Gap Penalties Research Articles

Alignment of Helical Membrane Protein Sequences Using AlignMe

Few sequence alignment methods have been designed specifically for integral membrane proteins, even though these important proteins have distinct evolutionary and structural properties that might affect their alignments. Existing approaches typically consider membrane-related information either by using membrane-specific substitution matrices or by assigning distinct penalties for gap creation in transmembrane and non-transmembrane regions. Here, we ask whether favoring matching of predicted transmembrane segments within a standard dynamic programming algorithm can improve the accuracy of pairwise membrane protein sequence alignments. We tested various strategies using a specifically designed program called AlignMe. An updated set of homologous membrane protein structures, called HOMEP2, was used as a reference for optimizing the gap penalties. The best of the membrane-protein optimized approaches were then tested on an independent reference set of membrane protein sequence alignments from the BAliBASE collection. When secondary structure (S) matching was combined with evolutionary information (using a position-specific substitution matrix (P)), in an approach we called AlignMePS, the resultant pairwise alignments were typically among the most accurate over a broad range of sequence similarities when compared to available methods. Matching transmembrane predictions (T), in addition to evolutionary information, and secondary-structure predictions, in an approach called AlignMePST, generally reduces the accuracy of the alignments of closely-related proteins in the BAliBASE set relative to AlignMePS, but may be useful in cases of extremely distantly related proteins for which sequence information is less informative. The open source AlignMe code is available at https://sourceforge.net/projects/alignme/, and at http://www.forrestlab.org, along with an online server and the HOMEP2 data set.

Empirical assessment of sequencing errors for high throughput pyrosequencing data

BackgroundSequencing-by-synthesis technologies significantly improve over the Sanger method in terms of speed and cost per base. However, they still usually fail to compete in terms of read length and quality. Current high-throughput implementations of the pyrosequencing technique yield reads whose length approach those of the capillary electrophoresis method. A less obvious question is whether their quality is affected by platform-specific sequencing errors.ResultsWe present an empirical study aimed at assessing the quality and characterising sequencing errors for high throughput pyrosequencing data. We have developed a procedure for extracting sequencing error data from genome assemblies and study their characteristics, in particular the length distribution of indel gaps and their relation to the sequence contexts where they occur. We used this procedure to analyse data from three prokaryotic genomes sequenced with the GS FLX technology. We also compared two models previously employed with success for peptide sequence alignment.ConclusionsWe observed an overall very low error rate in the analysed data, with indel errors being much more abundant than substitutions. We also observed a dependence between the length of the gaps and that of the homopolymer context where they occur. As with protein alignments, a power-law model seems to approximate the indel errors more accurately, although the results are not so conclusive as to justify a depart from the commonly used affine gap penalty scheme. In whichever case, however, our procedure can be used to estimate more realistic error model parameters.

N-W Algorithm and ANFIS Modeling on Alignment Similarity of Triplex Capsid Protein of Human Herpes Simplex Virus

Optimal sequence similarity of triplex capsid proteins of human herpes simplex virus (HHV) is a complex bioinformatics problem, which is controlled by alignment algorithms, substitution matrix, gap penalty and gap extension. A precise choice of mutation matrix is required to optimal the alignment similarity and appropriate computational approach required for similarity search. The present paper uses Adaptive Neuro-Fuzzy Inference System (ANFIS) approach to model and simulate the alignment similarity for PAM and Blosum substitution matrices. Mutation matrix and sequences of HHV-I and HHV-II were taken as model’s input parameters. The model is the combination of fuzzy inference, artificial neural network, and set of fuzzy rules has been developed directly from computational analysis using N-W algorithm. The proposed modeling approach is verified by comparing the expected results with the observed practical results obtained by computational analysis under specific conditions. The application of ANFIS test shows that the substitution matrix predicted by a proposed model is fully in agreement with the experimental values at 0.5% level of significance.

