Journal articles: 'Protein sequence alignment'

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Staritzbichler, René, Edoardo Sarti, Emily Yaklich, Antoniya Aleksandrova, Marcus Stamm, Kamil Khafizov, and Lucy R. Forrest. "Refining pairwise sequence alignments of membrane proteins by the incorporation of anchors." PLOS ONE 16, no. 4 (April 30, 2021): e0239881. http://dx.doi.org/10.1371/journal.pone.0239881.

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The alignment of primary sequences is a fundamental step in the analysis of protein structure, function, and evolution, and in the generation of homology-based models. Integral membrane proteins pose a significant challenge for such sequence alignment approaches, because their evolutionary relationships can be very remote, and because a high content of hydrophobic amino acids reduces their complexity. Frequently, biochemical or biophysical data is available that informs the optimum alignment, for example, indicating specific positions that share common functional or structural roles. Currently, if those positions are not correctly matched by a standard pairwise sequence alignment procedure, the incorporation of such information into the alignment is typically addressed in an ad hoc manner, with manual adjustments. However, such modifications are problematic because they reduce the robustness and reproducibility of the aligned regions either side of the newly matched positions. Previous studies have introduced restraints as a means to impose the matching of positions during sequence alignments, originally in the context of genome assembly. Here we introduce position restraints, or “anchors” as a feature in our alignment tool AlignMe, providing an aid to pairwise global sequence alignment of alpha-helical membrane proteins. Applying this approach to realistic scenarios involving distantly-related and low complexity sequences, we illustrate how the addition of anchors can be used to modify alignments, while still maintaining the reproducibility and rigor of the rest of the alignment. Anchored alignments can be generated using the online version of AlignMe available at www.bioinfo.mpg.de/AlignMe/.

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Pervez, Muhammad Tariq, Hayat Ali Shah, Masroor Ellahi Babar, Nasir Naveed, and Muhammad Shoaib. "SAliBASE: A Database of Simulated Protein Alignments." Evolutionary Bioinformatics 15 (January 2019): 117693431882108. http://dx.doi.org/10.1177/1176934318821080.

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Simulated alignments are alternatives to manually constructed multiple sequence alignments for evaluating performance of multiple sequence alignment tools. The importance of simulated sequences is recognized because their true evolutionary history is known, which is very helpful for reconstructing accurate phylogenetic trees and alignments. However, generating simulated alignments require expertise to use bioinformatics tools and consume several hours for reconstructing even a few hundreds of simulated sequences. It becomes a tedious job for an end user who needs a few datasets of variety of simulated sequences. Currently, there is no databank available which may help researchers to download simulated sequences/alignments for their study. Major focus of our study was to develop a database of simulated protein sequences (SAliBASE) based on different varying parameters such as insertion rate, deletion rate, sequence length, number of sequences, and indel size. Each dataset has corresponding alignment as well. This repository is very useful for evaluating multiple alignment methods.

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Cavanaugh, David, and Krishnan Chittur. "A hydrophobic proclivity index for protein alignments." F1000Research 4 (October 21, 2015): 1097. http://dx.doi.org/10.12688/f1000research.6348.1.

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Sequence alignment algorithms are fundamental to modern bioinformatics. Sequence alignments are widely used in diverse applications such as phylogenetic analysis, database searches for related sequences to aid identification of unknown protein domain structures and classification of proteins and protein domains. Additionally, alignment algorithms are integral to the location of related proteins to secure understanding of unknown protein functions, to suggest the folded structure of proteins of unknown structure from location of homologous proteins and/or by locating homologous domains of known 3D structure. For proteins, alignment algorithms depend on information about amino acid substitutions that allows for matching sequences that are similar, but not exact. When primary sequence percent identity falls below about 25%, algorithms often fail to identify proteins that may have similar 3D structure. We have created a hydrophobicity scale and a matching dynamic programming algorithm called TMATCH (unpublished report) that is able to match proteins with remote homologs with similar secondary/tertiary structure, even with very low primary sequence matches. In this paper, we describe how we arrived at the hydrophobic scale, how it provides much more information than percent identity matches and some of the implications for better alignments and understanding protein structure.

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Cavanaugh, David, and Krishnan Chittur. "A hydrophobic proclivity index for protein alignments." F1000Research 4 (October 15, 2020): 1097. http://dx.doi.org/10.12688/f1000research.6348.2.

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Sequence alignment algorithms are fundamental to modern bioinformatics. Sequence alignments are widely used in diverse applications such as phylogenetic analysis, database searches for related sequences to aid identification of unknown protein domain structures and classification of proteins and protein domains. Additionally, alignment algorithms are integral to the location of related proteins to secure understanding of unknown protein functions, to suggest the folded structure of proteins of unknown structure from location of homologous proteins and/or by locating homologous domains of known 3D structure. For proteins, alignment algorithms depend on information about amino acid substitutions that allows for matching sequences that are similar, but not exact. When primary sequence percent identity falls below about 25%, algorithms often fail to identify proteins that may have similar 3D structure. We have created a hydrophobicity scale and a matching dynamic programming algorithm called TMATCH (preprint report) that is able to match proteins with remote homologs with similar secondary/tertiary structure, even with very low primary sequence matches. In this paper, we describe how we arrived at the hydrophobic scale, how it provides much more information than percent identity matches and some of the implications for better alignments and understanding protein structure.

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Aadland, Kelsey, and Bryan Kolaczkowski. "Alignment-Integrated Reconstruction of Ancestral Sequences Improves Accuracy." Genome Biology and Evolution 12, no. 9 (August 12, 2020): 1549–65. http://dx.doi.org/10.1093/gbe/evaa164.

