Scoring profile-to-profile sequence alignments

Sequence alignment profiles have been shown to be very powerful in creating accurate sequence alignments. Profiles are often used to search a sequence database with a local alignment algorithm. More accurate and longer alignments have been obtained with profile-to-profile comparison. There are several steps that must be performed in creating profile-profile alignments, and each involves choices in parameters and algorithms. These steps include (1) what sequences to include in a multiple alignment used to build each profile, (2) how to weight similar sequences in the multiple alignment and how to determine amino acid frequencies from the weighted alignment, (3) how to score a column from one profile aligned to a column of the other profile, (4) how to score gaps in the profile-profile alignment, and (5) how to include structural information. Large-scale benchmarks consisting of pairs of homologous proteins with structurally determined sequence alignments are necessary for evaluating the efficacy of each scoring scheme. With such a benchmark, we have investigated the properties of profile-profile alignments and found that (1) with optimized gap penalties, most column-column scoring functions behave similarly to one another in alignment accuracy; (2) some functions, however, have much higher search sensitivity and specificity; (3) position-specific weighting schemes in determining amino acid counts in columns of multiple sequence alignments are better than sequence-specific schemes; (4) removing positions in the profile with gaps in the query sequence results in better alignments; and (5) adding predicted and known secondary structure information improves alignments.     

MINRMS: an efficient algorithm for determining protein structure similarity using root-mean-squared-distance

MOTIVATION: Existing algorithms for automated protein structure alignment generate contradictory results and are difficult to interpret. An algorithm which can provide a context for interpreting the alignment and uses a simple method to characterize protein structure similarity is needed. RESULTS: We describe a heuristic for limiting the search space for structure alignment comparisons between two proteins, and an algorithm for finding minimal root-mean-squared-distance (RMSD) alignments as a function of the number of matching residue pairs within this limited search space. Our alignment algorithm uses coordinates of alpha-carbon atoms to represent each amino acid residue and requires a total computation time of O(m(3) n(2)), where m and n denote the lengths of the protein sequences. This makes our method fast enough for comparisons of moderate-size proteins (fewer than approximately 800 residues) on current workstation-class computers and therefore addresses the need for a systematic analysis of multiple plausible shape similarities between two proteins using a widely accepted comparison metric.     

PROMALS3D: a tool for multiple protein sequence and structure alignments

Although multiple sequence alignments (MSAs) are essential for a wide range of applications from structure modeling to prediction of functional sites, construction of accurate MSAs for distantly related proteins remains a largely unsolved problem. The rapidly increasing database of spatial structures is a valuable source to improve alignment quality. We explore the use of 3D structural information to guide sequence alignments constructed by our MSA program PROMALS. The resulting tool, PROMALS3D, automatically identifies homologs with known 3D structures for the input sequences, derives structural constraints through structure-based alignments and combines them with sequence constraints to construct consistency-based multiple sequence alignments. The output is a consensus alignment that brings together sequence and structural information about input proteins and their homologs. PROMALS3D can also align sequences of multiple input structures, with the output representing a multiple structure-based alignment refined in combination with sequence constraints. The advantage of PROMALS3D is that it gives researchers an easy way to produce high-quality alignments consistent with both sequences and structures of proteins. PROMALS3D outperforms a number of existing methods for constructing multiple sequence or structural alignments using both reference-dependent and reference-independent evaluation methods.     

The origin of genes could be polyphyletic

The paradigm of the monophyletic origin of genes is deeply rooted in us all. For instance, this stems from the observation that the possibility of obtaining a good multiple alignment using the same protein from organisms from the three domains of life (Bacteria, Archaea and Eukarya) would seem to imply that the last universal common ancestor (LUCA) must have had that protein and, therefore, the origin of that gene must necessarily be monophyletic. The hypothesis of a polyphyletic origin of genes has to explain how it was possible to evolve highly conserved regions of multiple alignments of orthologous proteins from the three domains of life when these regions clearly seem to define a monophyletic origin of genes. If mRNAs were assembled at the stage of the LUCA through the trans-splicing of pieces of RNA representing mini-genes, and the translation of these mRNAs resulted in proteins whose genes (DNA) actually only evolved much later, i.e. only after the main domains of life were established, then this would explain why multiple alignments of orthologous proteins can be obtained from the three domains of life. Therefore, this makes these multiple alignments compatible with a polyphyletic origin of genes. I have analysed many multiple alignments of orthologous proteins from the three domains of life, reaching a conclusion that seems to suggest that these alignments are also compatible with a polyphyletic origin of genes because, for instance, they contain protein motifs characterising the domains of life. These motifs, and also genes, might have evolved late on, thus making their polyphyletic origin likely.     

