ProKinO: a unified resource for mining the cancer kinome

Role of the αC-β4 loop in protein kinase structure and dynamics

Although the αC-β4 loop is a stable feature of all protein kinases, the importance of this motif as a conserved element of secondary structure, as well as its links to the hydrophobic architecture of the kinase core, has been underappreciated. We first review the motif and then describe how it is linked to the hydrophobic spine architecture of the kinase core, which we first discovered using a computational tool, local spatial Pattern (LSP) alignment. Based on NMR predictions that a mutation in this motif abolishes the synergistic high-affinity binding of ATP and a pseudo substrate inhibitor, we used LSP to interrogate the F100A mutant. This comparison highlights the importance of the αC-β4 loop and key residues at the interface between the N- and C-lobes. In addition, we delved more deeply into the structure of the apo C-subunit, which lacks ATP. While apo C-subunit showed no significant changes in backbone dynamics of the αC-β4 loop, we found significant differences in the side chain dynamics of K105. The LSP analysis suggests disruption of communication between the N- and C-lobes in the F100A mutant, which would be consistent with the structural changes predicted by the NMR spectroscopy.

A Fast Method to Monitor Tyrosine Kinase Inhibitor Mechanisms

Methionine residues within the kinase domain of Src serve as unique NMR probes capable of distinguishing between distinct conformational states of full-length Src, including alternative drug-inhibited forms. This approach offers a rapid method to differentiate between various inhibition mechanisms at any stage of drug development, eliminating the need to resolve the structure of Src-drug complexes. Using selectively 13C-methyl-enriched methionine, spectra can be acquired in under an hour, while natural abundance spectra with comparable information are achievable within a few hours.

The αC-β4 loop controls the allosteric cooperativity between nucleotide and substrate in the catalytic subunit of protein kinase A

Allosteric cooperativity between ATP and substrates is a prominent characteristic of the cAMP-dependent catalytic subunit of protein kinase A (PKA-C). This long-range synergistic action is involved in substrate recognition and fidelity, and it may also regulate PKA's association with regulatory subunits and other binding partners. To date, a complete understanding of this intramolecular mechanism is still lacking. Here, we integrated NMR(Nuclear Magnetic Resonance)-restrained molecular dynamics simulations and a Markov State Model to characterize the free energy landscape and conformational transitions of PKA-C. We found that the apoenzyme populates a broad free energy basin featuring a conformational ensemble of the active state of PKA-C (ground state) and other basins with lower populations (excited states). The first excited state corresponds to a previously characterized inactive state of PKA-C with the αC helix swinging outward. The second excited state displays a disrupted hydrophobic packing around the regulatory (R) spine, with a flipped configuration of the F100 and F102 residues at the αC-β4 loop. We validated the second excited state by analyzing the F100A mutant of PKA-C, assessing its structural response to ATP and substrate binding. While PKA-CF100A preserves its catalytic efficiency with Kemptide, this mutation rearranges the αC-β4 loop conformation, interrupting the coupling of the two lobes and abolishing the allosteric binding cooperativity. The highly conserved αC-β4 loop emerges as a pivotal element to control the synergistic binding of nucleotide and substrate, explaining how mutations or insertions near or within this motif affect the function and drug sensitivity in homologous kinases.

Discovery of a Cushing's syndrome protein kinase A mutant that biases signaling through type I AKAPs

Adrenal Cushing's syndrome is a disease of cortisol hypersecretion often caused by mutations in protein kinase A catalytic subunit (PKAc). Using a personalized medicine screening platform, we discovered a Cushing's driver mutation, PKAc-W196G, in ~20% of patient samples analyzed. Proximity proteomics and photokinetic imaging reveal that PKAcW196G is unexpectedly distinct from other described Cushing's variants, exhibiting retained association with type I regulatory subunits (RI) and their corresponding A kinase anchoring proteins (AKAPs). Molecular dynamics simulations predict that substitution of tryptophan-196 with glycine creates a 653-cubic angstrom cleft between the catalytic core of PKAcW196G and type II regulatory subunits (RII), but only a 395-cubic angstrom cleft with RI. Endocrine measurements show that overexpression of RIα or redistribution of PKAcW196G via AKAP recruitment counteracts stress hormone overproduction. We conclude that a W196G mutation in the kinase catalytic core skews R subunit selectivity and biases AKAP association to drive Cushing's syndrome.

Dark kinase annotation, mining, and visualization using the Protein Kinase Ontology

The Protein Kinase Ontology (ProKinO) is an integrated knowledge graph that conceptualizes the complex relationships among protein kinase sequence, structure, function, and disease in a human and machine-readable format. In this study, we have significantly expanded ProKinO by incorporating additional data on expression patterns and drug interactions. Furthermore, we have developed a completely new browser from the ground up to render the knowledge graph visible and interactive on the web. We have enriched ProKinO with new classes and relationships that capture information on kinase ligand binding sites, expression patterns, and functional features. These additions extend ProKinO's capabilities as a discovery tool, enabling it to uncover novel insights about understudied members of the protein kinase family. We next demonstrate the application of ProKinO. Specifically, through graph mining and aggregate SPARQL queries, we identify the p21-activated protein kinase 5 (PAK5) as one of the most frequently mutated dark kinases in human cancers with abnormal expression in multiple cancers, including a previously unappreciated role in acute myeloid leukemia. We have identified recurrent oncogenic mutations in the PAK5 activation loop predicted to alter substrate binding and phosphorylation. Additionally, we have identified common ligand/drug binding residues in PAK family kinases, underscoring ProKinO's potential application in drug discovery. The updated ontology browser and the addition of a web component, ProtVista, which enables interactive mining of kinase sequence annotations in 3D structures and Alphafold models, provide a valuable resource for the signaling community. The updated ProKinO database is accessible at https://prokino.uga.edu.

