Kincore: a web resource for structural classification of protein kinases and their inhibitors

Guided Docking as a Data Generation Approach Facilitates Structure-Based Machine Learning on Kinases

Drug discovery pipelines nowadays rely on machine learning models to explore and evaluate large chemical spaces. While including 3D structural information is considered beneficial, structural models are hindered by the availability of protein-ligand complex structures. Exemplified for kinase drug discovery, we address this issue by generating kinase-ligand complex data using template docking for the kinase compound subset of available ChEMBL assay data. To evaluate the benefit of the created complex data, we use it to train a structure-based E(3)-invariant graph neural network. Our evaluation shows that binding affinities can be predicted with significantly higher precision by models that take synthetic binding poses into account compared to ligand- or drug-target interaction models alone.

Empowering AlphaFold2 for protein conformation selective drug discovery with AlphaFold2-RAVE

Small molecule drug design hinges on obtaining co-crystallized ligand-protein structures. Despite AlphaFold2's strides in protein native structure prediction, its focus on apo structures overlooks ligands and associated holo structures. Moreover, designing selective drugs often benefits from the targeting of diverse metastable conformations. Therefore, direct application of AlphaFold2 models in virtual screening and drug discovery remains tentative. Here, we demonstrate an AlphaFold2 based framework combined with all-atom enhanced sampling molecular dynamics and induced fit docking, named AF2RAVE-Glide, to conduct computational model based small molecule binding of metastable protein kinase conformations, initiated from protein sequences. We demonstrate the AF2RAVE-Glide workflow on three different protein kinases and their type I and II inhibitors, with special emphasis on binding of known type II kinase inhibitors which target the metastable classical DFG-out state. These states are not easy to sample from AlphaFold2. Here we demonstrate how with AF2RAVE these metastable conformations can be sampled for different kinases with high enough accuracy to enable subsequent docking of known type II kinase inhibitors with more than 50% success rates across docking calculations. We believe the protocol should be deployable for other kinases and more proteins generally.

The intrinsic substrate specificity of the human tyrosine kinome

Phosphorylation of proteins on tyrosine (Tyr) residues evolved in metazoan organisms as a mechanism of coordinating tissue growth1. Multicellular eukaryotes typically have more than 50 distinct protein Tyr kinases that catalyse the phosphorylation of thousands of Tyr residues throughout the proteome1-3. How a given Tyr kinase can phosphorylate a specific subset of proteins at unique Tyr sites is only partially understood4-7. Here we used combinatorial peptide arrays to profile the substrate sequence specificity of all human Tyr kinases. Globally, the Tyr kinases demonstrate considerable diversity in optimal patterns of residues surrounding the site of phosphorylation, revealing the functional organization of the human Tyr kinome by substrate motif preference. Using this information, Tyr kinases that are most compatible with phosphorylating any Tyr site can be identified. Analysis of mass spectrometry phosphoproteomic datasets using this compendium of kinase specificities accurately identifies specific Tyr kinases that are dysregulated in cells after stimulation with growth factors, treatment with anti-cancer drugs or expression of oncogenic variants. Furthermore, the topology of known Tyr signalling networks naturally emerged from a comparison of the sequence specificities of the Tyr kinases and the SH2 phosphotyrosine (pTyr)-binding domains. Finally we show that the intrinsic substrate specificity of Tyr kinases has remained fundamentally unchanged from worms to humans, suggesting that the fidelity between Tyr kinases and their protein substrate sequences has been maintained across hundreds of millions of years of evolution.

Exploring Kinase Asp-Phe-Gly (DFG) Loop Conformational Stability with AlphaFold2-RAVE

Kinases compose one of the largest fractions of the human proteome, and their misfunction is implicated in many diseases, in particular, cancers. The ubiquitousness and structural similarities of kinases make specific and effective drug design difficult. In particular, conformational variability due to the evolutionarily conserved Asp-Phe-Gly (DFG) motif adopting in and out conformations and the relative stabilities thereof are key in structure-based drug design for ATP competitive drugs. These relative conformational stabilities are extremely sensitive to small changes in sequence and provide an important problem for sampling method development. Since the invention of AlphaFold2, the world of structure-based drug design has noticeably changed. In spite of it being limited to crystal-like structure prediction, several methods have also leveraged its underlying architecture to improve dynamics and enhanced sampling of conformational ensembles, including AlphaFold2-RAVE. Here, we extend AlphaFold2-RAVE and apply it to a set of kinases: the wild type DDR1 sequence and three mutants with single point mutations that are known to behave drastically differently. We show that AlphaFold2-RAVE is able to efficiently recover the changes in relative stability using transferable learned order parameters and potentials, thereby supplementing AlphaFold2 as a tool for exploration of Boltzmann-weighted protein conformations (Meller, A.; Bhakat, S.; Solieva, S.; Bowman, G. R. Accelerating Cryptic Pocket Discovery Using AlphaFold. J. Chem. Theory Comput. 2023, 19, 4355-4363).

Analysis of the binding pattern of NIK inhibitors by computational simulation

NF-kappaB-Inducing Kinase (NIK) is a key kinase in the activation of the NF-κB non-classical signalling pathway, which has been shown to be over-activated in patients with inflammatory diseases, immune disorders and malignancies and solid tumours inducing activation of the NF-κB non-classical signalling pathway. The design of ATP-competitive small molecule inhibitors against NIK has been a hot topic in the last decade, and many efficient NIK inhibitors have been identified. In this work, I aim to unravel the mechanism of NIK inhibition by different representative NIK type I 1/2 kinase inhibitors, using ADME, molecular docking, molecular dynamics simulation, MM-PBSA analysis and 3D-QSAR analysis. This work contributes to the understanding of the efficiency of NIK inhibitor binding by revealing the basis of the efficiency of NIK inhibitors, the difference in binding modes between different inhibitors and the overall effect on NIK.Communicated by Ramaswamy H. Sarma.

High-throughput prediction of protein conformational distributions with subsampled AlphaFold2

This paper presents an innovative approach for predicting the relative populations of protein conformations using AlphaFold 2, an AI-powered method that has revolutionized biology by enabling the accurate prediction of protein structures. While AlphaFold 2 has shown exceptional accuracy and speed, it is designed to predict proteins' ground state conformations and is limited in its ability to predict conformational landscapes. Here, we demonstrate how AlphaFold 2 can directly predict the relative populations of different protein conformations by subsampling multiple sequence alignments. We tested our method against nuclear magnetic resonance experiments on two proteins with drastically different amounts of available sequence data, Abl1 kinase and the granulocyte-macrophage colony-stimulating factor, and predicted changes in their relative state populations with more than 80% accuracy. Our subsampling approach worked best when used to qualitatively predict the effects of mutations or evolution on the conformational landscape and well-populated states of proteins. It thus offers a fast and cost-effective way to predict the relative populations of protein conformations at even single-point mutation resolution, making it a useful tool for pharmacology, analysis of experimental results, and predicting evolution.

