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Journal articles on the topic 'Homology modeling'

Author: Grafiati

Published: 4 June 2021

Last updated: 15 March 2025

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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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2

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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3

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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4

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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5

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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6

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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7

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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8

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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9

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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10

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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11

HÅKANSSON, K. O., and P. L. JORGENSEN. "Homology Modeling of Na,K-ATPase." Annals of the New York Academy of Sciences 986, no. 1 (April 2003): 163–67. http://dx.doi.org/10.1111/j.1749-6632.2003.tb07155.x.

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12

Babu, Sathya, and Thirumurthy Madhavan. "Homology Modeling of Cysteinyl Leukotriene1 Receptor." Journal of the Chosun Natural Science 8, no. 1 (March 30, 2015): 13–18. http://dx.doi.org/10.13160/ricns.2015.8.1.13.

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13

Hatherley, Rowan, David K. Brown, Michael Glenister, and Özlem Tastan Bishop. "PRIMO: An Interactive Homology Modeling Pipeline." PLOS ONE 11, no. 11 (November 17, 2016): e0166698. http://dx.doi.org/10.1371/journal.pone.0166698.

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14

Edwards, D. J., R. E. Hubbard, and R. L. Brady. "Homology modeling of antibody combining sites." ImmunoMethods 1, no. 2 (October 1992): 71–79. http://dx.doi.org/10.1016/s1058-6687(05)80030-0.

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15

Rupasinghe, Sanjeewa, and Mary A. Schuler. "Homology modeling of plant cytochrome P450s." Phytochemistry Reviews 5, no. 2-3 (November 18, 2006): 473–505. http://dx.doi.org/10.1007/s11101-006-9028-y.

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16

Singh, Rajesh, and M. Elizabeth Sobhia. "Homology modeling of human CCR2 receptor." Medicinal Chemistry Research 20, no. 9 (November 21, 2010): 1704–12. http://dx.doi.org/10.1007/s00044-010-9497-9.

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17

Tramontano, Anna. "Homology Modeling with Low Sequence Identity." Methods 14, no. 3 (March 1998): 293–300. http://dx.doi.org/10.1006/meth.1998.0585.

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18

Monzon, Alexander Miguel, Diego Javier Zea, Cristina Marino-Buslje, and Gustavo Parisi. "Homology modeling in a dynamical world." Protein Science 26, no. 11 (September 28, 2017): 2195–206. http://dx.doi.org/10.1002/pro.3274.

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19

Cardozo, Timothy, Maxim Totrov, and Ruben Abagyan. "Homology modeling by the ICM method." Proteins: Structure, Function, and Genetics 23, no. 3 (November 1995): 403–14. http://dx.doi.org/10.1002/prot.340230314.

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20

Furnham, Nicholas, Stuart Ruffle, and Christopher Southan. "Splice variants: A homology modeling approach." Proteins: Structure, Function, and Bioinformatics 54, no. 3 (December 19, 2003): 596–608. http://dx.doi.org/10.1002/prot.10568.

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21

Farid, Anila, Beenish Haider, Kehkashan Akbar, Ammar Mehfooz, Ziyad Ahmad, Fazal E. Raheem, Furqan Arshad, et al. "Homology Modeling of Predicted Methyl Transferases (STY 3264): A Protein of Salmonella TYPHI CT18." Pakistan Journal of Medical and Health Sciences 16, no. 3 (March 26, 2022): 720–22. http://dx.doi.org/10.53350/pjmhs22163720.

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Background: Salmonella typhi gives rise to typhoid fever which is life threatening illness.It puts end to approximately 600,000 people per annum around the world.Food and water are the leading components through which this disease is passed on and becomes origin of typhoid.It lays out widely where cleanliness is very substandard. Objective: To construct 3 dimensional structure of protein Methyl Transferase of Salmonella typhi CT18 by homology modeling. Materials and Methods: Bioinformatic tools and programs like Comprehensive Microbial Resource (CMR), Interproscan, Basic Local Alignment Search Tool (BLAST), Modellor 9.10, Procheck and Prosa were helpful for the complete homology modeling of methyl transferases (STY 3264).The models were visualized by DS Viever. Results: Homology modeling is an effective method to find structure of methyl transferase protein for future discovery of drugs. Conclusion: Homology modeling is an effective method to find structure of protein which provides good solution for drug discovery. Keywords: Methyl transferase ,Homology modeling, Typhoid fever,Salmonella typhi CT18.

