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      An atlas of substrate specificities for the human serine/threonine kinome

      research-article
      1 , 2 , 1 , 2 , 3 , 4 , 5 , 1 , 2 , 1 , 2 , 3 , 1 , 2 , 1 , 2 , 1 , 2 , 6 , 1 , 2 , 1 , 2 , 1 , 2 , 7 , 8 , 1 , 2 , 9 , 10 , 11 , 12 , 10 , 10 , 13 , 13 , 14 , 14 , 15 , 1 , 2 , 1 , 16 , 17 , 3 , 4 , 18 , 19 , 20 , 21 , 22 , 22 , 22 , 22 , 22 , 23 , 1 , 24 , 25 , 22 , 11 , , 10 , 26 , 27 , , 1 , 2 ,
      Nature
      Nature Publishing Group UK
      Kinases, Cellular signalling networks, Bioinformatics, Phosphorylation

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          Abstract

          Protein phosphorylation is one of the most widespread post-translational modifications in biology 1, 2 . With advances in mass-spectrometry-based phosphoproteomics, 90,000 sites of serine and threonine phosphorylation have so far been identified, and several thousand have been associated with human diseases and biological processes 3, 4 . For the vast majority of phosphorylation events, it is not yet known which of the more than 300 protein serine/threonine (Ser/Thr) kinases encoded in the human genome are responsible 3 . Here we used synthetic peptide libraries to profile the substrate sequence specificity of 303 Ser/Thr kinases, comprising more than 84% of those predicted to be active in humans. Viewed in its entirety, the substrate specificity of the kinome was substantially more diverse than expected and was driven extensively by negative selectivity. We used our kinome-wide dataset to computationally annotate and identify the kinases capable of phosphorylating every reported phosphorylation site in the human Ser/Thr phosphoproteome. For the small minority of phosphosites for which the putative protein kinases involved have been previously reported, our predictions were in excellent agreement. When this approach was applied to examine the signalling response of tissues and cell lines to hormones, growth factors, targeted inhibitors and environmental or genetic perturbations, it revealed unexpected insights into pathway complexity and compensation. Overall, these studies reveal the intrinsic substrate specificity of the human Ser/Thr kinome, illuminate cellular signalling responses and provide a resource to link phosphorylation events to biological pathways.

          Abstract

          Analysis of the kinase activity of 300 protein Ser/Thr kinases reveals that the substrate specificity of the kinome is substantially more diverse than expected and is driven extensively by negative selectivity

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          Most cited references92

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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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            The Protein Data Bank.

            The Protein Data Bank (PDB; http://www.rcsb.org/pdb/ ) is the single worldwide archive of structural data of biological macromolecules. This paper describes the goals of the PDB, the systems in place for data deposition and access, how to obtain further information, and near-term plans for the future development of the resource.
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              The protein kinase complement of the human genome.

              G. Manning (2002)
              We have catalogued the protein kinase complement of the human genome (the "kinome") using public and proprietary genomic, complementary DNA, and expressed sequence tag (EST) sequences. This provides a starting point for comprehensive analysis of protein phosphorylation in normal and disease states, as well as a detailed view of the current state of human genome analysis through a focus on one large gene family. We identify 518 putative protein kinase genes, of which 71 have not previously been reported or described as kinases, and we extend or correct the protein sequences of 56 more kinases. New genes include members of well-studied families as well as previously unidentified families, some of which are conserved in model organisms. Classification and comparison with model organism kinomes identified orthologous groups and highlighted expansions specific to human and other lineages. We also identified 106 protein kinase pseudogenes. Chromosomal mapping revealed several small clusters of kinase genes and revealed that 244 kinases map to disease loci or cancer amplicons.
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                Author and article information

