Relevant bibliographies by topics / Protein sequence alignment

Academic literature on the topic 'Protein sequence alignment'

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Published: 14 March 2023

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Journal articles on the topic "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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Dissertations / Theses on the topic "Protein sequence alignment"

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Abhiman, Saraswathi. "Prediction of function shift in protein families /." Stockholm, 2006. http://diss.kib.ki.se/2006/91-7140-869-X/.

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Carroll, Hyrum D. "Biologically Relevant Multiple Sequence Alignment." Diss., CLICK HERE for online access, 2008. http://contentdm.lib.byu.edu/ETD/image/etd2623.pdf.

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Talbot, Danielle. "Identifying misalignments in sequence alignment for protein modelling." Thesis, University of Reading, 2005. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.445754.

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Garriga, Nogales Edgar 1990. "New algorithmic contributions for large scale multiple sequence alignments of protein sequences." Doctoral thesis, TDX (Tesis Doctorals en Xarxa), 2022. http://hdl.handle.net/10803/673526.

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In these days of significant changes and the rapid evolution of technology, the amount of datascience has to deal with the growth incredibly fast, and the size of data could be prohibitive.Multiple Sequence Alignments (MSA) are used in various areas of biology, and the increase ofdata has produced a degradation of the methods. That is why is proposed a new solution toperform the MSA. This novel paradigm allows the alignment of millions of sequences and theability to modularize the process. Regressive enables the parallelization of the process and thecombination of clustering methods (guide-tree) with whatever aligner is desired. On theclustering side, the guide-tree has to be rethought. A study of the current state of the methodsand their strength and weaknesses have been performed to shed some light on the topic. Theguide-tree cannot be the bottleneck, and it should provide a good starting point for the aligners.
En aquests dies de profunds canvis i una ràpida evolució de la tecnologia, la quantitat de dataque la ciència ha de treballar ha crescut increïblement ràpid i la grandària dels arxius ha crescutde manera quasi prohibitiva.Els alineaments múltiples de seqüència (MSA) es fan servir endiverses àrees de la biologia, i l'increment de les dades ha produït una degradació delsresultats. És per això, que es proposa una nova estratègia per realitzar els alineaments. Aquestnou paradigma permet alinear milions de seqüències i l'opcio de modularitzar el procés.'Regressive' permet la paral·lelització del procés i la combinació de diferents algoritmesd'agrupacio (guide-tree) amb el mètode de alineament que és desitgi. Dins del camp del'agrupació, s'ha de repensar l'estratègia per crear els guide-tree. Un estudi sobre l'estat actualdels mètodes i les seves virtuts i punts febles ha sigut realitzar per llençar una mica de llum enaquesta àrea. Els 'guide-tree' no poden ser el coll de botella, i haurien de servir per començarde la millor manera possible el procés d'alineament.
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Bonneau, Richard A. "Gene annotation using Ab initio protein structure prediction : method development and application to major protein families /." Thesis, Connect to this title online; UW restricted, 2001. http://hdl.handle.net/1773/9241.

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Lassmann, Timo. "Algorithms for building and evaluating multiple sequence alignments /." Stockholm, 2006. http://diss.kib.ki.se/2006/91-7140-887-8/.

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Hollich, Volker. "Orthology and protein domain architecture evolution /." Stockholm, 2006. http://diss.kib.ki.se/2006/91-7140-783-9/.

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Li, Yuheng. "Searching for remotely homologous sequences in protein databases with hybrid PSI-blast." The Ohio State University, 2006. http://rave.ohiolink.edu/etdc/view?acc_num=osu1164741421.

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DeBlasio, Dan, and John Kececioglu. "Core column prediction for protein multiple sequence alignments." BIOMED CENTRAL LTD, 2017. http://hdl.handle.net/10150/623957.