Predicting protein and pathway associations for understudied dark kinases using pattern-constrained knowledge graph embedding

The 534 protein kinases encoded in the human genome constitute a large druggable class of proteins that include both well-studied and understudied "dark" members. Accurate prediction of dark kinase functions is a major bioinformatics challenge. Here, we employ a graph mining approach that uses the evolutionary and functional context encoded in knowledge graphs (KGs) to predict protein and pathway associations for understudied kinases. We propose a new scalable graph embedding approach, RegPattern2Vec, which employs regular pattern constrained random walks to sample diverse aspects of node context within a KG flexibly. RegPattern2Vec learns functional representations of kinases, interacting partners, post-translational modifications, pathways, cellular localization, and chemical interactions from a kinase-centric KG that integrates and conceptualizes data from curated heterogeneous data resources. By contextualizing information relevant to prediction, RegPattern2Vec improves accuracy and efficiency in comparison to other random walk-based graph embedding approaches. We show that the predictions produced by our model overlap with pathway enrichment data produced using experimentally validated Protein-Protein Interaction (PPI) data from both publicly available databases and experimental datasets not used in training. Our model also has the advantage of using the collected random walks as biological context to interpret the predicted protein-pathway associations. We provide high-confidence pathway predictions for 34 dark kinases and present three case studies in which analysis of meta-paths associated with the prediction enables biological interpretation. Overall, RegPattern2Vec efficiently samples multiple node types for link prediction on biological knowledge graphs and the predicted associations between understudied kinases, pseudokinases, and known pathways serve as a conceptual starting point for hypothesis generation and testing.

Protein Kinase Structure and Dynamics: Role of the αC-β4 Loop

Although the αC-β4 loop is a stable feature of all protein kinases, the importance of this motif as a conserved element of secondary structure, as well as its links to the hydrophobic architecture of the kinase core, has been underappreciated. We first review the motif and then describe how it is linked to the hydrophobic spine architecture of the kinase core, which we first discovered using a computational tool, Local Spatial Pattern (LSP) alignment. Based on NMR predictions that a mutation in this motif abolishes the synergistic high-affinity binding of ATP and a pseudo substrate inhibitor, we used LSP to interrogate the F100A mutant. This comparison highlights the importance of the αC-β4 loop and key residues at the interface between the N- and C-lobes. In addition, we delved more deeply into the structure of the apo C-subunit, which lacks ATP. While apo C-subunit showed no significant changes in backbone dynamics of the αC-β4 loop, we found significant differences in the side chain dynamics of K105. The LSP analysis suggests disruption of communication between the N- and C-lobes in the F100A mutant, which would be consistent with the structural changes predicted by the NMR spectroscopy.

The αC-β4 loop controls the allosteric cooperativity between nucleotide and substrate in the catalytic subunit of protein kinase A

Allosteric cooperativity between ATP and substrates is a prominent characteristic of the cAMP-dependent catalytic (C) subunit of protein kinase A (PKA). Not only this long-range synergistic action is involved in substrate recognition and fidelity, but it is likely to regulate PKA association with regulatory subunits and other binding partners. To date, a complete understanding of the molecular determinants for this intramolecular mechanism is still lacking. Here, we used an integrated NMR-restrained molecular dynamics simulations and a Markov Model to characterize the free energy landscape and conformational transitions of the catalytic subunit of protein kinase A (PKA-C). We found that the apo-enzyme populates a broad free energy basin featuring a conformational ensemble of the active state of PKA-C (ground state) and other basins with lower populations (excited states). The first excited state corresponds to a previously characterized inactive state of PKA-C with the αC helix swinging outward. The second excited state displays a disrupted hydrophobic packing around the regulatory (R) spine, with a flipped configuration of the F100 and F102 residues at the tip of the αC-β4 loop. To experimentally validate the second excited state, we mutated F100 into alanine and used NMR spectroscopy to characterize the binding thermodynamics and structural response of ATP and a prototypical peptide substrate. While the activity of PKA-CF100A toward a prototypical peptide substrate is unaltered and the enzyme retains its affinity for ATP and substrate, this mutation rearranges the αC-β4 loop conformation interrupting the allosteric coupling between nucleotide and substrate. The highly conserved αC-β4 loop emerges as a pivotal element able to modulate the synergistic binding between nucleotide and substrate and may affect PKA signalosome. These results may explain how insertion mutations within this motif affect drug sensitivity in other homologous kinases.

KinFams: De-Novo Classification of Protein Kinases Using CATH Functional Units

Protein kinases are important targets for treating human disorders, and they are the second most targeted families after G-protein coupled receptors. Several resources provide classification of kinases into evolutionary families (based on sequence homology); however, very few systematically classify functional families (FunFams) comprising evolutionary relatives that share similar functional properties. We have developed the FunFam-MARC (Multidomain ARchitecture-based Clustering) protocol, which uses multi-domain architectures of protein kinases and specificity-determining residues for functional family classification. FunFam-MARC predicts 2210 kinase functional families (KinFams), which have increased functional coherence, in terms of EC annotations, compared to the widely used KinBase classification. Our protocol provides a comprehensive classification for kinase sequences from >10,000 organisms. We associate human KinFams with diseases and drugs and identify 28 druggable human KinFams, i.e., enriched in clinically approved drugs. Since relatives in the same druggable KinFam tend to be structurally conserved, including the drug-binding site, these KinFams may be valuable for shortlisting therapeutic targets. Information on the human KinFams and associated 3D structures from AlphaFold2 are provided via our CATH FTP website and Zenodo. This gives the domain structure representative of each KinFam together with information on any drug compounds available. For 32% of the KinFams, we provide information on highly conserved residue sites that may be associated with specificity.