Evolution of protein indels in plants, animals and fungi

BackgroundInsertions/deletions (indels) in protein sequences are useful as drug targets, protein structure predictors, species diagnostics and evolutionary markers. However there is limited understanding of indel evolutionary patterns. We sought to characterize indel patterns focusing first on the major groups of multicellular eukaryotes.ResultsComparisons of complete proteomes from a taxonically broad set of primarily Metazoa, Fungi and Viridiplantae yielded 299 substantial (>250aa) universal, single-copy (in-paralog only) proteins, from which 901 simple (present/absent) and 3,806 complex (multistate) indels were extracted. Simple indels are mostly small (1-7aa) with a most frequent size class of 1aa. However, even these simple looking indels show a surprisingly high level of hidden homoplasy (multiple independent origins). Among the apparently homoplasy-free simple indels, we identify 69 potential clade-defining indels (CDIs) that may warrant closer examination. CDIs show a very uneven taxonomic distribution among Viridiplante (13 CDIs), Fungi (40 CDIs), and Metazoa (0 CDIs). An examination of singleton indels shows an excess of insertions over deletions in nearly all examined taxa. This excess averages 2.31 overall, with a maximum observed value of 7.5 fold.ConclusionsWe find considerable potential for identifying taxon-marker indels using an automated pipeline. However, it appears that simple indels in universal proteins are too rare and homoplasy-rich to be used for pure indel-based phylogeny. The excess of insertions over deletions seen in nearly every genome and major group examined maybe useful in defining more realistic gap penalties for sequence alignment. This bias also suggests that insertions in highly conserved proteins experience less purifying selection than do deletions.

SPIC: A novel similarity metric for comparing transcription factor binding site motifs based on information contents

BackgroundDiscovering transcription factor binding sites (TFBS) is one of primary challenges to decipher complex gene regulatory networks encrypted in a genome. A set of short DNA sequences identified by a transcription factor (TF) is known as a motif, which can be expressed accurately in matrix form such as a position-specific scoring matrix (PSSM) and a position frequency matrix. Very frequently, we need to query a motif in a database of motifs by seeking its similar motifs, merge similar TFBS motifs possibly identified by the same TF, separate irrelevant motifs, or filter out spurious motifs. Therefore, a novel metric is required to seize slight differences between irrelevant motifs and highlight the similarity between motifs of the same group in all these applications. While there are already several metrics for motif similarity proposed before, their performance is still far from satisfactory for these applications.MethodsA novel metric has been proposed in this paper with name as SPIC (Similarity with Position Information Contents) for measuring the similarity between a column of a motif and a column of another motif. When defining this similarity score, we consider the likelihood that the column of the first motif's PFM can be produced by the column of the second motif's PSSM, and multiply the likelihood by the information content of the column of the second motif's PSSM, and vise versa. We evaluated the performance of SPIC combined with a local or a global alignment method having a function for affine gap penalty, for computing the similarity between two motifs. We also compared SPIC with seven existing state-of-the-arts metrics for their capability of clustering motifs from the same group and retrieving motifs from a database on three datasets.ResultsWhen used jointly with the Smith-Waterman local alignment method with an affine gap penalty function (gap open penalty is equal to1, gap extension penalty is equal to 0.5), SPIC outperforms the seven existing state-of-the-art motif similarity metrics combined with their best alignments for matching motifs in database searches, and clustering the same TF's sub-motifs or distinguishing relevant ones from a miscellaneous group of motifs.ConclusionsWe have developed a novel motif similarity metric that can more accurately match motifs in database searches, and more effectively cluster similar motifs and differentiate irrelevant motifs than do the other seven metrics we are aware of.

A Topology Structure Based Outer Membrane Proteins Segment Alignment Method

Outer membrane proteins (OMPs) are transmembrane proteins (TMPs) located in outer membranes. These proteins perform diverse biochemical functions and have immediate medical relevance, so that their spatial structures are important for studying. But the special physicochemical properties of OMP make it hard to obtain their structures experimentally. For the purpose of predicting OMP structures, discriminating OMPs and aligning their sequences to native structures are indispensable steps. We developed a novel method OMSA (Outer Membrane Segment Alignment), which implemented both steps in one program. OMSA integrates OMP-specific topology features to implement a sequence-to-structure alignment, for example, segment type and segment orientation, while a segment-dependent gap penalty model is employed to improve the alignment. Compared to peer top-leading methods, OMSA achieved higher accuracy in both OMP discrimination and alignment, which may further improve OMP structure studying.

