Local Sequence Alignment Research Articles

AWSEM-MD: Protein Structure Prediction Using Coarse-Grained Physical Potentials and Bioinformatically Based Local Structure Biasing

The associative memory, water mediated, structure and energy model (AWSEM) is a coarse-grained protein force field. AWSEM contains physically motivated terms, such as hydrogen bonding, as well as a bioinformatically based local structure biasing term, which efficiently takes into account many-body effects that are modulated by the local sequence. When combined with appropriate local or global alignments to choose memories, AWSEM can be used to perform de novo protein structure prediction. Herein we present structure prediction results for a particular choice of local sequence alignment method based on short residue sequences called fragments. We demonstrate the model's structure prediction capabilities for three levels of global homology between the target sequence and those proteins used for local structure biasing, all of which assume that the structure of the target sequence is not known. When there are no homologues in the database of structures used for local structure biasing, AWSEM calculations produce structural predictions that are somewhat improved compared with prior works using related approaches. The inclusion of a small number of structures from homologous sequences improves structure prediction only marginally, but when the fragment search is restricted to only homologous sequences, AWSEM can perform high resolution structure prediction and can be used for kinetics and dynamics studies.

Text-based similarity searching for hit- and lead-candidate identification

The Pharmacophore Alignment Search Tool (PhAST) is a string-based approach to virtual screening. Molecules are represented by linear sequences which describe their respective pattern of interaction possibilities. The problem of molecule linearization is tackled by applying Minimum Volume Embedding in combination with a Diffusion Kernel to the molecular graph [1,2]. Linear representations are compared using global pairwise sequence alignment [3]. PhAST exhibited enrichment capabilities comparable or superior to most common virtual screening approaches. Compound rankings were proven to be dissimilar to those of other virtual screening techniques. It was shown that emphasis on key interactions through the application of position specific weights in the alignment process significantly increases enrichment. Significance of chemical similarity was determined in form of p-values of global alignment scores, calculated in an approach that was adapted from its original application to local sequence alignments of protein sequences utilizing Marcov chain Monte Carlo simulation [4]. Bonferroni correction was used to correct p-values with respect to the size of the screening library [5]. PhAST was employed in two prospective applications: A screening for non-nucleoside analogue inhibitors of bacterial thymidine kinase yielded a hit with a distinct structural framework but only weak activity. Screenings for drugs that are not members of the NSAID (non-steroidal anti-inflammatory drug) class as modulators of gamma secretase resulted in a potent modulator with clear structural distinction from the reference compound.

Accelerating pairwise statistical significance estimation for local alignment by harvesting GPU's power

BackgroundPairwise statistical significance has been recognized to be able to accurately identify related sequences, which is a very important cornerstone procedure in numerous bioinformatics applications. However, it is both computationally and data intensive, which poses a big challenge in terms of performance and scalability.ResultsWe present a GPU implementation to accelerate pairwise statistical significance estimation of local sequence alignment using standard substitution matrices. By carefully studying the algorithm's data access characteristics, we developed a tile-based scheme that can produce a contiguous data access in the GPU global memory and sustain a large number of threads to achieve a high GPU occupancy. We further extend the parallelization technique to estimate pairwise statistical significance using position-specific substitution matrices, which has earlier demonstrated significantly better sequence comparison accuracy than using standard substitution matrices. The implementation is also extended to take advantage of dual-GPUs. We observe end-to-end speedups of nearly 250 (370) × using single-GPU Tesla C2050 GPU (dual-Tesla C2050) over the CPU implementation using Intel© Core™i7 CPU 920 processor.ConclusionsHarvesting the high performance of modern GPUs is a promising approach to accelerate pairwise statistical significance estimation for local sequence alignment.