Recent advances in targeting protein kinases and pseudokinases in cancer biology

Kinases still remain the most favorable members of the druggable genome, and there are an increasing number of kinase inhibitors approved by the FDA to treat a variety of cancers. Here, we summarize recent developments in targeting kinases and pseudokinases with some examples. Targeting the cell cycle machinery garnered significant clinical success, however, a large section of the kinome remains understudied. We also review recent developments in the understanding of pseudokinases and discuss approaches on how to effectively target in cancer.

Novel potential oncogenic and druggable mutations of FGFRs recur in the kinase domain across cancer types

Fibroblast growth factor receptors (FGFRs) are recurrently altered by single nucleotide variants (SNVs) in many human cancers. The prevalence of SNVs in FGFRs depends on the cancer type. In some tumors, such as the urothelial carcinoma, mutations of FGFRs occur at very high frequency (up to 60%). Many characterized mutations occur in the extracellular or transmembrane domains, while fewer known mutations are found in the kinase domain. In this study, we performed a bioinformatics analysis to identify novel putative cancer driver or therapeutically actionable mutations of the kinase domain of FGFRs. To pinpoint those mutations that may be clinically relevant, we exploited the recurrence of alterations on analogous amino acid residues within the kinase domain (PK_Tyr_Ser-Thr) of different kinases as a predictor of functional impact. By exploiting MutationAligner and LowMACA bioinformatics resources, we highlighted novel uncharacterized mutations of FGFRs which recur in other protein kinases. By revealing unanticipated correspondence with known variants, we were able to infer their functional effects, as alterations clustering on similar residues in analogous proteins have a high probability to elicit similar effects. As FGFRs represent an important class of oncogenes and drug targets, our study opens the way for further studies to validate their driver and/or actionable nature and, in the long term, for a more efficacious application of precision oncology.

PDBe-KB: collaboratively defining the biological context of structural data

The Protein Data Bank in Europe - Knowledge Base (PDBe-KB, https://pdbe-kb.org) is an open collaboration between world-leading specialist data resources contributing functional and biophysical annotations derived from or relevant to the Protein Data Bank (PDB). The goal of PDBe-KB is to place macromolecular structure data in their biological context by developing standardised data exchange formats and integrating functional annotations from the contributing partner resources into a knowledge graph that can provide valuable biological insights. Since we described PDBe-KB in 2019, there have been significant improvements in the variety of available annotation data sets and user functionality. Here, we provide an overview of the consortium, highlighting the addition of annotations such as predicted covalent binders, phosphorylation sites, effects of mutations on the protein structure and energetic local frustration. In addition, we describe a library of reusable web-based visualisation components and introduce new features such as a bulk download data service and a novel superposition service that generates clusters of superposed protein chains weekly for the whole PDB archive.

Computational tools and resources for pseudokinase research

Pseudokinases regulate diverse cellular processes associated with normal cellular functions and disease. They are defined bioinformatically based on the absence of one or more catalytic residues that are required for canonical protein kinase functions. The ability to define pseudokinases based on primary sequence comparison has enabled the systematic mapping and cataloging of pseudokinase orthologs across the tree of life. While these sequences contain critical information regarding pseudokinase evolution and functional specialization, extracting this information and generating testable hypotheses based on integrative mining of sequence and structural data requires specialized computational tools and resources. In this chapter, we review recent advances in the development and application of open-source tools and resources for pseudokinase research. Specifically, we describe the application of an interactive data analytics framework, KinView, for visualizing the patterns of conservation and variation in the catalytic domain motifs of pseudokinases and evolutionarily related canonical kinases using a consistent set of curated alignments organized based on the widely used kinome evolutionary hierarchy. We also demonstrate the application of an integrated Protein Kinase Ontology (ProKinO) and an interactive viewer, ProtVista, for mapping and analyzing primary sequence motifs and annotations in the context of 3D structures and AlphaFold2 models. We provide examples and protocols for generating testable hypotheses on pseudokinase functions both for bench biologists and advanced users.

Mutation in Abl kinase with altered drug-binding kinetics indicates a novel mechanism of imatinib resistance

Protein kinase inhibitors are potent anticancer therapeutics. For example, the Bcr-Abl kinase inhibitor imatinib decreases mortality for chronic myeloid leukemia by 80%, but 22 to 41% of patients acquire resistance to imatinib. About 70% of relapsed patients harbor mutations in the Bcr-Abl kinase domain, where more than a hundred different mutations have been identified. Some mutations are located near the imatinib-binding site and cause resistance through altered interactions with the drug. However, many resistance mutations are located far from the drug-binding site, and it remains unclear how these mutations confer resistance. Additionally, earlier studies on small sets of patient-derived imatinib resistance mutations indicated that some of these mutant proteins were in fact sensitive to imatinib in cellular and biochemical studies. Here, we surveyed the resistance of 94 patient-derived Abl kinase domain mutations annotated as disease relevant or resistance causing using an engagement assay in live cells. We found that only two-thirds of mutations weaken imatinib affinity by more than twofold compared to Abl wild type. Surprisingly, one-third of mutations in the Abl kinase domain still remain sensitive to imatinib and bind with similar or higher affinity than wild type. Intriguingly, we identified three clinical Abl mutations that bind imatinib with wild type-like affinity but dissociate from imatinib considerably faster. Given the relevance of residence time for drug efficacy, mutations that alter binding kinetics could cause resistance in the nonequilibrium environment of the body where drug export and clearance play critical roles.

