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      High Target Homology Does Not Guarantee Inhibition: Aminothiazoles Emerge as Inhibitors of Plasmodium falciparum

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      , , , , § , , , , , , § , § , # , , , , , § , , , , , , , , , , , # , β , α , , , , , § ,
      ACS Infectious Diseases
      American Chemical Society
      MEP pathway, malaria, Plasmodium falciparum, DXPS, Polypharmacology Browser

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          Abstract

          In this study, we identified three novel compound classes with potent activity against Plasmodium falciparum, the most dangerous human malarial parasite. Resistance of this pathogen to known drugs is increasing, and compounds with different modes of action are urgently needed. One promising drug target is the enzyme 1-deoxy- d-xylulose-5-phosphate synthase (DXPS) of the methylerythritol 4-phosphate (MEP) pathway for which we have previously identified three active compound classes against Mycobacterium tuberculosis. The close structural similarities of the active sites of the DXPS enzymes of P. falciparum and M. tuberculosis prompted investigation of their antiparasitic action, all classes display good cell-based activity. Through structure–activity relationship studies, we increased their antimalarial potency and two classes also show good metabolic stability and low toxicity against human liver cells. The most active compound 1 inhibits the growth of blood-stage P. falciparum with an IC 50 of 600 nM. The results from three different methods for target validation of compound 1 suggest no engagement of DXPS. All inhibitor classes are active against chloroquine-resistant strains, confirming a new mode of action that has to be further investigated.

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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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            SWISS-MODEL: homology modelling of protein structures and complexes

            Abstract Homology modelling has matured into an important technique in structural biology, significantly contributing to narrowing the gap between known protein sequences and experimentally determined structures. Fully automated workflows and servers simplify and streamline the homology modelling process, also allowing users without a specific computational expertise to generate reliable protein models and have easy access to modelling results, their visualization and interpretation. Here, we present an update to the SWISS-MODEL server, which pioneered the field of automated modelling 25 years ago and been continuously further developed. Recently, its functionality has been extended to the modelling of homo- and heteromeric complexes. Starting from the amino acid sequences of the interacting proteins, both the stoichiometry and the overall structure of the complex are inferred by homology modelling. Other major improvements include the implementation of a new modelling engine, ProMod3 and the introduction a new local model quality estimation method, QMEANDisCo. SWISS-MODEL is freely available at https://swissmodel.expasy.org.
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              Analyzing real-time PCR data by the comparative CT method

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                Author and article information

                Journal
                ACS Infect Dis
                ACS Infect Dis
                id
                aidcbc
                ACS Infectious Diseases
                American Chemical Society
                2373-8227
                17 February 2024
                08 March 2024
                : 10
                : 3
                : 1000-1022
                Affiliations
                []Helmholtz Institute for Pharmaceutical Research Saarland (HIPS) − Helmholtz Centre for Infection Research (HZI) , Campus Building E8.1, Saarbrücken 66123, Germany
                []Department of Pharmacy, Saarland University , Campus Building E8.1, Saarbrücken 66123, Germany
                [§ ]Stratingh Institute for Chemistry, University of Groningen , Nijenborgh 7, Groningen 9747 AG, The Netherlands
                []Unit for Drug Discovery, Department of Parasitology, Institute of Biomedical Sciences, University of São Paulo , Av. Prof. Lineu Prestes 1374, São Paulo-SP 05508-000, Brazil
                []Department of Pediatrics, Washington University School of Medicine , Saint Louis, Missouri 63110, United States
                [# ]Institute of Tropical Medicine, University of Tübingen , Wilhelmstraße 27, Tübingen 72074, Germany
                []Symeres , Kadijk 3, Groningen 9747 AT, The Netherlands
                []Department of Pediatrics, Children’s Hospital of Philadelphia, Perelman School of Medicine, University of Pennsylvania , Philadelphia, Pennsylvania 19104, United States
                []Proteomics & Metabolomics Facility, Center for Biotechnology, Department of Agronomy and Horticulture, University of Nebraska-Lincoln , Lincoln, Nebraska 68588, United States
                []Center for Bioinformatics, Saarland University , Campus Building E2.1, Saarbrücken 66123, Germany
                [β ]German Center for Infection Research (DZIF), Partner Site Tübingen , Tübingen 72074, Germany
                [α ]Centre de Recherches Médicales de Lambaréné (CERMEL) , B.P. 242 Lambaréné, Gabon
                Author notes
                Author information
                https://orcid.org/0000-0003-2306-2071
                https://orcid.org/0000-0001-6430-7392
                https://orcid.org/0000-0002-3857-5462
                https://orcid.org/0000-0002-2252-5751
                https://orcid.org/0000-0003-3168-6554
                https://orcid.org/0000-0001-8395-8537
                https://orcid.org/0000-0001-5987-1749
                https://orcid.org/0000-0001-8734-4663
                Article
                10.1021/acsinfecdis.3c00670
                10928712
                38367280
                d6faf2d7-7c56-4fcc-ac3f-91b8299f0aba
                © 2024 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
                : 06 December 2023
                : 24 January 2024
                : 24 January 2024
                Funding
                Funded by: Division of Intramural Research, National Institute of Allergy and Infectious Diseases, doi 10.13039/100006492;
                Award ID: R01-AI103280
                Funded by: Helmholtz Association, doi 10.13039/501100009318;
                Award ID: NA
                Funded by: Nederlandse Organisatie voor Wetenschappelijk Onderzoek, doi 10.13039/501100003246;
                Award ID: 731.015.414
                Funded by: Fundação de Amparo à Pesquisa do Estado de São Paulo, doi 10.13039/501100001807;
                Award ID: 2020/12277-0
                Funded by: Fundação de Amparo à Pesquisa do Estado de São Paulo, doi 10.13039/501100001807;
                Award ID: 2018/08820-0
                Funded by: Fundação de Amparo à Pesquisa do Estado de São Paulo, doi 10.13039/501100001807;
                Award ID: 2017/03966-4
                Funded by: Fundação de Amparo à Pesquisa do Estado de São Paulo, doi 10.13039/501100001807;
                Award ID: 2016/24790-9
                Funded by: Fundação de Amparo à Pesquisa do Estado de São Paulo, doi 10.13039/501100001807;
                Award ID: 2015/26722-8
                Funded by: Division of Intramural Research, National Institute of Allergy and Infectious Diseases, doi 10.13039/100006492;
                Award ID: R21-AI130584
                Funded by: Division of Intramural Research, National Institute of Allergy and Infectious Diseases, doi 10.13039/100006492;
                Award ID: R21-AI123808
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                id3c00670
                id3c00670

                mep pathway,malaria,plasmodium falciparum,dxps,polypharmacology browser

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