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Abstract Ancestral sequence reconstruction (ASR) uses an alignment of extant protein sequences, a phylogeny describing the history of the protein family and a model of the molecular-evolutionary process to infer the sequences of ancient proteins, allowing researchers to directly investigate the impact of sequence evolution on protein structure and function. Like all statistical inferences, ASR can be sensitive to violations of its underlying assumptions. Previous studies have shown that, whereas phylogenetic uncertainty has only a very weak impact on ASR accuracy, uncertainty in the protein sequence alignment can more strongly affect inferred ancestral sequences. Here, we show that errors in sequence alignment can produce errors in ASR across a range of realistic and simplified evolutionary scenarios. Importantly, sequence reconstruction errors can lead to errors in estimates of structural and functional properties of ancestral proteins, potentially undermining the reliability of analyses relying on ASR. We introduce an alignment-integrated ASR approach that combines information from many different sequence alignments. We show that integrating alignment uncertainty improves ASR accuracy and the accuracy of downstream structural and functional inferences, often performing as well as highly accurate structure-guided alignment. Given the growing evidence that sequence alignment errors can impact the reliability of ASR studies, we recommend that future studies incorporate approaches to mitigate the impact of alignment uncertainty. Probabilistic modeling of insertion and deletion events has the potential to radically improve ASR accuracy when the model reflects the true underlying evolutionary history, but further studies are required to thoroughly evaluate the reliability of these approaches under realistic conditions.

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Barton, Geoffrey J. "Protein Sequence Alignment Techniques." Acta Crystallographica Section D Biological Crystallography 54, no. 6 (November 1, 1998): 1139–46. http://dx.doi.org/10.1107/s0907444998008324.

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The basic algorithms for alignment of two or more protein sequences are explained. Alternative methods for scoring substitutions and gaps (insertions and deletions) are described, as are global and local alignment methods. Multiple alignment techniques are explained, including methods for profile comparison. A summary is given of programs for the alignment and analysis of protein sequences, either from sequence alone, or from three-dimensional structure.

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Kanagarajadurai, Karuppiah, Singaravelu Kalaimathy, Paramasivam Nagarajan, and Ramanathan Sowdhamini. "PASS2." International Journal of Knowledge Discovery in Bioinformatics 2, no. 4 (October 2011): 53–66. http://dx.doi.org/10.4018/jkdb.2011100104.

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A detailed comparison of protein domains that belong to families and superfamilies shows that structure is better conserved than sequence during evolutionary divergence. Sequence alignments, guided by structural features, permit a better sampling of the protein sequence space and effective construction of libraries for fold recognition. Sequence alignments are useful evolutionary models in defining structure-function relationships for protein superfamilies. The PASS2 database, maintained by the authors, presents alignments of proteins related at the superfamily level and characterised by low sequence similarity. The number of new superfamilies increased to 47% compared with the previous PASS2 version, which shows the crucial importance of updating the PASS2 database. In the current release of the PASS2 database, they align protein superfamilies using a structural alignment protocol. The authors also introduce two alignment assessment methods that depend on the average structural deviations of domains and the extent of conserved secondary structures. They also integrate new and important structural and sequence features at the superfamily level into the database. These features are conserved-unconserved blocks in proteins, spatial distribution of sequences using principal component analysis and a statistical view for each superfamily. The authors suggest that highly structurally deviant superfamily members could be removed as outliers, so that such extreme distant relationships will not obscure the alignment. They report a nearly-automated, updated version of the superfamily alignment database, consisting of 1776 superfamilies and 9536 protein domains, that is in direct correspondence with the SCOP (1.73) database.

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Pei, Jimin. "Multiple protein sequence alignment." Current Opinion in Structural Biology 18, no. 3 (June 2008): 382–86. http://dx.doi.org/10.1016/j.sbi.2008.03.007.

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PAI, TUN-WEN, RUEI-HSIANG CHANG, CHIEN-MING CHEN, PO-HAN SU, LEE-JYI WANG, KUEN-TSAIR LAY, and KUO-TORNG LAN. "MULTIPLE STRUCTURE ALIGNMENT BASED ON GEOMETRICAL CORRELATION OF SECONDARY STRUCTURE ELEMENTS." New Mathematics and Natural Computation 06, no. 01 (March 2010): 77–95. http://dx.doi.org/10.1142/s1793005710001621.

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Protein structure alignment facilitates the analysis of protein functionality. Through superimposed structures and the comparison of variant components, common or specific features of proteins can be identified. Several known protein families exhibit analogous tertiary structures but divergent primary sequences. These proteins in the same structural class are unable to be aligned by sequence-based methods. The main objective of the present study was to develop an efficient and effective algorithm for multiple structure alignment based on geometrical correlation of secondary structures, which are conserved in evolutionary heritage. The method utilizes mutual correlation analysis of secondary structure elements (SSEs) and selects representative segments as the key anchors for structural alignment. The system exploits a fast vector transformation technique to represent SSEs in vector format, and the mutual geometrical relationship among vectors is projected onto an angle-distance map. Through a scoring function and filtering mechanisms, the best candidates of vectors are selected, and an effective constrained multiple structural alignment module is performed. The correctness of the algorithm was verified by the multiple structure alignment of proteins in the SCOP database. Several protein sets with low sequence identities were aligned, and the results were compared with those obtained by three well-known structural alignment approaches. The results show that the proposed method is able to perform multiple structural alignments effectively and to obtain satisfactory results, especially for proteins possessing low sequence identity.

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Henneke, Christina M., Michael J. Danson, David W. Hough, and David J. Osguthorpe. "Sequence alignment of citrate synthase proteins using a multiple sequence alignment algorithm and multiple scoring matrices." "Protein Engineering, Design and Selection" 2, no. 8 (1989): 597–604. http://dx.doi.org/10.1093/protein/2.8.597.

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Sierk, Michael L., Michael E. Smoot, Ellen J. Bass, and William R. Pearson. "Improving pairwise sequence alignment accuracy using near-optimal protein sequence alignments." BMC Bioinformatics 11, no. 1 (2010): 146. http://dx.doi.org/10.1186/1471-2105-11-146.