The impact of structural bioinformatics tools and resources on SARS-CoV-2 research and therapeutic strategies

SARS-CoV-2 is the causative agent of COVID-19, the ongoing global pandemic. It has posed a worldwide challenge to human health as no effective treatment is currently available to combat the disease. Its severity has led to unprecedented collaborative initiatives for therapeutic solutions against COVID-19. Studies resorting to structure-based drug design for COVID-19 are plethoric and show good promise. Structural biology provides key insights into 3D structures, critical residues/mutations in SARS-CoV-2 proteins, implicated in infectivity, molecular recognition and susceptibility to a broad range of host species. The detailed understanding of viral proteins and their complexes with host receptors and candidate epitope/lead compounds is the key to developing a structure-guided therapeutic design. Since the discovery of SARS-CoV-2, several structures of its proteins have been determined experimentally at an unprecedented speed and deposited in the Protein Data Bank. Further, specialized structural bioinformatics tools and resources have been developed for theoretical models, data on protein dynamics from computer simulations, impact of variants/mutations and molecular therapeutics. Here, we provide an overview of ongoing efforts on developing structural bioinformatics tools and resources for COVID-19 research. We also discuss the impact of these resources and structure-based studies, to understand various aspects of SARS-CoV-2 infection and therapeutic development. These include (i) understanding differences between SARS-CoV-2 and SARS-CoV, leading to increased infectivity of SARS-CoV-2, (ii) deciphering key residues in the SARS-CoV-2 involved in receptor-antibody recognition, (iii) analysis of variants in host proteins that affect host susceptibility to infection and (iv) analyses facilitating structure-based drug and vaccine design against SARS-CoV-2.

Quantitative Structure-Mutation-Activity Relationship Tests (QSMART) model for protein kinase inhibitor response prediction

BACKGROUND

Protein kinases are a large family of druggable proteins that are genomically and proteomically altered in many human cancers. Kinase-targeted drugs are emerging as promising avenues for personalized medicine because of the differential response shown by altered kinases to drug treatment in patients and cell-based assays. However, an incomplete understanding of the relationships connecting genome, proteome and drug sensitivity profiles present a major bottleneck in targeting kinases for personalized medicine.

RESULTS

In this study, we propose a multi-component Quantitative Structure-Mutation-Activity Relationship Tests (QSMART) model and neural networks framework for providing explainable models of protein kinase inhibition and drug response ([Formula: see text]) profiles in cell lines. Using non-small cell lung cancer as a case study, we show that interaction terms that capture associations between drugs, pathways, and mutant kinases quantitatively contribute to the response of two EGFR inhibitors (afatinib and lapatinib). In particular, protein-protein interactions associated with the JNK apoptotic pathway, associations between lung development and axon extension, and interaction terms connecting drug substructures and the volume/charge of mutant residues at specific structural locations contribute significantly to the observed [Formula: see text] values in cell-based assays.

CONCLUSIONS

By integrating multi-omics data in the QSMART model, we not only predict drug responses in cancer cell lines with high accuracy but also identify features and explainable interaction terms contributing to the accuracy. Although we have tested our multi-component explainable framework on protein kinase inhibitors, it can be extended across the proteome to investigate the complex relationships connecting genotypes and drug sensitivity profiles.

Using phosphoproteomics data to understand cellular signaling: a comprehensive guide to bioinformatics resources

Mass spectrometry-based phosphoproteomics is becoming an essential methodology for the study of global cellular signaling. Numerous bioinformatics resources are available to facilitate the translation of phosphopeptide identification and quantification results into novel biological and clinical insights, a critical step in phosphoproteomics data analysis. These resources include knowledge bases of kinases and phosphatases, phosphorylation sites, kinase inhibitors, and sequence variants affecting kinase function, and bioinformatics tools that can predict phosphorylation sites in addition to the kinase that phosphorylates them, infer kinase activity, and predict the effect of mutations on kinase signaling. However, these resources exist in silos and it is challenging to select among multiple resources with similar functions. Therefore, we put together a comprehensive collection of resources related to phosphoproteomics data interpretation, compared the use of tools with similar functions, and assessed the usability from the standpoint of typical biologists or clinicians. Overall, tools could be improved by standardization of enzyme names, flexibility of data input and output format, consistent maintenance, and detailed manuals.

Emerging roles of the αC-β4 loop in protein kinase structure, function, evolution, and disease

The faithful propagation of cellular signals in most organisms relies on the coordinated functions of a large family of protein kinases that share a conserved catalytic domain. The catalytic domain is a dynamic scaffold that undergoes large conformational changes upon activation. Most of these conformational changes, such as movement of the regulatory αC-helix from an "out" to "in" conformation, hinge on a conserved, but understudied, loop termed the αC-β4 loop, which mediates conserved interactions to tether flexible structural elements to the kinase core. We previously showed that the αC-β4 loop is a unique feature of eukaryotic protein kinases. Here, we review the emerging roles of this loop in kinase structure, function, regulation, and diseases. Through a kinome-wide analysis, we define the boundaries of the loop for the first time and show that sequence and structural variation in the loop correlate with conformational and regulatory variation. Many recurrent disease mutations map to the αC-β4 loop and contribute to drug resistance and abnormal kinase activation by relieving key auto-inhibitory interactions associated with αC-helix and inter-lobe movement. The αC-β4 loop is a hotspot for post-translational modifications, protein-protein interaction, and Hsp90 mediated folding. Our kinome-wide analysis provides insights for hypothesis-driven characterization of understudied kinases and the development of allosteric protein kinase inhibitors.

