1
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Lu B, Liao SM, Liang SJ, Peng LX, Li JX, Liu XH, Huang RB, Zhou GP. The Bifunctional Effects of Lactoferrin (LFcinB11) in Inhibiting Neural Cell Adhesive Molecule (NCAM) Polysialylation and the Release of Neutrophil Extracellular Traps (NETs). Int J Mol Sci 2024; 25:4641. [PMID: 38731861 PMCID: PMC11083048 DOI: 10.3390/ijms25094641] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2024] [Revised: 04/17/2024] [Accepted: 04/19/2024] [Indexed: 05/13/2024] Open
Abstract
The expression of polysialic acid (polySia) on the neuronal cell adhesion molecule (NCAM) is called NCAM-polysialylation, which is strongly related to the migration and invasion of tumor cells and aggressive clinical status. Thus, it is important to select a proper drug to block tumor cell migration during clinical treatment. In this study, we proposed that lactoferrin (LFcinB11) may be a better candidate for inhibiting NCAM polysialylation when compared with CMP and low-molecular-weight heparin (LMWH), which were determined based on our NMR studies. Furthermore, neutrophil extracellular traps (NETs) represent the most dramatic stage in the cell death process, and the release of NETs is related to the pathogenesis of autoimmune and inflammatory disorders, with proposed involvement in glomerulonephritis, chronic lung disease, sepsis, and vascular disorders. In this study, the molecular mechanisms involved in the inhibition of NET release using LFcinB11 as an inhibitor were also determined. Based on these results, LFcinB11 is proposed as being a bifunctional inhibitor for inhibiting both NCAM polysialylation and the release of NETs.
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Affiliation(s)
- Bo Lu
- National Key Laboratory of Non-Food Biomass Energy Technology, National Engineering Research Center for Non-Food Biorefinery, Institute of Biological Science and Technology, Guangxi Academy of Sciences, 98 Daling Road, Nanning 530007, China; (B.L.); (S.-M.L.); (S.-J.L.); (L.-X.P.); (J.-X.L.)
| | - Si-Ming Liao
- National Key Laboratory of Non-Food Biomass Energy Technology, National Engineering Research Center for Non-Food Biorefinery, Institute of Biological Science and Technology, Guangxi Academy of Sciences, 98 Daling Road, Nanning 530007, China; (B.L.); (S.-M.L.); (S.-J.L.); (L.-X.P.); (J.-X.L.)
| | - Shi-Jie Liang
- National Key Laboratory of Non-Food Biomass Energy Technology, National Engineering Research Center for Non-Food Biorefinery, Institute of Biological Science and Technology, Guangxi Academy of Sciences, 98 Daling Road, Nanning 530007, China; (B.L.); (S.-M.L.); (S.-J.L.); (L.-X.P.); (J.-X.L.)
| | - Li-Xin Peng
- National Key Laboratory of Non-Food Biomass Energy Technology, National Engineering Research Center for Non-Food Biorefinery, Institute of Biological Science and Technology, Guangxi Academy of Sciences, 98 Daling Road, Nanning 530007, China; (B.L.); (S.-M.L.); (S.-J.L.); (L.-X.P.); (J.-X.L.)
| | - Jian-Xiu Li
- National Key Laboratory of Non-Food Biomass Energy Technology, National Engineering Research Center for Non-Food Biorefinery, Institute of Biological Science and Technology, Guangxi Academy of Sciences, 98 Daling Road, Nanning 530007, China; (B.L.); (S.-M.L.); (S.-J.L.); (L.-X.P.); (J.-X.L.)
| | - Xue-Hui Liu
- Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China;
| | - Ri-Bo Huang
- National Key Laboratory of Non-Food Biomass Energy Technology, National Engineering Research Center for Non-Food Biorefinery, Institute of Biological Science and Technology, Guangxi Academy of Sciences, 98 Daling Road, Nanning 530007, China; (B.L.); (S.-M.L.); (S.-J.L.); (L.-X.P.); (J.-X.L.)
- Rocky Mount Life Sciences Institute, Rocky Mount, NC 27804, USA
| | - Guo-Ping Zhou
- National Key Laboratory of Non-Food Biomass Energy Technology, National Engineering Research Center for Non-Food Biorefinery, Institute of Biological Science and Technology, Guangxi Academy of Sciences, 98 Daling Road, Nanning 530007, China; (B.L.); (S.-M.L.); (S.-J.L.); (L.-X.P.); (J.-X.L.)
