Journal articles: 'Structure-Based approaches'

1

Jiang, Lin, and David Eisenberg. "Structure-Based Approaches to Amyloid Inhibitors." Biophysical Journal 104, no. 2 (January 2013): 36a. http://dx.doi.org/10.1016/j.bpj.2012.11.236.

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Huang, Ta-Chou, Kung-Hao Liang, Tai-Jay Chang, Kai-Feng Hung, Mong-Lien Wang, Yen-Fu Cheng, Yi-Ting Liao, and De-Ming Yang. "Structure-based approaches against COVID-19." Journal of the Chinese Medical Association 87, no. 2 (December 20, 2023): 139–41. http://dx.doi.org/10.1097/jcma.0000000000001043.

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The coronavirus disease 2019 (COVID-19) pandemic has had a major impact on human life. This review highlights the versatile roles of both classical and modern structure-based approaches for COVID-19. X-ray crystallography, nuclear magnetic resonance spectroscopy, and cryogenic electron microscopy are the three cornerstones of classical structural biology. These technologies have helped provide fundamental and detailed knowledge regarding severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and the related human host proteins as well as enabled the identification of its target sites, facilitating the cessation of its transmission. Further progress into protein structure modeling was made using modern structure-based approaches derived from homology modeling and integrated with artificial intelligence (AI), facilitating advanced computational simulation tools to actively guide the design of new vaccines and the development of anti-SARS-CoV-2 drugs. This review presents the practical contributions and future directions of structure-based approaches for COVID-19.

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Vieira, Rafael Pinto, Viviane Corrêa Santos, and Rafaela Salgado Ferreira. "Structure-based Approaches Targeting Parasite Cysteine Proteases." Current Medicinal Chemistry 26, no. 23 (October 10, 2019): 4435–53. http://dx.doi.org/10.2174/0929867324666170810165302.

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Cysteine proteases are essential hydrolytic enzymes present in the majority of organisms, including viruses and unicellular parasites. Despite the high sequence identity displayed among these proteins, specific structural features across different species grant distinct functions to these biomolecules, frequently related to pathological conditions. Consequently, their relevance as promising targets for potential specific inhibitors has been highlighted and occasionally validated in recent decades. In this review, we discuss the recent outcomes of structure-based campaigns aiming the discovery of new inhibitor prototypes against cruzain and falcipain, as alternative therapeutic tools for Chagas disease and malaria treatments, respectively. Computational and synthetic approaches have been combined on hit optimization strategies and are also discussed herein. These rationales are extended to additional tropical infectious and neglected pathologies, such as schistosomiasis, leishmaniasis and babesiosis, and also to Alzheimer’s Disease, a widespread neurodegenerative disease poorly managed by currently available drugs and recently linked to particular physiopathological roles of human cysteine proteases.

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Gherardini, P. F., and M. Helmer-Citterich. "Structure-based function prediction: approaches and applications." Briefings in Functional Genomics and Proteomics 7, no. 4 (June 25, 2008): 291–302. http://dx.doi.org/10.1093/bfgp/eln030.

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Hubbard, Roderick E. "Fragment approaches in structure-based drug discovery." Journal of Synchrotron Radiation 15, no. 3 (April 18, 2008): 227–30. http://dx.doi.org/10.1107/s090904950705666x.

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Joseph-McCarthy, D. "Computational approaches to structure-based ligand design." Pharmacology & Therapeutics 84, no. 2 (November 1999): 179–91. http://dx.doi.org/10.1016/s0163-7258(99)00031-5.

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Simon J. Holton, Manfred S. Weiss, Paul A. Tucker, and Matthias Wilmanns. "Structure-Based Approaches to Drug Discovery Against Tuberculosis." Current Protein & Peptide Science 8, no. 4 (August 1, 2007): 365–75. http://dx.doi.org/10.2174/138920307781369445.

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Johnson, Sherida, and Maurizio Pellecchia. "Structure- and Fragment-Based Approaches to Protease Inhibition." Current Topics in Medicinal Chemistry 6, no. 4 (February 1, 2006): 317–29. http://dx.doi.org/10.2174/156802606776287072.