Cryo-EM Structures of CRAF2/14-3-32 and CRAF2/14-3-32/MEK12 Complexes

RAF protein kinases are essential effectors in the MAPK pathway and are important cancer drug targets. Structural understanding of RAF activation is so far based on cryo-electron microscopy (cryo-EM) and X-ray structures of BRAF in different conformational states as inactive or active complexes with KRAS, 14-3-3 and MEK1. In this study, we have solved the first cryo-EM structures of CRAF2/14-3-32 at 3.4 Å resolution and CRAF2/14-3-32/MEK12 at 4.2 Å resolution using CRAF kinase domain expressed as constitutively active Y340D/Y341D mutant in insect cells. The overall architecture of our CRAF2/14-3-32 and CRAF2/14-3-32/MEK12 cryo-EM structures is highly similar to corresponding BRAF structures in complex with 14-3-3 or 14-3-3/MEK1 and represent the activated dimeric RAF conformation. Our CRAF cryo-EM structures provide additional insights into structural understanding of the activated CRAF2/14-3-32/MEK12 complex.

A resource database for protein kinase substrate sequence-preference motifs based on large-scale mass spectrometry data

BACKGROUND

Protein phosphorylation is one of the most prevalent posttranslational modifications involved in molecular control of cellular processes, and is mediated by over 520 protein kinases in humans and other mammals. Identification of the protein kinases responsible for phosphorylation events is key to understanding signaling pathways. Unbiased phosphoproteomics experiments have generated a wealth of data that can be used to identify protein kinase targets and their preferred substrate sequences.

METHODS

This study utilized prior data from mass spectrometry-based studies identifying sites of protein phosphorylation after in vitro incubation of protein mixtures with recombinant protein kinases. PTM-Logo software was used with these data to generate position-dependent Shannon information matrices and sequence motif 'logos'. Webpages were constructed for facile access to logos for each kinase and a new stand-alone application was written in Python that uses the position-dependent Shannon information matrices to identify kinases most likely to phosphorylate a particular phosphorylation site.

RESULTS

A database of kinase substrate target preference logos allows browsing, searching, or downloading target motif data for each protein kinase ( https://esbl.nhlbi.nih.gov/Databases/Kinase_Logos/ ). These logos were combined with phylogenetic analysis of protein kinase catalytic sequences to reveal substrate preference patterns specific to particular groups of kinases ( https://esbl.nhlbi.nih.gov/Databases/Kinase_Logos/KinaseTree.html ). A stand-alone program, KinasePredictor, is provided ( https://esbl.nhlbi.nih.gov/Databases/Kinase_Logos/KinasePredictor.html ). It takes as input, amino-acid sequences surrounding a given phosphorylation site and generates a ranked list of protein kinases most likely to phosphorylate that site.

CONCLUSIONS

This study provides three new resources for protein kinase characterization. It provides a tool for prediction of kinase-substrate interactions, which in combination with other types of data (co-localization, etc.), can predict which kinases are likely responsible for a given phosphorylation event in a given tissue. Video Abstract.

Allo-targeting of the kinase domain: Insights from in silico studies and comparison with experiments

The eukaryotic protein kinase domain has been a broadly explored target for drug discovery, despite limitations imposed by its high sequence conservation as a shared modular domain and the development of resistance to drugs. One way of addressing those limitations has been to target its potential allosteric sites, shortly called allo-targeting, in conjunction with, or separately from, its conserved catalytic/orthosteric site that has been widely exploited. Allosteric regulation has gained importance as an alternative to overcome the drawbacks associated with the indiscriminate effect of targeting the active site, and it turned out to be particularly useful for these highly promiscuous and broadly shared kinase domains. Yet, allo-targeting often faces challenges as the allosteric sites are not as clearly defined as its orthosteric sites, and the effect on the protein function may not be unambiguously assessed. A robust understanding of the consequence of site-specific allo-targeting on the conformational dynamics of the target protein is essential to design effective allo-targeting strategies. Recent years have seen important advances in in silico identification of druggable sites and distinguishing among them those sites expected to allosterically mediate conformational switches essential to signal transmission. The present opinion underscores the utility of such computational approaches applied to the kinase domain, with the help of comparison between computational predictions and experimental observations.

Properties of FDA-approved small molecule protein kinase inhibitors: A 2024 update

Owing to the dysregulation of protein kinase activity in many diseases including cancer, this enzyme family has become one of the most important drug targets in the 21st century. There are 80 FDA-approved therapeutic agents that target about two dozen different protein kinases and seven of these drugs were approved in 2023. Of the approved drugs, thirteen target protein-serine/threonine protein kinases, four are directed against dual specificity protein kinases (MEK1/2), twenty block nonreceptor protein-tyrosine kinases, and 43 inhibit receptor protein-tyrosine kinases. The data indicate that 69 of these drugs are prescribed for the treatment of neoplasms. Six drugs (abrocitinib, baricitinib, deucravacitinib, ritlecitinib, tofacitinib, upadacitinib) are used for the treatment of inflammatory diseases (atopic dermatitis, rheumatoid arthritis, psoriasis, alopecia areata, and ulcerative colitis). Of the 80 approved drugs, nearly two dozen are used in the treatment of multiple diseases. The following seven drugs received FDA approval in 2023: capivasertib (HER2-positive breast cancer), fruquintinib (metastatic colorectal cancer), momelotinib (myelofibrosis), pirtobrutinib (mantle cell lymphoma, chronic lymphocytic leukemia, small lymphocytic lymphoma), quizartinib (Flt3-mutant acute myelogenous leukemia), repotrectinib (ROS1-positive lung cancer), and ritlecitinib (alopecia areata). All of the FDA-approved drugs are orally effective with the exception of netarsudil, temsirolimus, and trilaciclib. This review summarizes the physicochemical properties of all 80 FDA-approved small molecule protein kinase inhibitors including the molecular weight, number of hydrogen bond donors/acceptors, polar surface area, potency, solubility, lipophilic efficiency, and ligand efficiency.