- Rocky Mount Life Sciences Institute, Rocky Mount, NC 27804, USA
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2
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Ferrante L, Rajpoot K, Jeeves M, Ludwig C. Automated analysis for multiplet identification from ultra-high resolution 2D- 1H, 13C-HSQC NMR spectra. Wellcome Open Res 2023; 7:262. [PMID: 37008249 PMCID: PMC10050905 DOI: 10.12688/wellcomeopenres.18248.2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/27/2023] [Indexed: 05/26/2023] Open
Abstract
Background: Metabolism is essential for cell survival and proliferation. A deep understanding of the metabolic network and its regulatory processes is often vital to understand and overcome disease. Stable isotope tracing of metabolism using nuclear magnetic resonance (NMR) and mass spectrometry (MS) is a powerful tool to derive mechanistic information of metabolic network activity. However, to retrieve meaningful information, automated tools are urgently needed to analyse these complex spectra and eliminate the bias introduced by manual analysis. Here, we present a data-driven algorithm to automatically annotate and analyse NMR signal multiplets in 2D- 1H, 13C-HSQC NMR spectra arising from 13C - 13C scalar couplings. The algorithm minimises the need for user input to guide the analysis of 2D- 1H, 13C-HSQC NMR spectra by performing automated peak picking and multiplet analysis. This enables non-NMR specialists to use this technology. The algorithm has been integrated into the existing MetaboLab software package. Methods: To evaluate the algorithm performance two criteria are tested: is the peak correctly annotated and secondly how confident is the algorithm with its analysis. For the latter a coefficient of determination is introduced. Three datasets were used for testing. The first was to test reproducibility with three biological replicates, the second tested the robustness of the algorithm for different amounts of scaling of the apparent J-coupling constants and the third focused on different sampling amounts. Results: The algorithm annotated overall >90% of NMR signals correctly with average coefficient of determination ρ of 94.06 ± 5.08%, 95.47 ± 7.20% and 80.47 ± 20.98% respectively. Conclusions: Our results indicate that the proposed algorithm accurately identifies and analyses NMR signal multiplets in ultra-high resolution 2D- 1H, 13C-HSQC NMR spectra. It is robust to signal splitting enhancement and up to 25% of non-uniform sampling.
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Affiliation(s)
- Laura Ferrante
- School of Computer Sciences, University of Birmingham, Birmingham, B15 2TT, UK
| | - Kashif Rajpoot
- University of Birmingham Dubai, Dubai International Academic City, United Arab Emirates
| | - Mark Jeeves
- Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham, B15 2TT, UK
| | - Christian Ludwig
- Institute of Metabolism and Systems Research, University of Birmingham, Birmingham, B15 2TT, UK
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3
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Wang ZZ, Shi XX, Huang GY, Hao GF, Yang GF. Fragment-based drug discovery supports drugging 'undruggable' protein-protein interactions. Trends Biochem Sci 2023; 48:539-552. [PMID: 36841635 DOI: 10.1016/j.tibs.2023.01.008] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2022] [Revised: 01/05/2023] [Accepted: 01/31/2023] [Indexed: 02/26/2023]
Abstract
Protein-protein interactions (PPIs) have important roles in various cellular processes, but are commonly described as 'undruggable' therapeutic targets due to their large, flat, featureless interfaces. Fragment-based drug discovery (FBDD) has achieved great success in modulating PPIs, with more than ten compounds in clinical trials. Here, we highlight the progress of FBDD in modulating PPIs for therapeutic development. Targeting hot spots that have essential roles in both fragment binding and PPIs provides a shortcut for the development of PPI modulators via FBDD. We highlight successful cases of cracking the 'undruggable' problems of PPIs using fragment-based approaches. We also introduce new technologies and future trends. Thus, we hope that this review will provide useful guidance for drug discovery targeting PPIs.
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Affiliation(s)
- Zhi-Zheng Wang
- National Key Laboratory of Green Pesticide, Key Laboratory of Pesticide & Chemical Biology, Ministry of Education, Central China Normal University, Wuhan, 430079, PR China
| | - Xing-Xing Shi
- National Key Laboratory of Green Pesticide, Key Laboratory of Pesticide & Chemical Biology, Ministry of Education, Central China Normal University, Wuhan, 430079, PR China
| | - Guang-Yi Huang
- National Key Laboratory of Green Pesticide, Key Laboratory of Pesticide & Chemical Biology, Ministry of Education, Central China Normal University, Wuhan, 430079, PR China
| | - Ge-Fei Hao
- National Key Laboratory of Green Pesticide, Key Laboratory of Pesticide & Chemical Biology, Ministry of Education, Central China Normal University, Wuhan, 430079, PR China; National Key Laboratory of Green Pesticide, Key Laboratory of Green Pesticide and Agricultural Bioengineering, Ministry of Education, Center for R&D of Fine Chemicals, Guizhou University, Guiyang 550025, PR China.
| | - Guang-Fu Yang
- National Key Laboratory of Green Pesticide, Key Laboratory of Pesticide & Chemical Biology, Ministry of Education, Central China Normal University, Wuhan, 430079, PR China.
