Relevant bibliographies by topics / Homology modeling

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Homology modeling'

Author: Grafiati
Published: 4 June 2021
Last updated: 15 March 2025

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Journal articles on the topic "Homology modeling"

1

Pedersen, Jan, Stephen Searle, Andrew Henry, and Anthony R. Rees. "Antibody modeling: Beyond homology." ImmunoMethods 1, no. 2 (October 1992): 126–36. http://dx.doi.org/10.1016/s1058-6687(05)80035-x.

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Agami, Sarit, and Robert J. Adler. "Modeling of persistent homology." Communications in Statistics - Theory and Methods 49, no. 20 (May 20, 2019): 4871–88. http://dx.doi.org/10.1080/03610926.2019.1615091.

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Rashmi, Rashmi, Sunil Kumar Rai, M. Shah M. Shah, Dinesh Kumar Baitha, and Dr Royana Singh. "Structural Classification of Pax7 Using Homology Modeling: A Functional Approach." Indian Journal of Applied Research 4, no. 5 (October 1, 2011): 430–31. http://dx.doi.org/10.15373/2249555x/may2014/133.

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IWADATE, Mitsuo, and Hideaki UMEYAMA. "FAMS: A Homology Modeling Program." Seibutsu Butsuri 42, no. 6 (2002): 282–84. http://dx.doi.org/10.2142/biophys.42.282.

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Sudarsanam, Sucha, Carl J. March, and Subhashini Srinivasan. "Homology Modeling of Divergent Proteins." Journal of Molecular Biology 241, no. 2 (August 1994): 143–49. http://dx.doi.org/10.1006/jmbi.1994.1484.

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MENG, Zhen, Xiaoyan YOU, Chengying JIANG, and Juncai MA. "Homology Modeling for Sulfur Oxygenase/Reductase." Chinese Journal of Appplied Environmental Biology 16, no. 3 (August 20, 2010): 424–28. http://dx.doi.org/10.3724/sp.j.1145.2010.00424.

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Xiang, Zhexin. "Advances in Homology Protein Structure Modeling." Current Protein & Peptide Science 7, no. 3 (June 1, 2006): 217–27. http://dx.doi.org/10.2174/138920306777452312.

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Moutran, A., A. Balan, C. S. Perez, L. C. S. Ferreira, R. C. C. Ferreira, and G. Neshich. "Homology modeling ofXanthomonas Citrimolybdate-binding protein." Acta Crystallographica Section A Foundations of Crystallography 61, a1 (August 23, 2005): c168. http://dx.doi.org/10.1107/s0108767305092834.

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Viitanen, L., and T. A. Salminen. "Homology modeling ofArabidopsis thalianaglycolipid transfer protein." Acta Crystallographica Section A Foundations of Crystallography 64, a1 (August 23, 2008): C227—C228. http://dx.doi.org/10.1107/s0108767308092696.

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Kapp, Oscar H., and Jogeshwar Mukherjee. "MODELING OF RECEPTOR PROTEINS USING HOMOLOGY." INVESTIGATIVE RADIOLOGY 28, no. 12 (December 1993): 1212. http://dx.doi.org/10.1097/00004424-199312000-00149.

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Dissertations / Theses on the topic "Homology modeling"

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Meier, Armin. "Probabilistic protein homology modeling." Diss., Ludwig-Maximilians-Universität München, 2014. http://nbn-resolving.de/urn:nbn:de:bvb:19-171299.

