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      Dual Piperidine-Based Histamine H 3 and Sigma-1 Receptor Ligands in the Treatment of Nociceptive and Neuropathic Pain

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          Abstract

          In search of new dual-acting histamine H 3/sigma-1 receptor ligands, we designed a series of compounds structurally based on highly active in vivo ligands previously studied and described by our team. However, we kept in mind that within the previous series, a pair of closely related compounds, KSK67 and KSK68, differing only in the piperazine/piperidine moiety in the structural core showed a significantly different affinity at sigma-1 receptors (σ 1Rs). Therefore, we first focused on an in-depth analysis of the protonation states of piperazine and piperidine derivatives in the studied compounds. In a series of 16 new ligands, mainly based on the piperidine core, we selected three lead structures ( 3, 7, and 12) for further biological evaluation. Compound 12 showed a broad spectrum of analgesic activity in both nociceptive and neuropathic pain models based on the novel molecular mechanism.

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          Highly accurate protein structure prediction with AlphaFold

          Proteins are essential to life, and understanding their structure can facilitate a mechanistic understanding of their function. Through an enormous experimental effort 1 – 4 , the structures of around 100,000 unique proteins have been determined 5 , but this represents a small fraction of the billions of known protein sequences 6 , 7 . Structural coverage is bottlenecked by the months to years of painstaking effort required to determine a single protein structure. Accurate computational approaches are needed to address this gap and to enable large-scale structural bioinformatics. Predicting the three-dimensional structure that a protein will adopt based solely on its amino acid sequence—the structure prediction component of the ‘protein folding problem’ 8 —has been an important open research problem for more than 50 years 9 . Despite recent progress 10 – 14 , existing methods fall far short of atomic accuracy, especially when no homologous structure is available. Here we provide the first computational method that can regularly predict protein structures with atomic accuracy even in cases in which no similar structure is known. We validated an entirely redesigned version of our neural network-based model, AlphaFold, in the challenging 14th Critical Assessment of protein Structure Prediction (CASP14) 15 , demonstrating accuracy competitive with experimental structures in a majority of cases and greatly outperforming other methods. Underpinning the latest version of AlphaFold is a novel machine learning approach that incorporates physical and biological knowledge about protein structure, leveraging multi-sequence alignments, into the design of the deep learning algorithm. AlphaFold predicts protein structures with an accuracy competitive with experimental structures in the majority of cases using a novel deep learning architecture.
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            The Protein Data Bank.

            The Protein Data Bank (PDB; http://www.rcsb.org/pdb/ ) is the single worldwide archive of structural data of biological macromolecules. This paper describes the goals of the PDB, the systems in place for data deposition and access, how to obtain further information, and near-term plans for the future development of the resource.
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              AlphaFold Protein Structure Database: massively expanding the structural coverage of protein-sequence space with high-accuracy models

              The AlphaFold Protein Structure Database (AlphaFold DB, https://alphafold.ebi.ac.uk ) is an openly accessible, extensive database of high-accuracy protein-structure predictions. Powered by AlphaFold v2.0 of DeepMind, it has enabled an unprecedented expansion of the structural coverage of the known protein-sequence space. AlphaFold DB provides programmatic access to and interactive visualization of predicted atomic coordinates, per-residue and pairwise model-confidence estimates and predicted aligned errors. The initial release of AlphaFold DB contains over 360,000 predicted structures across 21 model-organism proteomes, which will soon be expanded to cover most of the (over 100 million) representative sequences from the UniRef90 data set.
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                Author and article information

