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The tryptophan rich basic protein/calcium signal-modulating cyclophilin ligand (WRB/CAML) and Get1p/Get2p complexes, in vertebrates and yeast, respectively, mediate the final step of tail-anchored protein insertion into the endoplasmic reticulum membrane via the Get pathway. While WRB appears to exist in all eukaryotes, CAML homologs were previously recognized only among chordates, raising the question as to how CAML's function is performed in other phyla. Furthermore, whereas WRB was recognized as the metazoan homolog of Get1, CAML and Get2, although functionally equivalent, were not considered to be homologous. CAML contains an N-terminal basic, TRC40/Get3-interacting, region, three transmembrane segments near the C-terminus, and a poorly conserved region between these domains. Here, I searched the NCBI protein database for remote CAML homologs in all eukaryotes, using position-specific iterated-basic local alignment search tool, with the C-terminal, the N-terminal or the full-length sequence of human CAML as query. The N-terminal basic region and full-length CAML retrieved homologs among metazoa, plants and fungi. In the latter group several hits were annotated as GET2. The C-terminal query did not return entries outside of the animal kingdom, but did retrieve over one hundred invertebrate metazoan CAML-like proteins, which all conserved the N-terminal TRC40-binding domain. The results indicate that CAML homologs exist throughout the eukaryotic domain of life, and suggest that metazoan CAML and yeast GET2 share a common evolutionary origin. They further reveal a tight link between the particular features of the metazoan membrane-anchoring domain and the TRC40-interacting region. The list of sequences presented here should provide a useful resource for future studies addressing structure-function relationships in CAML proteins.

The peak alignment is a vital preprocessing step before downstream analysis, such as biomarker discovery and pathway analysis, for two-dimensional gas chromatography mass spectrometry (2DGCMS)-based metabolomics data. Due to uncontrollable experimental conditions, e.g., the differences in temperature or pressure, matrix effects on samples, and stationary phase degradation, a shift of retention times among samples inevitably occurs during 2DGCMS experiments, making it difficult to align peaks. Various peak alignment algorithms have been developed to correct retention time shifts for homogeneous, heterogeneous or both type of mass spectrometry data. However, almost all existing algorithms have been focused on a local alignment and are suffering from low accuracy especially when aligning dense biological data with many peaks. We have developed four global peak alignment (GPA) algorithms using coherent point drift (CPD) point matching algorithms: retention time-based CPD-GPA (RT), prior CPD-GPA (P), mixture CPD-GPA (M), and prior mixture CPD-GPA (P+M). The method RT performs the peak alignment based only on the retention time distance, while the methods P, M, and P+M carry out the peak alignment using both the retention time distance and mass spectral similarity. The method P incorporates the mass spectral similarity through prior information and the methods M and P+M use the mixture distance measure. Four developed algorithms are applied to homogeneous and heterogeneous spiked-in data as well as two real biological data and compared with three existing algorithms, mSPA, SWPA, and BiPACE-2D. The results show that our CPD-GPA algorithms perform better than all existing algorithms in terms of F1 score.

Alignment is essential in many areas such as biological, chemical and criminal forensics. The well-known Smith-Waterman SW algorithm is able to retrieve the optimal local alignment with quadratic time and space complexity. There are several implementations that take advantage of computing parallelization, such as manycores, FPGAs or GPUs, in order to reduce the alignment effort. In this research, we adapt, develop and tune the SW algorithm named SWIMM on a heterogeneous platform based on Intel's Xeon and Xeon Phi coprocessor. SWIMM is a free tool available in a public git repositoryi¾?https://github.com/enzorucci/SWIMM. We efficiently exploit data and thread-level parallelism, reaching up to 380 GCUPS on heterogeneous architecture, 350 GCUPS for the isolated Xeon and 50 GCUPS on Xeon Phi. Despite the heterogeneous implementation obtaining the best performance, it is also the most energy-demanding. In fact, we also present a trade-off analysis between performance and power consumption. The greenest configuration is based on an isolated multicore system that exploits AVX2 instruction set architecture reaching 1.5 GCUPS/Watts. Copyright © 2015 John Wiley & Sons, Ltd.

In this paper, we present GSWABE, a graphics processing unit GPU-accelerated pairwise sequence alignment algorithm for a collection of short DNA sequences. This algorithm supports all-to-all pairwise global, semi-global and local alignment, and retrieves optimal alignments on Compute Unified Device Architecture CUDA-enabled GPUs. All of the three alignment types are based on dynamic programming and share almost the same computational pattern. Thus, we have investigated a general tile-based approach to facilitating fast alignment by deeply exploring the powerful compute capability of CUDA-enabled GPUs. The performance of GSWABE has been evaluated on a Kepler-based Tesla K40 GPU using a variety of short DNA sequence datasets. The results show that our algorithm can yield a performance of up to 59.1 billions cell updates per second GCUPS, 58.5 GCUPS and 50.3 GCUPS for global, semi-global and local alignment, respectively. Furthermore, on the same system GSWABE runs up to 156.0 times faster than the Streaming SIMD Extensions SSE-based SSW library and up to 102.4 times faster than the CUDA-based MSA-CUDA the first stage in terms of local alignment. Compared with the CUDA-based gpu-pairAlign, GSWABE demonstrates stable and consistent speedups with a maximum speedup of 11.2, 10.7, and 10.6 for global, semi-global, and local alignment, respectively. Copyright © 2014 John Wiley & Sons, Ltd.