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Tu, Shin-Lin, Jeannette Staheli, Colum McClay, Kathleen McLeod, Timothy Rose, and Chris Upton. "Base-By-Base Version 3: New Comparative Tools for Large Virus Genomes." Viruses 10, no. 11 (November 15, 2018): 637. http://dx.doi.org/10.3390/v10110637.

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Base-By-Base is a comprehensive tool for the creation and editing of multiple sequence alignments that is coded in Java and runs on multiple platforms. It can be used with gene and protein sequences as well as with large viral genomes, which themselves can contain gene annotations. This report describes new features added to Base-By-Base over the last 7 years. The two most significant additions are: (1) The recoding and inclusion of “consensus-degenerate hybrid oligonucleotide primers” (CODEHOP), a popular tool for the design of degenerate primers from a multiple sequence alignment of proteins; and (2) the ability to perform fuzzy searches within the columns of sequence data in multiple sequence alignments to determine the distribution of sequence variants among the sequences. The intuitive interface focuses on the presentation of results in easily understood visualizations and providing the ability to annotate the sequences in a multiple alignment with analytic and user data.

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Zhan, Qing, Yilei Fu, Qinghua Jiang, Bo Liu, Jiajie Peng, and Yadong Wang. "SpliVert: A Protein Multiple Sequence Alignment Refinement Method Based on Splitting-Splicing Vertically." Protein & Peptide Letters 27, no. 4 (March 17, 2020): 295–302. http://dx.doi.org/10.2174/0929866526666190806143959.

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Background: Multiple Sequence Alignment (MSA) is a fundamental task in bioinformatics and is required for many biological analysis tasks. The more accurate the alignments are, the more credible the downstream analyses. Most protein MSA algorithms realign an alignment to refine it by dividing it into two groups horizontally and then realign the two groups. However, this strategy does not consider that different regions of the sequences have different conservation; this property may lead to incorrect residue-residue or residue-gap pairs, which cannot be corrected by this strategy. Objective: In this article, our motivation is to develop a novel refinement method based on splitting- splicing vertically. Method: Here, we present a novel refinement method based on splitting-splicing vertically, called SpliVert. For an alignment, we split it vertically into 3 parts, remove the gap characters in the middle, realign the middle part alone, and splice the realigned middle parts with the other two initial pieces to obtain a refined alignment. In the realign procedure of our method, the aligner will only focus on a certain part, ignoring the disturbance of the other parts, which could help fix the incorrect pairs. Results: We tested our refinement strategy for 2 leading MSA tools on 3 standard benchmarks, according to the commonly used average SP (and TC) score. The results show that given appropriate proportions to split the initial alignment, the average scores are increased comparably or slightly after using our method. We also compared the alignments refined by our method with alignments directly refined by the original alignment tools. The results suggest that using our SpliVert method to refine alignments can also outperform direct use of the original alignment tools. Conclusion: The results reveal that splitting vertically and realigning part of the alignment is a good strategy for the refinement of protein multiple sequence alignments.

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Stamm, Marcus, René Staritzbichler, Kamil Khafizov, and Lucy R. Forrest. "AlignMe—a membrane protein sequence alignment web server." Nucleic Acids Research 42, W1 (April 21, 2014): W246—W251. http://dx.doi.org/10.1093/nar/gku291.

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TAYLOR, WILLIAM R. "Motif-Biased Protein Sequence Alignment." Journal of Computational Biology 1, no. 4 (January 1994): 297–310. http://dx.doi.org/10.1089/cmb.1994.1.297.

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Fox, Gearóid, Fabian Sievers, and Desmond G. Higgins. "Using de novo protein structure predictions to measure the quality of very large multiple sequence alignments." Bioinformatics 32, no. 6 (November 14, 2015): 814–20. http://dx.doi.org/10.1093/bioinformatics/btv592.

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Abstract Motivation: Multiple sequence alignments (MSAs) with large numbers of sequences are now commonplace. However, current multiple alignment benchmarks are ill-suited for testing these types of alignments, as test cases either contain a very small number of sequences or are based purely on simulation rather than empirical data. Results: We take advantage of recent developments in protein structure prediction methods to create a benchmark (ContTest) for protein MSAs containing many thousands of sequences in each test case and which is based on empirical biological data. We rank popular MSA methods using this benchmark and verify a recent result showing that chained guide trees increase the accuracy of progressive alignment packages on datasets with thousands of proteins. Availability and implementation: Benchmark data and scripts are available for download at http://www.bioinf.ucd.ie/download/ContTest.tar.gz. Contact: des.higgins@ucd.ie Supplementary information: Supplementary data are available at Bioinformatics online.

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MIAO, XIJIANG, PETER J. WADDELL, and HOMAYOUN VALAFAR. "TALI: LOCAL ALIGNMENT OF PROTEIN STRUCTURES USING BACKBONE TORSION ANGLES." Journal of Bioinformatics and Computational Biology 06, no. 01 (February 2008): 163–81. http://dx.doi.org/10.1142/s0219720008003370.

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Torsion angle alignment (TALI) is a novel approach to local structural motif alignment, based on backbone torsion angles (ϕ, ψ) rather than the more traditional atomic distance matrices. Representation of a protein structure in the form of a sequence of torsion angles enables easy integration of sequence and structural information, and adopts mature techniques in sequence alignment to improve performance and alignment quality. We show that TALI is able to match local structural motifs as well as identify global structural similarity. TALI is also compared to other structure alignment methods such as DALI, CE, and SSM, as well as sequence alignment based on PSI-BLAST; TALI is shown to be equally successful as, or more successful than, these other methods when applied to challenging structural alignments. The inference of the evolutionary tree of class II aminoacyl-tRNA synthetase shows the potential for TALI in estimating protein structural evolution and in identifying structural divergence among homologous structures. Availability: .