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Echalier, A., A. Merckx, A. Hole, J. Endicott, and M. Noble. "New approaches in structure based kinase drug discovery." Acta Crystallographica Section A Foundations of Crystallography 63, a1 (August 22, 2007): s287. http://dx.doi.org/10.1107/s010876730709352x.

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Cassidy, C. Keith, Benjamin A. Himes, Zaida Luthey-Schulten, and Peijun Zhang. "CryoEM-based hybrid modeling approaches for structure determination." Current Opinion in Microbiology 43 (June 2018): 14–23. http://dx.doi.org/10.1016/j.mib.2017.10.002.

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Thomas, Morgan, Andreas Bender, and Chris de Graaf. "Integrating structure-based approaches in generative molecular design." Current Opinion in Structural Biology 79 (April 2023): 102559. http://dx.doi.org/10.1016/j.sbi.2023.102559.

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Han, S., J. Kim, and S. H. Ko. "Advances in air filtration technologies: structure-based and interaction-based approaches." Materials Today Advances 9 (March 2021): 100134. http://dx.doi.org/10.1016/j.mtadv.2021.100134.

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Wilson, Gregory L., and Markus A. Lill. "Integrating structure-based and ligand-based approaches for computational drug design." Future Medicinal Chemistry 3, no. 6 (April 2011): 735–50. http://dx.doi.org/10.4155/fmc.11.18.

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Katz, Alan, and Craig Caufield. "Structure-Based Design Approaches to Cell Wall Biosynthesis Inhibitors." Current Pharmaceutical Design 9, no. 11 (April 1, 2003): 857–66. http://dx.doi.org/10.2174/1381612033455305.

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Fradera, Xavier, and Jordi Mestres. "Guided Docking Approaches to Structure-Based Design and Screening." Current Topics in Medicinal Chemistry 4, no. 7 (March 1, 2004): 687–700. http://dx.doi.org/10.2174/1568026043451104.

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Weingarth, Markus, and Marc Baldus. "Solid-State NMR-Based Approaches for Supramolecular Structure Elucidation." Accounts of Chemical Research 46, no. 9 (April 15, 2013): 2037–46. http://dx.doi.org/10.1021/ar300316e.

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Subramanian, Govindan, and Shashidhar N. Rao. "Comprehending renin inhibitor’s binding affinity using structure-based approaches." Bioorganic & Medicinal Chemistry Letters 23, no. 24 (December 2013): 6667–72. http://dx.doi.org/10.1016/j.bmcl.2013.10.044.

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18

Rognan, Didier. "Structure-Based Approaches to Target Fishing and Ligand Profiling." Molecular Informatics 29, no. 3 (March 5, 2010): 176–87. http://dx.doi.org/10.1002/minf.200900081.

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Graham, Barney S., Morgan S. A. Gilman, and Jason S. McLellan. "Structure-Based Vaccine Antigen Design." Annual Review of Medicine 70, no. 1 (January 27, 2019): 91–104. http://dx.doi.org/10.1146/annurev-med-121217-094234.

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Enabled by new approaches for rapid identification and selection of human monoclonal antibodies, atomic-level structural information for viral surface proteins, and capacity for precision engineering of protein immunogens and self-assembling nanoparticles, a new era of antigen design and display options has evolved. While HIV-1 vaccine development has been a driving force behind these technologies and concepts, clinical proof-of-concept for structure-based vaccine design may first be achieved for respiratory syncytial virus (RSV), where conformation-dependent access to neutralization-sensitive epitopes on the fusion glycoprotein determines the capacity to induce potent neutralizing activity. Success with RSV has motivated structure-based stabilization of other class I viral fusion proteins for use as immunogens and demonstrated the importance of structural information for developing vaccines against other viral pathogens, particularly difficult targets that have resisted prior vaccine development efforts. Solving viral surface protein structures also supports rapid vaccine antigen design and application of platform manufacturing approaches for emerging pathogens.

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20

Thai, Khac-Minh, and Gerhard Ecker. "Predictive Models for hERG Channel Blockers: Ligand-Based and Structure-Based Approaches." Current Medicinal Chemistry 14, no. 28 (December 1, 2007): 3003–26. http://dx.doi.org/10.2174/092986707782794087.