The POLYFIT rigid-body algorithm for automated global pairwise and multiple protein structural alignment is presented. Smith-Waterman local alignment is used to establish a set of seed equivalences that are extended using Needleman-Wunsch dynamic programming techniques. Structural and functional interaction constraints provided by evolution are encoded as one-dimensional residue physical environment strings for alignment of highly structurally overlapped protein pairs. Local structure alignment of more distantly related pairs is carried out using rigid-body conformational matching of 15-residue fragments, with allowance made for less stringent conformational matching of metal-ion and small molecule ligand-contact, disulphide bridge, and cis-peptide correspondences. Protein structural plasticity is accommodated through the stepped adjustment of a single empirical distance parameter value in the calculation of the Smith-Waterman dynamic programming matrix. Structural overlap is used both as a measure of similarity and to assess alignment quality. Pairwise alignment accuracy has been benchmarked against that of 10 widely used aligners on the Sippl and Wiederstein set of difficult pairwise structure alignment problems, and more extensively against that of Matt, SALIGN, and MUSTANG in pairwise and multiple structural alignments of protein domains with low shared sequence identity in the SCOP-ASTRAL 40% compendium. The results demonstrate the advantages of POLYFIT over other aligners in the efficient and robust identification of matching seed residue positions in distantly related protein targets and in the generation of longer structurally overlapped alignment lengths. Superposition-based application areas include comparative modeling and protein and ligand design. POLYFIT is available on the Web server at http://polyfit.insa-toulouse.fr. Proteins 2013; 81:1823-1839. (c) 2013 Wiley Periodicals, Inc.

With fierce competition between CPU and graphics processing unit (GPU) platforms, performance evaluation has become the focus of various sectors. In this paper, we take a well-known algorithm in the field of biosequence matching and database searching, the Smith–Waterman (S-W) algorithm as an example, and demonstrate approaches that fully exploit its performance potentials on CPU, GPU, and field-programmable gate array (FPGA) computing platforms. For CPU platforms, we perform two optimizations, single instruction, multiple data and multithread, with compiler options, to gain over 70 × speedups over naive CPU versions on quad-core CPU platforms. For GPU platforms, we propose the combination of coalesced global memory accesses, shared memory tiles, and loop unfolding, achieving 50 × speedups over initial GPU versions on an NVIDIA GeForce GTX 470 card. Experimental results show that the GPU GTX 470 gains 12 × speedups, instead of 100 × reported by some studies, over Intel quadcore CPU Q9400, under the same manufacturing technology and both with fully optimized schemes. In addition, for FPGA platforms, we customize a linear systolic array for the S-W algorithm in a 45-nm FPGA chip from Xilinx (XC6VLX760), with up to 1024 processing elements. Under only 133 MHz clock rate, the FPGA platform reaches the highest performance and becomes the most power-efficient platform, using only 25 W compared with 190 W of the GPU GTX 470. Copyright © 2011 John Wiley & Sons, Ltd.

The attainment of complete map-based sequence for rice (Oryza sativa) is clearly a major milestone for the research community. Identifying the localization of encoded proteins is the key to understanding their functional characteristics and facilitating their purification. Our proposed method, RSLpred, is an effort in this direction for genome-scale subcellular prediction of encoded rice proteins. First, the support vector machine (SVM)-based modules have been developed using traditional amino acid-, dipeptide- (i+1) and four parts-amino acid composition and achieved an overall accuracy of 81.43, 80.88 and 81.10%, respectively. Secondly, a similarity search-based module has been developed using position-specific iterated-basic local alignment search tool and achieved 68.35% accuracy. Another module developed using evolutionary information of a protein sequence extracted from position-specific scoring matrix achieved an accuracy of 87.10%. In this study, a large number of modules have been developed using various encoding schemes like higher-order dipeptide composition, N- and C-terminal, splitted amino acid composition and the hybrid information. In order to benchmark RSLpred, it was tested on an independent set of rice proteins where it outperformed widely used prediction methods such as TargetP, Wolf-PSORT, PA-SUB, Plant-Ploc and ESLpred. To assist the plant research community, an online web tool 'RSLpred' has been developed for subcellular prediction of query rice proteins, which is freely accessible at http://www.imtech.res.in/raghava/rslpred.