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Madhusudhan, M. S., Marc A. Marti-Renom, Roberto Sanchez, and Andrej Sali. "Variable gap penalty for protein sequence–structure alignment." Protein Engineering, Design and Selection 19, no. 3 (January 19, 2006): 129–33. http://dx.doi.org/10.1093/protein/gzj005.

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SALEM, SAEED, MOHAMMED J. ZAKI, and CHRISTOPHER BYSTROFF. "ITERATIVE NON-SEQUENTIAL PROTEIN STRUCTURAL ALIGNMENT." Journal of Bioinformatics and Computational Biology 07, no. 03 (June 2009): 571–96. http://dx.doi.org/10.1142/s0219720009004205.

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Structural similarity between proteins gives us insights into their evolutionary relationships when there is low sequence similarity. In this paper, we present a novel approach called SNAP for non-sequential pair-wise structural alignment. Starting from an initial alignment, our approach iterates over a two-step process consisting of a superposition step and an alignment step, until convergence. We propose a novel greedy algorithm to construct both sequential and non-sequential alignments. The quality of SNAP alignments were assessed by comparing against the manually curated reference alignments in the challenging SISY and RIPC datasets. Moreover, when applied to a dataset of 4410 protein pairs selected from the CATH database, SNAP produced longer alignments with lower rmsd than several state-of-the-art alignment methods. Classification of folds using SNAP alignments was both highly sensitive and highly selective. The SNAP software along with the datasets are available online at

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Kauffman, D. L., P. J. Keller, A. Bennick, and M. Blum. "Alignment of Amino Acid and DNA Sequences of Human Proline-rich Proteins." Critical Reviews in Oral Biology & Medicine 4, no. 3 (April 1993): 287–92. http://dx.doi.org/10.1177/10454411930040030501.

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Human proline-rich proteins (PRPs) constitute a complex family of salivary proteins that are encoded by a small number of genes. The primary gene product is cleaved by proteases, thereby giving rise to about 20 secreted proteins. To determine the genes for the secreted PRPs, therefore, it is necessary to obtain sequences of both the secreted proteins and the DNA encoding these proteins. We have sequenced most PRPs from one donor (D.K.) and aligned the protein sequences with available DNA sequences from unrelated individuals. Partial sequence data have now been obtained for an additional PRP from D.K. named II-1. This protein was purified from parotid saliva by gel filtration and ion-exchange chromatography. Peptides were obtained by cleavage with trypsin, clostripain, and N-bromosuccinimide, followed by column chromatography. The peptides were sequenced on a gas-phase protein sequenator. Overlapping peptide sequences were obtained for most of II-1 and aligned with translated DNA sequences. The best fit was obtained with clones containing sequences for the allele PRB4" (Lyons et al., 1988). However, there was not complete identity of the protein amino acid sequence and the DNA-derived sequences, indicating that II-1 is not encoded by PRB4". Other PRPs isolated from D.K. also fail to conform to any DNA structure so far reported. This shows the need to obtain amino acid sequences and corresponding DNA sequences from the same person to assign genes for the PRPs and to determine the location of the postribosomal cleavage points in the primary translation product.

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Manavalan, Mani. "Fast Model-based Protein Homology Discovery without Alignment." Asia Pacific Journal of Energy and Environment 1, no. 2 (December 31, 2014): 169–84. http://dx.doi.org/10.18034/apjee.v1i2.580.

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The need for quick gene categorization tools is growing as more genomes are sequenced. To evaluate a newly sequenced genome, the genes must first be identified and translated into amino acid sequences, which are then categorized into structural or functional classes. Protein homology detection using sequence alignment algorithms is the most effective way for protein categorization. Discriminative approaches such as support vector machines (SVMs) and position-specific scoring matrices (PSSM) derived from PSI-BLAST have recently been used to improve alignment algorithms. However, if a fresh sequence is being aligned, alignment algorithms take time. must be compared to a large number of previously published sequences — the same is true for SVMs. Building a PSSM for the PSSM is even more time-consuming than a fresh order It would take roughly 25 hours to implement the best-performing approaches to classify the sequences on today's computers. Describing a novel genome (20, 000 genes) as belonging to one single organism. There are hundreds of classes to choose from, though. Another flaw with alignment algorithms is that they do not construct a model of the positive class, instead of measuring the mutual distance between sequences or profiles. Only multiple alignments and hidden Markov models are common classification approaches for creating a positive class model, but they have poor classification performance. A model's advantage is that it may be evaluated for chemical features that are shared by all members of the class to get fresh insights into protein function and structure. We used LSTM to solve a well-known remote protein homology detection benchmark, in which a protein must be categorized as a member of the SCOP superfamily. LSTM achieves state-of-the-art classification performance while being significantly faster than other algorithms with similar classification performance. LSTM is five orders of magnitude quicker than the quickest SVM-based approaches and two orders of magnitude faster than methods that perform somewhat better in classification (which, however, have lower classification performance than LSTM). We applied LSTM to PROSITE classes and analyzed the derived patterns to test the modeling capabilities of the algorithm. Because it does not require established similarity metrics like BLOSUM or PAM matrices, LSTM is complementary to alignment-based techniques. The PROSITE motif was retrieved by LSTM in 8 out of 15 classes. In the remaining seven examples, alternative motifs are developed that, on average, outperform the PROSITE motifs in categorization.

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Th.Mevissen, Heina, and Martin Vingron. "Quantifying the local reliability of a sequence alignment." "Protein Engineering, Design and Selection" 9, no. 2 (1996): 127–32. http://dx.doi.org/10.1093/protein/9.2.127.

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Jeon, Yoon-Seong, Kihyun Lee, Sang-Cheol Park, Bong-Soo Kim, Yong-Joon Cho, Sung-Min Ha, and Jongsik Chun. "EzEditor: a versatile sequence alignment editor for both rRNA- and protein-coding genes." International Journal of Systematic and Evolutionary Microbiology 64, Pt_2 (February 1, 2014): 689–91. http://dx.doi.org/10.1099/ijs.0.059360-0.