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21

Li, Shuxiang, Shuqun Zhang, Dingyuan Chen, Xuan Jiang, Bin Liu, Hongbin Zhang, Munikishore Rachakunta, and Zhili Zuo. "Identification of Novel TRPC5 Inhibitors by Pharmacophore-Based and Structure-Based Approaches." Computational Biology and Chemistry 87 (August 2020): 107302. http://dx.doi.org/10.1016/j.compbiolchem.2020.107302.

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22

Brylinski, Michal, and Jeffrey Skolnick. "Comparison of structure-based and threading-based approaches to protein functional annotation." Proteins: Structure, Function, and Bioinformatics 78, no. 1 (August 5, 2009): 18–134. http://dx.doi.org/10.1002/prot.22566.

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23

Pakhrin, Subash C., Bikash Shrestha, Badri Adhikari, and Dukka B. KC. "Deep Learning-Based Advances in Protein Structure Prediction." International Journal of Molecular Sciences 22, no. 11 (May 24, 2021): 5553. http://dx.doi.org/10.3390/ijms22115553.

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Obtaining an accurate description of protein structure is a fundamental step toward understanding the underpinning of biology. Although recent advances in experimental approaches have greatly enhanced our capabilities to experimentally determine protein structures, the gap between the number of protein sequences and known protein structures is ever increasing. Computational protein structure prediction is one of the ways to fill this gap. Recently, the protein structure prediction field has witnessed a lot of advances due to Deep Learning (DL)-based approaches as evidenced by the success of AlphaFold2 in the most recent Critical Assessment of protein Structure Prediction (CASP14). In this article, we highlight important milestones and progresses in the field of protein structure prediction due to DL-based methods as observed in CASP experiments. We describe advances in various steps of protein structure prediction pipeline viz. protein contact map prediction, protein distogram prediction, protein real-valued distance prediction, and Quality Assessment/refinement. We also highlight some end-to-end DL-based approaches for protein structure prediction approaches. Additionally, as there have been some recent DL-based advances in protein structure determination using Cryo-Electron (Cryo-EM) microscopy based, we also highlight some of the important progress in the field. Finally, we provide an outlook and possible future research directions for DL-based approaches in the protein structure prediction arena.

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24

Tomlinson, S., R. Malmstrom, and S. Watowich. "New Approaches to Structure-Based Discovery of Dengue Protease Inhibitors." Infectious Disorders - Drug Targets 9, no. 3 (June 1, 2009): 327–43. http://dx.doi.org/10.2174/1871526510909030327.

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Brady, R., and Angus Cameron. "Structure-Based Approaches to the Development of Novel Anti-Malarials." Current Drug Targets 5, no. 2 (February 1, 2004): 137–49. http://dx.doi.org/10.2174/1389450043490587.

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26

Ho, P. Shing. "Thermogenomics: Thermodynamic-based approaches to genomic analyses of DNA structure." Methods 47, no. 3 (March 2009): 159–67. http://dx.doi.org/10.1016/j.ymeth.2008.09.007.

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27

Prathipati, Philip, and Kenji Mizuguchi. "Integration of Ligand and Structure Based Approaches for CSAR-2014." Journal of Chemical Information and Modeling 56, no. 6 (November 5, 2015): 974–87. http://dx.doi.org/10.1021/acs.jcim.5b00477.

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28

Mathlouthi, Houda, Kamel Abederrahim, Faouzi Msahli, and Gerard Favier. "Crosscumulants based approaches for the structure identification of Volterra models." International Journal of Automation and Computing 6, no. 4 (October 21, 2009): 420–30. http://dx.doi.org/10.1007/s11633-009-0420-0.

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29

Schott, Benedikt, Christoph Ager, and Wolfgang A. Wall. "Monolithic cut finite element–based approaches for fluid‐structure interaction." International Journal for Numerical Methods in Engineering 119, no. 8 (April 22, 2019): 757–96. http://dx.doi.org/10.1002/nme.6072.

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30

Quintana, Xavier D., D. Boix, A. Badosa, S. Brucet, J. Compte, S. Gascón, R. López-Flores, J. Sala, and R. Moreno-Amich. "Community structure in mediterranean shallow lentic ecosystems: size-based vs. taxon-based approaches." Limnetica 25, no. 1 (June 15, 2006): 303–20. http://dx.doi.org/10.23818/limn.25.21.