PDBe-KB: a community-driven resource for structural and functional annotations

The Protein Data Bank in Europe-Knowledge Base (PDBe-KB, https://pdbe-kb.org) is a community-driven, collaborative resource for literature-derived, manually curated and computationally predicted structural and functional annotations of macromolecular structure data, contained in the Protein Data Bank (PDB). The goal of PDBe-KB is two-fold: (i) to increase the visibility and reduce the fragmentation of annotations contributed by specialist data resources, and to make these data more findable, accessible, interoperable and reusable (FAIR) and (ii) to place macromolecular structure data in their biological context, thus facilitating their use by the broader scientific community in fundamental and applied research. Here, we describe the guidelines of this collaborative effort, the current status of contributed data, and the PDBe-KB infrastructure, which includes the data exchange format, the deposition system for added value annotations, the distributable database containing the assembled data, and programmatic access endpoints. We also describe a series of novel web-pages-the PDBe-KB aggregated views of structure data-which combine information on macromolecular structures from many PDB entries. We have recently released the first set of pages in this series, which provide an overview of available structural and functional information for a protein of interest, referenced by a UniProtKB accession.

Challenges in the annotation of pseudoenzymes in databases: the UniProtKB approach

The universal protein knowledgebase (UniProtKB) collects and centralises functional information on proteins across a wide range of species. In addition to the functional information added to all protein entries, for enzymes, which represent 20-40% of most proteomes, UniProtKB provides additional information about Enzyme Commission classification, catalytic activity, cofactors, enzyme regulation, kinetics and pathways, all based on critical assessment of published experimental data. Computer-based analysis and structural data are used to enrich the annotation of the sequence through the identification of active sites and binding sites. While the annotation of enzymes is well-defined, the curation of pseudoenzymes in UniProtKB has highlighted some challenges: how to identify them, how to assess their lack of catalytic activity, how to annotate their lack of catalytic activity in a consistent way and how much can be inferred and propagated from experimental data obtained from other species. Through various examples, we illustrate some of these issues and discuss some of the changes we propose to enhance the annotation and discovery of pseudoenzymes. Ultimately, improving the curation of pseudoenzymes will provide the scientific community with a comprehensive resource for pseudoenzymes, which in turn will lead to a better understanding of the evolution of these molecules, the aetiology of related diseases and the development of drugs.

The dynamic switch mechanism that leads to activation of LRRK2 is embedded in the DFGψ motif in the kinase domain

Little is known about the regulation of Leucine-rich repeat kinase 2 (LRRK2) associated with familial Parkinson’s disease (PD). To test whether the kinase domain drives LRRK2 activation, we applied the spine concept that describes the core architecture of every protein kinase. We discovered that mutation of Y2018, a regulatory spine residue, to Phe in the DFGψ motif created a hyperactive kinase similar to the PD-associated mutation G2019S. The hydroxyl moiety of Y2018 thus serves as a “brake,” stabilizing the inactive conformation; simply removing it destroys a key inhibitory hydrogen-bonding node. These data reveal an LRRK2-specific regulatory mechanism, confirming that the kinase domain functions as a classical kinase that controls overall conformational dynamics in full-length LRRK2 and drives therapeutic strategies.

Leucine-rich repeat kinase 2 (LRRK2) is a large multidomain protein, and LRRK2 mutants are recognized risk factors for Parkinson’s disease (PD). Although the precise mechanisms that control LRRK2 regulation and function are unclear, the importance of the kinase domain is strongly implicated, since 2 of the 5 most common familial LRRK2 mutations (G2019S and I2020T) are localized to the conserved DFGψ motif in the kinase core, and kinase inhibitors are under development. Combining the concept of regulatory (R) and catalytic (C) spines with kinetic and cell-based assays, we discovered a major regulatory mechanism embedded within the kinase domain and show that the DFG motif serves as a conformational switch that drives LRRK2 activation. LRRK2 is quite unusual in that the highly conserved Phe in the DFGψ motif, which is 1 of the 4 R-spine residues, is replaced with tyrosine (DY²⁰¹⁸GI). A Y2018F mutation creates a hyperactive phenotype similar to the familial mutation G2019S. The hydroxyl moiety of Y2018 thus serves as a “brake” that stabilizes an inactive conformation; simply removing it destroys a key hydrogen-bonding node. Y2018F, like the pathogenic mutant I2020T, spontaneously forms LRRK2-decorated microtubules in cells, while the wild type and G2019S require kinase inhibitors to form filaments. We also explored 3 different mechanisms that create kinase-dead pseudokinases, including D2017A, which further emphasizes the highly synergistic role of key hydrophobic and hydrophilic/charged residues in the assembly of active LRRK2. We thus hypothesize that LRRK2 harbors a classical protein kinase switch mechanism that drives the dynamic activation of full-length LRRK2.

Altered conformational landscape and dimerization dependency underpins the activation of EGFR by αC-β4 loop insertion mutations

Mutational activation of epidermal growth factor receptor (EGFR) in human cancers involves both point mutations and complex mutations (insertions and deletions). In particular, short in-frame insertion mutations within a conserved αC-β4 loop in the EGFR kinase domain are frequently observed in tumor samples and patients harboring these mutations are insensitive to first-generation EGFR inhibitors. Despite the prevalence and clinical relevance of insertion mutations, the mechanisms by which these mutations regulate EGFR activity and contribute to drug sensitivity are poorly understood. Using cell-based mutation screening, we find that the precise location, length, and sequence of the inserted segment are critical for ligand-independent EGFR activation and downstream signaling. We identify three insertion mutations (N771_P772insN, D770_N771insG, and D770>GY) that activate EGFR in a unique way by relying more on the "acceptor" interface for kinase activation. Our drug inhibition studies indicate that these activating insertion mutations respond more favorably to osimertinib, a recently Food and Drug Administration-approved EGFR inhibitor for T790M-positive patients with lung cancer. Molecular dynamics simulations and umbrella sampling of WT and mutant EGFR suggest a model in which activating insertion mutations increase catalytic activity by relieving key autoinhibitory interactions associated with αC-helix movement and by lowering the transition free energy ([Formula: see text]) between active and inactive states. Our studies also identify a transition state sampled by activating insertion mutations that can be exploited in the design of mutant-selective EGFR inhibitors.