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EzEditor is a Java-based molecular sequence editor allowing manipulation of both DNA and protein sequence alignments for phylogenetic analysis. It has multiple features optimized to connect initial computer-generated multiple alignment and subsequent phylogenetic analysis by providing manual editing with reference to biological information specific to the genes under consideration. It provides various functionalities for editing rRNA alignments using secondary structure information. In addition, it supports simultaneous editing of both DNA sequences and their translated protein sequences for protein-coding genes. EzEditor is, to our knowledge, the first sequence editing software designed for both rRNA- and protein-coding genes with the visualization of biologically relevant information and should be useful in molecular phylogenetic studies. EzEditor is based on Java, can be run on all major computer operating systems and is freely available from http://sw.ezbiocloud.net/ezeditor/.

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Lee, Sung Jong, Keehyoung Joo, Sangjin Sim, Juyong Lee, In-Ho Lee, and Jooyoung Lee. "CRFalign: A Sequence-Structure Alignment of Proteins Based on a Combination of HMM-HMM Comparison and Conditional Random Fields." Molecules 27, no. 12 (June 9, 2022): 3711. http://dx.doi.org/10.3390/molecules27123711.

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Sequence–structure alignment for protein sequences is an important task for the template-based modeling of 3D structures of proteins. Building a reliable sequence–structure alignment is a challenging problem, especially for remote homologue target proteins. We built a method of sequence–structure alignment called CRFalign, which improves upon a base alignment model based on HMM-HMM comparison by employing pairwise conditional random fields in combination with nonlinear scoring functions of structural and sequence features. Nonlinear scoring part is implemented by a set of gradient boosted regression trees. In addition to sequence profile features, various position-dependent structural features are employed including secondary structures and solvent accessibilities. Training is performed on reference alignments at superfamily levels or twilight zone chosen from the SABmark benchmark set. We found that CRFalign method produces relative improvement in terms of average alignment accuracies for validation sets of SABmark benchmark. We also tested CRFalign on 51 sequence–structure pairs involving 15 FM target domains of CASP14, where we could see that CRFalign leads to an improvement in average modeling accuracies in these hard targets (TM-CRFalign ≃42.94%) compared with that of HHalign (TM-HHalign ≃39.05%) and also that of MRFalign (TM-MRFalign ≃36.93%). CRFalign was incorporated to our template search framework called CRFpred and was tested for a random target set of 300 target proteins consisting of Easy, Medium and Hard sets which showed a reasonable template search performance.

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Kuchaiev, Oleksii, Tijana Milenković, Vesna Memišević, Wayne Hayes, and Nataša Pržulj. "Topological network alignment uncovers biological function and phylogeny." Journal of The Royal Society Interface 7, no. 50 (March 24, 2010): 1341–54. http://dx.doi.org/10.1098/rsif.2010.0063.

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Sequence comparison and alignment has had an enormous impact on our understanding of evolution, biology and disease. Comparison and alignment of biological networks will probably have a similar impact. Existing network alignments use information external to the networks, such as sequence, because no good algorithm for purely topological alignment has yet been devised. In this paper, we present a novel algorithm based solely on network topology, that can be used to align any two networks. We apply it to biological networks to produce by far the most complete topological alignments of biological networks to date. We demonstrate that both species phylogeny and detailed biological function of individual proteins can be extracted from our alignments. Topology-based alignments have the potential to provide a completely new, independent source of phylogenetic information. Our alignment of the protein–protein interaction networks of two very different species—yeast and human—indicate that even distant species share a surprising amount of network topology, suggesting broad similarities in internal cellular wiring across all life on Earth.

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Daniels, Noah M., Shilpa Nadimpalli, and Lenore J. Cowen. "Formatt: Correcting protein multiple structural alignments by incorporating sequence alignment." BMC Bioinformatics 13, no. 1 (2012): 259. http://dx.doi.org/10.1186/1471-2105-13-259.

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Carpentier, Mathilde, and Jacques Chomilier. "Protein multiple alignments: sequence-based versus structure-based programs." Bioinformatics 35, no. 20 (April 3, 2019): 3970–80. http://dx.doi.org/10.1093/bioinformatics/btz236.

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Abstract Motivation Multiple sequence alignment programs have proved to be very useful and have already been evaluated in the literature yet not alignment programs based on structure or both sequence and structure. In the present article we wish to evaluate the added value provided through considering structures. Results We compared the multiple alignments resulting from 25 programs either based on sequence, structure or both, to reference alignments deposited in five databases (BALIBASE 2 and 3, HOMSTRAD, OXBENCH and SISYPHUS). On the whole, the structure-based methods compute more reliable alignments than the sequence-based ones, and even than the sequence+structure-based programs whatever the databases. Two programs lead, MAMMOTH and MATRAS, nevertheless the performances of MUSTANG, MATT, 3DCOMB, TCOFFEE+TM_ALIGN and TCOFFEE+SAP are better for some alignments. The advantage of structure-based methods increases at low levels of sequence identity, or for residues in regular secondary structures or buried ones. Concerning gap management, sequence-based programs set less gaps than structure-based programs. Concerning the databases, the alignments of the manually built databases are more challenging for the programs. Availability and implementation All data and results presented in this study are available at: http://wwwabi.snv.jussieu.fr/people/mathilde/download/AliMulComp/. Supplementary information Supplementary data are available at Bioinformatics online.

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Piña, Johan S., Simon Orozco-Arias, Nicolas Tobón-Orozco, Leonardo Camargo-Forero, Reinel Tabares-Soto, and Romain Guyot. "G-SAIP: Graphical Sequence Alignment Through Parallel Programming in the Post-Genomic Era." Evolutionary Bioinformatics 19 (January 2023): 117693432211505. http://dx.doi.org/10.1177/11769343221150585.

