PROMALS: towards accurate multiple sequence alignments of distantly related proteins

MOTIVATION: Accurate multiple sequence alignments are essential in protein structure modeling, functional prediction and efficient planning of experiments. Although the alignment problem has attracted considerable attention, preparation of high-quality alignments for distantly related sequences remains a difficult task. RESULTS: We developed PROMALS, a multiple alignment method that shows promising results for protein homologs with sequence identity below 10%, aligning close to half of the amino acid residues correctly on average. This is about three times more accurate than traditional pairwise sequence alignment methods. PROMALS algorithm derives its strength from several sources: (i) sequence database searches to retrieve additional homologs; (ii) accurate secondary structure prediction; (iii) a hidden Markov model that uses a novel combined scoring of amino acids and secondary structures; (iv) probabilistic consistency-based scoring applied to progressive alignment of profiles. Compared to the best alignment methods that do not use secondary structure prediction and database searches (e.g. MUMMALS, ProbCons and MAFFT), PROMALS is up to 30% more accurate, with improvement being most prominent for highly divergent homologs. Compared to SPEM and HHalign, which also employ database searches and secondary structure prediction, PROMALS shows an accuracy improvement of several percent. AVAILABILITY: The PROMALS web server is available at: http://prodata.swmed.edu/promals/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.     

3DCoffee: combining protein sequences and structures within multiple sequence alignments

Most bioinformatics analyses require the assembly of a multiple sequence alignment. It has long been suspected that structural information can help to improve the quality of these alignments, yet the effect of combining sequences and structures has not been evaluated systematically. We developed 3DCoffee, a novel method for combining protein sequences and structures in order to generate high-quality multiple sequence alignments. 3DCoffee is based on TCoffee version 2.00, and uses a mixture of pairwise sequence alignments and pairwise structure comparison methods to generate multiple sequence alignments. We benchmarked 3DCoffee using a subset of HOMSTRAD, the collection of reference structural alignments. We found that combining TCoffee with the threading program Fugue makes it possible to improve the accuracy of our HOMSTRAD dataset by four percentage points when using one structure only per dataset. Using two structures yields an improvement of ten percentage points. The measures carried out on HOM39, a HOMSTRAD subset composed of distantly related sequences, show a linear correlation between multiple sequence alignment accuracy and the ratio of number of provided structure to total number of sequences. Our results suggest that in the case of distantly related sequences, a single structure may not be enough for computing an accurate multiple sequence alignment.     

Core column prediction for protein multiple sequence alignments

Background

In a computed protein multiple sequence alignment, the coreness of a column is the fraction of its substitutions that are in so-called core columns of the gold-standard reference alignment of its proteins. In benchmark suites of protein reference alignments, the core columns of the reference alignment are those that can be confidently labeled as correct, usually due to all residues in the column being sufficiently close in the spatial superposition of the known three-dimensional structures of the proteins. Typically the accuracy of a protein multiple sequence alignment that has been computed for a benchmark is only measured with respect to the core columns of the reference alignment. When computing an alignment in practice, however, a reference alignment is not known, so the coreness of its columns can only be predicted.

Results

We develop for the first time a predictor of column coreness for protein multiple sequence alignments. This allows us to predict which columns of a computed alignment are core, and hence better estimate the alignment’s accuracy. Our approach to predicting coreness is similar to nearest-neighbor classification from machine learning, except we transform nearest-neighbor distances into a coreness prediction via a regression function, and we learn an appropriate distance function through a new optimization formulation that solves a large-scale linear programming problem. We apply our coreness predictor to parameter advising, the task of choosing parameter values for an aligner’s scoring function to obtain a more accurate alignment of a specific set of sequences. We show that for this task, our predictor strongly outperforms other column-confidence estimators from the literature, and affords a substantial boost in alignment accuracy.

     

VISTAL--a new 2D visualization tool of protein 3D structural alignments

We offer a tool, denoted VISTAL, for two-dimensional visualization of protein structural alignments. VISTAL describes aligned structures as a series of matched secondary structure elements, colored according to the three-dimensional distance of their Calpha atoms. AVAILABILITY: VISTAL can be downloaded from http://trantor.bioc.columbia.edu/~kolodny/software.html.     

Greene SCPrimer: a rapid comprehensive tool for designing degenerate primers from multiple sequence alignments

Polymerase chain reaction (PCR) is widely applied in clinical and environmental microbiology. Primer design is key to the development of successful assays and is often performed manually by using multiple nucleic acid alignments. Few public software tools exist that allow comprehensive design of degenerate primers for large groups of related targets based on complex multiple sequence alignments. Here we present a method for designing such primers based on tree building followed by application of a set covering algorithm, and demonstrate its utility in compiling Multiplex PCR primer panels for detection and differentiation of viral pathogens.     

Pfam: multiple sequence alignments and HMM-profiles of protein domains.

