Although the functional roles of phospho-Ser2 and phospho-Ser5 and the kinases that place those marks have been extensively studied, the functions of the three so-called "orphan" residues (Tyr1, Thr4, and Ser7) and the identity of kinases that modify those sites remain poorly defined. Unbiased mass spectrometric mapping of the CTD revealed that the orphan sites are phosphorylated, and non-CDK kinases, such as HRR25, PLK3, and ABL1, modify those residues. Notably, unlike Ser2 or Ser5, whose phosphorylation has broad effects, mutations of the orphan residues or inhibition of kinases that act on them selectively disrupt the expression of limited sets of genes. The pathways mapped to those genes suggest that of the 250 phospho-acceptor residues that are densely packed within 360 residues of the human CTD, nearly 150 orphan sites may be used by other kinases to selectively regulate distinct sets of functionally coherent genes. To bridge the knowledge gap and identify previously unknown CTD-active kinases, we used three orthogonal kinome testing platforms in conjunction with machine learning algorithms to predict kinase-substrate pairings.

SARS-CoV-2, the causative virus of COVID-19 continues to cause an ongoing global pandemic. Therapeutics are still needed to treat mild and severe COVID-19. Drug repurposing provides an opportunity to deploy drugs for COVID-19 more rapidly than developing novel therapeutics. Some existing drugs have shown promise for treating COVID-19 in clinical trials. This in silico study uses structural similarity to clinical trial drugs to identify two drugs with potential applications to treat early COVID-19. We apply in silico validation to suggest a possible mechanism of action for both. Triamcinolone is a corticosteroid structurally similar to Dexamethasone. Gallopamil is a calcium channel blocker structurally similar to Verapamil. We propose that both these drugs could be useful to treat early COVID-19 infection due to the proximity of their targets within a SARS-CoV-2-induced protein-protein interaction network to kinases active in early infection, and the APOA1 protein which is linked to the spread of COVID-19.

In the search for new medicines against diseases such as cancer, a Leiden team has developed a new workflow. This approach combines artificial intelligence (AI) with molecular modelling and is suitable for finding unknown and innovative drug structures, the researchers proved. With their new method, the researchers of the Leiden Academic Centre for Drug Research (LACDR) and the Leiden Institute of Advanced Computer Science (LIACS) were able to find five substances with an inhibitory effect on a specific type of kinase. Kinases are enzymes that switch other proteins on or off and play an important role in the development of cancer. In their publication in the Journal of Chemical Information and Modeling, the team looked at so-called polypharmacology--drug development in which there are multiple targets in the body (see box below).

Park et al. question one out of seven findings from Hadzipasic et al.: whether TPX2 allosterically regulates the oldest Aurora. We had already addressed the two concerns raised--sparse sequence sampling and not forcing the gene to the species tree--before publication. Moreover, we believe their ancestral sequence reconstruction would be consistent with a nonallosteric common ancestor, and we show large sequence differences caused by species tree–enforced gene trees. The key findings in Hadzipasic et al. (1) are that (i) autophosphorylation is the ancient allosteric regulation for Aurora kinases; (ii) a gradual increase in allosteric activation took place during the holozoan evolution; (iii) an allosteric network in Aurora exists that, when mutated, alters allosteric activity; (iv) allosteric activation by TPX2 is entirely encoded in the kinase; (v) the interface between Aurora and TPX2 is co-conserved; (vi) evolution of specificity in signaling happens on binding affinity; and (vii) the oldest ancestral Aurora is not allosterically activated by TPX2. Notably, even though the ASR calculations differ, we believe the outcome is consistent with, rather than contradicting, the finding. The two concerns raised are (i) the small number of modern sequences used in the ASR calculations and (ii) the mismatch between the gene tree and the species tree.

Many biologists use machine learning (ML) as a computational tool to analyze a massive amount of data, helping them to recognise potential new drugs. MIT researchers have now integrated a new feature into these types of machine learning algorithms, enhancing their prediction-making ability. Using this new tool allows computer models to account for uncertainty in the data they are testing, MIT researchers detected several promising components that target a protein required by the bacteria that cause tuberculosis (TB). Although computer scientists previously used this technique, they have not taken off in biology. "It could also prove useful in protein design and many other fields of biology," says the Simons Professor of Mathematics and head of the Computation and Biology group in MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) Bonnie Berger.

Machine learning is a computational tool used by many biologists to analyze huge amounts of data, helping them to identify potential new drugs. MIT researchers have now incorporated a new feature into these types of machine-learning algorithms, improving their prediction-making ability. Using this new approach, which allows computer models to account for uncertainty in the data they're analyzing, the MIT team identified several promising compounds that target a protein required by the bacteria that cause tuberculosis. This method, which has previously been used by computer scientists but has not taken off in biology, could also prove useful in protein design and many other fields of biology, says Bonnie Berger, the Simons Professor of Mathematics and head of the Computation and Biology group in MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL). "This technique is part of a known subfield of machine learning, but people have not brought it to biology," Berger says.

Baricitinib, is an oral Janus kinase (JAK)1/JAK2 inhibitor approved for the treatment of rheumatoid arthritis (RA) that was independently predicted, using artificial intelligence (AI)‐algorithms, to be useful for COVID‐19 infection via a proposed anti‐cytokine effects and as an inhibitor of host cell viral propagation. We evaluated the in vitro pharmacology of baricitinib across relevant leukocyte subpopulations coupled to its in vivo pharmacokinetics and showed it inhibited signaling of cytokines implicated in COVID‐19 infection. We validated the AI‐predicted biochemical inhibitory effects of baricitinib on human numb‐associated kinase (hNAK) members measuring nanomolar affinities for AAK1, BIKE, and GAK. Inhibition of NAKs led to reduced viral infectivity with baricitinib using human primary liver spheroids. These effects occurred at exposure levels seen clinically.

SAN FRANCISCO – Forget the Terminator. The next robot on the horizon may be wearing a lab coat. Artificial intelligence (AI) is already helping scientists form testable hypotheses that enable experts to run real experiments, and the technology may soon be poised to help businesses make decisions, one scientist says. However, that doesn't mean the machines will be taking over from humans entirely. Instead, humans and machines have complementary skillsets, so AI could help researchers with the work they already do, Laura Haas, a computer scientist and director of the IBM Research Accelerated Discovery Lab in San Jose, California, said here Wednesday (Dec.

SAN FRANCISCO – Forget the Terminator. The next robot on the horizon may be wearing a lab coat. Artificial intelligence (AI) is already helping scientists form testable hypotheses that enable experts to run real experiments, and the technology may soon be poised to help businesses make decisions, one scientist says. However, that doesn't mean the machines will be taking over from humans entirely. Instead, humans and machines have complementary skillsets, so AI could help researchers with the work they already do, Laura Haas, a computer scientist and director of the IBM Research Accelerated Discovery Lab in San Jose, California, said here Wednesday (Dec.