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Searching sequence databases and building 3D models for proteins are important tasks for biologists. When the structure of a query protein is given, its function can be inferred. However, experimental methods for structure prediction are both expensive and time consuming. Fully automatic homology modeling refers to building a 3D model for a query sequence from an alignment to related homologous proteins with known structure (templates) by a computer. Current prediction servers can provide accurate models within a few hours to days. Our group has developed HHpred, which is one of the top performing structure prediction servers in the field. In general, homology based structure modeling consists of four steps: (1) finding homologous templates in a database, (2) selecting and (3) aligning templates to the query, (4) building a 3D model based on the alignment. In part one of this thesis, we will present improvements of step (2) and (4). Specifically, homology modeling has been shown to work best when multiple templates are selected instead of only a single one. Yet, current servers are using rather ad-hoc approaches to combine information from multiple templates. We provide a rigorous statistical framework for multi-template homology modeling. Given an alignment, we employ Modeller to calculate the most probable structure for a query. The 3D model is obtained by optimally satisfying spatial restraints derived from the alignment and expressed as probability density functions. We find that the query’s atomic distance restraints can be accurately described by two-component Gaussian mixtures. Moreover, we derive statistical weights to quantify the redundancy among related templates. This allows us to apply the standard rules of probability theory to combine restraints from several templates. Together with a heuristic template selection strategy, we have implemented this approach within HHpred and could significantly improve model quality. Furthermore, we took part in CASP, a community wide competition for structure prediction, where we were ranked first in template based modeling and, at the same time, were more than 450 times faster than all other top servers. Homology modeling heavily relies on detecting and correctly aligning templates to the query sequence (step (1) and (3) from above). But remote homologies are difficult to detect and hard to align on a pure sequence level. Hence, modern tools are based on profiles instead of sequences. A profile summarizes the evolutionary history of a given sequence and consists of position specific amino acid probabilities for each residue. In addition to the similarity score between profile columns, most methods use extra terms that compare 1D structural properties such as secondary structure or solvent accessibility. These can be predicted from local profile windows. In the second part of this thesis, we develop a new score that is independent of any predefined structural property. For this purpose, we learn a library of 32 profile patterns that are most conserved in alignments of remotely homologous, structurally aligned proteins. Each so called “context state” in the library consists of a 13-residue sequence profile. We integrate the new context score into our Hmm-Hmm alignment tool HHsearch and improve especially the sensitivity and precision of difficult pairwise alignments significantly. Taken together, we introduced probabilistic methods to improve all four main steps in homology based structure prediction.
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Diemand, Alexander Vasil. "Development of homology modeling techniques." [S.l. : s.n.], 2006.

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Chang, Jia-Ming 1978. "Influence of alignment uncertainty on homology and phylogenetic modeling." Doctoral thesis, Universitat Pompeu Fabra, 2013. http://hdl.handle.net/10803/129301.

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Most evolutionary analyses are based upon pre-estimated multiple sequence alignment models. From a computational point of view, it is too complex to estimate a correct alignment, as it is to derive a correct tree from that alignment. Several works have recently reported on the influence of alignment on downstream analysis, and on the uncertainty inherent to their estimation. Chapter 1 develops the notion of alignment uncertainty as either inherent to the data (internal) or resulting from methodological biases (external). Chapter 2 presents two contributions of mine for the improvement of MSA methods through the use of homology extension (TM-Coffee) and thanks to an improved word-matching algorithm (SymAlign). In Chapter 3, I show how alignment uncertainty can be used to improve the trustworthiness of phylogenetic analysis. Chapter 4 shows how a similar improvement can be obtained through a simple adaptation of the T-Coffee transitive score, thus allowing downstream analysis to take into account internal alignment uncertainty. The final chapter contained a discussion of our current results and possible future work.
La mayoría de los análisis evolutivos están basados en modelos establecidos de alineamiento de secuencia múltiple. Desde un punto de vista computacional, es igual de complejo la estimación de un alineamiento correcto, como la obtención de un árbol correcto a partir del alineamiento. Recientemente varios trabajos han informado sobre la influencia del alineamiento en los análisis posteriores, y en la incertidumbre inherente a su estimación. El Capítulo 1 desarrolla el concepto de incertidumbre de alineación, tanto inherente a los datos (internos), como resultante de los sesgos metodológicos (externo). El Capítulo 2 presenta dos contribuciones mías para la mejora de los métodos de MSA a través del uso de la extensión de homología (TM‐Coffee) y gracias a un algoritmo de coincidencia de palabra mejorado (SymAlign). En el capítulo 3, se muestra cómo la incertidumbre de alineación puede ser utilizada para mejorar la confiabilidad del análisis filogenético. El capítulo 4 nos muestra como se puede obtener una mejora similar por medio de una simple adaptación de la puntuación transitiva del T-- Coffee, lo cual permite un análisis posterior para tener en cuenta la incertidumbre de alineación interna. El último capítulo contiene un análisis de los resultados actuales y los posibles futuros trabajos.
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Wei, Tiandi. "Homology Modeling of Toll-Like Receptor Ligand-Binding Domains." Diss., lmu, 2010. http://nbn-resolving.de/urn:nbn:de:bvb:19-115642.