Epidermal growth factor receptor: Structure-function informing the design of anticancer therapeutics

Research on the epidermal growth factor (EGF) family and the family of receptors (EGFR) has progressed rapidly in recent times. New crystal structures of the ectodomains with different ligands, the activation of the kinase domain through oligomerisation and the use of fluorescence techniques have revealed profound conformational changes on ligand binding. The control of cell signaling from the EGFR-family is complex, with heterodimerisation, ligand affinity and signaling cross-talk influencing cellular outcomes. Analysis of tissue homeostasis indicates that the control of pro-ligand processing is likely to be as important as receptor activation events. Several members of the EGFR-family are overexpressed and/or mutated in cancer cells. The perturbation of EGFR-family signaling drives the malignant phenotype of many cancers and both inhibitors and antagonists of signaling from these receptors have already produced therapeutic benefits for patients. The design of affibodies, antibodies, small molecule inhibitors and even immunotherapeutic drugs targeting the EGFR-family has yielded promising new approaches to improving outcomes for cancer patients. In this review, we describe recent discoveries which have increased our understanding of the structure and dynamics of signaling from the EGFR-family, the roles of ligand processing and receptor cross-talk. We discuss the relevance of these studies to the development of strategies for designing more effective targeted treatments for cancer patients.

Integrative annotation and knowledge discovery of kinase post-translational modifications and cancer-associated mutations through federated protein ontologies and resources

Many bioinformatics resources with unique perspectives on the protein landscape are currently available. However, generating new knowledge from these resources requires interoperable workflows that support cross-resource queries. In this study, we employ federated queries linking information from the Protein Kinase Ontology, iPTMnet, Protein Ontology, neXtProt, and the Mouse Genome Informatics to identify key knowledge gaps in the functional coverage of the human kinome and prioritize understudied kinases, cancer variants and post-translational modifications (PTMs) for functional studies. We identify 32 functional domains enriched in cancer variants and PTMs and generate mechanistic hypotheses on overlapping variant and PTM sites by aggregating information at the residue, protein, pathway and species level from these resources. We experimentally test the hypothesis that S768 phosphorylation in the C-helix of EGFR is inhibitory by showing that oncogenic variants altering S768 phosphorylation increase basal EGFR activity. In contrast, oncogenic variants altering conserved phosphorylation sites in the ‘hydrophobic motif’ of PKCβII (S660F and S660C) are loss-of-function in that they reduce kinase activity and enhance membrane translocation. Our studies provide a framework for integrative, consistent, and reproducible annotation of the cancer kinomes.

Advances in kinome research of parasitic worms - implications for fundamental research and applied biotechnological outcomes

Protein kinases are enzymes that play essential roles in the regulation of many cellular processes. Despite expansions in the fields of genomics, transcriptomics and bioinformatics, there is limited information on the kinase complements (kinomes) of most eukaryotic organisms, including parasitic worms that cause serious diseases of humans and animals. The biological uniqueness of these worms and the draft status of their genomes pose challenges for the identification and classification of protein kinases using established tools. In this article, we provide an account of kinase biology, the roles of kinases in diseases and their importance as drug targets, and drug discovery efforts in key socioeconomically important parasitic worms. In this context, we summarise methods and resources commonly used for the curation, identification, classification and functional annotation of protein kinase sequences from draft genomes; review recent advances made in the characterisation of the worm kinomes; and discuss the implications of these advances for investigating kinase signalling and developing small-molecule inhibitors as new anti-parasitic drugs.

Cushing's syndrome mutant PKAL205R exhibits altered substrate specificity

The PKA^L205R hotspot mutation has been implicated in Cushing's syndrome through hyperactive gain-of-function PKA signaling; however, its influence on substrate specificity has not been investigated. Here, we employ the Proteomic Peptide Library (ProPeL) approach to create high-resolution models for PKA^WT and PKA^L205R substrate specificity. We reveal that the L205R mutation reduces canonical hydrophobic preference at the substrate P + 1 position, and increases acidic preference in downstream positions. Using these models, we designed peptide substrates that exhibit altered selectivity for specific PKA variants, and demonstrate the feasibility of selective PKA^L205R loss-of-function signaling. Through these results, we suggest that substrate rewiring may contribute to Cushing's syndrome disease etiology, and introduce a powerful new paradigm for investigating mutation-induced kinase substrate rewiring in human disease.

Computational and Experimental Characterization of Patient Derived Mutations Reveal an Unusual Mode of Regulatory Spine Assembly and Drug Sensitivity in EGFR Kinase

The catalytic activation of protein kinases requires precise positioning of key conserved catalytic and regulatory motifs in the kinase core. The Regulatory Spine (RS) is one such structural motif that is dynamically assembled upon kinase activation. The RS is also a mutational hotspot in cancers; however, the mechanisms by which cancer mutations impact RS assembly and kinase activity are not fully understood. In this study, through mutational analysis of patient derived mutations in the RS of EGFR kinase, we identify an activating mutation, M766T, at the RS3 position. RS3 is located in the regulatory αC-helix, and a series of mutations at the RS3 position suggest a strong correlation between the amino acid type present at the RS3 position and ligand (EGF) independent EGFR activation. Small polar amino acids increase ligand independent activity, while large aromatic amino acids decrease kinase activity. M766T relies on the canonical asymmetric dimer for full activation. Molecular modeling and molecular dynamics simulations of WT and mutant EGFR suggest a model in which M766T activates the kinase domain by disrupting conserved autoinhibitory interactions between M766 and hydrophobic residues in the activation segment. In addition, a water mediated hydrogen bond network between T766, the conserved K745-E762 salt bridge, and the backbone amide of the DFG motif is identified as a key determinant of M766T-mediated activation. M766T is resistant to FDA approved EGFR inhibitors such as gefitinib and erlotinib, and computational estimation of ligand binding free energy identifies key residues associated with drug sensitivity. In sum, our studies suggest an unusual mode of RS assembly and oncogenic EGFR activation, and provide new clues for the design of allosteric protein kinase inhibitors.

