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      Mechanistic and evolutionary insights into isoform-specific ‘supercharging’ in DCLK family kinases

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          Abstract

          Catalytic signaling outputs of protein kinases are dynamically regulated by an array of structural mechanisms, including allosteric interactions mediated by intrinsically disordered segments flanking the conserved catalytic domain. The Doublecortin Like Kinases (DCLKs) are a family of microtubule-associated proteins characterized by a flexible C-terminal autoregulatory ‘tail’ segment that varies in length across the various human DCLK isoforms. However, the mechanism whereby these isoform-specific variations contribute to unique modes of autoregulation is not well understood. Here, we employ a combination of statistical sequence analysis, molecular dynamics simulations and in vitro mutational analysis to define hallmarks of DCLK family evolutionary divergence, including analysis of splice variants within the DCLK1 sub-family, which arise through alternative codon usage and serve to ‘supercharge’ the inhibitory potential of the DCLK1 C-tail. We identify co-conserved motifs that readily distinguish DCLKs from all other Calcium Calmodulin Kinases (CAMKs), and a ‘Swiss-army’ assembly of distinct motifs that tether the C-terminal tail to conserved ATP and substrate-binding regions of the catalytic domain to generate a scaffold for auto-regulation through C-tail dynamics. Consistently, deletions and mutations that alter C-terminal tail length or interfere with co-conserved interactions within the catalytic domain alter intrinsic protein stability, nucleotide/inhibitor-binding, and catalytic activity, suggesting isoform-specific regulation of activity through alternative splicing. Our studies provide a detailed framework for investigating kinome–wide regulation of catalytic output through cis-regulatory events mediated by intrinsically disordered segments, opening new avenues for the design of mechanistically-divergent DCLK1 modulators, stabilizers or degraders.

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          Most cited references75

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          IQ-TREE: A Fast and Effective Stochastic Algorithm for Estimating Maximum-Likelihood Phylogenies

          Large phylogenomics data sets require fast tree inference methods, especially for maximum-likelihood (ML) phylogenies. Fast programs exist, but due to inherent heuristics to find optimal trees, it is not clear whether the best tree is found. Thus, there is need for additional approaches that employ different search strategies to find ML trees and that are at the same time as fast as currently available ML programs. We show that a combination of hill-climbing approaches and a stochastic perturbation method can be time-efficiently implemented. If we allow the same CPU time as RAxML and PhyML, then our software IQ-TREE found higher likelihoods between 62.2% and 87.1% of the studied alignments, thus efficiently exploring the tree-space. If we use the IQ-TREE stopping rule, RAxML and PhyML are faster in 75.7% and 47.1% of the DNA alignments and 42.2% and 100% of the protein alignments, respectively. However, the range of obtaining higher likelihoods with IQ-TREE improves to 73.3-97.1%.
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            VMD: Visual molecular dynamics

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              MAFFT: a novel method for rapid multiple sequence alignment based on fast Fourier transform.

              K Katoh (2002)
              A multiple sequence alignment program, MAFFT, has been developed. The CPU time is drastically reduced as compared with existing methods. MAFFT includes two novel techniques. (i) Homo logous regions are rapidly identified by the fast Fourier transform (FFT), in which an amino acid sequence is converted to a sequence composed of volume and polarity values of each amino acid residue. (ii) We propose a simplified scoring system that performs well for reducing CPU time and increasing the accuracy of alignments even for sequences having large insertions or extensions as well as distantly related sequences of similar length. Two different heuristics, the progressive method (FFT-NS-2) and the iterative refinement method (FFT-NS-i), are implemented in MAFFT. The performances of FFT-NS-2 and FFT-NS-i were compared with other methods by computer simulations and benchmark tests; the CPU time of FFT-NS-2 is drastically reduced as compared with CLUSTALW with comparable accuracy. FFT-NS-i is over 100 times faster than T-COFFEE, when the number of input sequences exceeds 60, without sacrificing the accuracy.
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                Author and article information

                Contributors
                Role: methodologyRole: investigationRole: data curationRole: formal analysisRole: validationRole: writing – original draftRole: writing – review and editingRole: visualizationRole: funding acquisition
                Role: methodologyRole: investigationRole: data curationRole: formal analysisRole: validationRole: writing – original draftRole: writing – review and editingRole: visualization
                Role: methodologyRole: investigationRole: formal analysisRole: validationRole: writing – original draftRole: writing – review and editingRole: visualizationRole: funding acquisition
                Role: investigationRole: data curationRole: formal analysisRole: writing – review and editingRole: visualization
                Role: investigationRole: formal analysisRole: writing – review and editingRole: visualization
                Role: investigationRole: validationRole: writing – review and editing
                Role: investigationRole: writing – review and editingRole: funding acquisition
                Role: investigationRole: writing – review and editingRole: funding acquisition
                Role: writing – review and editing
                Role: data curationRole: writing – review and editing
                Role: writing – review and editing
                Role: writing – review and editing
                Role: methodologyRole: writing – review and editingRole: funding acquisition
                Role: methodologyRole: writing – original draftRole: writing – review and editingRole: supervisionRole: funding acquisition
                Role: conceptualizationRole: methodologyRole: writing – original draftRole: writing – review and editingRole: supervisionRole: funding acquisition
                Journal
                bioRxiv
                BIORXIV
                bioRxiv
                Cold Spring Harbor Laboratory
                18 July 2023
                : 2023.03.29.534689
                Affiliations
                [1 ]Department of Biochemistry and Molecular Biology, University of Georgia, Athens, GA 30602, USA
                [2 ]Department of Biochemistry and Systems Biology, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, L69 7ZB, UK
                [3 ]Institute of Bioinformatics, University of Georgia, Athens, GA 30602, USA
                [4 ]Centre for Proteome Research, Department of Biochemistry and Systems Biology, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, L69 7ZB, UK
                Author notes
                [+]

                equal contributions

                [* ]Correspondence to: Natarajan Kannan, nkannan@ 123456uga.edu or Patrick Eyers, patrick.eyers@ 123456liverpool.ac.uk
                Article
                10.1101/2023.03.29.534689
                10081240
                37034755
                47802f3f-2e5b-4930-b100-a538d07b6bbd

                This work is licensed under a Creative Commons Attribution 4.0 International License, which allows reusers to distribute, remix, adapt, and build upon the material in any medium or format, so long as attribution is given to the creator. The license allows for commercial use.

                History
                Funding
                Funded by: University of Liverpool BBSRC IAA
                Award ID: R35 GM139656
                Funded by: ARCS Foundation
                Funded by: BBSRC
                Award ID: BB/S018514/1
                Award ID: BB/N021703/1
                Award ID: BB/X002780/1
                Funded by: North West Cancer Research (NWCR)
                Award ID: CR1208
                Funded by: DiMeN DTP studentship
                Award ID: 1961582
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