DNA reference alignment benchmarks based on tertiary structure of encoded proteins

MOTIVATION: Multiple sequence alignments (MSAs) are at the heart of bioinformatics analysis. Recently, a number of multiple protein sequence alignment benchmarks (i.e. BAliBASE, OXBench, PREFAB and SMART) have been released to evaluate new and existing MSA applications. These databases have been well received by researchers and help to quantitatively evaluate MSA programs on protein sequences. Unfortunately, analogous DNA benchmarks are not available, making evaluation of MSA programs difficult for DNA sequences. RESULTS: This work presents the first known multiple DNA sequence alignment benchmarks that are (1) comprised of protein-coding portions of DNA (2) based on biological features such as the tertiary structure of encoded proteins. These reference DNA databases contain a total of 3545 alignments, comprising of 68 581 sequences. Two versions of the database are available: mdsa_100s and mdsa_all. The mdsa_100s version contains the alignments of the data sets that TBLASTN found 100% sequence identity for each sequence. The mdsa_all version includes all hits with an E-value score above the threshold of 0.001. A primary use of these databases is to benchmark the performance of MSA applications on DNA data sets. The first such case study is included in the Supplementary Material.     

Assessing the Discordance of Multiple Sequence Alignments

Multiple sequence alignments have wide applicability in many areas of computational biology, including comparative genomics, functional annotation of proteins, gene finding, and modeling evolutionary processes. Because of the computational difficulty of multiple sequence alignment and the availability of numerous tools, it is critical to be able to assess the reliability of multiple alignments. We present a tool called StatSigMA to assess whether multiple alignments of nucleotide or amino acid sequences are contaminated with one or more unrelated sequences. There are numerous applications for which StatSigMA can be used. Two such applications are to distinguish homologous sequences from nonhomologous ones and to compare alignments produced by various multiple alignment tools. We present examples of both types of applications.     

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.     

PRALINETM: a strategy for improved multiple alignment of transmembrane proteins

MOTIVATION: Membrane-bound proteins are a special class of proteins. The regions that insert into the cell-membrane have a profoundly different hydrophobicity pattern compared with soluble proteins. Multiple alignment techniques use scoring schemes tailored for sequences of soluble proteins and are therefore in principle not optimal to align membrane-bound proteins. RESULTS: Transmembrane (TM) regions in protein sequences can be reliably recognized using state-of-the-art sequence prediction techniques. Furthermore, membrane-specific scoring matrices are available. We have developed a new alignment method, called PRALINETM, which integrates these two features to enhance multiple sequence alignment. We tested our algorithm on the TM alignment benchmark set by Bahr et al. (2001), and showed that the quality of TM alignments can be significantly improved compared with the quality produced by a standard multiple alignment technique. The results clearly indicate that the incorporation of these new elements into current state-of-the-art alignment methods is crucial for optimizing the alignment of TM proteins. AVAILABILITY: A webserver is available at http://www.ibi.vu.nl/programs/pralinewww.     

Widespread protein sequence similarities: origins of Escherichia coli genes.