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A common task in bioinformatics is to compare DNA sequences to identify similarities between organisms at the sequence level. An approach to such comparison is the dot-plots, a 2-dimensional graphical representation to analyze DNA or protein alignments. Dot-plots alignment software existed before the sequencing revolution, and now there is an ongoing limitation when dealing with large-size sequences, resulting in very long execution times. High-Performance Computing (HPC) techniques have been successfully used in many applications to reduce computing times, but so far, very few applications for graphical sequence alignment using HPC have been reported. Here, we present G-SAIP (Graphical Sequence Alignment in Parallel), a software capable of spawning multiple distributed processes on CPUs, over a supercomputing infrastructure to speed up the execution time for dot-plot generation up to 1.68× compared with other current fastest tools, improve the efficiency for comparative structural genomic analysis, phylogenetics because the benefits of pairwise alignments for comparison between genomes, repetitive structure identification, and assembly quality checking.

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CHUANG, LI-YEH, CHENG-HONG YANG, CHAO-CHING CHANG, WEN-SHYONG TZOU, and LI-CHENG JIN. "VSA-TOOL: A TOOL FOR DATA VISUALIZATION IN SEQUENCE ALIGNMENT." Biomedical Engineering: Applications, Basis and Communications 16, no. 02 (April 25, 2004): 68–72. http://dx.doi.org/10.4015/s1016237204000116.

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Sequence alignment is a fundamental and important tool for sequence data analysis in molecular biology. Many applications in molecular biology require the detection of a similarity pattern displayed by a number of DNA and protein sequences. Visual front-ends are useful for an intuitive viewing of alignment and help to analyze the structure, functions, and evolution of the DNA and protein. In this paper, we designed and implemented an interactive system for data visualization in DNA and proteins, which can be used in determining a sequence alignment, similarity search of sequence data, and function interference. Experimental results show that a user can easily operate the system after one hour's practice on the proposed system, which provides a clean output, easy identification of similarity and visualization of alignment data.

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Fallaize, Christopher J., Peter J. Green, Kanti V. Mardia, and Stuart Barber. "Bayesian protein sequence and structure alignment." Journal of the Royal Statistical Society: Series C (Applied Statistics) 69, no. 2 (January 8, 2020): 301–25. http://dx.doi.org/10.1111/rssc.12394.

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Gotoh, Osamu. "Direct mapping and alignment of protein sequences onto genomic sequence." Bioinformatics 24, no. 21 (August 26, 2008): 2438–44. http://dx.doi.org/10.1093/bioinformatics/btn460.

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Ahola, Virpi, Tero Aittokallio, Esa Uusipaikka, and Mauno Vihinen. "Statistical Methods for Identifying Conserved Residues in Multiple Sequence Alignment." Statistical Applications in Genetics and Molecular Biology 3, no. 1 (January 30, 2004): 1–28. http://dx.doi.org/10.2202/1544-6115.1074.

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The assessment of residue conservation in a multiple sequence alignment is a central issue in bioinformatics. Conserved residues and regions are used to determine structural and functional motifs or evolutionary relationships between the sequences of a multiple sequence alignment. For this reason, residue conservation is a valuable measure for database and motif search or for estimating the quality of alignments. In this paper, we present statistical methods for identifying conserved residues in multiple sequence alignments. While most earlier studies examine the positional conservation of the alignment, we focus on the detection of individual conserved residues at a position. The major advantages of multiple comparison methods originate from their ability to select conserved residues simultaneously and to consider the variability of the residue estimates. Large-scale simulations were used for the comparative analysis of the methods. Practical performance was studied by comparing the structurally and functionally important residues of Src homology 2 (SH2) domains to the assignments of the conservation indices. The applicability of the indices was also compared in three additional protein families comprising different degrees of entropy and variability in alignment positions. The results indicate that statistical multiple comparison methods are sensitive and reliable in identifying conserved residues.

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Roca, Alberto I., Aaron C. Abajian, and David J. Vigerust. "ProfileGrids solve the large alignment visualization problem: influenza hemagglutinin example." F1000Research 2 (January 4, 2013): 2. http://dx.doi.org/10.12688/f1000research.2-2.v1.

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Large multiple sequence alignments are a challenge for current visualization programs. ProfileGrids are a solution that reduces alignments to a matrix, color-shaded according to the residue frequency at each column position. ProfileGrids are not limited by the number of sequences and so solves this visualization problem. We demonstrate the new metadata searching and grep filtering features of the JProfileGrid version 2.0 software on an alignment of 11,900 hemagglutinin protein sequences. JProfileGrid is free and available from http://www.ProfileGrid.org.

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Kaur, Navjot, Rajbir Singh Cheema, and Harmandeep Singh Harmandeep Singh. "Multiple Sequence Alignment and Profile Analysis of Protein Family Utsing Hidden Markov Model." International Journal of Scientific Research 2, no. 6 (June 1, 2012): 208–11. http://dx.doi.org/10.15373/22778179/june2013/66.

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Lebsir, Rabah, Abdesslem Layeb, and Tahi Fariza. "A Greedy Clustering Algorithm for Multiple Sequence Alignment." International Journal of Cognitive Informatics and Natural Intelligence 15, no. 4 (October 2021): 1–17. http://dx.doi.org/10.4018/ijcini.20211001.oa41.

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This paper presents a strategy to tackle the Multiple Sequence Alignment (MSA) problem, which is one of the most important tasks in the biological sequence analysis. Its role is to align the sequences in their entirety to derive relationships and common characteristics between a set of protein or nucleotide sequences. The MSA problem was proved to be an NP-Hard problem. The proposed strategy incorporates a new idea based on the well-known divide and conquer paradigm. This paper presents a novel method of clustering sequences as a preliminary step to improve the final alignment; this decomposition can be used as an optimization procedure with any MSA aligner to explore promising alignments of the search space. In their solution, authors proposed to align the clusters in a parallel and distributed way in order to benefit from parallel architectures. The strategy was tested using classical benchmarks like BAliBASE, Sabre, Prefab4 and Oxm, and the experimental results show that it gives good results by comparing to the other aligners.

