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      A G358S mutation in the Plasmodium falciparum Na + pump PfATP4 confers clinically-relevant resistance to cipargamin

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          Abstract

          Diverse compounds target the Plasmodium falciparum Na + pump PfATP4, with cipargamin and (+)-SJ733 the most clinically-advanced. In a recent clinical trial for cipargamin, recrudescent parasites emerged, with most having a G358S mutation in PfATP4. Here, we show that PfATP4 G358S parasites can withstand micromolar concentrations of cipargamin and (+)-SJ733, while remaining susceptible to antimalarials that do not target PfATP4. The G358S mutation in PfATP4, and the equivalent mutation in Toxoplasma gondii ATP4, decrease the sensitivity of ATP4 to inhibition by cipargamin and (+)-SJ733, thereby protecting parasites from disruption of Na + regulation. The G358S mutation reduces the affinity of PfATP4 for Na + and is associated with an increase in the parasite’s resting cytosolic [Na +]. However, no defect in parasite growth or transmissibility is observed. Our findings suggest that PfATP4 inhibitors in clinical development should be tested against PfATP4 G358S parasites, and that their combination with unrelated antimalarials may mitigate against resistance development.

          Abstract

          In a recent clinical trial for oral administration of cipargamin in individuals with malaria, there was an emergence of recrudescent parasites with a G358S mutation in PfATP4. In this work, the authors investigate the effect of this mutation on the function of the ATPase, on parasite growth and susceptibility to antimalarial drugs.

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          Fast gapped-read alignment with Bowtie 2.

          As the rate of sequencing increases, greater throughput is demanded from read aligners. The full-text minute index is often used to make alignment very fast and memory-efficient, but the approach is ill-suited to finding longer, gapped alignments. Bowtie 2 combines the strengths of the full-text minute index with the flexibility and speed of hardware-accelerated dynamic programming algorithms to achieve a combination of high speed, sensitivity and accuracy.
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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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              VMD: Visual molecular dynamics

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                Author and article information

                Contributors
                adele.lehane@anu.edu.au
                Journal
                Nat Commun
                Nat Commun
                Nature Communications
                Nature Publishing Group UK (London )
                2041-1723
                30 September 2022
                30 September 2022
                2022
                : 13
                : 5746
                Affiliations
                [1 ]GRID grid.1001.0, ISNI 0000 0001 2180 7477, Research School of Biology, , Australian National University, ; Canberra, ACT 2600 Australia
                [2 ]GRID grid.21729.3f, ISNI 0000000419368729, Department of Microbiology & Immunology, , Columbia University Irving Medical Center, ; New York, NY 10032 USA
                [3 ]GRID grid.1042.7, ISNI 0000 0004 0432 4889, Bioinformatic Division, , The Walter & Eliza Hall Institute of Medical Research, ; Parkville, VIC 3052 Australia
                [4 ]GRID grid.21107.35, ISNI 0000 0001 2171 9311, Department of Molecular Microbiology & Immunology and Johns Hopkins Malaria Institute, , Johns Hopkins School of Public Health, ; Baltimore, MD 21205 USA
                [5 ]GRID grid.52788.30, ISNI 0000 0004 0427 7672, Wellcome Sanger Institute, , Wellcome Genome Campus, ; Hinxton, CB10 1SA UK
                [6 ]GRID grid.419481.1, ISNI 0000 0001 1515 9979, Novartis Pharma AG, Novartis Campus, ; Basel, 4056 Switzerland
                [7 ]GRID grid.418424.f, ISNI 0000 0004 0439 2056, Novartis Institute for Tropical Diseases, ; Emeryville, CA 94608 USA
                [8 ]GRID grid.1008.9, ISNI 0000 0001 2179 088X, Department of Medical Biology, , The University of Melbourne, ; Parkville, VIC 3052 Australia
                [9 ]GRID grid.21729.3f, ISNI 0000000419368729, Center for Malaria Therapeutics and Antimicrobial Resistance, Division of Infectious Diseases, Department of Medicine, , Columbia University Irving Medical Center, ; New York, NY 10032 USA
                Author information
                http://orcid.org/0000-0002-7297-8660
                http://orcid.org/0000-0003-3914-3116
                http://orcid.org/0000-0003-1561-0074
                http://orcid.org/0000-0002-7699-8837
                http://orcid.org/0000-0001-9848-0142
                http://orcid.org/0000-0003-2923-6060
                http://orcid.org/0000-0001-9519-487X
                http://orcid.org/0000-0002-6763-2366
                http://orcid.org/0000-0002-1102-8506
                http://orcid.org/0000-0002-4973-0915
                http://orcid.org/0000-0002-6324-442X
                http://orcid.org/0000-0003-2954-7547
                http://orcid.org/0000-0001-6753-8938
                http://orcid.org/0000-0003-2455-9821
                http://orcid.org/0000-0002-0050-9101
                Article
                33403
                10.1038/s41467-022-33403-9
                9525273
                36180431
                99fb4b73-6f3a-472a-9fe7-82caa7e07d0b
                © The Author(s) 2022

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 26 June 2022
                : 16 September 2022
                Funding
                Funded by: FundRef https://doi.org/10.13039/501100000925, Department of Health | National Health and Medical Research Council (NHMRC);
                Award ID: GNT1159648
                Award Recipient :
                Funded by: FundRef https://doi.org/10.13039/100004336, Novartis;
                Funded by: FundRef https://doi.org/10.13039/100004440, Wellcome Trust (Wellcome);
                Award ID: 206194/Z/17/Z
                Award Recipient :
                Funded by: FundRef https://doi.org/10.13039/100000002, U.S. Department of Health & Human Services | National Institutes of Health (NIH);
                Award ID: R01 AI132359
                Award Recipient :
                Categories
                Article
                Custom metadata
                © The Author(s) 2022

                Uncategorized
                parasite biology,malaria
                Uncategorized
                parasite biology, malaria

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