Mechanistic and evolutionary insights into isoform-specific 'supercharging' in DCLK family kinases.

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 autoregulation 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.

Mechanistic and evolutionary insights into isoform-specific 'supercharging' in DCLK family kinases.

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.

Identification of affective valence of Twitter generated sentiments during the COVID-19 outbreak.

This study aims to conduct text mining of affective valence of the sentiments generated on social media during the COVID-19 and measure their association with different outcomes of the disease. 50,000 tweets per day over 23 days during the pandemic were extracted using the VADER sentiment analysis tool. Overall, tweets could effectively be classified in terms of polarity, i.e., "positive," "negative" and "neutral" sentiments. Furthermore, on a day-to-day basis, the study identified a positive and significant relationship between COVID-19-related (a) global infections and negative tweets, (b) global deaths and negative tweets, (c) recoveries and negative tweets, and (d) recoveries and positive tweets. No significant association could be found between (e) infections and positive tweets and (f) deaths and positive tweets. Furthermore, the statistical analysis also indicated that the daily distribution of tweets based on polarity generates three distinct and significantly different numbers of tweets per category, i.e., positive, negative and neutral. As per the results generated through sentiment analysis of tweets in this study, the emergence of "positive" tweets in such a gloomy pandemic scenario shows the inherent resilience of humans. The significant association between news of COVID-19 recoveries and positive tweets seems to hint at a more optimistic scenario whenever the pandemic finally comes to an end or is controlled. Such public reactions-for good-have the potential to go viral and influence several others, especially those who are classified as "neutral" or fence-sitters.