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31

Inuiguchi, Masahiro. "Structure-Based Attribute Reduction in Variable Precision Rough Set Models." Journal of Advanced Computational Intelligence and Intelligent Informatics 10, no. 5 (September 20, 2006): 657–65. http://dx.doi.org/10.20965/jaciii.2006.p0657.

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In this paper, structure-enhancing approaches to attribute reduction are proposed. Ten kinds of meaningful reducts are defined. The relations among them are clarified. Moreover their relations to reducts by structure-preserving approaches are also investigated. A few computational approaches to the proposed reducts are briefly described.

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Carneiro, Marta G., Eiso AB, Stephan Theisgen, and Gregg Siegal. "NMR in structure-based drug design." Essays in Biochemistry 61, no. 5 (November 8, 2017): 485–93. http://dx.doi.org/10.1042/ebc20170037.

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NMR spectroscopy is a powerful technique that can provide valuable structural information for drug discovery endeavors. Here, we discuss the strengths (and limitations) of NMR applications to structure-based drug discovery, highlighting the different levels of resolution and throughput obtainable. Additionally, the emerging field of paramagnetic NMR in drug discovery and recent developments in approaches to speed up and automate protein-observed NMR data collection and analysis are discussed.

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Stehno, Abigail L., and Jeffrey A. Melby, Norberto Nadal-Caraballo, Victor Gonzalez. "COMPARING RESPONSE-BASED AND EVENT-BASED OVERTOPPING DESIGN." Coastal Engineering Proceedings, no. 37 (September 1, 2023): 21. http://dx.doi.org/10.9753/icce.v37.structures.21.

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Coastal structure crest elevations are routinely designed to a specific hazard level. In the U.S., for example, levee crest elevations often correspond to the 1 percent annual exceedance probability (AEP) overtopping rate at 90 percent confidence level (CL). Statistical methods to compute coastal structure response range from simply inputting wave and water level forcing conditions at a certain AEP into a response equation (i.e. event-based or frequency-based approach) to a fully stochastic Monte Carlo numerical simulation where thousands of storm responses are sampled and epistemic uncertainties incorporated (i.e. response-based approach). Event-based approaches oversimplify both statistics and physics; however, time, cost, and complexity can limit application of the response-based simulation. Response-based stochastic simulation approaches tend to more realistically characterize structure responses. Herein we compare common frequency-based and response-based stochastic approaches for levee and floodwall overtopping design. For frequency-based approaches, structure responses are computed using wave and water level forcings at a given AEP, and for response-based approaches, a large number of storms are sampled in a Monte Carlo simulation.

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Soni, Mohini, and J. Venkatesh Pratap. "Development of Novel Anti-Leishmanials: The Case for Structure-Based Approaches." Pathogens 11, no. 8 (August 22, 2022): 950. http://dx.doi.org/10.3390/pathogens11080950.

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The neglected tropical disease (NTD) leishmaniasis is the collective name given to a diverse group of illnesses caused by ~20 species belonging to the genus Leishmania, a majority of which are vector borne and associated with complex life cycles that cause immense health, social, and economic burdens locally, but individually are not a major global health priority. Therapeutic approaches against leishmaniasis have various inadequacies including drug resistance and a lack of effective control and eradication of the disease spread. Therefore, the development of a rationale-driven, target based approaches towards novel therapeutics against leishmaniasis is an emergent need. The utilization of Artificial Intelligence/Machine Learning methods, which have made significant advances in drug discovery applications, would benefit the discovery process. In this review, following a summary of the disease epidemiology and available therapies, we consider three important leishmanial metabolic pathways that can be attractive targets for a structure-based drug discovery approach towards the development of novel anti-leishmanials. The folate biosynthesis pathway is critical, as Leishmania is auxotrophic for folates that are essential in many metabolic pathways. Leishmania can not synthesize purines de novo, and salvage them from the host, making the purine salvage pathway an attractive target for novel therapeutics. Leishmania also possesses an organelle glycosome, evolutionarily related to peroxisomes of higher eukaryotes, which is essential for the survival of the parasite. Research towards therapeutics is underway against enzymes from the first two pathways, while the third is as yet unexplored.