Development of MPS1 Inhibitors: Recent Advances and Perspectives

Monopolar spindle kinase 1 (MPS1) plays a pivotal role as a dual-specificity kinase governing spindle assembly checkpoint activation and sister chromatid separation in mitosis. Its overexpression has been observed in various human malignancies. MPS1 reduces spindle assembly checkpoint sensitivity, allowing tumor cells with a high degree of aneuploidy to complete mitosis and survive. Thus, MPS1 has emerged as a promising candidate for cancer therapy. Despite the identification of numerous MPS1 inhibitors, only five have advanced to clinical trials with none securing FDA approval for cancer treatment. In this perspective, we provide a concise overview of the structural and functional characteristics of MPS1 by highlighting its relevance to cancer. Additionally, we explore the structure-activity relationships, selectivity, and pharmacokinetics of MPS1 inhibitors featuring diverse scaffolds. Moreover, we review the reported work on enhancing MPS1 inhibitor selectivity, offering valuable insights into the discovery of novel, highly potent small-molecule MPS1 inhibitors.

Biophysical and structural characterization of the impacts of MET phosphorylation on tepotinib binding

The receptor tyrosine kinase MET is activated by hepatocyte growth factor binding, followed by phosphorylation of the intracellular kinase domain (KD) mainly within the activation loop (A-loop) on Y1234 and Y1235. Dysregulation of MET can lead to both tumor growth and metastatic progression of cancer cells. Tepotinib is a highly selective, potent type Ib MET inhibitor and approved for treatment of non-small cell lung cancer harboring METex14 skipping alterations. Tepotinib binds to the ATP site of unphosphorylated MET with critical π-stacking contacts to Y1230 of the A-loop, resulting in a high residence time. In our study, we combined protein crystallography, biophysical methods (surface plasmon resonance, differential scanning fluorimetry), and mass spectrometry to clarify the impacts of A-loop conformation on tepotinib binding using different recombinant MET KD protein variants. We solved the first crystal structures of MET mutants Y1235D, Y1234E/1235E, and F1200I in complex with tepotinib. Our biophysical and structural data indicated a linkage between reduced residence times for tepotinib and modulation of A-loop conformation either by mutation (Y1235D), by affecting the overall Y1234/Y1235 phosphorylation status (L1195V and F1200I) or by disturbing critical π-stacking interactions with tepotinib (Y1230C). We corroborated these data with target engagement studies by fluorescence cross-correlation spectroscopy using KD constructs in cell lysates or full-length receptors from solubilized cellular membranes as WT or activated mutants (Y1235D and Y1234E/1235E). Collectively, our results provide further insight into the MET A-loop structural determinants that affect the binding of the selective inhibitor tepotinib.

Investigating the conformational landscape of AlphaFold2-predicted protein kinase structures

Summary

Protein kinases are a family of signaling proteins, crucial for maintaining cellular homeostasis. When dysregulated, kinases drive the pathogenesis of several diseases, and are thus one of the largest target categories for drug discovery. Kinase activity is tightly controlled by switching through several active and inactive conformations in their catalytic domain. Kinase inhibitors have been designed to engage kinases in specific conformational states, where each conformation presents a unique physico-chemical environment for therapeutic intervention. Thus, modeling kinases across conformations can enable the design of novel and optimally selective kinase drugs. Due to the recent success of AlphaFold2 in accurately predicting the 3D structure of proteins based on sequence, we investigated the conformational landscape of protein kinases as modeled by AlphaFold2. We observed that AlphaFold2 is able to model several kinase conformations across the kinome, however, certain conformations are only observed in specific kinase families. Furthermore, we show that the per residue predicted local distance difference test can capture information describing structural flexibility of kinases. Finally, we evaluated the docking performance of AlphaFold2 kinase structures for enriching known ligands. Taken together, we see an opportunity to leverage AlphaFold2 models for structure-based drug discovery against kinases across several pharmacologically relevant conformational states.

Availability and implementation

All code used in the analysis is freely available at https://github.com/Harmonic-Discovery/AF2-kinase-conformational-landscape.

Exploring kinase DFG loop conformational stability with AlphaFold2-RAVE

Kinases compose one of the largest fractions of the human proteome, and their misfunction is implicated in many diseases, in particular cancers. The ubiquitousness and structural similarities of kinases makes specific and effective drug design difficult. In particular, conformational variability due to the evolutionarily conserved DFG motif adopting in and out conformations and the relative stabilities thereof are key in structure-based drug design for ATP competitive drugs. These relative conformational stabilities are extremely sensitive to small changes in sequence, and provide an important problem for sampling method development. Since the invention of AlphaFold2, the world of structure-based drug design has noticably changed. In spite of it being limited to crystal-like structure prediction, several methods have also leveraged its underlying architecture to improve dynamics and enhanced sampling of conformational ensembles, including AlphaFold2-RAVE. Here, we extend AlphaFold2-RAVE and apply it to a set of kinases: the wild type DDR1 sequence and three mutants with single point mutations that are known to behave drastically differently. We show that AlphaFold2-RAVE is able to efficiently recover the changes in relative stability using transferable learnt order parameters and potentials, thereby supplementing AlphaFold2 as a tool for exploration of Boltzmann-weighted protein conformations.

AlphaFold2 models of the active form of all 437 catalytically competent human protein kinase domains

Humans have 437 catalytically competent protein kinase domains with the typical kinase fold, similar to the structure of Protein Kinase A (PKA). Only 155 of these kinases are in the Protein Data Bank in their active form. The active form of a kinase must satisfy requirements for binding ATP, magnesium, and substrate. From structural bioinformatics analysis of 40 unique substrate-bound kinases, we derived several criteria for the active form of protein kinases. We include requirements on the DFG motif of the activation loop but also on the positions of the N-terminal and C-terminal segments of the activation loop that must be placed appropriately to bind substrate. Because the active form of catalytic kinases is needed for understanding substrate specificity and the effects of mutations on catalytic activity in cancer and other diseases, we used AlphaFold2 to produce models of all 437 human protein kinases in the active form. This was accomplished with templates in the active form from the PDB and shallow multiple sequence alignments of orthologs and close homologs of the query protein. We selected models for each kinase based on the pLDDT scores of the activation loop residues, demonstrating that the highest scoring models have the lowest or close to the lowest RMSD to 22 non-redundant substrate-bound structures in the PDB. A larger benchmark of all 130 active kinase structures with complete activation loops in the PDB shows that 80% of the highest-scoring AlphaFold2 models have RMSD < 1.0 Å and 90% have RMSD < 2.0 Å over the activation loop backbone atoms. Models for all 437 catalytic kinases are available at http://dunbrack.fccc.edu/kincore/activemodels. We believe they may be useful for interpreting mutations leading to constitutive catalytic activity in cancer as well as for templates for modeling substrate and inhibitor binding for molecules which bind to the active state.