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Background: In a computed protein multiple sequence alignment, the coreness of a column is the fraction of its substitutions that are in so-called core columns of the gold-standard reference alignment of its proteins. In benchmark suites of protein reference alignments, the core columns of the reference alignment are those that can be confidently labeled as correct, usually due to all residues in the column being sufficiently close in the spatial superposition of the known three-dimensional structures of the proteins. Typically the accuracy of a protein multiple sequence alignment that has been computed for a benchmark is only measured with respect to the core columns of the reference alignment. When computing an alignment in practice, however, a reference alignment is not known, so the coreness of its columns can only be predicted. Results: We develop for the first time a predictor of column coreness for protein multiple sequence alignments. This allows us to predict which columns of a computed alignment are core, and hence better estimate the alignment's accuracy. Our approach to predicting coreness is similar to nearest-neighbor classification from machine learning, except we transform nearest-neighbor distances into a coreness prediction via a regression function, and we learn an appropriate distance function through a new optimization formulation that solves a large-scale linear programming problem. We apply our coreness predictor to parameter advising, the task of choosing parameter values for an aligner's scoring function to obtain a more accurate alignment of a specific set of sequences. We show that for this task, our predictor strongly outperforms other column-confidence estimators from the literature, and affords a substantial boost in alignment accuracy.
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Aniba, Mohamed Radhouane. "Knowledge based expert system development in bioinformatics : applied to multiple sequence alignment of protein sequences." Strasbourg, 2010. https://publication-theses.unistra.fr/public/theses_doctorat/2010/ANIBA_Mohamed_Radhouane_2010.pdf.

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L'objectif de ce projet de thèse a été le développement d'un système expert afin de tester, évaluer et d'optimiser toutes les étapes de la construction et l'analyse d'un alignement multiple de séquences. Le nouveau système a été validé en utilisant des alignements de référence et apporte une nouvelle vision pour le développement de logiciels en bioinformatique: les systèmes experts basés sur la connaissance. L'architecture utilisée pour construire le système expert est très modulaire et flexible, permettant à AlexSys d'évoluer en même temps que de nouveaux algorithmes seront mis à disposition. Ultérieurement, AlexSys sera utilisé pour optimiser davantage chaque étape du processus d'alignement, par exemple en optimisant les paramètres des différents programmes d 'alignement. Le moteur d'inférence pourrait également être étendu à identification des combinaisons d'algorithmes qui pourraient fournir des informations complémentaires sur les séquences. Par exemple, les régions bien alignées par différents algorithmes pourraient être identifiées et regroupées en un alignement consensus unique. Des informations structurales et fonctionnelles supplémentaires peuvent également être utilisées pour améliorer la précision de l'alignement final. Enfin, un aspect crucial de tout outil bioinformatique consiste en son accessibilité et la convivialité d' utilisation. Par conséquent, nous sommes en train de développer un serveur web, et un service web, nous allons également concevoir un nouveau module de visualisation qui fournira une interface intuitive et conviviale pour toutes les informa ions récupérées et construites par AlexSys
The objective of this PhD project was the development of an integrated expert system to test, evaluate and optimize all the stages of the construction and the analysis of a multiple sequence alignment. The new system was validated using standard benchmark cases and brings a ncw vision to software development in Bioinformatics: knowledge-guided systems. The architecture used to build the expert system is highly modular and flcxible, allowing AlcxSys to evolve as new algorithms are made available. In the future, AlexSys will he uscd to furthcr optimize each stage of the alignment process, for example by optimizing the input parameters of the different algorithms. The inference engine could also be extended to identify combinations of algorithms that could potentially provide complementary information about the input sequences. For example, well aligned regions from different aligners could be identified and combined into a single consensus alignment. Additional structural and functional information could also be exploited to improve the final alignment accuracy. Finally, a crucial aspect of any bioinformatics tool is its accessibility and usability. Therefore, we are currently developing a web server, and a web services based distributed system. We will also design a novel visualization module that will provide an intuitive, user-friendly interface to all the information retrieved and constructed by AlexSys
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Books on the topic "Protein sequence alignment"

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Russell, David James. Multiple sequence alignment methods. New York: Humana Press, 2014.

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Homology modeling: Methods and protocols. New York: Humana Press, 2012.

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Sheng wu ji suan: Sheng wu xu lie de fen xi fang fa yu ying yong. Beijing: Ke xue chu ban she, 2010.

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Book chapters on the topic "Protein sequence alignment"

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Do, Chuong B., and Kazutaka Katoh. "Protein Multiple Sequence Alignment." In Functional Proteomics, 379–413. Totowa, NJ: Humana Press, 2008. http://dx.doi.org/10.1007/978-1-59745-398-1_25.

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Barton, Geoffrey J., Robert B. Russell, and Craig D. Livingstone. "Prediction of Protein Structure from Multiple Sequence Alignment." In Methods in Protein Sequence Analysis, 209–20. Boston, MA: Springer US, 1993. http://dx.doi.org/10.1007/978-1-4899-1603-7_27.