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Bond, Charles Simon, and Alexander Wolfgang Schüttelkopf. "ALINE: a WYSIWYG protein-sequence alignment editor for publication-quality alignments." Acta Crystallographica Section D Biological Crystallography 65, no. 5 (April 18, 2009): 510–12. http://dx.doi.org/10.1107/s0907444909007835.

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Sauder, J. Michael, Jonathan W. Arthur, and Roland L. Dunbrack Jr. "Large-scale comparison of protein sequence alignment algorithms with structure alignments." Proteins: Structure, Function, and Genetics 40, no. 1 (July 1, 2000): 6–22. http://dx.doi.org/10.1002/(sici)1097-0134(20000701)40:1<6::aid-prot30>3.0.co;2-7.

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Chen, Jing, and Jia Huang. "A novel network aligner for the analysis of multiple protein-protein interaction networks." Computer Science and Information Systems, no. 00 (2021): 30. http://dx.doi.org/10.2298/csis200909030c.

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The analysis of protein-protein interaction networks can transfer the knowledge of well-studied biological functions to functions that are not yet adequately investigated by constructing networks and extracting similar network structures in different species. Multiple network alignment can be used to find similar regions among multiple networks. In this paper, we introduce Accurate Combined Clustering Multiple Network Alignment (ACCMNA), which is a new and accurate multiple network alignment algorithm. It uses both topology and sequence similarity information. First, the importance of all the nodes is calculated according to the network structures. Second, the seed-and-extend framework is used to conduct an iterative search. In each iteration, a clustering method is combined to generate the alignment. Extensive experimental results show that ACCMNA outperformed the state-of-the-art algorithms in producing functionally consistent and topological conservation alignments within an acceptable running time.

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Pazos, Florencio. "Prediction of Protein Sites and Physicochemical Properties Related to Functional Specificity." Bioengineering 8, no. 12 (December 3, 2021): 201. http://dx.doi.org/10.3390/bioengineering8120201.

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Specificity Determining Positions (SDPs) are protein sites responsible for functional specificity within a family of homologous proteins. These positions are extracted from a family’s multiple sequence alignment and complement the fully conserved positions as predictors of functional sites. SDP analysis is now routinely used for locating these specificity-related sites in families of proteins of biomedical or biotechnological interest with the aim of mutating them to switch specificities or design new ones. There are many different approaches for detecting these positions in multiple sequence alignments. Nevertheless, existing methods report the potential SDP positions but they do not provide any clue on the physicochemical basis behind the functional specificity, which has to be inferred a-posteriori by manually inspecting these positions in the alignment. In this work, a new methodology is presented that, concomitantly with the detection of the SDPs, automatically provides information on the amino-acid physicochemical properties more related to the change in specificity. This new method is applied to two different multiple sequence alignments of homologous of the well-studied RasH protein representing different cases of functional specificity and the results discussed in detail.

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Md Isa, Mohd Nazrin, Sohiful Anuar Zainol Murad, Mohamad Imran Ahmad, Muhammad M. Ramli, and Rizalafande Che Ismail. "An Efficient Scheduling Technique for Biological Sequence Alignment." Applied Mechanics and Materials 754-755 (April 2015): 1087–92. http://dx.doi.org/10.4028/www.scientific.net/amm.754-755.1087.

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Computing alignment matrix score to search for regions of homology between biological sequences is time consuming task. This is due to the recursive nature of the dynamic programming-based algorithms such as the Smith-Waterman and the Needleman-Wunsch algorithmns. Typical FPGA-based protein sequencer comprises of two main logic blocks. One for computing alignment scores i.e. the processing element (PE), while another logic block for configuring the PE with coefficients. During alignment matrix computation, the logic block for configuring the PE are left unused until the time consuming alignment matrix computation finished. Therefore, a new technique, known as overlap computation and configuration (OCC) is proposed to minimize the time overhead for performing biological sequence alignment. The OCC technique simultaneously updating substitution matrix in a processing element (PE) systolic array, while computing alignment matrix scores. Results showed that, the sequencer achieves more than two order of magnitude speed-up higher compared to the state of the art, at negligible area overhead, if any.

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ESKIN, ELEAZAR, and SAGI SNIR. "INCORPORATING HOMOLOGUES INTO SEQUENCE EMBEDDINGS FOR PROTEIN ANALYSIS." Journal of Bioinformatics and Computational Biology 05, no. 03 (June 2007): 717–38. http://dx.doi.org/10.1142/s0219720007002734.

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Statistical and learning techniques are becoming increasingly popular for different tasks in bioinformatics. Many of the most powerful statistical and learning techniques are applicable to points in a Euclidean space but not directly applicable to discrete sequences such as protein sequences. One way to apply these techniques to protein sequences is to embed the sequences into a Euclidean space and then apply these techniques to the embedded points. In this work we introduce a biologically motivated sequence embedding, the homology kernel, which takes into account intuitions from local alignment, sequence homology, and predicted secondary structure. This embedding allows us to directly apply learning techniques to protein sequences. We apply the homology kernel in several ways. We demonstrate how the homology kernel can be used for protein family classification and outperforms state-of-the-art methods for remote homology detection. We show that the homology kernel can be used for secondary structure prediction and is competitive with popular secondary structure prediction methods. Finally, we show how the homology kernel can be used to incorporate information from homologous sequences in local sequence alignment.

