Bioinformàtica: una eina essencial per als biòlegs del segle XXI

La bioinformàtica i la biologia computacional són dues cares d'una mateixa disciplina, que fa ús de tècniques i algoritmes informàtics per estudiar processos biològics. Ambdós termes tenen, però, matisos diferents. Històricament, el terme de biologia computacional s'ha emprat més en ambients de simulació computacional en què la física i les matemàtiques tenen un paper molt rellevant, mentre que el terme de bioinformàtica s'ha usat més en l'estudi de grans quantitats de dades en què l'estadística és essencial. Assignem el terme que assignem, l'augment de les dades òmiques juntament amb una comprensió més acurada dels processos estudiats, ha fet que la bioinformàtica resulti imprescindible en el currículum del biòleg. Com a conseqüència, en aquest segle que tot just estrenem, no tots els biòlegs hauran de ser bioinformàtics, però sí que hauran de saber fer servir la bioinformàtica.Bioinformatics and computational biology are parallel terms that refer to the application of computational science to the study of biological processes. However, these two terms have certain historical connotations. Computational biology has referred more specifically to a scientific approach to simulating biology in which physics and mathematics play an important role. Bioinformatics is often used to designate a discipline in which computational software allows the vast amounts of data now produced by biologists to be processed. Regardless of which term is used, the increase of biological data and the more accurate insights into the theory behind biological processes make bioinformatics essential for any biologists of the 21st century. Consequently, today not all biologists need to be bioinformaticians but they certainly all need to be proficient in bioinformatics

Bridging the Resolution Gap in Structural Modeling of 3D Genome Organization

Over the last decade, and especially after the advent of fluorescent in situ hybridization imaging and chromosome conformation capture methods, the availability of experimental data on genome three-dimensional organization has dramatically increased. We now have access to unprecedented details of how genomes organize within the interphase nucleus. Development of new computational approaches to leverage this data has already resulted in the first three-dimensional structures of genomic domains and genomes. Such approaches expand our knowledge of the chromatin folding principles, which has been classically studied using polymer physics and molecular simulations. Our outlook describes computational approaches for integrating experimental data with polymer physics, thereby bridging the resolution gap for structural determination of genomes and genomic domains.Spain. Ministerio de Ciencia e Innovación (BFU2010-19310)National Cancer Institute (U.S.)David H. Koch Institute for Integrative Cancer Research at MI

Evolutionary potentials: structure specific knowledge-based potentials exploiting the evolutionary record of sequence homologs

So-called ‘Evolutionary potentials’ for protein structure prediction are derived using a single experimental protein structure and all three-dimensional models of its homologous sequences

Exploring the three-dimensional organization of genomes: interpreting chromatin interaction data

How DNA is organized in three dimensions inside the cell nucleus and how this affects the ways in which cells access, read and interpret genetic information are among the longest standing questions in cell biology. Using newly developed molecular, genomic and computational approaches based on the chromosome conformation capture technology (such as 3C, 4C, 5C and Hi-C), the spatial organization of genomes is being explored at unprecedented resolution. Interpreting the increasingly large chromatin interaction data sets is now posing novel challenges. Here we describe several types of statistical and computational approaches that have recently been developed to analyse chromatin interaction data.National Institutes of Health (U.S.)National Human Genome Research Institute (U.S.) (HG003143)National Human Genome Research Institute (U.S.) (HG003143-06S1)W. M. Keck FoundationSpain. Ministerio de Ciencia e Innovación (BFU2010-19310/BMC)Human Frontier Science Program (Strasbourg, France) (RGP0044/2011)European Union (BLUEPRINT project (EU FP7 grant agreement 282510))National Cancer Institute (U.S.) (Physical Sciences Oncology Center at MIT, U54CA143874

STRUCTURE COMPARISON AND ALIGNMENT

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Characterization of Protein Hubs by Inferring Interacting Motifs from Protein Interactions

The characterization of protein interactions is essential for understanding biological systems. While genome-scale methods are available for identifying interacting proteins, they do not pinpoint the interacting motifs (e.g., a domain, sequence segments, a binding site, or a set of residues). Here, we develop and apply a method for delineating the interacting motifs of hub proteins (i.e., highly connected proteins). The method relies on the observation that proteins with common interaction partners tend to interact with these partners through a common interacting motif. The sole input for the method are binary protein interactions; neither sequence nor structure information is needed. The approach is evaluated by comparing the inferred interacting motifs with domain families defined for 368 proteins in the Structural Classification of Proteins (SCOP). The positive predictive value of the method for detecting proteins with common SCOP families is 75% at sensitivity of 10%. Most of the inferred interacting motifs were significantly associated with sequence patterns, which could be responsible for the common interactions. We find that yeast hubs with multiple interacting motifs are more likely to be essential than hubs with one or two interacting motifs, thus rationalizing the previously observed correlation between essentiality and the number of interacting partners of a protein. We also find that yeast hubs with multiple interacting motifs evolve slower than the average protein, contrary to the hubs with one or two interacting motifs. The proposed method will help us discover unknown interacting motifs and provide biological insights about protein hubs and their roles in interaction networks