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4
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Abstract
We demonstrate an automated microfluidic nuclear magnetic resonance (NMR) system that quantitatively characterizes protein-ligand interactions without user intervention and with minimal sample needs through protein-detected heteronuclear 2D NMR spectroscopy. Quantitation of protein-ligand interactions is of fundamental importance to the understanding of signaling and other life processes. As is well-known, NMR provides rich information both on the thermodynamics of binding and on the binding site. However, the required titrations are laborious and tend to require large amounts of sample, which are not always available. The present work shows how the analytical power of NMR detection can be brought in line with the trend of miniaturization and automation in life science workflows.
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Affiliation(s)
- Marek Plata
- School
of Chemistry, University of Southampton, SouthamptonSO17 1BJ, United Kingdom
| | - Manvendra Sharma
- School
of Chemistry, University of Southampton, SouthamptonSO17 1BJ, United Kingdom
| | - Marcel Utz
- School
of Chemistry, University of Southampton, SouthamptonSO17 1BJ, United Kingdom,Email
for M.U.:
| | - Jörn M. Werner
- School
for Biological Sciences, University of Southampton, B85 Life Science Building, University
Rd, SouthamptonSO17 1BJ, United Kingdom,Email for J.M.W.:
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5
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Ferrante L, Rajpoot K, Jeeves M, Ludwig C. Automated analysis for multiplet identification from ultra-high resolution 2D-1H,13C-HSQC NMR spectra. Wellcome Open Res 2022; 7:262. [PMID: 37008249 PMCID: PMC10050905 DOI: 10.12688/wellcomeopenres.18248.1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/07/2022] [Indexed: 11/20/2022] Open
Abstract
Background: Metabolism is essential for cell survival and proliferation. A deep understanding of the metabolic network and its regulatory processes is often vital to understand and overcome disease. Stable isotope tracing of metabolism using nuclear magnetic resonance (NMR) and mass spectrometry (MS) is a powerful tool to derive mechanistic information of metabolic network activity. However, to retrieve meaningful information, automated tools are urgently needed to analyse these complex spectra and eliminate the bias introduced by manual analysis. Here, we present a data-driven algorithm to automatically annotate and analyse NMR signal multiplets in 2D-1H,13C-HSQC NMR spectra arising from 13C -13C scalar couplings. The algorithm minimises the need for user input to guide the analysis of 2D-1H,13C-HSQC NMR spectra by performing automated peak picking and multiplet analysis. This enables non-NMR specialists to use this technology. The algorithm has been integrated into the existing MetaboLab software package. Methods: To evaluate the algorithm performance two criteria are tested: is the peak correctly annotated and secondly how confident is the algorithm with its analysis. For the latter a coefficient of determination is introduced. Three datasets were used for testing. The first was to test reproducibility with three biological replicates, the second tested the robustness of the algorithm for different amounts of scaling of the apparent J-coupling constants and the third focused on different sampling amounts. Results: The algorithm annotated overall >90% of NMR signals correctly with average coefficient of determination ρ of 94.06 ± 5.08%, 95.47 ± 7.20% and 80.47 ± 20.98% respectively. Conclusions: Our results indicate that the proposed algorithm accurately identifies and analyses NMR signal multiplets in ultra-high resolution 2D-1H,13C-HSQC NMR spectra. It is robust to signal splitting enhancement and up to 25% of non-uniform sampling.
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Affiliation(s)
- Laura Ferrante
- School of Computer Sciences, University of Birmingham, Birmingham, B15 2TT, UK
| | - Kashif Rajpoot
- University of Birmingham Dubai, Dubai International Academic City, United Arab Emirates
| | - Mark Jeeves
- Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham, B15 2TT, UK
| | - Christian Ludwig
- Institute of Metabolism and Systems Research, University of Birmingham, Birmingham, B15 2TT, UK
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6
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Abstract
Fragment-based drug discovery (FBDD) continues to evolve and make an impact in the pharmaceutical sciences. We summarize successful fragment-to-lead studies that were published in 2020. Having systematically analyzed annual scientific outputs since 2015, we discuss trends and best practices in terms of fragment libraries, target proteins, screening technologies, hit-optimization strategies, and the properties of hit fragments and the leads resulting from them. As well as the tabulated Fragment-to-Lead (F2L) programs, our 2020 literature review identifies several trends and innovations that promise to further increase the success of FBDD. These include developing structurally novel screening fragments, improving fragment-screening technologies, using new computer-aided design and virtual screening approaches, and combining FBDD with other innovative drug-discovery technologies.