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Dhilon S. Patel, Nigus Dessalew, Pansy Iqbal, and Prasad V. Bharatam. "Structure-Based Approaches in the Design of GSK-3 Selective Inhibitors." Current Protein & Peptide Science 8, no. 4 (August 1, 2007): 352–64. http://dx.doi.org/10.2174/138920307781369409.

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36

Kingdon, Alexander D. H., and Luke J. Alderwick. "Structure-based in silico approaches for drug discovery against Mycobacterium tuberculosis." Computational and Structural Biotechnology Journal 19 (2021): 3708–19. http://dx.doi.org/10.1016/j.csbj.2021.06.034.

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37

Blackburn, Ryan C., Robert Buscaglia, and Andrew J. Sánchez Meador. "Mixtures of airborne lidar-based approaches improve predictions of forest structure." Canadian Journal of Forest Research 51, no. 8 (August 2021): 1106–16. http://dx.doi.org/10.1139/cjfr-2020-0506.

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The most common method for modeling forest attributes with airborne lidar, the area-based approach, involves summarizing the point cloud of individual plots and relating this to attributes of interest. Tree- and voxel-based approaches have been considered as alternatives to the area-based approach but are rarely considered in an area-based context. We estimated three forest attributes (basal area, overstory biomass, and volume) across 1680 field plots in Arizona and New Mexico. Variables from the three lidar approaches (area, tree, and voxel) were created for each plot. Random forests were estimated using subsets of variables based on each individual lidar approach and mixtures of each approach. Boruta feature selection was performed on variable subsets, including the mixture of all lidar-approach predictors (KS-Boruta). A corrected paired t test was utilized to compare six validated models (area-Boruta, tree-Boruta, voxel-Boruta, KS-Boruta, KS-all, and ridge-all) for each forest attribute. Based on significant reductions in error (SMdAPE), basal area and biomass were best modeled with KS-Boruta, while volume was best modeled with KS-all. Analysis of variable importance shows that voxel-based predictors are critical for the prediction of the three forest attributes. This study highlights the importance of multiresolution voxel-based variables for modeling forest attributes in an area-based context.

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38

Ngo, Tony, Irina Kufareva, James LJ Coleman, Robert M. Graham, Ruben Abagyan, and Nicola J. Smith. "Identifying ligands at orphan GPCRs: current status using structure-based approaches." British Journal of Pharmacology 173, no. 20 (March 5, 2016): 2934–51. http://dx.doi.org/10.1111/bph.13452.

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39

Guido, Rafael V. C., Glaucius Oliva, and Adriano D. Andricopulo. "Structure- and ligand-based drug design approaches for neglected tropical diseases." Pure and Applied Chemistry 84, no. 9 (May 22, 2012): 1857–66. http://dx.doi.org/10.1351/pac-con-11-11-07.

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Drug discovery has moved toward more rational strategies based on our increasing understanding of the fundamental principles of protein–ligand interactions. Structure- (SBDD) and ligand-based drug design (LBDD) approaches bring together the most powerful concepts in modern chemistry and biology, linking medicinal chemistry with structural biology. The definition and assessment of both chemical and biological space have revitalized the importance of exploring the intrinsic complementary nature of experimental and computational methods in drug design. Major challenges in this field include the identification of promising hits and the development of high-quality leads for further development into clinical candidates. It becomes particularly important in the case of neglected tropical diseases (NTDs) that affect disproportionately poor people living in rural and remote regions worldwide, and for which there is an insufficient number of new chemical entities being evaluated owing to the lack of innovation and R&D investment by the pharmaceutical industry. This perspective paper outlines the utility and applications of SBDD and LBDD approaches for the identification and design of new small-molecule agents for NTDs.

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40

Congreve, Miles, Christine Oswald, and Fiona H. Marshall. "Applying Structure-Based Drug Design Approaches to Allosteric Modulators of GPCRs." Trends in Pharmacological Sciences 38, no. 9 (September 2017): 837–47. http://dx.doi.org/10.1016/j.tips.2017.05.010.

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41

Shortridge, Matthew D., and Gabriele Varani. "Structure based approaches for targeting non-coding RNAs with small molecules." Current Opinion in Structural Biology 30 (February 2015): 79–88. http://dx.doi.org/10.1016/j.sbi.2015.01.008.

