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      Insights into the molecular mechanism of yellow cuticle coloration by a chitin-binding carotenoprotein in gregarious locusts

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          Abstract

          Carotenoids are hydrophobic pigments binding to diverse carotenoproteins, many of which remain unexplored. Focusing on yellow gregarious locusts accumulating cuticular carotenoids, here we use engineered Escherichia coli cells to reconstitute a functional water-soluble β-carotene-binding protein, BBP. HPLC and Raman spectroscopy confirmed that recombinant BBP avidly binds β-carotene, inducing the unusual vibronic structure of its absorbance spectrum, just like native BBP extracted from the locust cuticles. Bound to recombinant BBP, β-carotene exhibits pronounced circular dichroism and allows BBP to withstand heating ( T 0.5 = 68 °C), detergents and pH variations. Using bacteria producing distinct xanthophylls we demonstrate that, while β-carotene is the preferred carotenoid, BBP can also extract from membranes ketocarotenoids and, very poorly, hydroxycarotenoids. We show that BBP-carotenoid complex reversibly binds to chitin, but not to chitosan, implying the role for chitin acetyl groups in cuticular BBP deposition. Reconstructing such locust coloration mechanism in vitro paves the way for structural studies and BBP applications.

          Abstract

          A unique soluble β-carotene-binding protein pigment extracted from yellow locust cuticle is biochemically and spectroscopically equivalent to the protein reconstituted in carotenoid-producing E. coli cells and capable of binding to chitin in vitro.

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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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            Cleavage of Structural Proteins during the Assembly of the Head of Bacteriophage T4

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              DISOPRED3: precise disordered region predictions with annotated protein-binding activity

              Motivation: A sizeable fraction of eukaryotic proteins contain intrinsically disordered regions (IDRs), which act in unfolded states or by undergoing transitions between structured and unstructured conformations. Over time, sequence-based classifiers of IDRs have become fairly accurate and currently a major challenge is linking IDRs to their biological roles from the molecular to the systems level. Results: We describe DISOPRED3, which extends its predecessor with new modules to predict IDRs and protein-binding sites within them. Based on recent CASP evaluation results, DISOPRED3 can be regarded as state of the art in the identification of IDRs, and our self-assessment shows that it significantly improves over DISOPRED2 because its predictions are more specific across the whole board and more sensitive to IDRs longer than 20 amino acids. Predicted IDRs are annotated as protein binding through a novel SVM based classifier, which uses profile data and additional sequence-derived features. Based on benchmarking experiments with full cross-validation, we show that this predictor generates precise assignments of disordered protein binding regions and that it compares well with other publicly available tools. Availability and implementation: http://bioinf.cs.ucl.ac.uk/disopred Contact: d.t.jones@ucl.ac.uk Supplementary information: Supplementary data are available at Bioinformatics online.
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                Author and article information

                Contributors
                nikolai.sluchanko@mail.ru
                Journal
                Commun Biol
                Commun Biol
                Communications Biology
                Nature Publishing Group UK (London )
                2399-3642
                11 April 2024
                11 April 2024
                2024
                : 7
                : 448
                Affiliations
                [1 ]A.N. Bach Institute of Biochemistry, Federal Research Centre of Biotechnology of the Russian Academy of Sciences, ( https://ror.org/0009wsb17) Moscow, Russia
                [2 ]M.V. Lomonosov Moscow State University, Faculty of Biology, ( https://ror.org/010pmpe69) Moscow, Russia
                [3 ]M.V. Lomonosov Moscow State University, Faculty of Chemistry, ( https://ror.org/010pmpe69) Moscow, Russia
                Author information
                http://orcid.org/0000-0001-6767-1501
                http://orcid.org/0000-0002-8608-1416
                Article
                6149
                10.1038/s42003-024-06149-x
                11009388
                38605243
                53926196-3deb-42ab-868b-fe897a19a240
                © The Author(s) 2024

                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 licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence 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 licence, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 21 December 2023
                : 5 April 2024
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                © Springer Nature Limited 2024

                entomology,raman spectroscopy,carrier proteins,protein purification,spectrophotometry

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