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22

Ilment, Nikita, and Ekaterina Zinina. "Protein modeling by homology using the example of tick-borne encephalitis serine protease NS3." Farmacevticheskoe delo i tehnologija lekarstv (Pharmacy and Pharmaceutical Technology), no. 3 (June 1, 2020): 59–66. http://dx.doi.org/10.33920/med-13-2003-05.

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Homology modeling is a process of obtaining a 3D structure of a protein using various algorithms based on already known structures of homologous proteins. The spatial structure of protein is required for in silico protein evaluation. 3D structures can be obtained using different methods: NMR, Xray crystallography (XRC), and cryo-electron microscopy (cryo-EM), but these methods require a lot of time and money. At the same time, the speed of nucleotide sequences analysis is increasing, thereby creating a mismatch between the number of decoded genomes and the investigated 3D protein structures that are encoded by these sequences. Also, homology modeling is the easiest and fastest way to obtain the model of the desired protein. This review describes free software for homology modelling — SWISS-MODEL and MODELLER, how to use it and how to evaluate the results.

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23

Makigaki, Shuichiro, and Takashi Ishida. "Sequence alignment using machine learning for accurate template-based protein structure prediction." Bioinformatics 36, no. 1 (June 14, 2019): 104–11. http://dx.doi.org/10.1093/bioinformatics/btz483.

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Abstract Motivation Template-based modeling, the process of predicting the tertiary structure of a protein by using homologous protein structures, is useful if good templates can be found. Although modern homology detection methods can find remote homologs with high sensitivity, the accuracy of template-based models generated from homology-detection-based alignments is often lower than that from ideal alignments. Results In this study, we propose a new method that generates pairwise sequence alignments for more accurate template-based modeling. The proposed method trains a machine learning model using the structural alignment of known homologs. It is difficult to directly predict sequence alignments using machine learning. Thus, when calculating sequence alignments, instead of a fixed substitution matrix, this method dynamically predicts a substitution score from the trained model. We evaluate our method by carefully splitting the training and test datasets and comparing the predicted structure’s accuracy with that of state-of-the-art methods. Our method generates more accurate tertiary structure models than those produced from alignments obtained by other methods. Availability and implementation https://github.com/shuichiro-makigaki/exmachina. Supplementary information Supplementary data are available at Bioinformatics online.

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24

Chaitanya, Amajala Krishna, Dr I. Bhaskar Reddy, P. Minakshi Kumari P. Minakshi Kumari, Kunal Zaveri Kunal Zaveri, and Dr DSVGK Kaladhar Dr. DSVGK. Kaladhar. "Homology Modeling and Structural Analysis of DNA Binding Response Regulator of Bacillus anthracis." International Journal of Scientific Research 2, no. 8 (June 1, 2012): 32–34. http://dx.doi.org/10.15373/22778179/aug2013/11.

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25

Mohamed, Noor Asidah, Ruzianisra Mohamed, and Teoh Teow Chong. "Homology Modeling of Coagulase in Staphylococcus aureus." Bioinformation 8, no. 9 (May 15, 2012): 412–14. http://dx.doi.org/10.6026/97320630008412.

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26

Chandramore, Kavita. "HOMOLOGY MODELING OF SUBCUTANEOUS FILARIASIS DHFR PROTEINS." International Journal of Current Pharmaceutical Research 9, no. 6 (November 14, 2017): 76. http://dx.doi.org/10.22159/ijcpr.2017v9i6.23433.

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Objective: A systematic technique for protein modeling offers great assistance in the study of protein function, dynamics, interactions with ligands, other proteins and even in drug discovery and drug design. Subcutaneous filariasis is rare parasitic disease caused by Loa Loa (eye worm) and monosonallastreptoscerca species. Methods: The present study develop three dimensional structure of dihydrofolatereductase present in Loa loa species. For this purpose knowledge based homology modeling is used by using Schrodinger Glide 5.6 software.Results: The procedure involves alignment that maps residues in the query sequence to residues in the template sequence to generate structural model of target, which was further refined and final result validated by using Ramchandran plot.Conclusion: In ramchandran plot majority of the amino acids are in the phi-psi distribution and thedevelop model is reliable and of good quality.

