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      Targeting Glial Cells by Organic Anion-Transporting Polypeptide 1C1 (OATP1C1)-Utilizing l-Thyroxine-Derived Prodrugs

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          Abstract

          OATP1C1 (organic anion-transporting polypeptide 1C1) transports thyroid hormones, particularly thyroxine (T 4), into human astrocytes. In this study, we investigated the potential of utilizing OATP1C1 to improve the delivery of anti-inflammatory drugs into glial cells. We designed and synthesized eight novel prodrugs by incorporating T 4 and 3,5-diiodo- l-tyrosine (DIT) as promoieties to selected anti-inflammatory drugs. The prodrug uptake in OATP1C1-expressing human U-87MG glioma cells demonstrated higher accumulation with T 4 promoiety compared to those with DIT promoiety or the parent drugs themselves. In silico models of OATP1C1 suggested dynamic binding for the prodrugs, wherein the pose changed from vertical to horizontal. The predicted binding energies correlated with the transport profiles, with T 4 derivatives exhibiting higher binding energies when compared to prodrugs with a DIT promoiety. Interestingly, the prodrugs also showed utilization of oatp1a4/1a5/1a6 in mouse primary astrocytes, which was further supported by docking studies and a great potential for improved brain drug delivery.

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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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            Comparison of simple potential functions for simulating liquid water

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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
                06 November 2023
                23 November 2023
                : 66
                : 22
                : 15094-15114
                Affiliations
                []School of Pharmacy, Faculty of Health Sciences, University of Eastern Finland , P.O. Box 1627, 70211 Kuopio, Finland
                []Department of Pharmaceutical and Medicinal Chemistry, Institute of Pharmaceutical Sciences, Eberhard-Karls-Universität , Tuebingen, Auf der Morgenstelle 8, 72076 Tuebingen, Germany
                [§ ]Tuebingen Center for Academic Drug Discovery & Development (TüCAD2) , 72076 Tuebingen, Germany
                []Department of Internal Medicine VIII, University Hospital Tübingen , DE 72076 Tübingen, Germany
                []Cluster of Excellence iFIT (EXC 2180) “Image-Guided and Functionally Instructed Tumor Therapies”, University of Tübingen , 72076 Tübingen, Germany
                Author notes
                Author information
                https://orcid.org/0000-0001-6986-4198
                https://orcid.org/0000-0002-9354-903X
                https://orcid.org/0000-0002-7526-0419
                https://orcid.org/0000-0003-4511-467X
                https://orcid.org/0000-0003-4196-4204
                https://orcid.org/0000-0002-1175-8517
                Article
                10.1021/acs.jmedchem.3c01026
                10683023
                37930268
                4f7e248b-b355-4447-8fd8-6921aeb539fb
                © 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
                : 08 June 2023
                : 25 October 2023
                : 20 October 2023
                Funding
                Funded by: Horizon 2020 Framework Programme, doi 10.13039/100010661;
                Award ID: NA
                Funded by: Sigrid Juséliuksen Säätiö, doi 10.13039/501100006306;
                Award ID: NA
                Funded by: Terveyden Tutkimuksen Toimikunta, doi 10.13039/501100005878;
                Award ID: 338693
                Funded by: Ministerium für Wissenschaft, Forschung und Kunst Baden-Württemberg, doi 10.13039/501100003542;
                Award ID: NA
                Funded by: Bundesministerium für Bildung und Forschung, doi 10.13039/501100002347;
                Award ID: NA
                Funded by: European Proteomics Infrastructure Consortium providing access, doi 10.13039/100017790;
                Award ID: 823839
                Categories
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                Custom metadata
                jm3c01026
                jm3c01026

                Pharmaceutical chemistry
                Pharmaceutical chemistry

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