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Shatsky, Maxim, Ruth Nussinov, and Haim J. Wolfson. "Algorithms for Multiple Protein Structure Alignment and Structure-Derived Multiple Sequence Alignment." In Protein Structure Prediction, 125–46. Totowa, NJ: Humana Press, 2008. http://dx.doi.org/10.1007/978-1-59745-574-9_5.

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Liu, Yongchao, and Bertil Schmidt. "Multiple Protein Sequence Alignment with MSAProbs." In Methods in Molecular Biology, 211–18. Totowa, NJ: Humana Press, 2013. http://dx.doi.org/10.1007/978-1-62703-646-7_14.

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Barton, Geoffrey J. "The AMPS Package for Multiple Protein Sequence Alignment." In Computer Analysis of Sequence Data, 327–47. Totowa, NJ: Humana Press, 1994. http://dx.doi.org/10.1385/0-89603-276-0:327.

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Do, Chuong B., Samuel S. Gross, and Serafim Batzoglou. "CONTRAlign: Discriminative Training for Protein Sequence Alignment." In Lecture Notes in Computer Science, 160–74. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11732990_15.

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Higgins, Desmond G. "Clustal V: Multiple Alignment of DNA and Protein Sequences." In Computer Analysis of Sequence Data, 307–18. Totowa, NJ: Humana Press, 1994. http://dx.doi.org/10.1385/0-89603-276-0:307.

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Kumar, Manish, Ranjeet Kumar, and R. Nidhya. "WOAMSA: Whale Optimization Algorithm for Multiple Sequence Alignment of Protein Sequence." In Computational Vision and Bio-Inspired Computing, 131–39. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-37218-7_15.

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Manikandan, P., and D. Ramyachitra. "Influence of Parameters in Multiple Sequence Alignment Methods for Protein Sequences." In Advances in Intelligent Systems and Computing, 183–91. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-7871-2_18.

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Reizer, Aiala, and Jonathan Reizer. "Progressive Multiple Alignment of Protein Sequences and the Construction of Phylogenetic Trees." In Computer Analysis of Sequence Data, 319–25. Totowa, NJ: Humana Press, 1994. http://dx.doi.org/10.1385/0-89603-276-0:319.

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Conference papers on the topic "Protein sequence alignment"

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Hasan, L., M. Kentie, and Z. Al-Ars. "GPU-accelerated protein sequence alignment." In 2011 33rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE, 2011. http://dx.doi.org/10.1109/iembs.2011.6090679.

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Midic, Uros, A. Keith Dunker, and Zoran Obradovic. "Protein sequence alignment and structural disorder." In the KDD-09 Workshop. New York, New York, USA: ACM Press, 2009. http://dx.doi.org/10.1145/1562090.1562096.

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Hung, Che-Lun, Chun-Yuan Lin, Yeh-Ching Chung, and Chuan Yi Tang. "Introducing Variable Gap Penalties into Three-Sequence Alignment for Protein Sequences." In 22nd International Conference on Advanced Information Networking and Applications - Workshops (aina workshops 2008). IEEE, 2008. http://dx.doi.org/10.1109/waina.2008.101.

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Carvalho, Leonardo Reboucas de, Alba Cristina Alves Melo, and Aleteia Araujo. "A Framework for Executing Protein Sequence Alignment in Cloud Computing Services." In Simpósio em Sistemas Computacionais de Alto Desempenho. Sociedade Brasileira de Computação, 2021. http://dx.doi.org/10.5753/wscad.2021.18511.

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Protein sequence alignment is a task of great relevance in Bioinformatics and the Hirschberg algorithm is widely used for this task. This work proposes a framework for executing sequence alignment with the Hirschberg algorithm in different cloud computing services. In experiments, our framework was used to align HIV-1 protease sequences using different instances of AWS EC2 and different configurations of AWS Lambda functions.The results show that, for this application, there is a tradeoff between the expected execution time and the cost, e.g., in most cases AWS Lambda provides the best runtime, however at a higher USD cost. In this context, it is important to have a framework that helps in deciding which approach is most appropriate.
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Wise, Michael J. "Alignment algorithms revisited: Alignment algorithms for low similarity protein sequence comparisons." In 2003 European Control Conference (ECC). IEEE, 2003. http://dx.doi.org/10.23919/ecc.2003.7086563.