Fast protein structure alignment using Gaussian overlap scoring of backbone peptide fragment similarity

Aligning and comparing protein structures is important for understanding their evolutionary and functional relationships. With the rapid growth of protein structure databases in recent years, the need to align, superpose and compare protein structures rapidly and accurately has never been greater. Many structural alignment algorithms have been described in the past 20 years. However, achieving an algorithm that is both accurate and fast remains a considerable challenge. We have developed a novel protein structure alignment algorithm called 'Kpax', which exploits the highly predictable covalent geometry of C(α) atoms to define multiple local coordinate frames in which backbone peptide fragments may be oriented and compared using sensitive Gaussian overlap scoring functions. A global alignment and hence a structural superposition may then be found rapidly using dynamic programming with secondary structure-specific gap penalties. When superposing pairs of structures, Kpax tends to give tighter secondary structure overlays than several popular structure alignment algorithms. When searching the CATH database, Kpax is faster and more accurate than the very efficient Yakusa algorithm, and it gives almost the same high level of fold recognition as TM-Align while being more than 100 times faster.

A global optimization method for continuous network design problems

The continuous network design problem (CNDP) is generally formulated as a mathematical program with equilibrium constraints (MPEC). It aims to optimize the network performance via expansion of existing links subject to the Wardrop user equilibrium constraint. As one of the extremely challenging problems in the transportation research field, various solution methods have been proposed for solving the CNDP. However, most of the algorithms developed up to date can only find a local optimum due to inherent nonconvexity of the MPEC. This paper proposes a viable global optimization method for the CNDP. Based on the concepts of gap function and penalty, the CNDP is transferred into a sequence of single level concave programs, which is amenable to a global solution. It is proved that any accumulation of the solutions to the sequence of concave programs is a globally optimal solution to the original CNDP. Owing to their special structure, all concave programs can be solved by a multicutting plane method. The penalty term in each step of the inner subproblem can be calculated by simply executing an all-or-nothing assignment.

Specific Residues of a Conserved Domain in the N Terminus of the Human Cytomegalovirus pUL50 Protein Determine Its Intranuclear Interaction with pUL53

Herpesviral capsids are assembled in the host cell nucleus and are subsequently translocated to the cytoplasm. During this process it has been demonstrated that the human cytomegalovirus proteins pUL50 and pUL53 interact and form, together with other viral and cellular proteins, the nuclear egress complex at the nuclear envelope. In this study we provide evidence that specific residues of a conserved N-terminal region of pUL50 determine its intranuclear interaction with pUL53. In silico evaluation and biophysical analyses suggested that the conserved region forms a regular secondary structure adopting a globular fold. Importantly, site-directed replacement of individual amino acids by alanine indicated a strong functional influence of specific residues inside this globular domain. In particular, mutation of the widely conserved residues Glu-56 or Tyr-57 led to a loss of interaction with pUL53. Consistent with the loss of binding properties, mutants E56A and Y57A showed a defective function in the recruitment of pUL53 to the nuclear envelope in expression plasmid-transfected and human cytomegalovirus-infected cells. In addition, in silico analysis suggested that residues 3-20 form an amphipathic α-helix that appears to be conserved among Herpesviridae. Point mutants revealed a structural role of this N-terminal α-helix for pUL50 stability rather than a direct role in the binding of pUL53. In contrast, the central part of the globular domain including Glu-56 and Tyr-57 is directly responsible for the functional interaction with pUL53 and thus determines formation of the basic nuclear egress complex.

Treelength optimization for phylogeny estimation.