To learn more about the evolutionary origins of Escherichia coli genes, we surveyed systematically for extended sequence similarities among the 1,264 amino acid sequences encoded by chromosomal genes of E. coli K-12 in SwissProt release 26 by using the FASTA program and imposing the following criteria: (i) alignment of segments at least 100 amino acids long and (ii) at least 20% amino acid identity. Altogether, 624 extended alignments meeting the two criteria were identified, corresponding to 577 protein sequences (45.6% of the 1,264 E. coli protein sequences) that had an extended alignment with at least one other E. coli protein sequence. To exclude alignments of questionable biological significance, we imposed a high threshold on the number of gaps allowed in each of the 624 extended alignments, giving us a subset of 464 proteins. The population of 464 alignments has the following characteristics expressed as median values of the group: 254 amino acids in the alignment, representing 86% of the length of the protein, 33% of the amino acids in the alignment being identical, and 1.1 gaps introduced per 100 amino acids of alignment. Where functions are known, nearly all pairs consist of functionally related proteins. This implies that the sequence similarity we detected has biological meaning and did not arise by chance. That a major fraction of E. coli proteins form extended alignments strongly suggests the predominance of duplication and divergence of ancestral genes in the evolution of E. coli genes. The range of degrees of similarity shows that some genes originated more recently than others. There is no evidence of genome doubling in the past, since map distances between genes of sequence-related proteins show no coherent pattern of favored separations.     

Multiple alignment of protein sequences with repeats and rearrangements

Multiple sequence alignments are the usual starting point for analyses of protein structure and evolution. For proteins with repeated, shuffled and missing domains, however, traditional multiple sequence alignment algorithms fail to provide an accurate view of homology between related proteins, because they either assume that the input sequences are globally alignable or require locally alignable regions to appear in the same order in all sequences. In this paper, we present ProDA, a novel system for automated detection and alignment of homologous regions in collections of proteins with arbitrary domain architectures. Given an input set of unaligned sequences, ProDA identifies all homologous regions appearing in one or more sequences, and returns a collection of local multiple alignments for these regions. On a subset of the BAliBASE benchmarking suite containing curated alignments of proteins with complicated domain architectures, ProDA performs well in detecting conserved domain boundaries and clustering domain segments, achieving the highest accuracy to date for this task. We conclude that ProDA is a practical tool for automated alignment of protein sequences with repeats and rearrangements in their domain architecture.     

Pandit: a database of protein and associated nucleotide domains with inferred trees

MOTIVATION: A large, high-quality database of homologous sequence alignments with good estimates of their corresponding phylogenetic trees will be a valuable resource to those studying phylogenetics. It will allow researchers to compare current and new models of sequence evolution across a large variety of sequences. The large quantity of data may provide inspiration for new models and methodology to study sequence evolution and may allow general statements about the relative effect of different molecular processes on evolution. RESULTS: The Pandit 7.6 database contains 4341 families of sequences derived from the seed alignments of the Pfam database of amino acid alignments of families of homologous protein domains (Bateman et al., 2002). Each family in Pandit includes an alignment of amino acid sequences that matches the corresponding Pfam family seed alignment, an alignment of DNA sequences that contain the coding sequence of the Pfam alignment when they can be recovered (overall, 82.9% of sequences taken from Pfam) and the alignment of amino acid sequences restricted to only those sequences for which a DNA sequence could be recovered. Each of the alignments has an estimate of the phylogenetic tree associated with it. The tree topologies were obtained using the neighbor joining method based on maximum likelihood estimates of the evolutionary distances, with branch lengths then calculated using a standard maximum likelihood approach.     

Analysis on sliding helices and strands in protein structural comparisons: a case study with protein kinases

Protein structural alignments are generally considered as 'golden standard' for the alignment at the level of amino acid residues. In this study we have compared the quality of pairwise and multiple structural alignments of about 5900 homologous proteins from 718 families of known 3-D structures. We observe shifts in the alignment of regular secondary structural elements (helices and strands) between pairwise and multiple structural alignments. The differences between pairwise and multiple structural alignments within helical and beta-strand regions often correspond to 4 and 2 residue positions respectively. Such shifts correspond approximately to "one turn" of these regular secondary structures. We have performed manual analysis explicitly on the family of protein kinases. We note shifts of one or two turns in helix-helix alignments obtained using pairwise and multiple structural alignments. Investigations on the quality of the equivalent helix-helix, strand-strand pairs in terms of their residue side-chain accessibilities have been made. Our results indicate that the quality of the pairwise alignments is comparable to that of the multiple structural alignments and, in fact, is often better. We propose that pairwise alignment of protein structures should also be used in formulation of methods for structure prediction and evolutionary analysis.     