Exploring the Druggable Conformational Space of Protein Kinases Using AI-Generated Structures

Protein kinase function and interactions with drugs are controlled in part by the movement of the DFG and ɑC-Helix motifs, which enable kinases to adopt various conformational states. Small molecule ligands elicit therapeutic effects with distinct selectivity profiles and residence times that often depend on the kinase conformation(s) they bind. However, the limited availability of experimentally determined structural data for kinases in inactive states restricts drug discovery efforts for this major protein family. Modern AI-based structural modeling methods hold potential for exploring the previously experimentally uncharted druggable conformational space for kinases. Here, we first evaluated the currently explored conformational space of kinases in the PDB and models generated by AlphaFold2 (AF2) (1) and ESMFold (2), two prominent AI-based structure prediction methods. We then investigated AF2's ability to predict kinase structures in different conformations at various multiple sequence alignment (MSA) depths, based on this parameter's ability to explore conformational diversity. Our results showed a bias within the PDB and predicted structural models generated by AF2 and ESMFold toward structures of kinases in the active state over alternative conformations, particularly those conformations controlled by the DFG motif. Finally, we demonstrate that predicting kinase structures using AF2 at lower MSA depths allows the exploration of the space of these alternative conformations, including identifying previously unobserved conformations for 398 kinases. The results of our analysis of structural modeling by AF2 create a new avenue for the pursuit of new therapeutic agents against a notoriously difficult-to-target family of proteins.

Significance Statement

Greater abundance of kinase structural data in inactive conformations, currently lacking in structural databases, would improve our understanding of how protein kinases function and expand drug discovery and development for this family of therapeutic targets. Modern approaches utilizing artificial intelligence and machine learning have potential for efficiently capturing novel protein conformations. We provide evidence for a bias within AlphaFold2 and ESMFold to predict structures of kinases in their active states, similar to their overrepresentation in the PDB. We show that lowering the AlphaFold2 algorithm's multiple sequence alignment depth can help explore kinase conformational space more broadly. It can also enable the prediction of hundreds of kinase structures in novel conformations, many of whose models are likely viable for drug discovery.

A biophysical framework for double-drugging kinases

Selective orthosteric inhibition of kinases has been challenging due to the conserved active site architecture of kinases and emergence of resistance mutants. Simultaneous inhibition of distant orthosteric and allosteric sites, which we refer to as "double-drugging", has recently been shown to be effective in overcoming drug resistance. However, detailed biophysical characterization of the cooperative nature between orthosteric and allosteric modulators has not been undertaken. Here, we provide a quantitative framework for double-drugging of kinases employing isothermal titration calorimetry, Förster resonance energy transfer, coupled-enzyme assays, and X-ray crystallography. We discern positive and negative cooperativity for Aurora A kinase (AurA) and Abelson kinase (Abl) with different combinations of orthosteric and allosteric modulators. We find that a conformational equilibrium shift is the main principle governing cooperativity. Notably, for both kinases, we find a synergistic decrease of the required orthosteric and allosteric drug dosages when used in combination to inhibit kinase activities to clinically relevant inhibition levels. X-ray crystal structures of the double-drugged kinase complexes reveal the molecular principles underlying the cooperative nature of double-drugging AurA and Abl with orthosteric and allosteric inhibitors. Finally, we observe a fully closed conformation of Abl when bound to a pair of positively cooperative orthosteric and allosteric modulators, shedding light on the puzzling abnormality of previously solved closed Abl structures. Collectively, our data provide mechanistic and structural insights into rational design and evaluation of double-drugging strategies.

Small molecule protein kinase inhibitors approved by regulatory agencies outside of the United States

Owing to genetic alterations and overexpression, the dysregulation of protein kinases plays a significant role in the pathogenesis of many autoimmune and neoplastic disorders and protein kinase antagonists have become an important drug target. Although the efficacy of imatinib in the treatment of chronic myelogenous leukemia in the United States in 2001 was the main driver of protein kinase inhibitor drug discovery, this was preceded by the approval of fasudil (a ROCK antagonist) in Japan in 1995 for the treatment of cerebral vasospasm. There are 21 small molecule protein kinase inhibitors that are approved in China, Japan, Europe, and South Korea that are not approved in the United Sates and 75 FDA-approved inhibitors in the United States. Of the 21 agents, eleven target receptor protein-tyrosine kinases, eight inhibit nonreceptor protein-tyrosine kinases, and two block protein-serine/threonine kinases. All 21 drugs are orally bioavailable or topically effective. Of the non-FDA approved drugs, sixteen are prescribed for the treatment of neoplastic diseases, three are directed toward inflammatory disorders, one is used for glaucoma, and fasudil is used in the management of vasospasm. The leading targets of kinase inhibitors approved by both international regulatory agencies and by the FDA are members of the EGFR family, the VEGFR family, and the JAK family. One-third of the 21 internationally approved drugs are not compliant with Lipinski's rule of five for orally bioavailable drugs. The rule of five relies on four parameters including molecular weight, number of hydrogen bond donors and acceptors, and the Log of the partition coefficient.

Conformational changes in the essential E. coli septal cell wall synthesis complex suggest an activation mechanism

The bacterial divisome is a macromolecular machine composed of more than 30 proteins that controls cell wall constriction during division. Here, we present a model of the structure and dynamics of the core complex of the E. coli divisome, supported by a combination of structure prediction, molecular dynamics simulation, single-molecule imaging, and mutagenesis. We focus on the septal cell wall synthase complex formed by FtsW and FtsI, and its regulators FtsQ, FtsL, FtsB, and FtsN. The results indicate extensive interactions in four regions in the periplasmic domains of the complex. FtsQ, FtsL, and FtsB support FtsI in an extended conformation, with the FtsI transpeptidase domain lifted away from the membrane through interactions among the C-terminal domains. FtsN binds between FtsI and FtsL in a region rich in residues with superfission (activating) and dominant negative (inhibitory) mutations. Mutagenesis experiments and simulations suggest that the essential domain of FtsN links FtsI and FtsL together, potentially modulating interactions between the anchor-loop of FtsI and the putative catalytic cavity of FtsW, thus suggesting a mechanism of how FtsN activates the cell wall synthesis activities of FtsW and FtsI.