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27

Sircar, A., E. T. Kim, and J. J. Gray. "RosettaAntibody: antibody variable region homology modeling server." Nucleic Acids Research 37, Web Server (May 20, 2009): W474—W479. http://dx.doi.org/10.1093/nar/gkp387.

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28

Thomas, Trayder, Kimberley C. McLean, Fiona M. McRobb, David T. Manallack, David K. Chalmers, and Elizabeth Yuriev. "Homology Modeling of Human Muscarinic Acetylcholine Receptors." Journal of Chemical Information and Modeling 54, no. 1 (December 23, 2013): 243–53. http://dx.doi.org/10.1021/ci400502u.

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29

Tuccinardi, Tiziano, Maurizio Botta, Antonio Giordano, and Adriano Martinelli. "Protein Kinases: Docking and Homology Modeling Reliability." Journal of Chemical Information and Modeling 50, no. 8 (July 27, 2010): 1432–41. http://dx.doi.org/10.1021/ci100161z.

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30

Kawakita, S., R. Kuroki, and T. Yao. "Protein modeling by homology using distance information." Journal of Molecular Graphics 10, no. 1 (March 1992): 58–59. http://dx.doi.org/10.1016/0263-7855(92)80042-c.

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31

Farce, Amaury, Anton O. Chugunov, Cédric Logé, Ahmed Sabaouni, Saïd Yous, Sébastien Dilly, Nicolas Renault, Gérard Vergoten, Roman G. Efremov, and Daniel Lesieur. "Homology modeling of MT1 and MT2 receptors." European Journal of Medicinal Chemistry 43, no. 9 (September 2008): 1926–44. http://dx.doi.org/10.1016/j.ejmech.2007.12.001.

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32

Alexandrov, Nickolai N., and Roland Luethy. "Alignment algorithm for homology modeling and threading." Protein Science 7, no. 2 (February 1998): 254–58. http://dx.doi.org/10.1002/pro.5560070204.

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33

Weber, Irene T. "Evaluation of homology modeling of HIV Protease." Proteins: Structure, Function, and Genetics 7, no. 2 (1990): 172–84. http://dx.doi.org/10.1002/prot.340070206.

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34

Pisabarro, M. T., A. R. Ortiz, L. Serrano, and R. C. Wade. "Homology modeling of the Abl-SH3 domain." Proteins: Structure, Function, and Genetics 20, no. 3 (November 1994): 203–15. http://dx.doi.org/10.1002/prot.340200302.

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35

Kim, Minseon, and David A. Tiberi. "New insights into template-based protein modeling techniques." McGill Science Undergraduate Research Journal 5, no. 1 (March 31, 2010): 49–54. http://dx.doi.org/10.26443/msurj.v5i1.84.

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introduction: While the development of genomic sequencing methods has greatly improved the efficiency of collecting sequence data, experimental methods to obtain structure information have been lagging significantly. In order to elucidate protein structures, researchers have developed computational structural modeling techniques such as homology modeling and fold recognition (threading). The general consensus is that homology modeling is a superior approach with templates of high sequence similarity to the desired target (>30%), whereas threading is better suited for lower (<30%) sequence similarity templates. We compared recently improved threading algorithms with homology modeling to test the validity of this consensus. Methods: The most current versions of moDelleR and I-TasseR were used for model generation. We then used common assessment criteria (n-Dope, Q-mean and pRoCheCK) to verify the validity of the models. structure comparisons were also made using Chimera’s Cα root-mean-square deviation. results: Contrary to our prior expectations, the model determined by threading showed similar or even better assessment results in some criteria compared to the model generated from homology modeling. Furthermore, the structure analysis showed that homology modeling and threading protocols yield models with root-mean-square deviations of under 2 Å when used on protein sequences that share sequence identities of at least 30% to the experimentally determined protein template. discussion: We believe that recent improvements in threading algorithms will allow for broader applications of this methodology in large-scale modeling efforts. The fully automated steps could provide time efficacy. In contrast to popular belief in the modeling community, we have shown that threading could be a competitive means of modeling rather than a mere backup method.