                Contributors
                ben.turk@yale.edu
                myaffe@mit.edu
                LCantley@med.cornell.edu
                Journal
                Nature
                Nature
                Nature
                Nature Publishing Group UK (London )
                0028-0836
                1476-4687
                11 January 2023
                11 January 2023
                2023
                : 613
                : 7945
                : 759-766
                Affiliations
                [1 ]GRID grid.5386.8, ISNI 000000041936877X, Meyer Cancer Center, , Weill Cornell Medicine, ; New York, NY USA
                [2 ]GRID grid.5386.8, ISNI 000000041936877X, Department of Medicine, , Weill Cornell Medicine, ; New York, NY USA
                [3 ]GRID grid.5386.8, ISNI 000000041936877X, Englander Institute for Precision Medicine, Institute for Computational Biomedicine, , Weill Cornell Medicine, ; New York, NY USA
                [4 ]GRID grid.5386.8, ISNI 000000041936877X, Department of Physiology and Biophysics, , Weill Cornell Medicine, ; New York, NY USA
                [5 ]GRID grid.5386.8, ISNI 000000041936877X, Tri-Institutional PhD Program in Computational Biology & Medicine, , Weill Cornell Medicine, Memorial Sloan Kettering Cancer Center and The Rockefeller University, ; New York, NY USA
                [6 ]GRID grid.5386.8, ISNI 000000041936877X, Weill Cornell Graduate School of Medical Sciences, , Cell and Developmental Biology Program, ; New York, NY USA
                [7 ]GRID grid.239585.0, ISNI 0000 0001 2285 2675, Department of Medicine, Division of Hematology/Oncology, , Columbia University Irving Medical Center, ; New York, NY USA
                [8 ]GRID grid.239585.0, ISNI 0000 0001 2285 2675, Herbert Irving Comprehensive Cancer Center, , Columbia University Irving Medical Center, ; New York, NY USA
                [9 ]GRID grid.116068.8, ISNI 0000 0001 2341 2786, Computer Science and Artificial Intelligence Laboratory, , Massachusetts Institute of Technology, ; Cambridge, MA USA
                [10 ]GRID grid.116068.8, ISNI 0000 0001 2341 2786, Center for Precision Cancer Medicine, Koch Institute for Integrative Cancer Biology, Departments of Biology and Biological Engineering, , Massachusetts Institute of Technology, ; Cambridge, MA USA
                [11 ]GRID grid.47100.32, ISNI 0000000419368710, Department of Pharmacology, , Yale School of Medicine, ; New Haven, CT USA
                [12 ]GRID grid.47100.32, ISNI 0000000419368710, Department of Chemistry, , Yale University, ; New Haven, CT USA
                [13 ]GRID grid.6738.a, ISNI 0000 0001 1090 0254, Institute of Genetics, , Technische Universität Braunschweig, ; Braunschweig, Germany
                [14 ]GRID grid.430387.b, ISNI 0000 0004 1936 8796, Department of Pharmacology, , Rutgers Robert Wood Johnson Medical School, ; Piscataway, NJ USA
                [15 ]GRID grid.257413.6, ISNI 0000 0001 2287 3919, Department of Biochemistry and Molecular Biology, , Indiana University School of Medicine, ; Indianapolis, IN USA
                [16 ]GRID grid.5386.8, ISNI 000000041936877X, Division of Endocrinology, , Weill Cornell Medicine, ; New York, NY USA
                [17 ]GRID grid.59734.3c, ISNI 0000 0001 0670 2351, Department of Genetics and Genomic Sciences, , Icahn School of Medicine at Mount Sinai, ; New York, NY USA
                [18 ]GRID grid.266190.a, ISNI 0000000096214564, Department of Biochemistry, , University of Colorado, ; Boulder, CO USA
                [19 ]GRID grid.8390.2, ISNI 0000 0001 2180 5818, SABNP, , Univ Evry, INSERM U1204, Université Paris-Saclay, ; Evry, France
                [20 ]GRID grid.267625.2, ISNI 0000 0001 0685 5104, Department of Investigative Medicine, Graduate School of Medicine, , University of the Ryukyus, ; Nishihara-cho, Japan
                [21 ]GRID grid.67033.31, ISNI 0000 0000 8934 4045, Department of Developmental, Molecular and Chemical Biology, , Tufts University School of Medicine, ; Boston, MA USA
                [22 ]GRID grid.420530.0, ISNI 0000 0004 0580 0138, Department Of Bioinformatics, , Cell Signaling Technology, ; Danvers, MA USA
                [23 ]GRID grid.7468.d, ISNI 0000 0001 2248 7639, Rewire Tx, , Humboldt-Universität zu Berlin, ; Berlin, Germany
                [24 ]GRID grid.5386.8, ISNI 000000041936877X, Department of Pharmacology, , Weill Cornell Medicine, ; New York, NY USA
                [25 ]GRID grid.5386.8, ISNI 000000041936877X, Department of Biochemistry, , Weill Cornell Medicine, ; New York, NY USA
                [26 ]GRID grid.38142.3c, ISNI 000000041936754X, Divisions of Acute Care Surgery, Trauma, and Surgical Critical Care, and Surgical Oncology, Department of Surgery, Beth Israel Deaconess Medical Center, , Harvard Medical School, ; Boston, MA USA
                [27 ]GRID grid.94365.3d, ISNI 0000 0001 2297 5165, Surgical Oncology Program, National Cancer Institute, , National Institutes of Health, ; Bethesda, MD USA
                Author information
                http://orcid.org/0000-0003-1802-6527
                http://orcid.org/0000-0001-6574-7314
                http://orcid.org/0000-0003-0898-8749
                http://orcid.org/0000-0001-8994-3416
                http://orcid.org/0000-0002-7353-0518
                http://orcid.org/0000-0003-0379-4945
                http://orcid.org/0000-0002-1128-1480
                http://orcid.org/0000-0002-0988-6798
                http://orcid.org/0000-0002-1872-212X
                http://orcid.org/0000-0002-0784-9248
                http://orcid.org/0000-0003-4444-5688
                http://orcid.org/0000-0003-2947-6654
                http://orcid.org/0000-0002-8240-7132
                http://orcid.org/0000-0002-6242-2300
                http://orcid.org/0000-0002-1179-2045
                http://orcid.org/0000-0003-3622-2337
                http://orcid.org/0000-0001-9275-4069
                http://orcid.org/0000-0002-9547-3251
                http://orcid.org/0000-0002-1298-7653
                Article
                5575
                10.1038/s41586-022-05575-3
                9876800
                36631611
                1acfe970-5a83-486e-ae73-e7b6076f878d
                © The Author(s) 2023

                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
                : 1 May 2022
                : 17 November 2022
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                kinases,cellular signalling networks,bioinformatics,phosphorylation
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                kinases, cellular signalling networks, bioinformatics, phosphorylation

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