Mating type specific chromosome conformation in Saccharomyces cerevisiae

Budding yeast switch their mating type by a gene conversion event at the MAT locus which uses either of two silent loci (HML or HMR) on opposite ends of chromosome three as a template. InMATa cells the left arm of Chr. Ill is “activated” which allows for the preferential recombination ofHML with the MAT locus. The left arm is otherwise “repressed” for recombination in MATα cells which then prefer to use HMR, on the right arm, as a template for gene conversion. We set out to analyze the potential role of chromosome conformation in this “activation”/”repression” phenomenon observed on the left arm of Chr. III. We used Chromosome Conformation Capture Carbon Copy (5C) to comprehensively analyze the conformation of chromosomes III, V, and XII in the two mating types. Our data reveals that the yeast genome is organized in a unique way compared to other species. We have found that global nuclearorganization such ascentromereclustering, telomere tethering to the periphery, and sequestration of the rDNA array into the nucleolus affect both the specific conformations of each chromosome but also the interactions between these chromosomes. Our analysis indicates that the overall architecture for these 3 chromosomes is very similar between the two mating types. Interestingly, a mating type specific difference in conformation of the left arm of Chr. Ill was identified. Furthermore, the 5C data was used, in conjunction with the Integrative Modeling Platform (IMP), to generate three dimensional models of Chr. III in both mating types. This method provides a more intuitive way of viewing 5C data and reveals that, in general, Chr. Ill has a more crumpled conformation in MATacells than in MATα. However, this crumpling is most evident on the left arm of the chromosome. Thus the phenomenon of “activation”/”repression” of the left arm of Chr. III which is associated with mating type-specific switching preference is, in fact, associated with a difference in the innate conformation of Chr. Ill between the two mating types. This difference in structure between mating types will be used as a phenotype to analyze the effect of cis and trans acting factors that play a role in switching preference through alteration of chromosome conformation

MODBASE, a database of annotated comparative protein structure models and associated resources.

MODBASE (http://salilab.org/modbase) is a database of annotated comparative protein structure models. The models are calculated by MODPIPE, an automated modeling pipeline that relies primarily on MODELLER for fold assignment, sequence-structure alignment, model building and model assessment (http:/salilab.org/modeller). MODBASE currently contains 5,152,695 reliable models for domains in 1,593,209 unique protein sequences; only models based on statistically significant alignments and/or models assessed to have the correct fold are included. MODBASE also allows users to calculate comparative models on demand, through an interface to the MODWEB modeling server (http://salilab.org/modweb). Other resources integrated with MODBASE include databases of multiple protein structure alignments (DBAli), structurally defined ligand binding sites (LIGBASE), predicted ligand binding sites (AnnoLyze), structurally defined binary domain interfaces (PIBASE) and annotated single nucleotide polymorphisms and somatic mutations found in human proteins (LS-SNP, LS-Mut). MODBASE models are also available through the Protein Model Portal (http://www.proteinmodelportal.org/)

An Alternative Model of Amino Acid Replacement

The observed correlations between pairs of homologous protein sequences are typically explained in terms of a Markovian dynamic of amino acid substitution. This model assumes that every location on the protein sequence has the same background distribution of amino acids, an assumption that is incompatible with the observed heterogeneity of protein amino acid profiles and with the success of profile multiple sequence alignment. We propose an alternative model of amino acid replacement during protein evolution based upon the assumption that the variation of the amino acid background distribution from one residue to the next is sufficient to explain the observed sequence correlations of homologs. The resulting dynamical model of independent replacements drawn from heterogeneous backgrounds is simple and consistent, and provides a unified homology match score for sequence-sequence, sequence-profile and profile-profile alignment.Comment: Minor improvements. Added figure and reference

Polar/Ionizable Residues in Transmembrane Segments: Effects on Helix-Helix Packing

The vast majority of membrane proteins are anchored to biological membranes through hydrophobic α-helices. Sequence analysis of high-resolution membrane protein structures show that ionizable amino acid residues are present in transmembrane (TM) helices, often with a functional and/or structural role. Here, using as scaffold the hydrophobic TM domain of the model membrane protein glycophorin A (GpA), we address the consequences of replacing specific residues by ionizable amino acids on TM helix insertion and packing, both in detergent micelles and in biological membranes. Our findings demonstrate that ionizable residues are stably inserted in hydrophobic environments, and tolerated in the dimerization process when oriented toward the lipid face, emphasizing the complexity of protein-lipid interactions in biological membranes