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Meier, Armin [Verfasser], and Johannes [Akademischer Betreuer] Söding. "Probabilistic protein homology modeling / Armin Meier. Betreuer: Johannes Söding." München : Universitätsbibliothek der Ludwig-Maximilians-Universität, 2014. http://d-nb.info/1053913818/34.

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LIMA, Sheyla Carla Barbosa da Silva. "Isolamento e caracterização in silico de ciclotídeos em milho (Zea mays) e centeio (Secale cereale)." Universidade Federal de Pernambuco, 2015. https://repositorio.ufpe.br/handle/123456789/16744.

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Submitted by Haroudo Xavier Filho (haroudo.xavierfo@ufpe.br) on 2016-04-20T16:40:36Z No. of bitstreams: 2 license_rdf: 1232 bytes, checksum: 66e71c371cc565284e70f40736c94386 (MD5) Dissertacao_SheylaSilvaLima_2015.pdf: 4958893 bytes, checksum: 21511e1c9e1a86ea210befeb33c91543 (MD5)
Made available in DSpace on 2016-04-20T16:40:36Z (GMT). No. of bitstreams: 2 license_rdf: 1232 bytes, checksum: 66e71c371cc565284e70f40736c94386 (MD5) Dissertacao_SheylaSilvaLima_2015.pdf: 4958893 bytes, checksum: 21511e1c9e1a86ea210befeb33c91543 (MD5) Previous issue date: 2015-02-24
FACEPE
Ciclotídeos são uma classe de peptídeos antimicrobianos (AMPs - do inglês Antimicrobial peptide) cíclicos de plantas, compostos de, aproximadamente, 30 resíduos de aminoácidos, sendo seis cisteínas conservadas e conectadas por três pontes de dissulfeto. Sua expressão é constitutiva, tendo sua principal função na defesa vegetal contra patógenos, que podem causar perdas significativas em culturas importantes para a agricultura, como no caso da família Poaceae que apresenta destacada importância econômica no Brasil e no mundo. Nesse estudo foi conduzida uma busca por genes relacionados a ciclotídeos vegetais, disponíveis em bancos de dados de acesso restrito e público, com vistas ao isolamento e caracterização in silico desses peptídeos. Através da busca nos genomas de Hevea brasiliensis, Manihot esculenta, Ricinus communis, Sorghum bicolor e Zea mays; bem como no transcriptoma de Vigna unguiculata foi verificado que apenas o genoma de Zea mays apresentou dois possíveis genes codificadores de ciclotídeos. Assim, primers foram desenhados para o isolamento destes genes em milho. Além da espécie Z. mays, as espécies Triticum aestivum (trigo) e Secale cereale (centeio), foram utilizadas para a tentativa de isolamento a partir dos pares de primers desenhados. Foram obtidos 19 fragmentos (amplicons), sendo quatro deles (zm315, zm316, zm317, sc359) com o domínio ciclotídeo, os três primeiros de milho e o último de centeio. Essas quatro sequências foram, então, submetidas a uma caracterização in silico, para predição da estrutura secundaria, terciaria e função predita. Verificou-se que esses peptídeos apresentam as seis cisteínas conservadas, três pontes dissulfeto e o padrão de aminoácidos entre as cisteínas, similar aos encontrados em ciclotídeos. Ainda foi possível a predição de algumas propriedades físico-químicas e modelagem por homologia para as quatro proteínas, o que mostrou a qualidade e confiabilidade dos modelos. Sugere-se que dois dos ciclotídeos isolados (zm315, zm316) pertençam a uma nova classe de peptídeos lineares, mas com características de ciclotídeos.
Cyclotides are a class of cyclic antimicrobial peptides (AMPs) present on plants, composed by approximately 30 amino acid residues, including six conserved cysteines connected by three disulphide bridges. Its expression is constitutive, with main function on plant defense against pathogens, that may cause significant losses in important cultivars, as in the case of Poaceae, a family that presents economic importance for the agriculture in Brazil and worldwide. This study performed a search for genes related to plant cyclotides, available in restricted and public access databases, aimed at their in silico isolation and characterization. Searching for these peptides in Hevea brasiliensis, Manihot esculenta, Ricinus communis, Sorghum bicolor, Vigna unguiculata and Zea mays genomes, we obtained two possible genes encoding Cyclotides in Z. mays. Thus, primers were designed for the isolation of these genes in maize as well in wheat (Triticum aestivum) and rye (Secale cereale) species. We obtained 19 amplicons and four of them (zm315, zm316, zm317, sc359) presented cyclotide domain. These four sequences were then subjected to in silico characterization, for predicting their secondary and tertiary structures, as well their function. It was found that these peptides present six conserved cysteines, three disulphide bridges and the amino acid pattern between the cysteines similar to those found in cyclotides. It was also possible to predict some physical chemical properties and also building a 3D protein by homology modeling for the four peptides, presenting high quality and reliability. Our analysis indicates that two isolated cyclotides (zm315, zm316) appear to belong to a new class of linear peptides, but with cyclotide features.
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López, Muñoz Laura. "Homology modeling and structural analysis of the antipsychotic drugs receptorome." Doctoral thesis, Universitat Pompeu Fabra, 2010. http://hdl.handle.net/10803/7228.

