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      Ksp1 is an autophagic receptor protein for the Snx4-assisted autophagy of Ssn2/Med13

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          ABSTRACT

          Ksp1 is a casein II-like kinase whose activity prevents aberrant macroautophagy/autophagy induction in nutrient-rich conditions in yeast. Here, we describe a kinase-independent role of Ksp1 as a novel autophagic receptor protein for Ssn2/Med13, a known cargo of Snx4-assisted autophagy of transcription factors. In this pathway, a subset of conserved transcriptional regulators, Ssn2/Med13, Rim15, and Msn2, are selectively targeted for vacuolar proteolysis following nitrogen starvation, assisted by the sorting nexin heterodimer Snx4-Atg20. Here we show that phagophores also engulf Ksp1 alongside its cargo for vacuolar proteolysis. Ksp1 directly associates with Atg8 following nitrogen starvation at the interface of an Atg8-family interacting motif (AIM)/LC3-interacting region (LIR) in Ksp1 and the LIR/AIM docking site (LDS) in Atg8. Mutating the LDS site prevents the autophagic degradation of Ksp1. However, deletion of the C terminal canonical AIM still permitted Ssn2/Med13 proteolysis, suggesting that additional non-canonical AIMs may mediate the Ksp1-Atg8 interaction. Ksp1 is recruited to the perivacuolar phagophore assembly site by Atg29, a member of the trimeric scaffold complex. This interaction is independent of Atg8 and Snx4, suggesting that Ksp1 is recruited early to phagophores, with Snx4 delivering Ssn2/Med13 thereafter. Finally, normal cell survival following prolonged nitrogen starvation requires Ksp1. Together, these studies define a kinase-independent role for Ksp1 as an autophagic receptor protein mediating Ssn2/Med13 degradation. They also suggest that phagophores built by the trimeric scaffold complex are capable of receptor-mediated autophagy. These results demonstrate the dual functionality of Ksp1, whose kinase activity prevents autophagy while it plays a scaffolding role supporting autophagic degradation.

          Abbreviations: 3-AT: 3-aminotriazole; 17C: Atg17-Atg31-Atg29 trimeric scaffold complex; AIM: Atg8-family interacting motif; ATG: autophagy related; CKM: CDK8 kinase module; Cvt: cytoplasm-to-vacuole targeting; IDR: intrinsically disordered region; LIR: LC3-interacting region; LDS: LIR/AIM docking site; MoRF: molecular recognition feature; NPC: nuclear pore complex; PAS: phagophore assembly site; PKA: protein kinase A; RBP: RNA-binding protein; UPS: ubiquitin-proteasome system. SAA-TF: Snx4-assisted autophagy of transcription factors; Y2H: yeast two-hybrid.

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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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            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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              Mechanism and medical implications of mammalian autophagy

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                Author and article information

                Journal
                Autophagy
                Autophagy
                Autophagy
                Taylor & Francis
                1554-8627
                1554-8635
                21 September 2023
                2024
                21 September 2023
                : 20
                : 2
                : 397-415
                Affiliations
                [a ]Department of Molecular Biology, Rowan-Virtua School of Translational Biomedical Engineering & Sciences, Rowan University; , Stratford, NJ, USA
                [b ]School of Osteopathic Medicine, Rowan University; , Stratford, NJ, USA
                Author notes
                CONTACT Katrina F. Cooper cooperka@ 123456rowan.edu Department of Molecular Biology, Rowan-Virtua School of Translational Biomedical Engineering & Sciences, Rowan University; , Stratford, NJ 08084, USA
                Author information
                https://orcid.org/0000-0003-4619-7534
                Article
                2259708
                10.1080/15548627.2023.2259708
                10813586
                37733395
                0b1062c3-09c2-441b-b22d-f1ee18276cc7
                © 2023 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives License ( http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited, and is not altered, transformed, or built upon in any way. The terms on which this article has been published allow the posting of the Accepted Manuscript in a repository by the author(s) or with their consent.

                History
                Page count
                Figures: 9, References: 127, Pages: 19
                Categories
                Research Article
                Research Paper

                Molecular biology
                atg8,ksp1,phagophore assembly site (pas),receptor proteins,ssn2/med13,trimeric scaffold complex (17c)

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