Par-PSSE: Software for Pairwise Statistical Significance Estimation in Parallel for Local Sequence Alignment

Pairwise statistical significance (PSS) has been recognized as a very useful method for homology detection. It can help in estimating whether the output of sequence alignment is evolutionarily link or just arisen by accident. However, pairwise statistical significance estimation (PSSE) poses a big challenge in terms of performance and scalability since it is both computationally intensive and data intensive to construct the empirical score distribution during the estimation. This paper presents a software library for estimating pairwise statistical significance in parallel, named Par-PSSE, implemented in C++ using OpenMP, MPI paradigms and their hybrids. Further, we apply the parallelization technique to estimate non-conservative PSS using standard, sequence-specific, and position-specific substitution matrices. These extensions have been found superior compared to the standard pairwise statistical significance in term of retrieval accuracy. Through distributing the compute-intensive kernels of the pairwise statistical significance estimation across multiple computational units, we achieve a speedup of up to 621.73× over the corresponding sequential implementation when using1024 cores.

Genome-scale NCRNA homology search using a Hamming distance-based filtration strategy.

BackgroundNCRNAs (noncoding RNAs) play important roles in many biological processes. Existing genome-scale ncRNA search tools identify ncRNAs in local sequence alignments generated by conventional sequence comparison methods. However, some types of ncRNA lack strong sequence conservation and tend to be missed or mis-aligned by conventional sequence comparison.ResultsIn this paper, we propose an ncRNA identification framework that is complementary to existing sequence comparison tools. By integrating a filtration step based on Hamming distance and ncRNA alignment programs such as FOLDALIGN or PLAST-ncRNA, the proposed ncRNA search framework can identify ncRNAs that lack strong sequence conservation. In addition, as the ratio of transition and transversion mutation is often used as a discriminative feature for functional ncRNA identification, we incorporate this feature into the filtration step using a coding strategy. We apply Hamming distance seeds to ncRNA search in the intergenic regions of human and mouse genomes and between the Burkholderia cenocepacia J2315 genome and the Ralstonia solanacearum genome. The experimental results demonstrate that a carefully designed Hamming distance seed can achieve better sensitivity in searching for poorly conserved ncRNAs than conventional sequence comparison tools.ConclusionsHamming distance seeds provide better sensitivity as a filtration strategy for genome-wide ncRNA homology search than the existing seeding strategies used in BLAST-like tools. By combining Hamming distance seeds matching and ncRNA alignment, we are able to find ncRNAs with sequence similarities below 60%.

Substitution Transformation of Score Matrix for Improving Alignment Quality of Local Sequence of Distantly Related Proteins

This paper presents a transformation method of original score matrix by correspondently normalizing its elements in row and column. The sequence alignment of 4830 superfamilies and 9570 fold related protein pairs showed that the quality of the alignment based on the transformed matrices is significantly improved compared to that based on the original matrices, especially for the fold related protein pairs, which is possibly due to that the elements in the transformed matrix contain more similarity information between two residues than those in the original matrix. These results indicate that for the sequence alignment of distantly related proteins, the original score matrix should probably be replaced by its correspondently transformed matrix to enhance the quality of sequence alignment. Keywords: Distantly-related proteins, Matrix transformation, Local sequence alignment, Protrein Structure Prediction, Score Matrix, LGscore, IDENTITY, BLOSUM, parameters, MaxSub score

Introducing EzTaxon-e: a prokaryotic 16S rRNA gene sequence database with phylotypes that represent uncultured species

Despite recent advances in commercially optimized identification systems, bacterial identification remains a challenging task in many routine microbiological laboratories, especially in situations where taxonomically novel isolates are involved. The 16S rRNA gene has been used extensively for this task when coupled with a well-curated database, such as EzTaxon, containing sequences of type strains of prokaryotic species with validly published names. Although the EzTaxon database has been widely used for routine identification of prokaryotic isolates, sequences from uncultured prokaryotes have not been considered. Here, the next generation database, named EzTaxon-e, is formally introduced. This new database covers not only species within the formal nomenclatural system but also phylotypes that may represent species in nature. In addition to an identification function based on Basic Local Alignment Search Tool (blast) searches and pairwise global sequence alignments, a new objective method of assessing the degree of completeness in sequencing is proposed. All sequences that are held in the EzTaxon-e database have been subjected to phylogenetic analysis and this has resulted in a complete hierarchical classification system. It is concluded that the EzTaxon-e database provides a useful taxonomic backbone for the identification of cultured and uncultured prokaryotes and offers a valuable means of communication among microbiologists who routinely encounter taxonomically novel isolates. The database and its analytical functions can be found at http://eztaxon-e.ezbiocloud.net/.