The standard approach to phylogeny estimation uses two phases, in which the first phase produces an alignment on a set of homologous sequences, and the second phase estimates a tree on the multiple sequence alignment. POY, a method which seeks a tree/alignment pair minimizing the total treelength, is the most widely used alternative to this two-phase approach. The topological accuracy of trees computed under treelength optimization is, however, controversial. In particular, one study showed that treelength optimization using simple gap penalties produced poor trees and alignments, and suggested the possibility that if POY were used with an affine gap penalty, it might be able to be competitive with the best two-phase methods. In this paper we report on a study addressing this possibility. We present a new heuristic for treelength, called BeeTLe (Better Treelength), that is guaranteed to produce trees at least as short as POY. We then use this heuristic to analyze a large number of simulated and biological datasets, and compare the resultant trees and alignments to those produced using POY and also maximum likelihood (ML) and maximum parsimony (MP) trees computed on a number of alignments. In general, we find that trees produced by BeeTLe are shorter and more topologically accurate than POY trees, but that neither POY nor BeeTLe produces trees as topologically accurate as ML trees produced on standard alignments. These findings, taken as a whole, suggest that treelength optimization is not as good an approach to phylogenetic tree estimation as maximum likelihood based upon good alignment methods.

Nrf2b, Novel Zebrafish Paralog of Oxidant-responsive Transcription Factor NF-E2-related Factor 2 (NRF2)

NF-E2-related factor 2 (NRF2; also called NFE2L2) and related NRF family members regulate antioxidant defenses by activating gene expression via antioxidant response elements (AREs), but their roles in embryonic development are not well understood. We report here that zebrafish (Danio rerio), an important developmental model species, possesses six nrf genes, including duplicated nrf1 and nrf2 genes. We cloned a novel zebrafish nrf2 paralog, nrf2b. The predicted Nrf2b protein sequence shares several domains with the original Nrf2 (now Nrf2a) but lacks the Neh4 transactivation domain. Zebrafish-human comparisons demonstrate conserved synteny involving nrf2 and hox genes, indicating that nrf2a and nrf2b are co-orthologs of human NRF2. nrf2a and nrf2b displayed distinct patterns of expression during embryonic development; nrf2b was more highly expressed at all stages. Embryos in which Nrf2a expression had been knocked down with morpholino oligonucleotides were more sensitive to tert-butylhydroperoxide but not tert-butylhydroquinone, whereas knockdown of Nrf2b did not affect sensitivity of embryos to either chemical. Gene expression profiling by microarray identified a specific role for Nrf2b as a negative regulator of several genes, including p53, cyclin G1, and heme oxygenase 1, in embryos. Nrf2a and Nrf2b exhibited different mechanisms of cross-talk with the Ahr2 signaling pathway. Together, these results demonstrate distinct roles for nrf2a and nrf2b, consistent with subfunction partitioning, and identify a novel negative regulatory role for Nrf2b during development. The identification of zebrafish nrf2 co-orthologs will facilitate new understanding of the multiple roles of NRF2 in protecting vertebrate embryos from oxidative damage.

A Novel Center Star Multiple Sequence Alignment Algorithm Based on Affine Gap Penalty and K-Band

Abstract Multiple sequence alignment is one of the most important topics in computational biology, but it cannot deal with the large data so far. As the development of copy-number variant(CNV) and Single Nucleotide Polymorphisms(SNP) research, many researchers want to align numbers of similar sequences for detecting CNV and SNP. In this paper, we propose a novel multiple sequence alignment algorithm based on affine gap penalty and k-band. It can align more quickly and accurately, that will be helpful for mining CNV and SNP. Experiments prove the performance of our algorithm.

Protein sequence alignment with family-specific amino acid similarity matrices

BackgroundAlignment of amino acid sequences by means of dynamic programming is a cornerstone sequence comparison method. The quality of alignments produced by dynamic programming critically depends on the choice of the alignment scoring function. Therefore, for a specific alignment problem one needs a way of selecting the best performing scoring function. This work is focused on the issue of finding optimized protein family- and fold-specific scoring functions for global similarity matrix-based sequence alignment.FindingsI utilize a comprehensive set of reference alignments obtained from structural superposition of homologous and analogous proteins to design a quantitative statistical framework for evaluating the performance of alignment scoring functions in global pairwise sequence alignment. This framework is applied to study how existing general-purpose amino acid similarity matrices perform on individual protein families and structural folds, and to compare them to family-specific and fold-specific matrices derived in this work. I describe an adaptive alignment procedure that automatically selects an appropriate similarity matrix and optimized gap penalties based on the properties of the sequences being aligned.ConclusionsThe results of this work indicate that using family-specific similarity matrices significantly improves the quality of the alignment of homologous sequences over the traditional sequence alignment based on a single general-purpose similarity matrix. However, using fold-specific similarity matrices can only marginally improve sequence alignment of proteins that share the same structural fold but do not share a common evolutionary origin. The family-specific matrices derived in this work and the optimized gap penalties are available at http://taurus.crc.albany.edu/fsm.