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Affiliation(s)
- Iwan J. P. de Esch
- Division
of Medicinal Chemistry, Amsterdam Institute of Molecular and Life
Sciences (AIMMS), Vrije Universiteit Amsterdam, De Boelelaan 1108, 1081 HZ Amsterdam, The Netherlands
| | - Daniel A. Erlanson
- Frontier
Medicines, 151 Oyster
Point Blvd., South San Francisco, California 94080, United States
| | - Wolfgang Jahnke
- Novartis
Institutes for Biomedical Research, Chemical
Biology and Therapeutics, 4002 Basel, Switzerland
| | - Christopher N. Johnson
- Astex
Pharmaceuticals, 436 Cambridge Science Park, Milton Road, Cambridge CB4 0QA, United Kingdom
| | - Louise Walsh
- Astex
Pharmaceuticals, 436 Cambridge Science Park, Milton Road, Cambridge CB4 0QA, United Kingdom
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7
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Laveglia V, Giachetti A, Cerofolini L, Haubrich K, Fragai M, Ciulli A, Rosato A. Automated Determination of Nuclear Magnetic Resonance Chemical Shift Perturbations in Ligand Screening Experiments: The PICASSO Web Server. J Chem Inf Model 2021; 61:5726-5733. [PMID: 34843238 PMCID: PMC8715503 DOI: 10.1021/acs.jcim.1c00871] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2021] [Indexed: 11/28/2022]
Abstract
Nuclear magnetic resonance (NMR) is an effective, commonly used experimental approach to screen small organic molecules against a protein target. A very popular method consists of monitoring the changes of the NMR chemical shifts of the protein nuclei upon addition of the small molecule to the free protein. Multidimensional NMR experiments allow the interacting residues to be mapped along the protein sequence. A significant amount of human effort goes into manually tracking the chemical shift variations, especially when many signals exhibit chemical shift changes and when many ligands are tested. Some computational approaches to automate the procedure are available, but none of them as a web server. Furthermore, some methods require the adoption of a fairly specific experimental setup, such as recording a series of spectra at increasing small molecule:protein ratios. In this work, we developed a tool requesting a minimal amount of experimental data from the user, implemented it as an open-source program, and made it available as a web application. Our tool compares two spectra, one of the free protein and one of the small molecule:protein mixture, based on the corresponding peak lists. The performance of the tool in terms of correct identification of the protein-binding regions has been evaluated on different protein targets, using experimental data from interaction studies already available in the literature. For a total of 16 systems, our tool achieved between 79% and 100% correct assignments, properly identifying the protein regions involved in the interaction.
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Affiliation(s)
- Vincenzo Laveglia
- Consorzio
Interuniversitario di Risonanze Magnetiche di Metallo Proteine, Via Luigi Sacconi 6, 50019 Sesto Fiorentino, Italy
| | - Andrea Giachetti
- Consorzio
Interuniversitario di Risonanze Magnetiche di Metallo Proteine, Via Luigi Sacconi 6, 50019 Sesto Fiorentino, Italy
| | - Linda Cerofolini
- Consorzio
Interuniversitario di Risonanze Magnetiche di Metallo Proteine, Via Luigi Sacconi 6, 50019 Sesto Fiorentino, Italy
| | - Kevin Haubrich
- School
of Life Sciences, Division of Biological Chemistry and Drug Discovery, The University of Dundee, James Black Centre, Dow Street, DD1 5EH, Dundee, United Kingdom
| | - Marco Fragai
- Consorzio
Interuniversitario di Risonanze Magnetiche di Metallo Proteine, Via Luigi Sacconi 6, 50019 Sesto Fiorentino, Italy
- Magnetic
Resonance Center (CERM), University of Florence, Via Luigi Sacconi 6, 50019 Sesto Fiorentino, Italy
- Department
of Chemistry, University of Florence, Via della Lastruccia 3, 50019 Sesto Fiorentino, Italy
| | - Alessio Ciulli
- School
of Life Sciences, Division of Biological Chemistry and Drug Discovery, The University of Dundee, James Black Centre, Dow Street, DD1 5EH, Dundee, United Kingdom
| | - Antonio Rosato
- Consorzio
Interuniversitario di Risonanze Magnetiche di Metallo Proteine, Via Luigi Sacconi 6, 50019 Sesto Fiorentino, Italy
- Magnetic
Resonance Center (CERM), University of Florence, Via Luigi Sacconi 6, 50019 Sesto Fiorentino, Italy