Protein–Protein Binding Sites Prediction by 3D Structural Similarities

Identifying the location of binding sites on proteins is of fundamental importance for a wide range of applications including molecular docking, de novo drug design, structure identification, and comparison of functional sites. In this paper, we develop an efficient approach for finding binding sites between proteins. Our approach consists of four steps: local sequence alignment, protein surface detection, 3D structure comparison, and candidate binding site selection. A comparison of our method with the LSA algorithm shows that the binding sites predicted by our method are somewhat closer to the actual binding sites in the protein-protein complexes. The software package is available at http://sites.google.com/site/guofeics/pro-bs for noncommercial use.

Applications of the pipeline environment for visual informatics and genomics computations

BackgroundContemporary informatics and genomics research require efficient, flexible and robust management of large heterogeneous data, advanced computational tools, powerful visualization, reliable hardware infrastructure, interoperability of computational resources, and detailed data and analysis-protocol provenance. The Pipeline is a client-server distributed computational environment that facilitates the visual graphical construction, execution, monitoring, validation and dissemination of advanced data analysis protocols.ResultsThis paper reports on the applications of the LONI Pipeline environment to address two informatics challenges - graphical management of diverse genomics tools, and the interoperability of informatics software. Specifically, this manuscript presents the concrete details of deploying general informatics suites and individual software tools to new hardware infrastructures, the design, validation and execution of new visual analysis protocols via the Pipeline graphical interface, and integration of diverse informatics tools via the Pipeline eXtensible Markup Language syntax. We demonstrate each of these processes using several established informatics packages (e.g., miBLAST, EMBOSS, mrFAST, GWASS, MAQ, SAMtools, Bowtie) for basic local sequence alignment and search, molecular biology data analysis, and genome-wide association studies. These examples demonstrate the power of the Pipeline graphical workflow environment to enable integration of bioinformatics resources which provide a well-defined syntax for dynamic specification of the input/output parameters and the run-time execution controls.ConclusionsThe LONI Pipeline environment http://pipeline.loni.ucla.edu provides a flexible graphical infrastructure for efficient biomedical computing and distributed informatics research. The interactive Pipeline resource manager enables the utilization and interoperability of diverse types of informatics resources. The Pipeline client-server model provides computational power to a broad spectrum of informatics investigators - experienced developers and novice users, user with or without access to advanced computational-resources (e.g., Grid, data), as well as basic and translational scientists. The open development, validation and dissemination of computational networks (pipeline workflows) facilitates the sharing of knowledge, tools, protocols and best practices, and enables the unbiased validation and replication of scientific findings by the entire community.

Faster Smith-Waterman database searches with inter-sequence SIMD parallelisation

BackgroundThe Smith-Waterman algorithm for local sequence alignment is more sensitive than heuristic methods for database searching, but also more time-consuming. The fastest approach to parallelisation with SIMD technology has previously been described by Farrar in 2007. The aim of this study was to explore whether further speed could be gained by other approaches to parallelisation.ResultsA faster approach and implementation is described and benchmarked. In the new tool SWIPE, residues from sixteen different database sequences are compared in parallel to one query residue. Using a 375 residue query sequence a speed of 106 billion cell updates per second (GCUPS) was achieved on a dual Intel Xeon X5650 six-core processor system, which is over six times more rapid than software based on Farrar's 'striped' approach. SWIPE was about 2.5 times faster when the programs used only a single thread. For shorter queries, the increase in speed was larger. SWIPE was about twice as fast as BLAST when using the BLOSUM50 score matrix, while BLAST was about twice as fast as SWIPE for the BLOSUM62 matrix. The software is designed for 64 bit Linux on processors with SSSE3. Source code is available from http://dna.uio.no/swipe/ under the GNU Affero General Public License.ConclusionsEfficient parallelisation using SIMD on standard hardware makes it possible to run Smith-Waterman database searches more than six times faster than before. The approach described here could significantly widen the potential application of Smith-Waterman searches. Other applications that require optimal local alignment scores could also benefit from improved performance.