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BIANCHETTI, LAURENT, JULIE DAWN THOMPSON, ODILE LECOMPTE, FREDERIC PLEWNIAK, and OLIVIER POCH. "vALId: VALIDATION OF PROTEIN SEQUENCE QUALITY BASED ON MULTIPLE ALIGNMENT DATA." Journal of Bioinformatics and Computational Biology 03, no. 04 (August 2005): 929–47. http://dx.doi.org/10.1142/s0219720005001326.

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The validation of sequences is essential to perform accurate phylogeny and structure/function analysis. However among the thousands of protein sequences available in the public databases, most have been predicted in silico and have not systematically undergone a quality verification. It has recently become evident that they often contain sequence errors. To address the problem of automatic protein quality control, we have developed vALId, an interactive web interfaced software. Taking advantage of high quality multiple alignments of complete protein sequences (MACS), vALId first warns about the presence of suspicious insertions, deletions (indels) and divergent segments, and second, proposes corrections based on transcripts and genome contigs. In a first evaluation test, hundreds of indels and divergent segments were randomly generated in a manually refined MACS. The sensitivity (Sn) and specificity (Sp) of indel detection were excellent (0.96) while the mean Sn(0.49) and Sp(0.56) of divergent segment delineation depended on the percent identity between sequence neighbors. In a second test, 6195 sequences in 100 MACS corresponding to different functional and structural protein families were analyzed. 65% of the sequences were in silico predictions and 44% of eukaryote predicted proteins were partially incorrect with at least one suspicious indel or divergent segment.

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43

Lee, Justin, and Shawn X. Wang. "A software tool for protein sequence alignment." International Journal of Bioinformatics Research and Applications 16, no. 4 (2020): 319. http://dx.doi.org/10.1504/ijbra.2020.113018.

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Lee, Justin, and Shawn X. Wang. "A software tool for protein sequence alignment." International Journal of Bioinformatics Research and Applications 16, no. 4 (2020): 319. http://dx.doi.org/10.1504/ijbra.2020.10035352.

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Somasundar, K., and S. Radhakrish. "Nimble Protein Sequence Alignment in Grid (NPSAG)." Journal of Computer Science 4, no. 1 (January 1, 2008): 36–41. http://dx.doi.org/10.3844/jcssp.2008.36.41.

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Md. Isa, Mohd Nazrin, Ku Noor Dhaniah Ku Muhsen, Dayana Saiful Nurdin, Muhammad Imran Ahmad, Sohiful Anuar Zainol Murad, Shaiful Nizam Mohyar, Azizi Harun, and Razaidi Hussin. "FPGA-based protein sequence alignment : A review." EPJ Web of Conferences 162 (2017): 01075. http://dx.doi.org/10.1051/epjconf/201716201075.

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Rangwala, H., and G. Karypis. "Incremental window-based protein sequence alignment algorithms." Bioinformatics 23, no. 2 (January 15, 2007): e17-e23. http://dx.doi.org/10.1093/bioinformatics/btl297.

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Elofsson, Arne. "A study on protein sequence alignment quality." Proteins: Structure, Function, and Genetics 46, no. 3 (January 29, 2002): 330–39. http://dx.doi.org/10.1002/prot.10043.

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Sherman, Westley Arthur, Durga Bhavani Kuchibhatla, Vachiranee Limviphuvadh, Sebastian Maurer-Stroh, Birgit Eisenhaber, and Frank Eisenhaber. "HPMV: Human protein mutation viewer — relating sequence mutations to protein sequence architecture and function changes." Journal of Bioinformatics and Computational Biology 13, no. 05 (October 2015): 1550028. http://dx.doi.org/10.1142/s0219720015500286.

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Next-generation sequencing advances are rapidly expanding the number of human mutations to be analyzed for causative roles in genetic disorders. Our Human Protein Mutation Viewer (HPMV) is intended to explore the biomolecular mechanistic significance of non-synonymous human mutations in protein-coding genomic regions. The tool helps to assess whether protein mutations affect the occurrence of sequence-architectural features (globular domains, targeting signals, post-translational modification sites, etc.). As input, HPMV accepts protein mutations — as UniProt accessions with mutations (e.g. HGVS nomenclature), genome coordinates, or FASTA sequences. As output, HPMV provides an interactive cartoon showing the mutations in relation to elements of the sequence architecture. A large variety of protein sequence architectural features were selected for their particular relevance to mutation interpretation. Clicking a sequence feature in the cartoon expands a tree view of additional information including multiple sequence alignments of conserved domains and a simple 3D viewer mapping the mutation to known PDB structures, if available. The cartoon is also correlated with a multiple sequence alignment of similar sequences from other organisms. In cases where a mutation is likely to have a straightforward interpretation (e.g. a point mutation disrupting a well-understood targeting signal), this interpretation is suggested. The interactive cartoon can be downloaded as standalone viewer in Java jar format to be saved and viewed later with only a standard Java runtime environment. The HPMV website is: http://hpmv.bii.a-star.edu.sg/ .

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Long, Hai Xia, Li Hua Wu, and Yu Zhang. "Multiple Sequence Alignment Based on Profile Hidden Markov Model and Quantum-Behaved Particle Swarm Optimization with Selection Method." Advanced Materials Research 282-283 (July 2011): 7–12. http://dx.doi.org/10.4028/www.scientific.net/amr.282-283.7.

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Multiple sequence alignment (MSA) is an NP-complete and important problem in bioinformatics. Currently, profile hidden Markov model (HMM) is widely used for multiple sequence alignment. In this paper, Quantum-behaved Particle Swarm Optimization with selection operation (SQPSO) is presented, which is used to train profile HMM. Furthermore, an integration algorithm based on the profile HMM and SQPSO for the MSA is constructed. The approach is examined by using multiple nucleotides and protein sequences and compared with other algorithms. The results of the comparisons show that the HMM trained with SQPSO and QPSO yield better alignments than other most commonly used HMM training methods such as Baum–Welch and PSO.

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Journal articles on the topic 'Protein sequence alignment'

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