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36

Larsson, Per, Björn Wallner, Erik Lindahl, and Arne Elofsson. "Using multiple templates to improve quality of homology models in automated homology modeling." Protein Science 17, no. 6 (June 2008): 990–1002. http://dx.doi.org/10.1110/ps.073344908.

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37

Kumar, Amit, and Mahejibin Khan. "Homology Modeling and Docking Studies of Cold Shock Protein Homologs (Isoforms) of E. coli." Current Proteomics 11, no. 4 (January 21, 2015): 281–88. http://dx.doi.org/10.2174/157016461104150121114835.

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38

Mohamed, Ruzianisra. "Homology Modeling of Human DNA Repair Protein RAD51 Homolog 3 (RAD51C) in Breast Cancer." International Journal of Pharmaceuticals, Nutraceuticals and Cosmetic Science 7, no. 2 (December 30, 2024): 136–49. https://doi.org/10.24191/ijpnacs.v7i2.11.

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Breast cancer is known as one of the most predominant cancers that affect both females and males worldwide. The most crucial risk factor in breast cancer is the mutations in the RAD51C gene that have been considered in most hereditary breast cancers. RAD51C, the RAD51 paralogs, is also a deoxyribonucleic acid (DNA) repair protein related to breast and ovarian cancers. DNA double-strand breaks (DSBs) account for the significant detrimental form of DNA damage. RAD51C mutants also have been recognized in breast/ovarian cancer patients. However, the role of the RAD51C protein in hereditary breast cancer and its three-dimensional (3D) structures remains unclear. Thus, this study was conducted to identify the 3D structure of RAD51C protein from its amino acid sequences. The homology modeling for the 3D structure of the RAD51C protein was carried out by using three automated webservers: I-TASSER, SWISS-MODEL, and Phyre2. PyMOL was applied to visualize the 3D structure of RAD51C protein. Next, the MolProbity, ProSA, and SAVES v6.0 programs have been employed to check the stereo-chemical quality of RAD51C protein. The RAD51C-IT models were found to be the best models for the RAD1C protein after being evaluated and validated, and the models were constructed using full-length RAD51C protein sequences. Thus, these protein models can be utilized as a virtual screening tool in discovering potential inhibitors of RAD51C protein.

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39

Szarecka, Agnieszka, and Christopher Dobson. "A Tool to Teach Evolution of Protein Sequences and Structures." American Biology Teacher 86, no. 2 (February 1, 2024): 108–15. http://dx.doi.org/10.1525/abt.2024.86.2.108.

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Computer modeling and protein structure visualization tools are effective and engaging ways of presenting various molecular biology concepts to high school and college students. Here, we describe a series of activities and exercises that use online bioinformatics databases and programs to search for and obtain protein sequence and structure data and use it to build homology models of proteins. Exercises in homology modeling can serve the pedagogical purpose of introducing and illustrating the concept of homology within gene and protein families, which results in conservation of the 3D structures of proteins and allows us to predict structures when experimental data are not available.

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40

Karami, Yasaman, Julien Rey, Guillaume Postic, Samuel Murail, Pierre Tufféry, and Sjoerd J. de Vries. "DaReUS-Loop: a web server to model multiple loops in homology models." Nucleic Acids Research 47, W1 (May 22, 2019): W423—W428. http://dx.doi.org/10.1093/nar/gkz403.

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AbstractLoop regions in protein structures often have crucial roles, and they are much more variable in sequence and structure than other regions. In homology modeling, this leads to larger deviations from the homologous templates, and loop modeling of homology models remains an open problem. To address this issue, we have previously developed the DaReUS-Loop protocol, leading to significant improvement over existing methods. Here, a DaReUS-Loop web server is presented, providing an automated platform for modeling or remodeling loops in the context of homology models. This is the first web server accepting a protein with up to 20 loop regions, and modeling them all in parallel. It also provides a prediction confidence level that corresponds to the expected accuracy of the loops. DaReUS-Loop facilitates the analysis of the results through its interactive graphical interface and is freely available at http://bioserv.rpbs.univ-paris-diderot.fr/services/DaReUS-Loop/.

