Large Multiple Sequence Alignments Research Articles

INTREPID—INformation-theoretic TREe traversal for Protein functional site IDentification

Motivation: Identification of functionally important residues in proteins plays a significant role in biological discovery. Here, we present INTREPID—an information–theoretic approach for functional site identification that exploits the information in large diverse multiple sequence alignments (MSAs). INTREPID uses a traversal of the phylogeny in combination with a positional conservation score, based on Jensen–Shannon divergence, to rank positions in an MSA. While knowledge of protein 3D structure can significantly improve the accuracy of functional site identification, since structural information is not available for a majority of proteins, INTREPID relies solely on sequence information. We evaluated INTREPID on two tasks: predicting catalytic residues and predicting specificity determinants.Results: In catalytic residue prediction, INTREPID provides significant improvements over Evolutionary Trace, ConSurf as well as over a baseline global conservation method on a set of 100 manually curated enzymes from the Catalytic Site Atlas. In particular, INTREPID is able to better predict catalytic positions that are not globally conserved and hence, attains improved sensitivity at high values of specificity. We also investigated the performance of INTREPID as a function of the evolutionary divergence of the protein family. We found that INTREPID is better able to exploit the diversity in such families and that accuracy improves when homologs with very low sequence identity are included in an alignment. In specificity determinant prediction, when subtype information is known, INTREPID-SPEC, a variant of INTREPID, attains accuracies that are competitive with other approaches for this task.Availability: INTREPID is available for 16919 families in the PhyloFacts resource (http://phylogenomics.berkeley.edu/phylofacts).Contact: sriram_s@cs.berkeley.eduSupplementary information: Relevant online supplementary material is available at http://phylogenomics.berkeley.edu/INTREPID.

Rewiring Bacteria, Two Components at a Time

In this issue, Skerker et al. (2008) present a rational method for rewiring the protein-protein interactions and output responses of prokaryotic two-component signal transduction systems. This work has important implications for understanding the specificity of protein interactions and for designing protein-based synthetic signaling cascades.

Using inferred residue contacts to distinguish between correct and incorrect protein models

Motivation: The de novo prediction of 3D protein structure is enjoying a period of dramatic improvements. Often, a remaining difficulty is to select the model closest to the true structure from a group of low-energy candidates. To what extent can inter-residue contact predictions from multiple sequence alignments, information which is orthogonal to that used in most structure prediction algorithms, be used to identify those models most similar to the native protein structure?Results: We present a Bayesian inference procedure to identify residue pairs that are spatially proximal in a protein structure. The method takes as input a multiple sequence alignment, and outputs an accurate posterior probability of proximity for each residue pair. We exploit a recent metagenomic sequencing project to create large, diverse and informative multiple sequence alignments for a test set of 1656 known protein structures. The method infers spatially proximal residue pairs in this test set with good accuracy: top-ranked predictions achieve an average accuracy of 38% (for an average 21-fold improvement over random predictions) in cross-validation tests. Notably, the accuracy of predicted 3D models generated by a range of structure prediction algorithms strongly correlates with how well the models satisfy probable residue contacts inferred via our method. This correlation allows for confident rejection of incorrect structural models.Availability: An implementation of the method is freely available at http://www.doe-mbi.ucla.edu/servicesContact: david@mbi.ucla.eduSupplementary information: Supplementary data are available at Bioinformatics online.

On the design of high-performance algorithms for aligning multiple protein sequences on mesh-based multiprocessor architectures

In this paper, we address the problem of multiple sequence alignment (MSA) for handling very large number of proteins sequences on mesh-based multiprocessor architectures. As the problem has been conclusively shown to be computationally complex, we employ divisible load paradigm (also, referred to as divisible load theory, DLT) to handle such large number of sequences. We design an efficient computational engine that is capable of conducting MSAs by exploiting the underlying parallelism embedded in the computational steps of multiple sequence algorithms. Specifically, we consider the standard Smith–Waterman (SW) algorithm in our implementation, however, our approach is by no means restrictive to SW class of algorithms alone. The treatment used in this paper is generic to a class of similar dynamic programming problems. Our approach is recursive in the sense that the quality of solutions can be refined continuously till an acceptable level of quality is achieved. After first phase of computation, we design a heuristic scheme that renders the final solution for MSA. We conduct rigorous simulation experiments using several hundreds of homologous protein sequences derived from the Rattus Norvegicus and Mus Musculus databases of olfactory receptors. We quantify the performance based on speed-up metric. We compare our algorithms to serial or single machine processing approaches. We testify our findings by comparing with conventional equal load partitioning (ELP) strategy that is commonly used in the parallel processing literature. Based on our extensive simulation study, we observe that DLT paradigm offers an excellent speed-up characteristics and provides avenues for its use in several other biological sequence processing related problem. This study is a first time attempt in using the DLT paradigm to devise efficient strategies to handle large scale multiple protein sequence alignment problem on mesh-based multiprocessor systems.

TaxMan: a taxonomic database manager.