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Classically it was assumed that the compounds with therapeutic effect exert their action interacting with a single receptor. Nowadays it is widely recognized that the pharmacological effect of most drugs is more complex and involves a set of receptors, some associated to their positive effects and some others to the side effects and toxicity. Antipsychotic drugs are an example of effective compounds characterized by a complex pharmacological profile binding to several receptors (mainly G protein-coupled-receptors, GPCR). In this work we will present a detailed study of known antipsychotic drugs and the receptors potentially involved in their binding profile, in order to understand the molecular mechanisms of the antipsychotic pharmacologic effects.

The study started with obtaining homology models for all the receptors putatively involved in the antipsychotic drugs receptorome, suitable for building consistent drug-receptor complexes. These complexes were structurally analyzed and compared using multivariate statistical methods, which in turn allowed the identification of the relationship between the pharmacological properties of the antipsychotic drugs and the structural differences in the receptor targets. The results can be exploited for the design of safer and more effective antipsychotic drugs with an optimum binding profile.
Tradicionalmente se asumía que los fármacos terapéuticamente efectivos actuaban interaccionando con un único receptor. Actualmente está ampliamente reconocido que el efecto farmacológico de la mayoría de los fármacos es más complejo y abarca a un conjunto de receptores, algunos asociados a los efectos terapéuticos y otros a los secundarios y toxicidad. Los fármacos antipsicóticos son un ejemplo de compuestos eficaces que se caracterizan por unirse a varios receptores simultáneamente (principalmente a receptores unidos a proteína G, GPCR). El trabajo de la presente tesis se ha centrado en el estudio de los mecanismos moleculares que determinan el perfil de afinidad de unión por múltiples receptores de los fármacos antipsicóticos.

En primer lugar se construyeron modelos de homología para todos los receptores potencialmente implicados en la actividad farmacológica de dichos fármacos, usando una metodología adecuada para construir complejos fármaco-receptor consistentes. La estructura de estos complejos fue analizada y se llevó a cabo una comparación mediante métodos estadísticos multivariantes, que permitió la identificación de asociaciones entre la actividad farmacológica de los fármacos antipsicóticos y diferencias estructurales de los receptores diana. Los resultados obtenidos tienen interés para ser explotados en el diseño de fármacos antipsicóticos con un perfil farmacológico óptimo, más seguros y eficaces.
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Grundy, William Noble. "A bayesian approach to motif-based protein modeling /." Diss., Connect to a 24 p. preview or request complete full text in PDF format. Access restricted to UC campuses, 1998. http://wwwlib.umi.com/cr/ucsd/fullcit?p9904723.