- Department
of Chemistry, University of Florence, Via della Lastruccia 3, 50019 Sesto Fiorentino, Italy
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8
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Zak KM, Bostock MJ, Waligorska I, Thøgersen IB, Enghild JJ, Popowicz GM, Grudnik P, Potempa J, Ksiazek M. Latency, thermal stability, and identification of an inhibitory compound of mirolysin, a secretory protease of the human periodontopathogen Tannerella forsythia. J Enzyme Inhib Med Chem 2021; 36:1267-1281. [PMID: 34210221 PMCID: PMC8259862 DOI: 10.1080/14756366.2021.1937619] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022] Open
Abstract
Mirolysin is a secretory protease of Tannerella forsythia, a member of the dysbiotic oral microbiota responsible for periodontitis. In this study, we show that mirolysin latency is achieved by a “cysteine-switch” mechanism exerted by Cys23 in the N-terminal profragment. Mutation of Cys23 shortened the time needed for activation of the zymogen from several days to 5 min. The mutation also decreased the thermal stability and autoproteolysis resistance of promirolysin. Mature mirolysin is a thermophilic enzyme and shows optimal activity at 65 °C. Through NMR-based fragment screening, we identified a small molecule (compound (cpd) 9) that blocks promirolysin maturation and functions as a competitive inhibitor (Ki = 3.2 µM), binding to the S1′ subsite of the substrate-binding pocket. Cpd 9 shows superior specificity and does not interact with other T. forsythia proteases or Lys/Arg-specific proteases.
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Affiliation(s)
- Krzysztof M Zak
- Helmholtz Zentrum München, Institute of Structural Biology, Neuherberg, Germany.,Malopolska Centre of Biotechnology, Jagiellonian University, Kraków, Poland
| | - Mark J Bostock
- Helmholtz Zentrum München, Institute of Structural Biology, Neuherberg, Germany.,Biomolecular NMR and Center for Integrated Protein Science Munich at Department Chemie, Technical University of Munich, Garching, Germany
| | - Irena Waligorska
- Department of Microbiology, Faculty of Biochemistry, Biophysics, and Biotechnology, Jagiellonian University, Krakow, Poland
| | - Ida B Thøgersen
- Department of Molecular Biology and Genetics, Aarhus University, Aarhus, Denmark
| | - Jan J Enghild
- Department of Molecular Biology and Genetics, Aarhus University, Aarhus, Denmark
| | - Grzegorz M Popowicz
- Helmholtz Zentrum München, Institute of Structural Biology, Neuherberg, Germany.,Biomolecular NMR and Center for Integrated Protein Science Munich at Department Chemie, Technical University of Munich, Garching, Germany
| | - Przemyslaw Grudnik
- Malopolska Centre of Biotechnology, Jagiellonian University, Kraków, Poland
| | - Jan Potempa
- Department of Microbiology, Faculty of Biochemistry, Biophysics, and Biotechnology, Jagiellonian University, Krakow, Poland.,Department of Oral Immunology and Infectious Diseases, University of Louisville School of Dentistry, Louisville, KY, USA
| | - Miroslaw Ksiazek
- Department of Microbiology, Faculty of Biochemistry, Biophysics, and Biotechnology, Jagiellonian University, Krakow, Poland.,Department of Oral Immunology and Infectious Diseases, University of Louisville School of Dentistry, Louisville, KY, USA
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9
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Abstract
During the past decades, solution nuclear magnetic resonance (NMR) spectroscopy has demonstrated itself as a promising tool in drug discovery. Especially, fragment-based drug discovery (FBDD) has benefited a lot from the NMR development. Multiple candidate compounds and FDA-approved drugs derived from FBDD have been developed with the assistance of NMR techniques. NMR has broad applications in different stages of the FBDD process, which includes fragment library construction, hit generation and validation, hit-to-lead optimization and working mechanism elucidation, etc. In this manuscript, we reviewed the current progresses of NMR applications in fragment-based drug discovery, which were illustrated by multiple reported cases. Moreover, the NMR applications in protein-protein interaction (PPI) modulators development and the progress of in-cell NMR for drug discovery were also briefly summarized.
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