Protein kinases on carbon metabolism: potential targets for alternative chemotherapies against toxoplasmosis

The apicomplexan parasite Toxoplasma gondii is the causative agent of toxoplasmosis, a global disease that significantly impacts human health. The clinical manifestations are mainly observed in immunocompromised patients, including ocular damage and neuronal alterations leading to psychiatric disorders. The congenital infection leads to miscarriage or severe alterations in the development of newborns. The conventional treatment is limited to the acute phase of illness, without effects in latent parasites; consequently, a cure is not available yet. Furthermore, considerable toxic effects and long-term therapy contribute to high treatment abandonment rates. The investigation of exclusive parasite pathways would provide new drug targets for more effective therapies, eliminating or reducing the side effects of conventional pharmacological approaches. Protein kinases (PKs) have emerged as promising targets for developing specific inhibitors with high selectivity and efficiency against diseases. Studies in T. gondii have indicated the presence of exclusive PKs without homologs in human cells, which could become important targets for developing new drugs. Knockout of specific kinases linked to energy metabolism have shown to impair the parasite development, reinforcing the essentiality of these enzymes in parasite metabolism. In addition, the specificities found in the PKs that regulate the energy metabolism in this parasite could bring new perspectives for safer and more efficient therapies for treating toxoplasmosis. Therefore, this review provides an overview of the limitations for reaching an efficient treatment and explores the role of PKs in regulating carbon metabolism in Toxoplasma, discussing their potential as targets for more applied and efficient pharmacological approaches.

AI-Assisted chemical probe discovery for the understudied Calcium-Calmodulin Dependent Kinase, PNCK

PNCK, or CAMK1b, is an understudied kinase of the calcium-calmodulin dependent kinase family which recently has been identified as a marker of cancer progression and survival in several large-scale multi-omics studies. The biology of PNCK and its relation to oncogenesis has also begun to be elucidated, with data suggesting various roles in DNA damage response, cell cycle control, apoptosis and HIF-1-alpha related pathways. To further explore PNCK as a clinical target, potent small-molecule molecular probes must be developed. Currently, there are no targeted small molecule inhibitors in pre-clinical or clinical studies for the CAMK family. Additionally, there exists no experimentally derived crystal structure for PNCK. We herein report a three-pronged chemical probe discovery campaign which utilized homology modeling, machine learning, virtual screening and molecular dynamics to identify small molecules with low-micromolar potency against PNCK activity from commercially available compound libraries. We report the discovery of a hit-series for the first targeted effort towards discovering PNCK inhibitors that will serve as the starting point for future medicinal chemistry efforts for hit-to-lead optimization of potent chemical probes.

Unified access to up-to-date residue-level annotations from UniProtKB and other biological databases for PDB data

More than 61,000 proteins have up-to-date correspondence between their amino acid sequence (UniProtKB) and their 3D structures (PDB), enabled by the Structure Integration with Function, Taxonomy and Sequences (SIFTS) resource. SIFTS incorporates residue-level annotations from many other biological resources. SIFTS data is available in various formats like XML, CSV and TSV format or also accessible via the PDBe REST API but always maintained separately from the structure data (PDBx/mmCIF file) in the PDB archive. Here, we extended the wwPDB PDBx/mmCIF data dictionary with additional categories to accommodate SIFTS data and added the UniProtKB, Pfam, SCOP2, and CATH residue-level annotations directly into the PDBx/mmCIF files from the PDB archive. With the integrated UniProtKB annotations, these files now provide consistent numbering of residues in different PDB entries allowing easy comparison of structure models. The extended dictionary yields a more consistent, standardised metadata description without altering the core PDB information. This development enables up-to-date cross-reference information at the residue level resulting in better data interoperability, supporting improved data analysis and visualisation.

Redefining pseudokinases: A look at the untapped enzymatic potential of pseudokinases

Catalytically inactive kinases, known as pseudokinases, are conserved in all three domains of life. Due to the lack of catalytic residues, pseudokinases are considered to act as allosteric regulators and scaffolding proteins with no enzymatic function. However, since these "dead" kinases are conserved along with their active counterparts, a role for pseudokinases may have been overlooked. In this review, we will discuss the recently characterized pseudokinases Selenoprotein O, Legionella effector SidJ, and the SARS-CoV2 protein nsp12 which catalyze AMPylation, glutamylation, and RNAylation, respectively. These studies provide structural and mechanistic insight into the versatility and diversity of the kinase fold.

Strategy toward Kinase-Selective Drug Discovery

Kinase drug selectivity is the ground challenge in cancer research. Due to the structurally similar kinase drug pockets, off-target inhibitor toxicity has been a major cause for clinical trial failures. The pockets are similar but not identical. Here, we describe a transformation invariant protocol to identify distinct geometric features in the drug pocket that can distinguish one kinase from all others. We integrate available experimental structures with the artificial intelligence-based structural kinome, performing a kinome-wide structural bioinformatic analysis to establish the structural principles of kinase drug selectivity. We generate the structural landscape from the experimental kinase-ligand complexes and propose a binary network that encapsulates the information. The results show that all kinases contain binary units that are shared by less than seven other kinases in the kinome. 331 kinases contain unique binary units that may distinguish them from all others. The structural features encoded by these binary units in the network represent the inhibitor-accessible geometric space that may capture the kinome-wide selectivity. Our proposed binary network with the unsupervised clustering can serve as a general structural bioinformatic protocol for extracting the distinguishing structural features for any protein from their families. We apply the binary network to epidermal growth factor receptor tyrosine kinase inhibitor selectivity by targeting the gate area and the AKT1 serine/threonine kinase selectivity by binding to the αC-helix region and the allosteric pocket. Finally, we develop the cross-platform software, KDS (Kinase Drug Selectivity), for customized visualization and analysis of the binary networks in the human kinome (https://github.com/CBIIT/KDS).

Identification of potential ATP-competitive cyclin-dependent kinase 1 inhibitors: De novo drug generation, molecular docking, and molecular dynamics simulation

Cyclin-dependent kinases 1 (CDK1) has been identified as a potential target for the search for new antitumor drugs. However, no clinically effective CDK1 inhibitors are now available for cancer treatment. Therefore, this study aimed to offer potential CDK1 inhibitors using de novo drug generation, molecular docking, and molecular dynamics (MD) simulation studies. We first utilized the BREED algorithm (a de novo drug generation approach) to produce a novel library of small molecules targeting CDK1. To initially obtain novel potential CDK1 inhibitors with favorable physicochemical properties and excellent druggability, we performed a virtual rule-based rational drug screening on our generated library and found ten initial hits. Then, the molecular interactions and dynamic stability of these ten initial hits and CDK1 complexes during their all-atom MD simulations (total 18 μs) and binding pose metadynamics simulations were investigated, resulting in five final hits. Furthermore, another MD simulation (total 2.1 μs) with different force fields demonstrated the binding ability of the five hits to CDK1. It was found that these five hits, CBMA001 (ΔG = -29.88 kcal/mol), CBMA002 (ΔG = -34.89 kcal/mol), CBMA004 (ΔG = -32.47 kcal/mol), CBMA007 (ΔG = -31.16 kcal/mol), and CBMA008 (ΔG = -34.78 kcal/mol) possessed much greater binding affinity to CDK1 than positive compound Flavopiridol (FLP, ΔG = -25.38 kcal/mol). Finally, CBMA002 and CBMA004 were identified as excellent selective CDK1 inhibitors in silico. Together, this study provides a workflow for rational drug design and two promising selective CDK1 inhibitors that deserve further investigation.