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41

Adebiyi, Marion Olubunmi, and Oludayo Olufolorunsho Olugbara. "Homology Modeling of CYP6Z3 Protein of Anopheles Mosquito." Advances in Science, Technology and Engineering Systems Journal 6, no. 2 (March 2021): 580–85. http://dx.doi.org/10.25046/aj060266.

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42

Jalily Hasani, Horia, and Khaled Barakat. "Homology Modeling: an Overview of Fundamentals and Tools." International Review on Modelling and Simulations (IREMOS) 10, no. 2 (April 30, 2017): 129. http://dx.doi.org/10.15866/iremos.v10i2.11412.

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43

Komari, Noer, Samsul Hadi, and Eko Suhartono. "Pemodelan Protein dengan Homology Modeling menggunakan SWISS-MODEL." Jurnal Jejaring Matematika dan Sains 2, no. 2 (December 30, 2020): 65–70. http://dx.doi.org/10.36873/jjms.2020.v2.i2.408.

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The three-dimensional (3D) structure of proteins is necessary to understand the properties and functions of proteins. Determining protein structure by laboratory equipment is quite complicated and expensive. An alternative method to predict the 3D structure of proteins in the in silico method. One of the in silico methods is homology modeling. Homology modeling is done using the SWISS-MODEL server. Proteins that will be modeled in the 3D structure are proteins that do not yet have a structure in the RCSB PDB database. Protein sequences were obtained from the UniProt database with code A0A0B6VWS2. The results showed that there were two models selected, namely model-1 with the PDB code template 1q0e and model-2 with the PDB code template 3gtv. The results of sequence alignment and model visualization show that model-1 and model-2 are identical. The evaluation and assessment of model-1 on the Ramachandran Plot have a Favored area of ??97.36%, a MolProbity score of 0.79, and a QMEAN value is 1.13. Model-1 is a good 3D protein structure model.

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44

Shaji., Divya. "MULTI-TEMPLATE HOMOLOGY MODELING OF HUMAN MCT8 PROTEIN." International Journal of Advanced Research 5, no. 7 (July 31, 2017): 1025–36. http://dx.doi.org/10.21474/ijar01/4811.

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45

Ma, Buyong, Junjie Xiong, Jacek Lubkowski, and Ruth Nussinov. "Homology modeling and molecular dynamics simulations of lymphotactin." Protein Science 9, no. 11 (2000): 2192–99. http://dx.doi.org/10.1110/ps.9.11.2192.

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46

Schwede, T. "SWISS-MODEL: an automated protein homology-modeling server." Nucleic Acids Research 31, no. 13 (July 1, 2003): 3381–85. http://dx.doi.org/10.1093/nar/gkg520.

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47

Pellikka, M., S. Suuriniemi, L. Kettunen, and C. Geuzaine. "Homology and Cohomology Computation in Finite Element Modeling." SIAM Journal on Scientific Computing 35, no. 5 (January 2013): B1195—B1214. http://dx.doi.org/10.1137/130906556.

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48

Hazai, Eszter, and Zsolt Bikádi. "Homology modeling of breast cancer resistance protein (ABCG2)." Journal of Structural Biology 162, no. 1 (April 2008): 63–74. http://dx.doi.org/10.1016/j.jsb.2007.12.001.

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49

Selwyne, R. A., Kh T. Kholmurodov, and N. A. Koltovaya. "Homology modeling of yeast cyclin-dependent protein kinase." Physics of Particles and Nuclei Letters 4, no. 4 (July 2007): 339–42. http://dx.doi.org/10.1134/s1547477107040085.

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50

Marinelli, Luciana, Kay-E. Gottschalk, Axel Meyer, Ettore Novellino, and Horst Kessler. "Human Integrin αvβ5: Homology Modeling and Ligand Binding." Journal of Medicinal Chemistry 47, no. 17 (August 2004): 4166–77. http://dx.doi.org/10.1021/jm030635j.

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