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Vulpetti, Anna, Patrizia Crivori, Alexander Cameron, Jay Bertrand, Maria Gabriella Brasca, Roberto D'Alessio, and Paolo Pevarello. "Structure-Based Approaches to Improve Selectivity: CDK2−GSK3β Binding Site Analysis." Journal of Chemical Information and Modeling 45, no. 5 (August 12, 2005): 1282–90. http://dx.doi.org/10.1021/ci0500280.

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43

Masegosa, Andrés R., and Serafín Moral. "New skeleton-based approaches for Bayesian structure learning of Bayesian networks." Applied Soft Computing 13, no. 2 (February 2013): 1110–20. http://dx.doi.org/10.1016/j.asoc.2012.09.029.

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44

Berezov, Alan, Mark I. Greene, and Ramachandran Murali. "Structure-Based Approaches to Inhibition of erbB Receptors with Peptide Mimetics." Immunologic Research 27, no. 2-3 (2003): 303–8. http://dx.doi.org/10.1385/ir:27:2-3:303.

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Vyas, Vivek K., Ashutosh Goel, Manjunath Ghate, and Palak Patel. "Ligand and structure-based approaches for the identification of SIRT1 activators." Chemico-Biological Interactions 228 (February 2015): 9–17. http://dx.doi.org/10.1016/j.cbi.2015.01.001.

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Hoffer, Laurent, Christophe Muller, Philippe Roche, and Xavier Morelli. "Chemistry-driven Hit-to-lead Optimization Guided by Structure-based Approaches." Molecular Informatics 37, no. 9-10 (July 27, 2018): 1800059. http://dx.doi.org/10.1002/minf.201800059.

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Xu, Bangzhen, Xingyu Lu, Hong Gu, and Weimin Su. "Tensor structure-based ground clutter suppression approaches for pulse doppler radar." Remote Sensing Letters 15, no. 5 (April 3, 2024): 457–65. http://dx.doi.org/10.1080/2150704x.2024.2334195.

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48

Fourches, Denis, Eugene Muratov, Feng Ding, Nikolay V. Dokholyan, and Alexander Tropsha. "Predicting Binding Affinity of CSAR Ligands Using Both Structure-Based and Ligand-Based Approaches." Journal of Chemical Information and Modeling 53, no. 8 (July 17, 2013): 1915–22. http://dx.doi.org/10.1021/ci400216q.

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Chandni, Khatri, Prof Mrudang Pandya, and Dr Sunil Jardosh. "Deep Learning Approaches for Protein Structure Prediction." International Journal of Engineering & Technology 7, no. 4.5 (September 22, 2018): 168. http://dx.doi.org/10.14419/ijet.v7i4.5.20037.

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In recent years, Machine Learning techniques that are based on Deep Learning networks that show a great promise in research communities.Successful methods for deep learning involve Artificial Neural Networks and Machine Learning. Deep Learning solves severa problems in bioinformatics. Protein Structure Prediction is one of the most important fields that can be solving using Deep Learning approaches.These protein are categorized on basis of occurrence of amino acid patterns occur to extract the feature. In these paper aimed to review work based on protein structure prediction solve using Deep Learning Networks. Objective is to review motivate and facilitatethese deep learn the network for predicting protein sequences using Deep Learning.

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Beran, Gregory. "Modeling molecular crystals with fragment-based electronic structure techniques." Acta Crystallographica Section A Foundations and Advances 70, a1 (August 5, 2014): C1616. http://dx.doi.org/10.1107/s2053273314083831.

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"A proper theoretical description of molecular crystal packing requires a uniformly high-quality treatment of the diverse intra- and intermolecular interactions. Fragment-based electronic structure methods enable the application of accurate electronic structure approaches to chemically interesting molecular crystals by decomposing the total crystal energy into the sum of many smaller ""fragment"" calculations. In this talk, we will discuss (1) the hybrid quantum/classical fragment based approach we have developed for molecular crystal problems, (2) state-of-the-art electronic structure approaches for treating the individual fragments with the requisite accuracy and acceptable computational effort, and (3) applications of these techniques to interesting molecular crystal problems."

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Journal articles on the topic 'Structure-Based approaches'

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