Deucravacitinib is an allosteric TYK2 protein kinase inhibitor FDA-approved for the treatment of psoriasis

Psoriasis is a heterogeneous, inflammatory, autoimmune skin disease that affects up to 2% of the world's population. There are many treatment modalities including topical medicines, ultraviolet light therapy, monoclonal antibodies, and several oral medications. Cytokines play a central role in the pathogenesis of this disorder including TNF-α, (tumor necrosis factor-α) IL-17A (interleukin-17A), IL-17F, IL-22, and IL-23. Cytokine signaling involves transduction mediated by the JAK-STAT pathway. There are four JAKS (JAK1/2/3 and TYK2) and six STATS (signal transducer and activators of transcription). Janus kinases contain an inactive JH2 domain that is aminoterminal to the active JH1 domain. Under basal conditions, the JH2 domain inhibits the activity of the JH1 domain. Deucravacitinib is an orally effective N-trideuteromethyl-pyridazine derivative that targets and stabilizes the TYK2 JH2 domain and thereby blocks TYK2 JH1 activity. Seven other JAK inhibitors, which target the JAK family JH1 domain, are prescribed for the treatment of neoplastic and other inflammatory diseases. The use of deuterium in the trimethylamide decreases the rate of demethylation and slows the production of a metabolite that is active against a variety of targets in addition to TYK2. A second unique aspect in the development of deucravacitinib is the targeting of a pseudokinase domain. Deucravacitinib is rather specific for TYK2 and its toxic effects are much less than those of the other FDA-approved JAK inhibitors. The successful development of deucravacitinib may stimulate the development of additional pseudokinase ligands for the JAK family and for other kinase families as well.

Protein kinases: Role of their dysregulation in carcinogenesis, identification and inhibition

Protein kinases belong to the phosphor-transferases superfamily of enzymes, which "activate" enzymes via phosphorylation. The kinome of an organism is the total set of genes in the genome, which encode for all the protein kinases. Certain mutations in the kinome have been linked to dysregulation of protein kinases, which in turn can lead to several diseases and disorders including cancer. In this review, we have briefly discussed the role of protein kinases in various biochemical processes by categorizing cancer associated phenotypes and giving their protein kinase examples. Various techniques have also been discussed, which are being used to analyze the structure of protein kinases, and associate their roles in the oncogenesis. We have also discussed protein kinase inhibitors and United States Federal Drug Administration (USFDA) approved drugs, which target protein kinases and can serve as a counter to protein kinase dysregulation and mitigate the effects of oncogenesis. Overall, this review briefs about the importance of protein kinases, their roles in oncogenesis on dysregulation and how their inhibition via various drugs can be used to mitigate their effects.

Properties of FDA-approved small molecule protein kinase inhibitors: A 2023 update

Owing to the dysregulation of protein kinase activity in many diseases including cancer, this enzyme family has become one of the most important drug targets in the 21st century. There are 72 FDA-approved therapeutic agents that target about two dozen different protein kinases and three of these drugs were approved in 2022. Of the approved drugs, twelve target protein-serine/threonine protein kinases, four are directed against dual specificity protein kinases (MEK1/2), sixteen block nonreceptor protein-tyrosine kinases, and 40 target receptor protein-tyrosine kinases. The data indicate that 62 of these drugs are prescribed for the treatment of neoplasms (57 against solid tumors including breast, lung, and colon, ten against nonsolid tumors such as leukemia, and four against both solid and nonsolid tumors: acalabrutinib, ibrutinib, imatinib, and midostaurin). Four drugs (abrocitinib, baricitinib, tofacitinib, upadacitinib) are used for the treatment of inflammatory diseases (atopic dermatitis, psoriatic arthritis, rheumatoid arthritis, Crohn disease, and ulcerative colitis). Of the 72 approved drugs, eighteen are used in the treatment of multiple diseases. The following three drugs received FDA approval in 2022 for the treatment of these specified diseases: abrocitinib (atopic dermatitis), futibatinib (cholangiocarcinomas), pacritinib (myelofibrosis). All of the FDA-approved drugs are orally effective with the exception of netarsudil, temsirolimus, and trilaciclib. This review summarizes the physicochemical properties of all 72 FDA-approved small molecule protein kinase inhibitors including lipophilic efficiency and ligand efficiency.

The Power of Biocatalysts for Highly Selective and Efficient Phosphorylation Reactions

Reactions involving the transfer of phosphorus-containing groups are of key importance for maintaining life, from biological cells, tissues and organs to plants, animals, humans, ecosystems and the whole planet earth. The sustainable utilization of the nonrenewable element phosphorus is of key importance for a balanced phosphorus cycle. Significant advances have been achieved in highly selective and efficient biocatalytic phosphorylation reactions, fundamental and applied aspects of phosphorylation biocatalysts, novel phosphorylation biocatalysts, discovery methodologies and tools, analytical and synthetic applications, useful phosphoryl donors and systems for their regeneration, reaction engineering, product recovery and purification. Biocatalytic phosphorylation reactions with complete conversion therefore provide an excellent reaction platform for valuable analytical and synthetic applications.

HProteome-BSite: predicted binding sites and ligands in human 3D proteome

Atomic-level knowledge of protein-ligand interactions allows a detailed understanding of protein functions and provides critical clues to discovering molecules regulating the functions. While recent innovative deep learning methods for protein structure prediction dramatically increased the structural coverage of the human proteome, molecular interactions remain largely unknown. A new database, HProteome-BSite, provides predictions of binding sites and ligands in the enlarged 3D human proteome. The model structures for human proteins from the AlphaFold Protein Structure Database were processed to structural domains of high confidence to maximize the coverage and reliability of interaction prediction. For ligand binding site prediction, an updated version of a template-based method GalaxySite was used. A high-level performance of the updated GalaxySite was confirmed. HProteome-BSite covers 80.74% of the UniProt entries in the AlphaFold human 3D proteome. Predicted binding sites and binding poses of potential ligands are provided for effective applications to further functional studies and drug discovery. The HProteome-BSite database is available at https://galaxy.seoklab.org/hproteome-bsite/database and is free and open to all users.