Overlap digraph: An effective model for finding good spaced seeds for biological sequence local alignment

Spaced seeds technology, which was proposed by PatternHunter, has been proven to be more sensitive and faster than continuous seeds, and it is now widely used for bio-sequence local alignments. However, finding optimal spaced seeds is an NP-hard problem. A seed digraph model is proposed to find good spaced seeds, which are very close to optimal, in a very different but effective way. Using this different approach, some good long spaced seeds which cannot be calculated by normal optimal sensitivity formulas due to their exponential complexity can be found.

Accurate statistics for local sequence alignment with position-dependent scoring by rare-event sampling

BackgroundMolecular database search tools need statistical models to assess the significance for the resulting hits. In the classical approach one asks the question how probable a certain score is observed by pure chance. Asymptotic theories for such questions are available for two random i.i.d. sequences. Some effort had been made to include effects of finite sequence lengths and to account for specific compositions of the sequences. In many applications, such as a large-scale database homology search for transmembrane proteins, these models are not the most appropriate ones. Search sensitivity and specificity benefit from position-dependent scoring schemes or use of Hidden Markov Models. Additional, one may wish to go beyond the assumption that the sequences are i.i.d. Despite their practical importance, the statistical properties of these settings have not been well investigated yet.ResultsIn this paper, we discuss an efficient and general method to compute the score distribution to any desired accuracy. The general approach may be applied to different sequence models and and various similarity measures that satisfy a few weak assumptions. We have access to the low-probability region ("tail") of the distribution where scores are larger than expected by pure chance and therefore relevant for practical applications. Our method uses recent ideas from rare-event simulations, combining Markov chain Monte Carlo simulations with importance sampling and generalized ensembles. We present results for the score statistics of fixed and random queries against random sequences. In a second step, we extend the approach to a model of transmembrane proteins, which can hardly be described as i.i.d. sequences. For this case, we compare the statistical properties of a fixed query model as well as a hidden Markov sequence model in connection with a position based scoring scheme against the classical approach.ConclusionsThe results illustrate that the sensitivity and specificity strongly depend on the underlying scoring and sequence model. A specific ROC analysis for the case of transmembrane proteins supports our observation.

Deterministic local alignment methods improved by a simple genetic algorithm

Multiple sequence local alignment, often deployed for de novo discovery of biological motifs hidden in a set of DNA or protein sequences, remains a challenge in bioinformatics and computational biology. Many algorithms and software packages have been developed to address the problem. Expectation maximization (EM), one of the popular local alignment methods, is often used to solve the motif-finding problem. However, EM largely depends on its initialization and can be easily trapped in local optima. This paper presents the Genetic-enabled EM Motif-Finding Algorithm (GEMFA) in an effort to mitigate the difficulties confronted the EM-based motif discovery algorithms. The new algorithm integrates a simple genetic algorithm (GA) with a local searcher to explore the local alignment space, that is, it combines deterministic local alignment methods with a simple GA to effectively perform de novo motif discovery. It first initializes a population of multiple local alignments each of which is encoded on a chromosome that represents a potential solution. GEMFA then performs heuristic search in the whole alignment space using minimum distance length (MDL) as the fitness function, which is generalized from maximum log-likelihood. The genetic algorithm gradually moves this population towards the best alignment from which the motif model is derived. Simulated and real biological sequence analysis showed that GEMFA significantly improved deterministic local alignment methods especially in the subtle motif sequence alignment, and it also outperformed other algorithms tested.