Comparison of linear gap penalties and profile-based variable gap penalties in profile–profile alignments

Profile–profile alignment algorithms have proven powerful for recognizing remote homologs and generating alignments by effectively integrating sequence evolutionary information into scoring functions. In comparison to scoring function, the development of gap penalty functions has rarely been addressed in profile–profile alignment algorithms. Although indel frequency profiles have been used to construct profile-based variable gap penalties in some profile–profile alignment algorithms, there is still no fair comparison between variable gap penalties and traditional linear gap penalties to quantify the improvement of alignment accuracy. We compared two linear gap penalty functions, the traditional affine gap penalty (AGP) and the bilinear gap penalty (BGP), with two profile-based variable gap penalty functions, the Profile-based Gap Penalty used in SP5 (SPGP) and a new Weighted Profile-based Gap Penalty (WPGP) developed by us, on some well-established benchmark datasets. Our results show that profile-based variable gap penalties get limited improvements than linear gap penalties, whether incorporated with secondary structure information or not. Secondary structure information appears less powerful to be incorporated into gap penalties than into scoring functions. Analysis of gap length distributions indicates that gap penalties could stably maintain corresponding distributions of gap lengths in their alignments, but the distribution difference from reference alignments does not reflect the performance of gap penalties. There is useful information in indel frequency profiles, but it is still not good enough for improving alignment accuracy when used in profile-based variable gap penalties. All of the methods tested in this work are freely accessible at http://protein.cau.edu.cn/gppat/.

Protein alignment algorithms with an efficient backtracking routine on multiple GPUs

BackgroundPairwise sequence alignment methods are widely used in biological research. The increasing number of sequences is perceived as one of the upcoming challenges for sequence alignment methods in the nearest future. To overcome this challenge several GPU (Graphics Processing Unit) computing approaches have been proposed lately. These solutions show a great potential of a GPU platform but in most cases address the problem of sequence database scanning and computing only the alignment score whereas the alignment itself is omitted. Thus, the need arose to implement the global and semiglobal Needleman-Wunsch, and Smith-Waterman algorithms with a backtracking procedure which is needed to construct the alignment.ResultsIn this paper we present the solution that performs the alignment of every given sequence pair, which is a required step for progressive multiple sequence alignment methods, as well as for DNA recognition at the DNA assembly stage. Performed tests show that the implementation, with performance up to 6.3 GCUPS on a single GPU for affine gap penalties, is very efficient in comparison to other CPU and GPU-based solutions. Moreover, multiple GPUs support with load balancing makes the application very scalable.ConclusionsThe article shows that the backtracking procedure of the sequence alignment algorithms may be designed to fit in with the GPU architecture. Therefore, our algorithm, apart from scores, is able to compute pairwise alignments. This opens a wide range of new possibilities, allowing other methods from the area of molecular biology to take advantage of the new computational architecture. Performed tests show that the efficiency of the implementation is excellent. Moreover, the speed of our GPU-based algorithms can be almost linearly increased when using more than one graphics card.

Local structural alignment of RNA with affine gap model

BackgroundPredicting new non-coding RNAs (ncRNAs) of a family can be done by aligning the potential candidate with a member of the family with known sequence and secondary structure. Existing tools either only consider the sequence similarity or cannot handle local alignment with gaps.ResultsIn this paper, we consider the problem of finding the optimal local structural alignment between a query RNA sequence (with known secondary structure) and a target sequence (with unknown secondary structure) with the affine gap penalty model. We provide the algorithm to solve the problem.ConclusionsBased on an experiment, we show that there are ncRNA families in which considering local structural alignment with gap penalty model can identify real hits more effectively than using global alignment or local alignment without gap penalty model.