Pfam contains multiple alignments and hidden Markov model based profiles (HMM-profiles) of complete protein domains. The definition of domain boundaries, family members and alignment is done semi-automatically based on expert knowledge, sequence similarity, other protein family databases and the ability of HMM-profiles to correctly identify and align the members. Release 2.0 of Pfam contains 527 manually verified families which are available for browsing and on-line searching via the World Wide Web in the UK at http://www.sanger.ac.uk/Pfam/ and in the US at http://genome.wustl. edu/Pfam/ Pfam 2.0 matches one or more domains in 50% of Swissprot-34 sequences, and 25% of a large sample of predicted proteins from the Caenorhabditis elegans genome.     

COMPASS: a tool for comparison of multiple protein alignments with assessment of statistical significance

We present a novel method for the comparison of multiple protein alignments with assessment of statistical significance (COMPASS). The method derives numerical profiles from alignments, constructs optimal local profile-profile alignments and analytically estimates E-values for the detected similarities. The scoring system and E-value calculation are based on a generalization of the PSI-BLAST approach to profile-sequence comparison, which is adapted for the profile-profile case. Tested along with existing methods for profile-sequence (PSI-BLAST) and profile-profile (prof_sim) comparison, COMPASS shows increased abilities for sensitive and selective detection of remote sequence similarities, as well as improved quality of local alignments. The method allows prediction of relationships between protein families in the PFAM database beyond the range of conventional methods. Two predicted relations with high significance are similarities between various Rossmann-type folds and between various helix-turn-helix-containing families. The potential value of COMPASS for structure/function predictions is illustrated by the detection of an intricate homology between the DNA-binding domain of the CTF/NFI family and the MH1 domain of the Smad family.     

ParaAT: a parallel tool for constructing multiple protein-coding DNA alignments

Constructing multiple homologous alignments for protein-coding DNA sequences is crucial for a variety of bioinformatic analyses but remains computationally challenging. With the growing amount of sequence data available and the ongoing efforts largely dependent on protein-coding DNA alignments, there is an increasing demand for a tool that can process a large number of homologous groups and generate multiple protein-coding DNA alignments. Here we present a parallel tool - ParaAT that is capable of parallelly constructing multiple protein-coding DNA alignments for a large number of homologs. As testified on empirical datasets, ParaAT is well suited for large-scale data analysis in the high-throughput era, providing good scalability and exhibiting high parallel efficiency for computationally demanding tasks. ParaAT is freely available for academic use only at http://cbb.big.ac.cn/software.     

AlignMiner: a Web-based tool for detection of divergent regions in multiple sequence alignments of conserved sequences

Background  

Multiple sequence alignments are used to study gene or protein function, phylogenetic relations, genome evolution hypotheses and even gene polymorphisms. Virtually without exception, all available tools focus on conserved segments or residues. Small divergent regions, however, are biologically important for specific quantitative polymerase chain reaction, genotyping, molecular markers and preparation of specific antibodies, and yet have received little attention. As a consequence, they must be selected empirically by the researcher. AlignMiner has been developed to fill this gap in bioinformatic analyses.     

THoR: a tool for domain discovery and curation of multiple alignments

We describe a tool, THoR, that automatically creates and curates multiple sequence alignments representing protein domains. This exploits both PSI-BLAST and HMMER algorithms and provides an accurate and comprehensive alignment for any domain family. The entire process is designed for use via a web-browser, with simple links and cross-references to relevant information, to assist the assessment of biological significance. THoR has been benchmarked for accuracy using the SMART and pufferfish genome databases.     

PFAAT version 2.0: A tool for editing,annotating, and analyzing multiple sequence alignments

Background  

By virtue of their shared ancestry, homologous sequences are similar in their structure and function. Consequently, multiple sequence alignments are routinely used to identify trends that relate to function. This type of analysis is particularly productive when it is combined with structural and phylogenetic analysis.     

Multiple sequence alignments as tools for protein structure and function prediction

Multiple sequence alignments have much to offer to the understanding of protein structure, evolution and function. We are developing approaches to use this information in predicting protein-binding specificity, intra-protein and protein-protein interactions, and in reconstructing protein interaction networks.     

Motif3D: Relating protein sequence motifs to 3D structure

Motif3D is a web-based protein structure viewer designed to allow sequence motifs, and in particular those contained in the fingerprints of the PRINTS database, to be visualised on three-dimensional (3D) structures. Additional functionality is provided for the rhodopsin-like G protein-coupled receptors, enabling fingerprint motifs of any of the receptors in this family to be mapped onto the single structure available, that of bovine rhodopsin. Motif3D can be used via the web interface available at: http://www.bioinf.man.ac.uk/dbbrowser/motif3d/motif3d.html.     

COMBOSA3D: combining sequence alignments with three-dimensional structures

COMBOSA3D is a program that allows sequence conservation to be viewed in its proper three-dimensional environment. It superimposes sequence alignment information onto a protein structure using a customizable color scheme, which is also applied to a textual sequence alignment for reference. AVAILABILITY: The program can be tested at http://www.bioinformatics.org/combosa3d/, and the source code is freely available.     

AltAVisT: comparing alternative multiple sequence alignments

We introduce a WWW-based tool that is able to compare two alternative multiple alignments of a given sequence set. Regions where both alignments coincide are color-coded to visualize the local agreement between the two alignments and to identify those regions that can be considered to be reliably aligned. AVAILABILITY: http://bibiserv.techfak.uni-bielefeld.de/altavist/.     