Performance results of running parallel applications on the InteGrade

AbstractThe InteGrade middleware intends to exploit the idle time of computing resources in computer laboratories. In this work we investigate the performance of running parallel applications with communication among processors on the InteGrade grid. As costly communication on a grid can be prohibitive, we explore the so‐called systolic or wavefront paradigm to design the parallel algorithms in which no global communication is used. To evaluate the InteGrade middleware we considered three parallel algorithms that solve the matrix chain product problem, the 0–1 Knapsack Problem, and the local sequence alignment problem, respectively. We show that these three applications running under the InteGrade middleware and MPI take slightly more time than the same applications running on a cluster with only LAM‐MPI support. The results can be considered promising and the time difference between the two is not substantial. The overhead of the InteGrade middleware is acceptable, in view of the benefits obtained to facilitate the use of grid computing by the user. These benefits include job submission, checkpointing, security, job migration, etc. Copyright © 2009 John Wiley & Sons, Ltd.

A similarity network approach for the analysis and comparison of protein sequence/structure sets

A set of proteins is a complex system whose elements are interrelated on the concept of sequence- and structure-based similarity. Here, we applied a similarity network-based methodology for the representation and analysis of protein sequences and structures sets using a non-redundant set of 311 proteins and three different information criteria based on sequence-derived features, sequence local alignment and structural alignment. A wide set of measurements, like network degree, clustering coefficient, characteristic path length and vertex centrality were utilized to characterize the networks’ topology. Protein similarity networks were found medium or highly interconnected and the existence of both clusters and random edges classified their fully connected versions as Small World Networks (SWNs). The SWN architecture was able to host the continuous similarity transition among proteins and model the protein information flow during evolution. Recently reported ancestral elements, like the α/β class and certain folds, were remarkably found to act as hubs in the networks. Additionally, the moderate information value of sequence-derived features when used for fold and class assignment was shown on a network basis. The methodology described here can be applied for the analysis of other complex systems which consist of interrelated elements and a certain information flow.

Finite-temperature local protein sequence alignment: Percolation and free-energy distribution

Sequence alignment is a tool in bioinformatics that is used to find homological relationships in large molecular databases. It can be mapped on the physical model of directed polymers in random media. We consider the finite-temperature version of local sequence alignment for proteins and study the transition between the linear phase and the biologically relevant logarithmic phase, where the free energy grows linearly or logarithmically with the sequence length. By means of numerical simulations and finite-size-scaling analysis, we determine the phase diagram in the plane that is spanned by the gap costs and the temperature. We use the most frequently used parameter set for protein alignment. The critical exponents that describe the parameter-driven transition are found to be explicitly temperature dependent. Furthermore, we study the shape of the (free-) energy distribution close to the transition by rare-event simulations down to probabilities on the order 10(-64). It is well known that in the logarithmic region, the optimal score distribution (T=0) is described by a modified Gumbel distribution. We confirm that this also applies for the free-energy distribution (T>0). However, in the linear phase, the distribution crosses over to a modified Gaussian distribution.

Heuristic Reusable Dynamic Programming: Efficient Updates of Local Sequence Alignment

Recomputation of the previously evaluated similarity results between biological sequences becomes inevitable when researchers realize errors in their sequenced data or when the researchers have to compare nearly similar sequences, e.g., in a family of proteins. We present an efficient scheme for updating local sequence alignments with an affine gap model. In principle, using the previous matching result between two amino acid sequences, we perform a forward-backward alignment to generate heuristic searching bands which are bounded by a set of suboptimal paths. Given a correctly updated sequence, we initially predict a new score of the alignment path for each contour to select the best candidates among them. Then, we run the Smith-Waterman algorithm in this confined space. Furthermore, our heuristic alignment for an updated sequence shows that it can be further accelerated by using reusable dynamic programming (rDP), our prior work. In this study, we successfully validate "relative node tolerance bound" (RNTB) in the pruned searching space. Furthermore, we improve the computational performance by quantifying the successful RNTB tolerance probability and switch to rDP on perturbation-resilient columns only. In our searching space derived by a threshold value of 90 percent of the optimal alignment score, we find that 98.3 percent of contours contain correctly updated paths. We also find that our method consumes only 25.36 percent of the runtime cost of sparse dynamic programming (sDP) method, and to only 2.55 percent of that of a normal dynamic programming with the Smith-Waterman algorithm.