Annotating Mutational Effects on Proteins and Protein Interactions: Designing Novel and Revisiting Existing Protocols

In this review we describe a protocol to annotate the effects of missense mutations on proteins, their functions, stability, and binding. For this purpose we present a collection of the most comprehensive databases which store different types of sequencing data on missense mutations, we discuss their relationships, possible intersections, and unique features. Next, we suggest an annotation workflow using the state-of-the art methods and highlight their usability, advantages, and limitations for different cases. Finally, we address a particularly difficult problem of deciphering the molecular mechanisms of mutations on proteins and protein complexes to understand the origins and mechanisms of diseases.

Tribbles in the 21st Century: The Evolving Roles of Tribbles Pseudokinases in Biology and Disease

The Tribbles (TRIB) pseudokinases control multiple aspects of eukaryotic cell biology and evolved unique features distinguishing them from all other protein kinases. The atypical pseudokinase domain retains a regulated binding platform for substrates, which are ubiquitinated by context-specific E3 ligases. This plastic configuration has also been exploited as a scaffold to support the modulation of canonical MAPK and AKT modules. In this review, we discuss the evolution of TRIBs and their roles in vertebrate cell biology. TRIB2 is the most ancestral member of the family, whereas the emergence of TRIB3 homologs in mammals supports additional biological roles, many of which are currently being dissected. Given their pleiotropic role in diseases, the unusual TRIB pseudokinase conformation provides a highly attractive opportunity for drug design.

Pseudoenzymes are inactive counterparts of classical enzymes and have evolved in all kingdoms of life, where they regulate a vast array of biological processes. The pseudokinases are one of the best-studied families of human pseudoenzymes.

Eukaryotic TRIB pseudokinases evolved from a common ancestor (the human TRIB2 homolog), and contain a highly atypical pseudokinase domain fused to a unique docking site in an extended C tail that binds to ubiquitin E3 ligases.

TRIB evolution has led to the appearance of three mammalian TRIB pseudokinases, termed TRIB1, TRIB2, and TRIB3, which contain both unique and shared features.

In cells, TRIB pseudokinases act as modulators of substrate ubiquitination and as molecular scaffolds for the assembly and regulation of signaling modules, including the C/EBPα transcription factor and AKT and ERK networks.

TRIB1 and TRIB2 have potent oncogenic activities in vertebrate cells, and recent evidence also suggests that TRIB2 acts as a tumour suppressor, consistent with the requirement for balanced TRIB signaling in the regulation of transcription, differentiation, proliferation, and apoptosis.

KinView: a visual comparative sequence analysis tool for integrated kinome research

Multiple sequence alignments (MSAs) are a fundamental analysis tool used throughout biology to investigate relationships between protein sequence, structure, function, evolutionary history, and patterns of disease-associated variants. However, their widespread application in systems biology research is currently hindered by the lack of user-friendly tools to simultaneously visualize, manipulate and query the information conceptualized in large sequence alignments, and the challenges in integrating MSAs with multiple orthogonal data such as cancer variants and post-translational modifications, which are often stored in heterogeneous data sources and formats. Here, we present the Multiple Sequence Alignment Ontology (MSAOnt), which represents a profile or consensus alignment in an ontological format. Subsets of the alignment are easily selected through the SPARQL Protocol and RDF Query Language for downstream statistical analysis or visualization. We have also created the Kinome Viewer (KinView), an interactive integrative visualization that places eukaryotic protein kinase cancer variants in the context of natural sequence variation and experimentally determined post-translational modifications, which play central roles in the regulation of cellular signaling pathways. Using KinView, we identified differential phosphorylation patterns between tyrosine and serine/threonine kinases in the activation segment, a major kinase regulatory region that is often mutated in proliferative diseases. We discuss cancer variants that disrupt phosphorylation sites in the activation segment, and show how KinView can be used as a comparative tool to identify differences and similarities in natural variation, cancer variants and post-translational modifications between kinase groups, families and subfamilies. Based on KinView comparisons, we identify and experimentally characterize a regulatory tyrosine (Y177PLK4) in the PLK4 C-terminal activation segment region termed the P+1 loop. To further demonstrate the application of KinView in hypothesis generation and testing, we formulate and validate a hypothesis explaining a novel predicted loss-of-function variant (D523NPKCβ) in the regulatory spine of PKCβ, a recently identified tumor suppressor kinase. KinView provides a novel, extensible interface for performing comparative analyses between subsets of kinases and for integrating multiple types of residue specific annotations in user friendly formats.