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Stanton, Suzanne Louise. "Homology Modeling and Molecular Docking of Antagonists to Class B G-Protein Coupled Receptor Pituitary Adenylate Cyclase Type 1 (PAC1R)." ScholarWorks @ UVM, 2016. http://scholarworks.uvm.edu/graddis/624.

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Recent studies have identified the Class B g-protein coupled receptor (GPCR) pituitary adenylate cyclase activating polypeptide type 1 (PAC1R) as a key component in physiological stress management. Over-activity of neurological stress response systems due to prolonged or extreme exposure to traumatic events has led researchers to investigate PAC1R inhibition as a possible treatment for anxiety disorders such as post-traumatic stress disorder (PTSD). In 2008, Beebe and coworkers identified two such small molecule hydrazide antagonists and a general pharmacaphore for PAC1R inhibition. However, a relative dearth of information about Class B GPCRs in general, and PAC1R in specific, has significantly hindered progress toward the development of small molecule antagonists of PAC1R. The recent crystallization of the homologically similar glucagon receptor (GCGR) by Siu and coworkers in 2013, also a Class B receptor, has provided an experimentally resolved template from which to base computationally derived models of PAC1R. Initially, this research was focused towards synthesizing small molecule antagonists for PAC1R which were to be biologically screened via a qualitative western blot assay followed by a radioisotope binding assay for those hydrazides exhibiting down-stream signaling inhibitory capabilities. However, the resolution of the GCGR crystal structure shifted research objectives towards developing a homology model of PAC1R and evaluating that computationally created model with Beebe's known small molecule antagonists. Created using academic versions of on-line resources including UniProtKB, Swiss-Model and Maestro, a homology model for PAC1R is presented here. The model is validated and evaluated for the presence of conserved Class B GPCR residues and motifs, including expected disulfide bridges, a conserved tyrosine residue, a GWGxP motif, a conserved glutamic acid residue and the extension of the transmembrane helix 1 (TM1) into the extra-cellular domain. Having determined this virtual PAC1R an acceptable model, ligand docking studies of known antagonists to the receptor were undertaken using AutoDock Vina in conjunction with AutoDock Tools and PyMol. Computational docking results were evaluated via comparison of theoretical binding affinity results to Beebe's experimental data. Based on hydrogen bonding capabilities, several residues possibly key to the ligand-receptor binding complex are identified and include ASN 240, TYR 241 and HIST 365. Although the docking software does not identify non-bonding interactions other than hydrogen-bonding, the roles of additional proposed binding pocket residues are discussed in terms of hydrophobic interactions, π-π interactions and halogen bonding. These residues include TYR 161, PHE 196, VAL 203, PHE 204, ILE 209, LEU 210, VAL 237, TRP 297, PHE 362 and LEU 386. Although theoretical in nature, this reported homology modeling and docking exercise details a proposed binding site that may potentially further the development of drugs designed for the treatment of PTSD.
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Arnold, Matthew Scott. "Characterization of the thermostable nature of the alpha and beta tubulin proteins in Cyanidium caldarium and Cyanidioschyzon merolae." Thesis, [Blacksburg, Va. : University Libraries, Virginia Polytechnic Institute and State University, 2004. http://scholar.lib.vt.edu/theses/available/etd-03222004-144731.

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Books on the topic "Homology modeling"

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Orry, Andrew J. W., and Ruben Abagyan, eds. Homology Modeling. Totowa, NJ: Humana Press, 2012. http://dx.doi.org/10.1007/978-1-61779-588-6.

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Filipek, Sławomir, ed. Homology Modeling. New York, NY: Springer US, 2023. http://dx.doi.org/10.1007/978-1-0716-2974-1.

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Xu, Tianchuan. Loop Prediction and Homology Modeling with High Resolution. [New York, N.Y.?]: [publisher not identified], 2020.

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Limited, Oxford Molecular. Cameleon: Protein sequence homology modelling system : user's guide : [version 2.0]. Oxford: Oxford Molecular, 1992.