                Journal
                J Med Chem
                J Med Chem
                jm
                jmcmar
                Journal of Medicinal Chemistry
                American Chemical Society
                0022-2623
                1520-4804
                07 July 2023
                27 July 2023
                : 66
                : 14
                : 9658-9683
                Affiliations
                []Department of Technology and Biotechnology of Drugs, Faculty of Pharmacy, Jagiellonian University Medical College , Medyczna 9, 30-688 Kraków, Poland
                []Department of Medicinal Chemistry, Maj Institute of Pharmacology, Polish Academy of Sciences , Smętna 12, 31-343 Kraków, Poland
                [§ ]Department of Drug and Health Sciences, University of Catania , V.le A. Doria, 95125 Catania, Italy
                []Department of Pharmacodynamics, Faculty of Pharmacy, Jagiellonian University Medical College , Medyczna 9, 30-688 Kraków, Poland
                []Department of Crystal Chemistry and Crystal Physics, Faculty of Chemistry, Jagiellonian University , Gronostajowa 2, 30-387 Kraków, Poland
                [# ]Cerko Sp. z o.o. Sp.k , Al. Zwycięstwa 96/98, 81-451 Gdynia, Poland
                []Celon Pharma S.A., R&D Centre , Marymoncka 15, 05-152 Kazuń Nowy, Poland
                []Department of Neurochemistry, Maj Institute of Pharmacology, Polish Academy of Sciences , Smętna 12, 31-343 Kraków, Poland
                []Institute of Pharmacy, Faculty of Chemistry and Pharmacy, University of Regensburg , Universitätsstraße 31, D-93053 Regensburg, Germany
                []Department of Pharmacology and Neurosciences Institute (Biomedical Research Center), University of Granada, and Biosanitary Research Institute ibs. Granada , Avenida de la Investigación 11, 18016 Granada, Spain
                [& ]Institute of Pharmaceutical and Medicinal Chemistry, Heinrich Heine University Düsseldorf , Universitaetsstr. 1, 40225 Düsseldorf, Germany
                []Bioprojet-Biotech, 4rue du Chesnay Beauregard , 35762 Saint-Gregoire Cedex, France
                Author notes
                Author information
                https://orcid.org/0000-0002-6110-9400
                https://orcid.org/0000-0001-6380-7176
                https://orcid.org/0000-0002-8563-7929
                https://orcid.org/0000-0002-2891-5603
                https://orcid.org/0000-0003-4019-5538
                https://orcid.org/0000-0001-6968-0946
                https://orcid.org/0000-0001-8454-4440
                https://orcid.org/0000-0002-2211-9868
                https://orcid.org/0000-0002-8728-8857
                https://orcid.org/0000-0003-3336-1710
                https://orcid.org/0000-0003-1417-6333
                https://orcid.org/0000-0002-4750-3479
                Article
                10.1021/acs.jmedchem.3c00430
                10388327
                37418295
                a2137c7d-9d30-4c5b-bf3b-e7447ced2336
                © 2023 The Authors. Published by American Chemical Society

                Permits the broadest form of re-use including for commercial purposes, provided that author attribution and integrity are maintained ( https://creativecommons.org/licenses/by/4.0/).

                History
                : 10 March 2023
                Funding
                Funded by: Uniwersytet Jagiellonski Collegium Medicum, doi 10.13039/100009045;
                Award ID: N42/DBS/000242
                Funded by: Ministerstwo Edukacji i Nauki, doi 10.13039/501100004569;
                Award ID: 6903/IA/SP/2018
                Funded by: Università di Catania, doi 10.13039/501100004505;
                Award ID: 57722172136
                Funded by: Narodowe Centrum Nauki, doi 10.13039/501100004281;
                Award ID: 2020/36/C/NZ7/00284
                Funded by: Narodowe Centrum Nauki, doi 10.13039/501100004281;
                Award ID: 2020/04/X/NZ7/01338
                Funded by: Narodowe Centrum Nauki, doi 10.13039/501100004281;
                Award ID: 2019/35/D/NZ7/01042
                Funded by: Deutsche Forschungsgemeinschaft, doi 10.13039/501100001659;
                Award ID: GRK 1910
                Funded by: European Cooperation in Science and Technology, doi 10.13039/501100000921;
                Award ID: 18133
                Funded by: Fonds der Chemischen Industrie, doi 10.13039/100018992;
                Award ID: 661688
                Categories
                Article
                Custom metadata
                jm3c00430
                jm3c00430

                Pharmaceutical chemistry
                Pharmaceutical chemistry

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