Multiple sequence alignments of partially coding nucleic acid sequences

Background  

High quality sequence alignments of RNA and DNA sequences are an important prerequisite for the comparative analysis of genomic sequence data. Nucleic acid sequences, however, exhibit a much larger sequence heterogeneity compared to their encoded protein sequences due to the redundancy of the genetic code. It is desirable, therefore, to make use of the amino acid sequence when aligning coding nucleic acid sequences. In many cases, however, only a part of the sequence of interest is translated. On the other hand, overlapping reading frames may encode multiple alternative proteins, possibly with intermittent non-coding parts. Examples are, in particular, RNA virus genomes.     

Probabilistic divergence measures for detecting interspecies recombination

This paper proposes a graphical method for detecting interspecies recombination in multiple alignments of DNA sequences. A fixed-size window is moved along a given DNA sequence alignment. For every position, the marginal posterior probability over tree topologies is determined by means of a Markov chain Monte Carlo simulation. Two probabilistic divergence measures are plotted along the alignment, and are used to identify recombinant regions. The method is compared with established detection methods on a set of synthetic benchmark sequences and two real-world DNA sequence alignments.     

GeneSilico protein structure prediction meta-server

Rigorous assessments of protein structure prediction have demonstrated that fold recognition methods can identify remote similarities between proteins when standard sequence search methods fail. It has been shown that the accuracy of predictions is improved when refined multiple sequence alignments are used instead of single sequences and if different methods are combined to generate a consensus model. There are several meta-servers available that integrate protein structure predictions performed by various methods, but they do not allow for submission of user-defined multiple sequence alignments and they seldom offer confidentiality of the results. We developed a novel WWW gateway for protein structure prediction, which combines the useful features of other meta-servers available, but with much greater flexibility of the input. The user may submit an amino acid sequence or a multiple sequence alignment to a set of methods for primary, secondary and tertiary structure prediction. Fold-recognition results (target-template alignments) are converted into full-atom 3D models and the quality of these models is uniformly assessed. A consensus between different FR methods is also inferred. The results are conveniently presented on-line on a single web page over a secure, password-protected connection. The GeneSilico protein structure prediction meta-server is freely available for academic users at http://genesilico.pl/meta.     

A strategy for the rapid multiple alignment of protein sequences. Confidence levels from tertiary structure comparisons

An algorithm is presented for the multiple alignment of protein sequences that is both accurate and rapid computationally. The approach is based on the conventional dynamic-programming method of pairwise alignment. Initially, two sequences are aligned, then the third sequence is aligned against the alignment of both sequences one and two. Similarly, the fourth sequence is aligned against one, two and three. This is repeated until all sequences have been aligned. Iteration is then performed to yield a final alignment. The accuracy of sequence alignment is evaluated from alignment of the secondary structures in a family of proteins. For the globins, the multiple alignment was on average 99% accurate compared to 90% for pairwise comparison of sequences. For the alignment of immunoglobulin constant and variable domains, the use of many sequences yielded an alignment of 63% average accuracy compared to 41% average for individual variable/constant alignments. The multiple alignment algorithm yields an assignment of disulphide connectivity in mammalian serotransferrin that is consistent with crystallographic data, whereas pairwise alignments give an alternative assignment.     

Information about the protein secondary structure improves quality of an alignment of protein sequences

All popular algorithms of pair-wise alignment of protein primary structures (e.g. Smith-Waterman (SW), FASTA, BLAST, et al.) utilize only amino acid sequences. The SW-algorithm is the most accurate among them, i.e. it produces alignments that are most similar to the alignments obtained by superposition of protein 3D-structures. But even the SW-algorithm is unable to restore the 3D-based alignment if similarity of amino acid sequences (%id) is below 30%. We have proposed a novel alignment method that explicitly takes into account the secondary structure of the compared proteins. We have shown that it creates significantly more accurate alignments compared to SW-algorithm. In particular, for sequences with %id < 30% the average accuracy of the new method is 58% compared to 35% for SW-algorithm (the accuracy of an algorithmic sequence alignment is the part of restored position of a "golden standard" alignment obtained by superposition of corresponding 3D-structures). The accuracy of the proposed method is approximately identical both for experimental, and for theoretically predicted secondary structures. Thus the method can be applied for alignment of protein sequences even if protein 3D-structure is unknown. The program is available at ftp://194.149.64.196/STRUSWER/.