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Ait, Layal Al, Eduardo Corel, and Burkhard Morgenstern. "Using protein-domain information for multiple sequence alignment." In 2012 IEEE 12th International Conference on Bioinformatics & Bioengineering (BIBE). IEEE, 2012. http://dx.doi.org/10.1109/bibe.2012.6399667.

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Nord, Alex, Peter Hornbeck, Kaitlin Carey, and Travis Wheeler. "Splice-Aware Multiple Sequence Alignment of Protein Isoforms." In BCB '18: 9th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics. New York, NY, USA: ACM, 2018. http://dx.doi.org/10.1145/3233547.3233592.

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"IMPROVEMENTS TO A MULTIPLE PROTEIN SEQUENCE ALIGNMENT TOOL." In International Conference on Bioinformatics Models, Methods and Algorithms. SciTePress - Science and and Technology Publications, 2012. http://dx.doi.org/10.5220/0003789202260233.

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Iryanto, Syam B., Wisnu A. Kusuma, Rifki Sadikin, and I. Wayan A. Swardiana. "GPU-accelerated protein sequence alignment for Jamu prediction." In 2017 International Conference on Computer, Control, Informatics and its Applications (IC3INA). IEEE, 2017. http://dx.doi.org/10.1109/ic3ina.2017.8251754.

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Qi-wen Dong, Lei Lin, Xiao-Long Wang, and Ming-Hui Li. "Contact-based Simulated Annealing Protein Sequence Alignment Method." In 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference. IEEE, 2005. http://dx.doi.org/10.1109/iembs.2005.1617054.

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Reports on the topic "Protein sequence alignment"

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Rangwala, Huzefa, and George Karypis. Incremental Window-based Protein Sequence Alignment Algorithms. Fort Belvoir, VA: Defense Technical Information Center, March 2006. http://dx.doi.org/10.21236/ada444856.

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Rafaeli, Ada, and Russell Jurenka. Molecular Characterization of PBAN G-protein Coupled Receptors in Moth Pest Species: Design of Antagonists. United States Department of Agriculture, December 2012. http://dx.doi.org/10.32747/2012.7593390.bard.

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The proposed research was directed at determining the activation/binding domains and gene regulation of the PBAN-R’s thereby providing information for the design and screening of potential PBAN-R-blockers and to indicate possible ways of preventing the process from proceeding to its completion. Our specific aims included: (1) The identification of the PBAN-R binding domain by a combination of: (a) in silico modeling studies for identifying specific amino-acid side chains that are likely to be involved in binding PBAN with the receptor and; (b) bioassays to verify the modeling studies using mutant receptors, cell lines and pheromone glands (at tissue and organism levels) against selected, designed compounds to confirm if compounds are agonists or antagonists. (2) The elucidation ofthemolecular regulationmechanisms of PBAN-R by:(a) age-dependence of gene expression; (b) the effect of hormones and; (c) PBAN-R characterization in male hair-pencil complexes. Background to the topic Insects have several closely related G protein-coupled receptors (GPCRs) belonging to the pyrokinin/PBAN family, one with the ligand pheromone biosynthesis activating neuropeptide or pyrokinin-2 and another with diapause hormone or pyrokinin-1 as a ligand. We were unable to identify the diapause hormone receptor from Helicoverpa zea despite considerable effort. A third, related receptor is activated by a product of the capa gene, periviscerokinins. The pyrokinin/PBAN family of GPCRs and their ligands has been identified in various insects, such as Drosophila, several moth species, mosquitoes, Triboliumcastaneum, Apis mellifera, Nasoniavitripennis, and Acyrthosiphon pisum. Physiological functions of pyrokinin peptides include muscle contraction, whereas PBAN regulates pheromone production in moths plus other functions indicating the pleiotropic nature of these ligands. Based on the alignment of annotated genomic sequences, the primary and secondary structures of the pyrokinin/PBAN family of receptors have similarity with the corresponding structures of the capa or periviscerokinin receptors of insects and the neuromedin U receptors found in vertebrates. Major conclusions, solutions, achievements Evolutionary trace analysisof receptor extracellular domains exhibited several class-specific amino acid residues, which could indicate putative domains for activation of these receptors by ligand recognition and binding. Through site-directed point mutations, the 3rd extracellular domain of PBAN-R was shown to be critical for ligand selection. We identified three receptors that belong to the PBAN family of GPCRs and a partial sequence for the periviscerokinin receptor from the European corn borer, Ostrinianubilalis. Functional expression studies confirmed that only the C-variant of the PBAN-R is active. We identified a non-peptide agonist that will activate the PBAN-receptor from H. zea. We determined that there is transcriptional control of the PBAN-R in two moth species during the development of the pupa to adult, and we demonstrated that this transcriptional regulation is independent of juvenile hormone biosynthesis. This transcriptional control also occurs in male hair-pencil gland complexes of both moth species indicating a regulatory role for PBAN in males. Ultimate confirmation for PBAN's function in the male tissue was revealed through knockdown of the PBAN-R using RNAi-mediated gene-silencing. Implications, both scientific and agricultural The identification of a non-peptide agonist can be exploited in the future for the design of additional compounds that will activate the receptor and to elucidate the binding properties of this receptor. The increase in expression levels of the PBAN-R transcript was delineated to occur at a critical period of 5 hours post-eclosion and its regulation can now be studied. The mysterious role of PBAN in the males was elucidated by using a combination of physiological, biochemical and molecular genetics techniques.
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Rafaeli, Ada, Russell Jurenka, and Chris Sander. Molecular characterisation of PBAN-receptors: a basis for the development and screening of antagonists against Pheromone biosynthesis in moth pest species. United States Department of Agriculture, January 2008. http://dx.doi.org/10.32747/2008.7695862.bard.