Looking lively: emerging principles of pseudokinase signaling

Progress towards understanding catalytically 'dead' protein kinases - pseudokinases - in biology and disease has hastened over the past decade. An especially lively area for structural biology, pseudokinases appear to be strikingly similar to their kinase relatives, despite lacking key catalytic residues. Distinct active- and inactive-like conformation states, which are crucial for regulating bona fide protein kinases, are conserved in pseudokinases and appear to be essential for function. We discuss recent structural data on conformational transitions and nucleotide binding by pseudokinases, from which some common principles emerge. In both pseudokinases and bona fide kinases, a conformational toggle appears to control the ability to interact with signaling effectors. We also discuss how biasing this conformational toggle may provide opportunities to target pseudokinases pharmacologically in disease.

Fumarate suppresses B-cell activation and function through direct inactivation of LYN

Activated B cells increase central carbon metabolism to fulfill their bioenergetic demands, yet the mechanistic basis for this, as well as metabolic regulation in B cells, remains largely unknown. Here, we demonstrate that B-cell activation reprograms the tricarboxylic acid cycle and boosts the expression of fumarate hydratase (FH), leading to decreased cellular fumarate abundance. Fumarate accumulation by FH inhibition or dimethyl-fumarate treatment suppresses B-cell activation, proliferation and antibody production. Mechanistically, fumarate is a covalent inhibitor of tyrosine kinase LYN, a key component of the BCR signaling pathway. Fumarate can directly succinate LYN at C381 and abrogate LYN activity, resulting in a block to B-cell activation and function in vitro and in vivo. Therefore, our findings uncover a previously unappreciated metabolic regulation of B cells, and reveal LYN is a natural sensor of fumarate, connecting cellular metabolism to B-cell antigen receptor signaling.

Identification of NEK7 inhibitors: structure based virtual screening, molecular docking, density functional theory calculations and molecular dynamics simulations

NEK7 is a NIMA related-protein kinase that plays a crucial role in spindle assembly and cell division. Dysregulation of NEK7 protein leads to development and progression of different types of malignancies including colon and breast cancers. Therefore, NEK7 could be considered as an attractive target for anti-cancer drug discovery. However, few efforts have been made for the development of selective inhibitors of NIMA-related kinase but still no FDA approved drug is known to selectively inhibit the NEK7 protein. Dacomitinib and Neratinib are two Enamide derivatives that were approved for treatment against non-small cell lung cancer and breast cancer respectively. Drug repurposing is a time and cost-efficient method for re-evaluating the activities of previously authorized medications. Thus, the present research involves the repurposing of two FDA-approved medications via comprehensive in silico approach including Density functional theory (DFTs) studies which were conducted to determine the electronic properties of the Dacomitinib and Neratinib. Afterward, binding orientation of selected drugs inside NEK7 activation loop was evaluated through molecular docking approach. Selected drugs exhibited potential molecular interactions engaging important amino acid residues of active site. The docking score of Dacomitinib and Neratinib was -30.77 and -26.78 kJ/mol, respectively. The top ranked pose obtained from molecular docking was subjected to Molecular Dynamics (MD) Simulations for investigating the stability of protein-ligand complex. The RMSD pattern revealed the stability of protein-ligand complex throughout simulated trajectory. In conclusion, both drugs displayed inhibitory efficacy against NEK7 protein and provide a prospective therapy option for malignant malignancies linked with NEK7. Communicated by Ramaswamy H. Sarma.

Structural features of the protein kinase domain and targeted binding by small-molecule inhibitors

Protein kinases are key components in cellular signaling pathways as they carry out the phosphorylation of proteins, primarily on Ser, Thr, and Tyr residues. The catalytic activity of protein kinases is regulated, and they can be thought of as molecular switches that are controlled through protein-protein interactions and post-translational modifications. Protein kinases exhibit diverse structural mechanisms of regulation and have been fascinating subjects for structural biologists from the first crystal structure of a protein kinase over 30 years ago, to recent insights into kinase assemblies enabled by the breakthroughs in cryo-EM. Protein kinases are high-priority targets for drug discovery in oncology and other disease settings, and kinase inhibitors have transformed the outcomes of specific groups of patients. Most kinase inhibitors are ATP competitive, deriving potency by occupying the deep hydrophobic pocket at the heart of the kinase domain. Selectivity of inhibitors depends on exploiting differences between the amino acids that line the ATP site and exploring the surrounding pockets that are present in inactive states of the kinase. More recently, allosteric pockets outside the ATP site are being targeted to achieve high selectivity and to overcome resistance to current therapeutics. Here, we review the key regulatory features of the protein kinase family, describe the different types of kinase inhibitors, and highlight examples where the understanding of kinase regulatory mechanisms has gone hand in hand with the development of inhibitors.

Janus kinase (JAK) inhibitors in the treatment of neoplastic and inflammatory disorders

The Janus kinase (JAK) family of nonreceptor protein-tyrosine kinases consists of JAK1, JAK2, JAK3, and TYK2 (Tyrosine Kinase 2). Each of these proteins contains a JAK homology pseudokinase (JH2) domain that interacts with and regulates the activity of the adjacent protein kinase domain (JH1). The Janus kinase family is regulated by numerous cytokines including interferons, interleukins, and hormones such as erythropoietin and thrombopoietin. Ligand binding to cytokine receptors leads to the activation of associated Janus kinases, which then catalyze the phosphorylation of the receptors. The SH2 domain of signal transducers and activators of transcription (STAT) binds to the cytokine receptor phosphotyrosines thereby promoting STAT phosphorylation and activation by the Janus kinases. STAT dimers are then translocated into the nucleus where they participate in the regulation and expression of dozens of proteins. JAK1/3 signaling participates in the pathogenesis of inflammatory disorders while JAK1/2 signaling contributes to the development of myeloproliferative neoplasms as well as several malignancies including leukemias and lymphomas. An activating JAK2 V617F mutation occurs in 95% of people with polycythemia vera and about 50% of cases of myelofibrosis and essential thrombocythemia. Abrocitinib, ruxolitinib, and upadacitinib are JAK inhibitors that are FDA-approved for the treatment of atopic dermatitis. Baricitinib is used for the treatment of rheumatoid arthritis and covid 19. Tofacitinib and upadacitinib are JAK antagonists that are used for the treatment of rheumatoid arthritis and ulcerative colitis. Additionally, ruxolitinib is approved for the treatment of polycythemia vera while fedratinib, pacritinib, and ruxolitinib are approved for the treatment of myelofibrosis.

