advances in this field will require a better understanding of the molecular basis of the co‐evolution of protein families. Thus, it will be important to decipher the molecular mechanisms underlying the similarity observed in phylogenetic trees of interacting proteins, distinguishing direct specific molecular interactions from other general functional constraints. In particular, it will be important to separate the effects of physical interactions within protein complexes (‘co‐adaptation’) from other forces that, in a less specific way, can also create general patterns of co‐evolution.

Genetic polymorphisms among C57BL/6 mouse inbred strains

Mice from the inbred C57BL/6 strain have been commonly used for the generation and analysis of transgenic and knockout animal models. However, several C57BL/6 substrains exist, and these are genetically and phenotypically different. In addition, each of these substrains can be purchased from different animal providers and, in some cases, they have maintained their breeding stocks separated for a long time, allowing genetic differences to accumulate due to individual variability and genetic drift. With the aim of describing the differences in the genotype of several C57BL/6 substrains, we applied the Illumina® Mouse Medium Density Linkage Mapping panel, with 1,449 single nucleotide polymorphisms (SNPs), to individuals from ten C57BL/6-related strains: C57BL/6JArc, C57BL/6J from The Jackson Lab, C57BL/6J from Crl, C57BL6/JRccHsd, C57BL/6JOlaHsd, C57BL/6JBomTac, B6(Cg)-Tyrc−2j/J, C57BL/6NCrl, C57BL/6NHsd and C57BL/6NTac. Twelve SNPs were found informative to discriminate among the mouse strains considered. Mice derived from the original C57BL/6J: C57BL/6JArc, C57BL/6J from The Jackson Lab and C57BL/6J from Crl, were indistinguishable. Similarly, all C57BL/6N substrains displayed the same genotype, whereas the additional substrains showed intermediate cases with substrain-specific polymorphisms. These results will be instrumental for the correct genetic monitoring and appropriate mouse colony handling of different transgenic and knockout mice produced in distinct C57BL/6 inbred substrains.

Protein interactions and ligand binding: from protein subfamilies to functional specificity.

The divergence accumulated during the evolution of protein families translates into their internal organization as subfamilies, and it is directly reflected in the characteristic patterns of differentially conserved residues. These specifically conserved positions in protein subfamilies are known as “specificity determining positions” (SDPs). Previous studies have limited their analysis to the study of the relationship between these positions and ligand-binding specificity, demonstrating significant yet limited predictive capacity. We have systematically extended this observation to include the role of differential protein interactions in the segregation of protein subfamilies and explored in detail the structural distribution of SDPs at protein interfaces. Our results show the extensive influence of protein interactions in the evolution of protein families and the widespread association of SDPs with protein interfaces. The combined analysis of SDPs in interfaces and ligand-binding sites provides a more complete picture of the organization of protein families, constituting the necessary framework for a large scale analysis of the evolution of protein function.

MBRole: enrichment analysis of metabolomic data

UNLABELLED While many tools exist for performing enrichment analysis of transcriptomic and proteomic data in order to interpret them in biological terms, almost no equivalent tools exist for metabolomic data. We present Metabolite Biological Role (MBRole), a web server for carrying out over-representation analysis of biological and chemical annotations in arbitrary sets of metabolites (small chemical compounds) coming from metabolomic data of any organism or sample. AVAILABILITY AND IMPLEMENTATION The web server is freely available at http://csbg.cnb.csic.es/mbrole. It was tested in the main web browsers.

Towards the prediction of protein interaction partners using physical docking

Deciphering the whole network of protein interactions for a given proteome (‘interactome’) is the goal of many experimental and computational efforts in Systems Biology. Separately the prediction of the structure of protein complexes by docking methods is a well‐established scientific area. To date, docking programs have not been used to predict interaction partners. We provide a proof of principle for such an approach. Using a set of protein complexes representing known interactors in their unbound form, we show that a standard docking program can distinguish the true interactors from a background of 922 non‐redundant potential interactors. We additionally show that true interactions can be distinguished from non‐likely interacting proteins within the same structural family. Our approach may be put in the context of the proposed ‘funnel‐energy model’; the docking algorithm may not find the native complex, but it distinguishes binding partners because of the higher probability of favourable models compared with a collection of non‐binders. The potential exists to develop this proof of principle into new approaches for predicting interaction partners and reconstructing biological networks.

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.