We present a comprehensive evaluation of a new structure mining method called PB-ALIGN. It is based on the encoding of protein structure as 1D sequence of a combination of 16 short structural motifs or protein blocks (PBs). PBs are short motifs capable of representing most of the local structural features of a protein backbone. Using derived PB substitution matrix and simple dynamic programming algorithm, PB sequences are aligned the same way amino acid sequences to yield structure alignment. PBs are short motifs capable of representing most of the local structural features of a protein backbone. Alignment of these local features as sequence of symbols enables fast detection of structural similarities between two proteins. Ability of the method to characterize and align regions beyond regular secondary structures, for example, N and C caps of helix and loops connecting regular structures, puts it a step ahead of existing methods, which strongly rely on secondary structure elements. PB-ALIGN achieved efficiency of 85% in extracting true fold from a large database of 7259 SCOP domains and was successful in 82% cases to identify true super-family members. On comparison to 13 existing structure comparison/mining methods, PB-ALIGN emerged as the best on general ability test dataset and was at par with methods like YAKUSA and CE on nontrivial test dataset. Furthermore, the proposed method performed well when compared to flexible structure alignment method like FATCAT and outperforms in processing speed (less than 45 s per database scan). This work also establishes a reliable cut-off value for the demarcation of similar folds. It finally shows that global alignment scores of unrelated structures using PBs follow an extreme value distribution. PB-ALIGN is freely available on web server called Protein Block Expert (PBE) at http://bioinformatics.univ-reunion.fr/PBE/.

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.

A detailed analysis of the performance of hybrid, a new sequence alignment algorithm developed by Yu and coworkers that combines Smith Waterman local dynamic programming with a local version of the maximum-likelihood approach, was made to access the applicability of this algorithm to the detection of distant homologs by sequence comparison. We analyzed the statistics of hybrid with a set of nonhomologous protein sequences from the SCOP database and found that the statistics of the scores from hybrid algorithm follows an Extreme Value Distribution with lambda ∼1, as previously shown by Yu et al. for the case of artificially generated sequences. Local dynamic programming was compared to the hybrid algorithm by using two different test data sets of distant homologs from the PFAM and COGs protein sequence databases. The studies were made with several score functions in current use including OPTIMA, a new score function originally developed to detect remote homologs with the Smith Waterman algorithm. We found OPTIMA to be the best score function for both both dynamic programming and the hybrid algorithms. The ability of dynamic programming to discriminate between homologs and nonhomologs in the two sets of distantly related sequences is slightly better than that of hybrid algorithm. The advantage of producing accurate score statistics with only a few simulations may overcome the small differences in performance and make this new algorithm suitable for detection of homologs in conjunction with a wide range of score functions and gap penalties. Proteins 2002;48:367–376. © 2002 Wiley-Liss, Inc.

We present a fast algorithm to search for repeating fragments within protein sequences. The technique is based on an extension of the Smith-Waterman algorithm that allows the calculation of sub-optimal alignments of a sequence against itself. We are able to estimate the statistical significance of all sub-optimal alignment scores. We also rapidly determine the length of the repeating fragment and the number of times it is found in a sequence. The technique is applied to sequences in the Swissprot database, and to 16 complete genomes. We find that eukaryotic proteins contain more internal repeats than those of prokaryotic and archael organisms. The finding that 18% of yeast sequences and 28% of the known human sequences contain detectable repeats emphasizes the importance of internal duplication in protein evolution.

We have compared commonly used sequence comparison algorithms, scoring matrices, and gap penalties using a method that identifies statistically significant differences in performance. Search sensitivity with either the Smith-Waterman algorithm or FASTA is significantly improved by using modern scoring matrices, such as BLOSUM45-55, and optimized gap penalties instead of the conventional PAM250 matrix. More dramatic improvement can be obtained by scaling similarity scores by the logarithm of the length of the library sequence (In()-scaling). With the best modern scoring matrix (BLOSUM55 or JO93) and optimal gap penalties (-12 for the first residue in the gap and -2 for additional residues), Smith-Waterman and FASTA performed significantly better than BLASTP. With In()-scaling and optimal scoring matrices (BLOSUM45 or Gonnet92) and gap penalties (-12, -1), the rigorous Smith-Waterman algorithm performs better than either BLASTP and FASTA, although with the Gonnet92 matrix the difference with FASTA was not significant. Ln()-scaling performed better than normalization based on other simple functions of library sequence length. Ln()-scaling also performed better than scores based on normalized variance, but the differences were not statistically significant for the BLOSUM50 and Gonnet92 matrices. Optimal scoring matrices and gap penalties are reported for Smith-Waterman and FASTA, using conventional or In()-scaled similarity scores. Searches with no penalty for gap extension, or no penalty for gap opening, or an infinite penalty for gaps performed significantly worse than the best methods. Differences in performance between FASTA and Smith-Waterman were not significant when partial query sequences were used. However, the best performance with complete query sequences was obtained with the Smith-Waterman algorithm and In()-scaling.

The Smith–Waterman algorithm is a computer algorithm that finds regions of local similarity between DNA or protein sequences. Keywords: DNA; protein; sequence alignment