Drugging the Undruggable: How Isoquinolines and PKA Initiated the Era of Designed Protein Kinase Inhibitor Therapeutics.

In 1984, Japanese researchers led by the biochemist Hiroyoshi Hidaka described the first synthetic protein kinase inhibitors based on an isoquinoline sulfonamide structure (Hidaka et al. Biochemistry, 1984 Oct 9; 23(21): 5036-41. doi: 10.1021/bi00316a032). These led to the first protein kinase inhibitor approved for medical use (fasudil), an inhibitor of the AGC subfamily Rho kinase. With potencies strong enough to compete against endogenous ATP, the isoquinoline compounds established the druggability of the ATP binding site. Crystal structures of their protein kinase complexes, including with cAMP-dependent protein kinase (PKA), showed interactions that, on the one hand, could mimic ATP but, on the other hand, could be optimized for high potency binding, kinase selectivity, and diversification away from adenosine. They also showed the flexibility of the glycine-rich loop, and PKA became a major prototype for crystallographic and nuclear magnetic resonance (NMR) studies of protein kinase mechanism and dynamic activity control. Since fasudil, more than 70 kinase inhibitors have been approved for clinical use, involving efforts that progressively have introduced new paradigms of data-driven drug discovery. Publicly available data alone comprise over 5000 protein kinase crystal structures and hundreds of thousands of binding data. Now, new methods, including artificial intelligence techniques and expansion of protein kinase targeting approaches, together with the expiration of patent protection for optimized inhibitor scaffolds, promise even greater advances in drug discovery. Looking back to the time of the first isoquinoline hinge binders brings the current state-of-the-art into stark contrast. Appropriately for this Perspective article, many of the milestone papers during this time were published in Biochemistry (now ACS Biochemistry).

Mass cytometry and type 1 diabetes research in the age of single-cell data science.

PURPOSE OF REVIEW: New single-cell tec. hnologies developed over the past decade have considerably reshaped the biomedical research landscape, and more recently have found their way into studies probing the pathogenesis of type 1 diabetes (T1D). In this context, the emergence of mass cytometry in 2009 revolutionized immunological research in two fundamental ways that also affect the T1D world: first, its ready embrace by the community and rapid dissemination across academic and private science centers alike established a new standard of analytical complexity for the high-dimensional proteomic stratification of single-cell populations; and second, the somewhat unexpected arrival of mass cytometry awoke the flow cytometry field from its seeming sleeping beauty stupor and precipitated substantial technological advances that by now approach a degree of analytical dimensionality comparable to mass cytometry. RECENT FINDINGS: Here, we summarize in detail how mass cytometry has thus far been harnessed for the pursuit of discovery studies in T1D science; we provide a succinct overview of other single-cell analysis platforms that already have been or soon will be integrated into various T1D investigations; and we briefly consider how effective adoption of these technologies requires an adjusted model for expense allocation, prioritization of experimental questions, division of labor, and recognition of scientific contributions. SUMMARY: The introduction of contemporary single-cell technologies in general, and of mass cytometry, in particular, provides important new opportunities for current and future T1D research; the necessary reconfiguration of research strategies to accommodate implementation of these technologies, however, may both broaden research endeavors by fostering genuine team science, and constrain their actual practice because of the need for considerable investments into infrastructure and technical expertise.

Social Media, Open Science, and Data Science Are Inextricably Linked.

Should scientists use social media? Why practice open science? What is data science? Ten years ago, these phrases hardly existed. Now they are ubiquitous. Here I argue that these phenomena are inextricably linked and reflect similar underlying social and technological transformations.