BackgroundPhylogenetic analysis of large, multiple-gene datasets, assembled from public sequence databases, is rapidly becoming a popular way to approach difficult phylogenetic problems. Supermatrices (concatenated multiple sequence alignments of multiple genes) can yield more phylogenetic signal than individual genes. However, manually assembling such datasets for a large taxonomic group is time-consuming and error-prone. Additionally, sequence curation, alignment and assessment of the results of phylogenetic analysis are made particularly difficult by the potential for a given gene in a given species to be unrepresented, or to be represented by multiple or partial sequences. We have developed a software package, TaxMan, that largely automates the processes of sequence acquisition, consensus building, alignment and taxon selection to facilitate this type of phylogenetic study.ResultsTaxMan uses freely available tools to allow rapid assembly, storage and analysis of large, aligned DNA and protein sequence datasets for user-defined sets of species and genes. The user provides GenBank format files and a list of gene names and synonyms for the loci to analyse. Sequences are extracted from the GenBank files on the basis of annotation and sequence similarity. Consensus sequences are built automatically. Alignment is carried out (where possible, at the protein level) and aligned sequences are stored in a database. TaxMan can automatically determine the best subset of taxa to examine phylogeny at a given taxonomic level. By using the stored aligned sequences, large concatenated multiple sequence alignments can be generated rapidly for a subset and output in analysis-ready file formats. Trees resulting from phylogenetic analysis can be stored and compared with a reference taxonomy.ConclusionTaxMan allows rapid automated assembly of a multigene datasets of aligned sequences for large taxonomic groups. By extracting sequences on the basis of both annotation and BLAST similarity, it ensures that all available sequence data can be brought to bear on a phylogenetic problem, but remains fast enough to cope with many thousands of records. By automatically assisting in the selection of the best subset of taxa to address a particular phylogenetic problem, TaxMan greatly speeds up the process of generating multiple sequence alignments for phylogenetic analysis. Our results indicate that an automated phylogenetic workbench can be a useful tool when correctly guided by user knowledge.

Evolutionarily Conserved Allosteric Network in the Cys Loop Family of Ligand-gated Ion Channels Revealed by Statistical Covariance Analyses

The Cys loop family of ligand-gated ion channels mediate fast synaptic transmission for communication between neurons. They are allosteric proteins, in which binding of a neurotransmitter to its binding site in the extracellular amino-terminal domain triggers structural changes in distant transmembrane domains to open a channel for ion flow. Although the locations of binding site and channel gating machinery are well defined, the structural basis of the activation pathway coupling binding and channel opening remains to be determined. In this paper, by analyzing amino acid covariance in a multiple sequence alignment, we have identified an energetically interconnected network in the Cys loop family of ligand-gated ion channels. Statistical coupling and correlated mutational analyses along with clustering revealed a highly coupled cluster. Mapping the positions in the cluster onto a three-dimensional structural model demonstrated that these highly coupled positions form an interconnected network linking experimentally identified binding domains through the coupling region to the gating machinery. In addition, these highly coupled positions are also condensed in the transmembrane domains, which are a recent focus for the sites of action of many allosteric modulators. Thus, our results revealed a genetically interconnected network that potentially plays an important role in the allosteric activation and modulation of the Cys loop family of ligand-gated ion channels.

Large scale multiple sequence alignment with simultaneous phylogeny inference

Multiple sequence alignment (MSA) and phylogenetic tree reconstruction are one of the most important problems in the computational biology. While both these problems are of great practical significance, in most cases they are very computationally demanding. In this paper we propose a new approach to the MSA problem which simultaneously infers an underlying phylogenetic tree. To process large data sets we provide parallel implementation of our method, which is based on the distributed caching of intermediate results. Finally, we show a parallel server designed for grid environments, and we report results of experiments performed with actual biological data, e.g. 1000 ribosomal RNA sequences.

The Jalview Java alignment editor.

Multiple sequence alignment remains a crucial method for understanding the function of groups of related nucleic acid and protein sequences. However, it is known that automatic multiple sequence alignments can often be improved by manual editing. Therefore, tools are needed to view and edit multiple sequence alignments. Due to growth in the sequence databases, multiple sequence alignments can often be large and difficult to view efficiently. The Jalview Java alignment editor is presented here, which enables fast viewing and editing of large multiple sequence alignments.

Assessing functional divergence in EF-1alpha and its paralogs in eukaryotes and archaebacteria.

A number of methods have recently been published that use phylogenetic information extracted from large multiple sequence alignments to detect sites that have changed properties in related protein families. In this study we use such methods to assess functional divergence between eukaryotic EF-1alpha (eEF-1alpha), archaebacterial EF-1alpha (aEF-1alpha) and two eukaryote-specific EF-1alpha paralogs-eukaryotic release factor 3 (eRF3) and Hsp70 subfamily B suppressor 1 (HBS1). Overall, the evolutionary modes of aEF-1alpha, HBS1 and eRF3 appear to significantly differ from that of eEF-1alpha. However, functionally divergent (FD) sites detected between aEF-1alpha and eEF-1alpha only weakly overlap with sites implicated as putative EF-1beta or aminoacyl-tRNA (aa-tRNA) binding residues in EF-1alpha, as expected based on the shared ancestral primary translational functions of these two orthologs. In contrast, FD sites detected between eEF-1alpha and its paralogs significantly overlap with the putative EF-1beta and/or aa-tRNA binding sites in EF-1alpha. In eRF3 and HBS1, these sites appear to be released from functional constraints, indicating that they bind neither eEF-1beta nor aa-tRNA. These results are consistent with experimental observations that eRF3 does not bind to aa-tRNA, but do not support the 'EF-1alpha-like' function recently proposed for HBS1. We re-assess the available genetic data for HBS1 in light of our analyses, and propose that this protein may function in stop codon-independent peptide release.