Restriction versus guidance in protein structure prediction

Conformational restriction by fragment assembly and guidance in molecular dynamics are alternate conformational search strategies in protein structure prediction. We examine both approaches using a version of the associative memory Hamiltonian that incorporates the influence of water-mediated interactions (AMW). For short proteins (<70 residues), fragment assembly, while searching a restricted space, compares well to molecular dynamics and is often sufficient to fold such proteins to near-native conformations (4A) via simulated annealing. Longer proteins encounter kinetic sampling limitations in fragment assembly not seen in molecular dynamics which generally samples more native-like conformations. We also present a fragment enriched version of the standard AMW energy function, AMW-FME, which incorporates the local sequence alignment derived fragment libraries from fragment assembly directly into the energy function. This energy function, in which fragment information acts as a guide not a restriction, is found by molecular dynamics to improve on both previous approaches.

Enhanced protein fold recognition through a novel data integration approach

BackgroundProtein fold recognition is a key step in protein three-dimensional (3D) structure discovery. There are multiple fold discriminatory data sources which use physicochemical and structural properties as well as further data sources derived from local sequence alignments. This raises the issue of finding the most efficient method for combining these different informative data sources and exploring their relative significance for protein fold classification. Kernel methods have been extensively used for biological data analysis. They can incorporate separate fold discriminatory features into kernel matrices which encode the similarity between samples in their respective data sources.ResultsIn this paper we consider the problem of integrating multiple data sources using a kernel-based approach. We propose a novel information-theoretic approach based on a Kullback-Leibler (KL) divergence between the output kernel matrix and the input kernel matrix so as to integrate heterogeneous data sources. One of the most appealing properties of this approach is that it can easily cope with multi-class classification and multi-task learning by an appropriate choice of the output kernel matrix. Based on the position of the output and input kernel matrices in the KL-divergence objective, there are two formulations which we respectively refer to as MKLdiv-dc and MKLdiv-conv. We propose to efficiently solve MKLdiv-dc by a difference of convex (DC) programming method and MKLdiv-conv by a projected gradient descent algorithm. The effectiveness of the proposed approaches is evaluated on a benchmark dataset for protein fold recognition and a yeast protein function prediction problem.ConclusionOur proposed methods MKLdiv-dc and MKLdiv-conv are able to achieve state-of-the-art performance on the SCOP PDB-40D benchmark dataset for protein fold prediction and provide useful insights into the relative significance of informative data sources. In particular, MKLdiv-dc further improves the fold discrimination accuracy to 75.19% which is a more than 5% improvement over competitive Bayesian probabilistic and SVM margin-based kernel learning methods. Furthermore, we report a competitive performance on the yeast protein function prediction problem.

A Parallel Pairwise Local Sequence Alignment Algorithm

Researchers are compelled to use heuristic-based pairwise sequence alignment tools instead of Smith-Waterman (SW) algorithm due to space and time constraints, thereby losing significant amount of sensitivity. Parallelization is a possible solution, though, till date, the parallelization is restricted to database searching through database fragmentation. In this paper, the power of a cluster computer is utilized for developing a parallel algorithm, RPAlign, involving, first, the detection of regions that are potentially alignable, followed by their actual alignment. RPAlign is found to reduce the timing requirement by a factor of upto 9 and 99 when used with the basic local alignment search tool (BLAST) and SW, respectively, while keeping the sensitivity similar to the corresponding method. For distantly related sequences, which remain undetected by BLAST, RPAlign with SW can be used. Again, for megabase-scale sequences, when SW becomes computationally intractable, the proposed method can still align them reasonably fast with high sensitivity.