KalignP: Improved multiple sequence alignments using position specific gap penalties in Kalign2

Summary: Kalign2 is one of the fastest and most accurate methods for multiple alignments. However, in contrast to other methods Kalign2 does not allow externally supplied position specific gap penalties. Here, we present a modification to Kalign2, KalignP, so that it accepts such penalties. Further, we show that KalignP using position specific gap penalties obtained from predicted secondary structures makes steady improvement over Kalign2 when tested on Balibase 3.0 as well as on a dataset derived from Pfam-A seed alignments.Availability and Implementation: KalignP is freely available at http://kalignp.cbr.su.se. The source code of KalignP is available under the GNU General Public License, Version 2 or later from the same website.Contact: arne@bioinfo.seSupplementary information: Supplementary data are available at Bioinformatics online.

Revisiting gap locations in amino acid sequence alignments and a proposal for a method to improve them by introducing solvent accessibility

In comparative modeling, the quality of amino acid sequence alignment still constitutes a major bottleneck in the generation of high quality models of protein three-dimensional (3D) structures. Substantial efforts have been made to improve alignment quality by revising the substitution matrix, introducing multiple sequences, replacing dynamic programming with hidden Markov models, and incorporating 3D structure information. Improvements in the gap penalty have not been a major focus, however, following the development of the affine gap penalty and of the secondary structure dependent gap penalty. We revisited the correlation between protein 3D structure and gap location in a large protein 3D structure data set, and found that the frequency of gap locations approximated to an exponential function of the solvent accessibility of the inserted residues. The nonlinearity of the gap frequency as a function of accessibility corresponded well to the relationship between residue mutation pattern and residue accessibility. By introducing this relationship into the gap penalty calculation for pairwise alignment between template and target amino acid sequences, we were able to obtain a sequence alignment much closer to the structural alignment. The quality of the alignments was substantially improved on a pair of sequences with identity in the “twilight zone” between 20 and 40%. The relocation of gaps by our new method made a significant improvement in comparative modeling, exemplified here by the Bacillus subtilis yitF protein. The method was implemented in a computer program, ALAdeGAP (ALignment with Accessibility dependent GAp Penalty), which is available at http://cib.cf.ocha.ac.jp/target_protein/. Proteins 2011; © 2011 Wiley-Liss, Inc.

Uncovering the Human Methyltransferasome

We present a comprehensive analysis of the human methyltransferasome. Primary sequences, predicted secondary structures, and solved crystal structures of known methyltransferases were analyzed by hidden Markov models, Fisher-based statistical matrices, and fold recognition prediction-based threading algorithms to create a model, or profile, of each methyltransferase superfamily. These profiles were used to scan the human proteome database and detect novel methyltransferases. 208 proteins in the human genome are now identified as known or putative methyltransferases, including 38 proteins that were not annotated previously. To date, 30% of these proteins have been linked to disease states. Possible substrates of methylation for all of the SET domain and SPOUT methyltransferases as well as 100 of the 131 seven-β-strand methyltransferases were surmised from sequence similarity clusters based on alignments of the substrate-specific domains.

A performance enhanced PSI-BLAST based on hybrid alignment

Sequence alignment is one of the most popular tools of modern biology. NCBI's PSI-BLAST utilizes iterative model building in order to better detect distant homologs with greater sensitivity than non-iterative BLAST. However, PSI-BLAST's performance is limited by the fact that it relies on deterministic alignments. Using a semi-probabilistic alignment scheme such as Hybrid alignment should allow for better informed model building and improved identification of homologous sequences, particularly remote homologs. We have built a new version of the tool in which the Smith-Waterman alignment algorithm core is replaced by the hybrid alignment algorithm. The favorable statistical properties of the hybrid algorithm allow the introduction of position-specific gap penalties in Hybrid PSI-BLAST. This improves the position-specific modeling of protein families and results in an overall improvement of performance. Source code is freely available for download at http://bioserv.mps.ohio-state.edu/HybridPSI, implemented in C and supported on linux.