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

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Sławomir Filipek. Homology Modeling: Methods and Protocols. Springer, 2023.

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Orry, Andrew J. W., and Ruben Abagyan. Homology Modeling: Methods and Protocols. Humana Press, 2016.

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Maia, Rafael Trindade, Rômulo Maciel de Moraes Filho, and Magnólia de Araújo Campos. Homology Molecular Modeling: Perspectives and Applications. IntechOpen, 2021.

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Homology Molecular Modeling - Perspectives and Applications [Working Title]. IntechOpen, 2021. http://dx.doi.org/10.5772/intechopen.91624.

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Thakur, Aman, Vineet Mehta, Priyanka Nagu, and Kiran Goutam. Computer-Aided Drug Design: QSAR, Molecular Docking, Virtual Screening, Homology and Pharmacophore Modeling. de Gruyter GmbH, Walter, 2024.

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Book chapters on the topic "Homology modeling"

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Krieger, Elmar, Sander B. Nabuurs, and Gert Vriend. "Homology Modeling." In Structural Bioinformatics, 509–23. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2005. http://dx.doi.org/10.1002/0471721204.ch25.

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Fomenko, Anatolij T., and Tosiyasu L. Kinii. "Homology." In Topological Modeling for Visualization, 211–44. Tokyo: Springer Japan, 1997. http://dx.doi.org/10.1007/978-4-431-66956-2_11.

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Simms, John, Nathan E. Hall, Polo H. C. Lam, Laurence J. Miller, Arthur Christopoulos, Ruben Abagyan, and Patrick M. Sexton. "Homology Modeling of GPCRs." In Methods in Molecular Biology, 97–113. Totowa, NJ: Humana Press, 2009. http://dx.doi.org/10.1007/978-1-60327-317-6_7.

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Stamboulian, Mouses, and Nashat Mansour. "Scatter Search for Homology Modeling." In Lecture Notes in Computer Science, 66–73. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-41000-5_7.

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Ubhayasekera, Wimal. "Homology Modeling for Enzyme Design." In Cellulases, 301–20. New York, NY: Springer New York, 2018. http://dx.doi.org/10.1007/978-1-4939-7877-9_21.

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Schwede, Torsten. "Homology Modeling of Protein Structures." In Encyclopedia of Biophysics, 992–98. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-16712-6_417.

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Stavrou, Anastasios, Sudad Dayl, and Ralf Schmid. "Homology Modeling of P2X Receptors." In Methods in Molecular Biology, 65–75. New York, NY: Springer New York, 2019. http://dx.doi.org/10.1007/978-1-4939-9717-6_4.

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Bordner, Andrew J. "Force Fields for Homology Modeling." In Methods in Molecular Biology, 83–106. Totowa, NJ: Humana Press, 2011. http://dx.doi.org/10.1007/978-1-61779-588-6_4.

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Smith, Andrew T., Ping Du, and Gilda H. Loew. "Homology Modeling of Horseradish Peroxidase." In Nuclear Magnetic Resonance of Paramagnetic Macromolecules, 75–93. Dordrecht: Springer Netherlands, 1995. http://dx.doi.org/10.1007/978-94-015-8573-6_4.

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Sylte, Ingebrigt, Mari Gabrielsen, and Kurt Kristiansen. "Homology Modeling of Transporter Proteins." In Methods in Molecular Biology, 247–64. New York, NY: Springer US, 2023. http://dx.doi.org/10.1007/978-1-0716-2974-1_14.

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Conference papers on the topic "Homology modeling"

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Zomorodian, Afra, and Gunnar Carlsson. "Localized Homology." In IEEE International Conference on Shape Modeling and Applications 2007 (SMI '07). IEEE, 2007. http://dx.doi.org/10.1109/smi.2007.25.

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Vieira, Diogo Munaro, Elvismary Molina de Armas, Maria L. G. Jaramillo, Marcos Catanho, Antonio B. Miranda, Edward Hermann Haeusler, and Sérgio Lifschitz. "A New Data Modeling Approach for Alignment-free Biological Applications." In Simpósio Brasileiro de Banco de Dados. Sociedade Brasileira de Computação - SBC, 2023. http://dx.doi.org/10.5753/sbbd.2023.232471.