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The original objectives of the approved proposal included: (a) The determination of species- and tissue-specificity of the PBAN-R; (b) the elucidation of the role of juvenile hormone in gene regulation of the PBAN-R; (c) the identificationof the ligand binding domains in the PBAN-R and (d) the development of efficient screening assays in order to screen potential antagonists that will block the PBAN-R. Background to the topic: Moths constitute one of the major groups of pest insects in agriculture and their reproductive behavior is dependent on chemical communication. Sex-pheromone blends are utilised by a variety of moth species to attract conspecific mates. In most of the moth species sex-pheromone biosynthesis is under circadian control by the neurohormone, PBAN (pheromone-biosynthesis-activating neuropeptide). In order to devise ideal strategies for mating disruption/prevention, we proposed to study the interactions between PBAN and its membrane-bound receptor in order to devise potential antagonists. Major conclusions: Within the framework of the planned objectives we have confirmed the similarities between the two Helicoverpa species: armigera and zea. Receptor sequences of the two Helicoverpa spp. are 98% identical with most changes taking place in the C-terminal. Our findings indicate that PBAN or PBAN-like receptors are also present in the neural tissues and may represent a neurotransmitter-like function for PBAN-like peptides. Surprisingly the gene encoding the PBAN-receptor was also present in the male homologous tissue, but it is absent at the protein level. The presence of the receptor (at the gene- and protein-levels), and the subsequent pheromonotropic activity are age-dependent and up-regulated by Juvenile Hormone in pharate females but down-regulated by Juvenile Hormone in adult females. Lower levels of pheromonotropic activity were observed when challenged with pyrokinin-like peptides than with HezPBAN as ligand. A model of the 3D structure of the receptor was created using the X-ray structure of rhodopsin as a template after sequence alignment of the HezPBAN-R with several other GPCRs and computer simulated docking with the model predicted putative binding sites. Using in silico mutagenesis the predicted docking model was validated with experimental data obtained from expressed chimera receptors in Sf9 cells created by exchanging between the three extracellular loops of the HezPBAN-R and the Drosophila Pyrokinin-R (CG9918). The chimera receptors also indicated that the 3ʳᵈ extracellular loop is important for recognition of PBAN or Diapause hormone ligands. Implications: The project has successfully completed all the objectives and we are now in a position to be able to design and screen potential antagonists for pheromone production. The successful docking simulation-experiments encourage the use of in silico experiments for initial (high-throughput) screening of potential antagonists. However, the differential responses between the expressed receptor (Sf9 cells) and the endogenous receptor (pheromone glands) emphasize the importance of assaying lead compounds using several alternative bioassays (at the cellular, tissue and organism levels). The surprising discovery of the presence of the gene encoding the PBAN-R in the male homologous tissue, but its absence at the protein level, launches opportunities for studying molecular regulation pathways and the evolution of these GPCRs. Overall this research will advance research towards the goal of finding antagonists for this important class of receptors that might encompass a variety of essential insect functions.
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