Heads or tails: a simple reliability check for multiple sequence alignments

The question of multiple sequence alignment quality has received much attention from developers of alignment methods. Less forthcoming, however, are practical measures for addressing alignment quality issues in real life settings. Here, we present a simple methodology to help identify and quantify the uncertainties in multiple sequence alignments and their effects on subsequent analyses. The proposed methodology is based upon the a priori expectation that sequence alignment results should be independent of the orientation of the input sequences. Thus, for totally unambiguous cases, reversing residue order prior to alignment should yield an exact reversed alignment of that obtained by using the unreversed sequences. Such "ideal" alignments, however, are the exception in real life settings, and the two alignments, which we term the heads and tails alignments, are usually different to a greater or lesser degree. The degree of agreement or discrepancy between these two alignments may be used to assess the reliability of the sequence alignment. Furthermore, any alignment dependent sequence analysis protocol can be carried out separately for each of the two alignments, and the two sets of results may be compared with each other, providing us with valuable information regarding the robustness of the whole analytical process. The heads-or-tails (HoT) methodology can be easily implemented for any choice of alignment method and for any subsequent analytical protocol. We demonstrate the utility of HoT for phylogenetic reconstruction for the case of 130 sequences belonging to the chemoreceptor superfamily in Drosophila melanogaster, and by analysis of the BaliBASE alignment database. Surprisingly, Neighbor-Joining methods of phylogenetic reconstruction turned out to be less affected by alignment errors than maximum likelihood and Bayesian methods.     

ViTO: tool for refinement of protein sequence-structure alignments

SUMMARY: ViTO is a graphical application, including an editor, of multiple sequence alignment and a three-dimensional (3D) structure viewer. It is possible to manipulate alignments containing hundreds of sequences and to display a dozen structures. ViTO can handle so-called 'multiparts' alignments to allow the visualization of complex structures (multi-chain proteins and/or small molecules and DNA) and the editing of the corresponding alignment. The 3D viewer and the alignment editor are connected together allowing rapid refinement of sequence-structure alignment by taking advantage of the immediate visualization of resulting insertions/deletions and strict conservations in their structural context. More generally, it allows the mapping of informations about the sequence conservation extracted from the alignment onto the 3D structures in a dynamic way. ViTO is also connected to two comparative modelling programs, SCWRL and MODELLER. These features make ViTO a powerful tool to characterize protein families and to optimize the alignments for comparative modelling. AVAILABILITY: http://bioserv.cbs.cnrs.fr/VITO/DOC/. SUPPLEMENTARY INFORMATION: http://bioserv.cbs.cnrs.fr/VITO/DOC/index.html.     

Using the T-Coffee package to build multiple sequence alignments of protein, RNA, DNA sequences and 3D structures

T-Coffee (Tree-based consistency objective function for alignment evaluation) is a versatile multiple sequence alignment (MSA) method suitable for aligning most types of biological sequences. The main strength of T-Coffee is its ability to combine third party aligners and to integrate structural (or homology) information when building MSAs. The series of protocols presented here show how the package can be used to multiply align proteins, RNA and DNA sequences. The protein section shows how users can select the most suitable T-Coffee mode for their data set. Detailed protocols include T-Coffee, the default mode, M-Coffee, a meta version able to combine several third party aligners into one, PSI (position-specific iterated)-Coffee, the homology extended mode suitable for remote homologs and Expresso, the structure-based multiple aligner. We then also show how the T-RMSD (tree based on root mean square deviation) option can be used to produce a functionally informative structure-based clustering. RNA alignment procedures are described for using R-Coffee, a mode able to use predicted RNA secondary structures when aligning RNA sequences. DNA alignments are illustrated with Pro-Coffee, a multiple aligner specific of promoter regions. We also present some of the many reformatting utilities bundled with T-Coffee. The package is an open-source freeware available from http://www.tcoffee.org/.     

Genomic multiple sequence alignments: refinement using a genetic algorithm

Background  

Genomic sequence data cannot be fully appreciated in isolation. Comparative genomics – the practice of comparing genomic sequences from different species – plays an increasingly important role in understanding the genotypic differences between species that result in phenotypic differences as well as in revealing patterns of evolutionary relationships. One of the major challenges in comparative genomics is producing a high-quality alignment between two or more related genomic sequences. In recent years, a number of tools have been developed for aligning large genomic sequences. Most utilize heuristic strategies to identify a series of strong sequence similarities, which are then used as anchors to align the regions between the anchor points. The resulting alignment is globally correct, but in many cases is suboptimal locally. We describe a new program, GenAlignRefine, which improves the overall quality of global multiple alignments by using a genetic algorithm to improve local regions of alignment. Regions of low quality are identified, realigned using the program T-Coffee, and then refined using a genetic algorithm. Because a better COFFEE (Consistency based Objective Function For alignmEnt Evaluation) score generally reflects greater alignment quality, the algorithm searches for an alignment that yields a better COFFEE score. To improve the intrinsic slowness of the genetic algorithm, GenAlignRefine was implemented as a parallel, cluster-based program.