KiSSim: Predicting Off-Targets from Structural Similarities in the Kinome

Protein kinases are among the most important drug targets because their dysregulation can cause cancer, inflammatory and degenerative diseases, and many more. Developing selective inhibitors is challenging due to the highly conserved binding sites across the roughly 500 human kinases. Thus, detecting subtle similarities on a structural level can help explain and predict off-targets among the kinase family. Here, we present the kinase-focused, subpocket-enhanced KiSSim fingerprint (Kinase Structural Similarity). The fingerprint builds on the KLIFS pocket definition, composed of 85 residues aligned across all available protein kinase structures, which enables residue-by-residue comparison without a computationally expensive alignment. The residues' physicochemical and spatial properties are encoded within their structural context including key subpockets at the hinge region, the DFG motif, and the front pocket. Since structure was found to contain information complementary to sequence, we used the fingerprint to calculate all-against-all similarities within the structurally covered kinome. We could identify off-targets that are unexpected if solely considering the sequence-based kinome tree grouping; for example, Erlobinib's known kinase off-targets SLK and LOK show high similarities to the key target EGFR (TK group), although belonging to the STE group. KiSSim reflects profiling data better or at least as well as other approaches such as KLIFS pocket sequence identity, KLIFS interaction fingerprints (IFPs), or SiteAlign. To rationalize observed (dis)similarities, the fingerprint values can be visualized in 3D by coloring structures with residue and feature resolution. We believe that the KiSSim fingerprint is a valuable addition to the kinase research toolbox to guide off-target and polypharmacology prediction. The method is distributed as an open-source Python package on GitHub and as a conda package: https://github.com/volkamerlab/kissim.

Regulatory spine RS3 residue of protein kinases: a lipophilic bystander or a decisive element in the small-molecule kinase inhibitor binding?

In recent years, protein kinases have been one of the most pursued drug targets. These determined efforts have resulted in ever increasing numbers of small-molecule kinase inhibitors reaching to the market, offering novel treatment options for patients with distinct diseases. One essential component related to the activation and normal functionality of a protein kinase is the regulatory spine (R-spine). The R-spine is formed of four conserved residues named as RS1–RS4. One of these residues, RS3, located in the C-terminal part of αC-helix, is usually accessible for the inhibitors from the ATP-binding cavity as its side chain is lining the hydrophobic back pocket in many protein kinases. Although the role of RS3 has been well acknowledged in protein kinase function, this residue has not been actively considered in inhibitor design, even though many small-molecule kinase inhibitors display interactions to this residue. In this minireview, we will cover the current knowledge of RS3, its relationship with the gatekeeper, and the role of RS3 in kinase inhibitor interactions. Finally, we comment on the future perspectives how this residue could be utilized in the kinase inhibitor design.

Targeting Protein Kinases and Epigenetic Control as Combinatorial Therapy Options for Advanced Prostate Cancer Treatment

Prostate cancer (PC), the fifth leading cause of cancer-related mortality worldwide, is known as metastatic bone cancer when it spreads to the bone. Although there is still no effective treatment for advanced/metastatic PC, awareness of the molecular events that contribute to PC progression has opened up opportunities and raised hopes for the development of new treatment strategies. Androgen deprivation and androgen-receptor-targeting therapies are two gold standard treatments for metastatic PC. However, acquired resistance to these treatments is a crucial challenge. Due to the role of protein kinases (PKs) in the growth, proliferation, and metastases of prostatic tumors, combinatorial therapy by PK inhibitors may help pave the way for metastatic PC treatment. Additionally, PC is known to have epigenetic involvement. Thus, understanding epigenetic pathways can help adopt another combinatorial treatment strategy. In this study, we reviewed the PKs that promote PC to advanced stages. We also summarized some PK inhibitors that may be used to treat advanced PC and we discussed the importance of epigenetic control in this cancer. We hope the information presented in this article will contribute to finding an effective treatment for the management of advanced PC.

The 2022 Nucleic Acids Research database issue and the online molecular biology database collection

The 2022 Nucleic Acids Research Database Issue contains 185 papers, including 87 papers reporting on new databases and 85 updates from resources previously published in the Issue. Thirteen additional manuscripts provide updates on databases most recently published elsewhere. Seven new databases focus specifically on COVID-19 and SARS-CoV-2, including SCoV2-MD, the first of the Issue's Breakthrough Articles. Major nucleic acid databases reporting updates include MODOMICS, JASPAR and miRTarBase. The AlphaFold Protein Structure Database, described in the second Breakthrough Article, is the stand-out in the protein section, where the Human Proteoform Atlas and GproteinDb are other notable new arrivals. Updates from DisProt, FuzDB and ELM comprehensively cover disordered proteins. Under the metabolism and signalling section Reactome, ConsensusPathDB, HMDB and CAZy are major returning resources. In microbial and viral genomes taxonomy and systematics are well covered by LPSN, TYGS and GTDB. Genomics resources include Ensembl, Ensembl Genomes and UCSC Genome Browser. Major returning pharmacology resource names include the IUPHAR/BPS guide and the Therapeutic Target Database. New plant databases include PlantGSAD for gene lists and qPTMplants for post-translational modifications. The entire Database Issue is freely available online on the Nucleic Acids Research website (https://academic.oup.com/nar). Our latest update to the NAR online Molecular Biology Database Collection brings the total number of entries to 1645. Following last year's major cleanup, we have updated 317 entries, listing 89 new resources and trimming 80 discontinued URLs. The current release is available at http://www.oxfordjournals.org/nar/database/c/.

Properties of FDA-approved small molecule protein kinase inhibitors: A 2022 update

Owing to the dysregulation of protein kinase activity in many diseases including cancer, this enzyme family has become one of the most important drug targets in the 21st century. There are 68 FDA-approved therapeutic agents that target about two dozen different protein kinases and six of these drugs were approved in 2021. Of the approved drugs, twelve target protein-serine/threonine protein kinases, four are directed against dual specificity protein kinases (MEK1/2), thirteen block nonreceptor protein-tyrosine kinases, and 39 target receptor protein-tyrosine kinases. The data indicate that 58 of these drugs are prescribed for the treatment of neoplasms (49 against solid tumors including breast, lung, and colon, five against nonsolid tumors such as leukemias, and four against both solid and nonsolid tumors: acalabrutinib, ibrutinib, imatinib, and midostaurin). Three drugs (baricitinib, tofacitinib, upadacitinib) are used for the treatment of inflammatory diseases including rheumatoid arthritis. Of the 68 approved drugs, eighteen are used in the treatment of multiple diseases. The following six drugs received FDA approval in 2021 for the treatment of these specified diseases: belumosudil (graft vs. host disease), infigratinib (cholangiocarcinomas), mobocertinib and tepotinib (specific forms of non-small cell lung cancer), tivozanib (renal cell carcinoma), and trilaciclib (to decrease chemotherapy-induced myelosuppression). All of the FDA-approved drugs are orally effective with the exception of netarsudil, temsirolimus, and the newly approved trilaciclib. This review summarizes the physicochemical properties of all 68 FDA-approved small molecule protein kinase inhibitors including lipophilic efficiency and ligand efficiency.