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Finding homologous proteins and grouping them are tasks of utmost importance in biology, which currently rely on tools based on information from these proteins' DNA or amino acid sequences. These tasks require identifying evolutionary patterns that are challenging to obtain automatically using traditional methods. This work proposes a data modeling approach to leverage evolutionary patterns in homology searching, ranking, and clustering tasks through an alignment-free process using image similarity algorithms. This strategy is valuable even for distant homologs and contributes to data privacy and security.
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Yanbo Zhang, Meiying Hu, Guohua Zhong, and Shaohua Chen. "Homology modeling and docking study of GyrB." In 2011 International Symposium on Water Resource and Environmental Protection (ISWREP). IEEE, 2011. http://dx.doi.org/10.1109/iswrep.2011.5893514.

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Doong, Shing-hwang. "Protein Homology Modeling with Heuristic Search for Sequence Alignment." In Proceedings of the 40th Annual Hawaii International Conference on System Sciences. IEEE, 2007. http://dx.doi.org/10.1109/hicss.2007.453.

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Godshall, Brian G., and Brian Y. Chen. "Improving accuracy in binding site comparison with homology modeling." In 2012 IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW). IEEE, 2012. http://dx.doi.org/10.1109/bibmw.2012.6470291.

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Dalila Wan Chik, Wan, Ruzianisra Mohamed, Abu Bakar Abdul Majeed, and Siti Azma Jusoh. "Sequence analysis and homology modeling of TRPV5 and TRPV6 channels." In 2012 IEEE Symposium on Business, Engineering and Industrial Applications (ISBEIA). IEEE, 2012. http://dx.doi.org/10.1109/isbeia.2012.6422900.

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Vergne, A., I. Flint, L. Decreusefond, and P. Martins. "Homology based algorithm for disaster recovery in wireless networks." In 2014 12th International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks (WiOpt). IEEE, 2014. http://dx.doi.org/10.1109/wiopt.2014.6850366.

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Yang, Jiaoyan, Mingjun Liao, Qingye Zhang, Wenjing Xiao, Zhibo Cai, Yonghong Yang, Jian Wan, and Shao Yang. "Expression and Homology Modeling of Sterol 14alpha-Demethylase from Magnaporthe Grisea." In 2008 2nd International Conference on Bioinformatics and Biomedical Engineering. IEEE, 2008. http://dx.doi.org/10.1109/icbbe.2008.27.

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Chen, LiHua, GuoHui Liu, Qiang Wang, and WanGuo Hou. "Homology modeling of the three-dimensional structure of bovine serum albumin." In 2010 3rd International Conference on Biomedical Engineering and Informatics (BMEI). IEEE, 2010. http://dx.doi.org/10.1109/bmei.2010.5639708.

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Ravi, D., S. Santhi, R. Parthasarathy, and V. Vijayabharathi. "Homology Modeling of Receptor Kinase (SERK) Enzyme from Embryogenesis of Endosperms." In 2014 International Conference on Intelligent Computing Applications (ICICA). IEEE, 2014. http://dx.doi.org/10.1109/icica.2014.12.

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Reports on the topic "Homology modeling"

1

GRIBSKOV, M. INTEGRATION OF STRUCTURAL AND SEQUENCE INFORMATION FOR HOMOLOGY-BASED MODELING OF PROTEINS. Office of Scientific and Technical Information (OSTI), June 2003. http://dx.doi.org/10.2172/814008.

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Avdjieva, Irena, Ivan Terziyski, Gergana Zahmanova, Valeria Simeonova, Ognyan Kulev, Evgeny Krustev, Milko Krachunov, Maria Nisheva, and Dimitar Vassilev. Homology Based Computational Modelling of Hepatitis-E Viral Fusion Capsid Protein. Balkan, Black sea and Caspian sea Regional Network for Space Weather Studies, March 2019. http://dx.doi.org/10.7546/crabs.2019.03.10.

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