Landscape of activating cancer mutations in FGFR kinases and their differential responses to inhibitors in clinical use

Frequent genetic alterations discovered in FGFRs and evidence implicating some as drivers in diverse tumors has been accompanied by rapid progress in targeting FGFRs for anticancer treatments. Wider assessment of the impact of genetic changes on the activation state and drug responses is needed to better link the genomic data and treatment options. We here apply a direct comparative and comprehensive analysis of FGFR3 kinase domain variants representing the diversity of point-mutations reported in this domain. We reinforce the importance of N540K and K650E and establish that not all highly activating mutations (for example R669G) occur at high-frequency and conversely, that some "hotspots" may not be linked to activation. Further structural characterization consolidates a mechanistic view of FGFR kinase activation and extends insights into drug binding. Importantly, using several inhibitors of particular clinical interest (AZD4547, BGJ-398, TKI258, JNJ42756493 and AP24534), we find that some activating mutations (including different replacements of the same residue) result in distinct changes in their efficacy. Considering that there is no approved inhibitor for anticancer treatments based on FGFR-targeting, this information will be immediately translatable to ongoing clinical trials.

Single-cell Transcriptome Study as Big Data

The rapid growth of single-cell RNA-seq studies (scRNA-seq) demands efficient data storage, processing, and analysis. Big-data technology provides a framework that facilitates the comprehensive discovery of biological signals from inter-institutional scRNA-seq datasets. The strategies to solve the stochastic and heterogeneous single-cell transcriptome signal are discussed in this article. After extensively reviewing the available big-data applications of next-generation sequencing (NGS)-based studies, we propose a workflow that accounts for the unique characteristics of scRNA-seq data and primary objectives of single-cell studies.

wKinMut-2: Identification and Interpretation of Pathogenic Variants in Human Protein Kinases

Most genomic alterations are tolerated while only a minor fraction disrupts molecular function sufficiently to drive disease. Protein kinases play a central biological function and the functional consequences of their variants are abundantly characterized. However, this heterogeneous information is often scattered across different sources, which makes the integrative analysis complex and laborious. wKinMut-2 constitutes a solution to facilitate the interpretation of the consequences of human protein kinase variation. Nine methods predict their pathogenicity, including a kinase-specific random forest approach. To understand the biological mechanisms causative of human diseases and cancer, information from pertinent reference knowledge bases and the literature is automatically mined, digested, and homogenized. Variants are visualized in their structural contexts and residues affecting catalytic and drug binding are identified. Known protein-protein interactions are reported. Altogether, this information is intended to assist the generation of new working hypothesis to be corroborated with ulterior experimental work. The wKinMut-2 system, along with a user manual and examples, is freely accessible at http://kinmut2.bioinfo.cnio.es, the code for local installations can be downloaded from https://github.com/Rbbt-Workflows/KinMut2.

Mechanistic Insights into R776H Mediated Activation of Epidermal Growth Factor Receptor Kinase

The epidermal growth factor receptor (EGFR) kinase is activated by a variety of mutations in human cancers. R776H is one such recurrent mutation (R752H in another numbering system) in the αC-β4 loop of the tyrosine kinase domain that activates EGFR in the absence of the activating EGF ligand. However, the mechanistic details of how R776H contributes to kinase activation are not well understood. Here using cell-based cotransfection assays, we show that the R776H mutation activates EGFR in a dimerization-dependent manner by preferentially adopting the acceptor position in the asymmetric dimer. The acceptor function, but not the donor function, is enhanced for the R776H mutant, supporting the "superacceptor" hypothesis proposed for oncogenic mutations in EGFR. We also find that phosphorylation of monomeric EGFR is increased by R776H mutation, providing insights into EGFR lateral phosphorylation and oligomerization. On the basis of molecular modeling and molecular dynamics simulation, we propose a model in which loss of key autoinhibitory αC-helix capping interaction and alteration of coconserved cis regulatory interactions between the kinase domain and the flanking regulatory segments contribute to mutational activation. Since the R776 equivalent position is mutated in ErbB2 and ErbB4, our studies have implications for understanding kinase mutational activation in other ErbB family members as well.

The Tribbles 2 (TRB2) pseudokinase binds to ATP and autophosphorylates in a metal-independent manner

The human Tribbles (TRB)-related pseudokinases are CAMK (calcium/calmodulin-dependent protein kinase)-related family members that have evolved a series of highly unusual motifs in the 'pseudocatalytic' domain. In canonical kinases, conserved amino acids bind to divalent metal ions and align ATP prior to efficient phosphoryl-transfer to substrates. However, in pseudokinases, atypical residues give rise to diverse and often unstudied biochemical and structural features that are thought to be central to cellular functions. TRB proteins play a crucial role in multiple signalling networks and overexpression confers cancer phenotypes on human cells, marking TRB pseudokinases out as a novel class of drug target. In the present paper, we report that the human pseudokinase TRB2 retains the ability to both bind and hydrolyse ATP weakly in vitro. Kinase activity is metal-independent and involves a catalytic lysine residue, which is conserved in TRB proteins throughout evolution alongside several unique amino acids in the active site. A similar low level of autophosphorylation is also preserved in the closely related human TRB3. By employing chemical genetics, we establish that the nucleotide-binding site of an 'analogue-sensitive' (AS) TRB2 mutant can be targeted with specific bulky ligands of the pyrazolo-pyrimidine (PP) chemotype. Our analysis confirms that TRB2 retains low levels of ATP binding and/or catalysis that is targetable with small molecules. Given the significant clinical successes associated with targeting of cancer-associated kinases with small molecule inhibitors, it is likely that similar approaches will be useful for further evaluating the TRB pseudokinases, with the translation of this information likely to furnish new leads for drug discovery.

Integration of signaling in the kinome: Architecture and regulation of the αC Helix

Eukaryotic protein kinases have evolved to be highly regulated and dynamic molecular switches that are typically kept in an inactive state and then activated in response to extracellular signals. The hallmark signature of an active kinase is a hydrophobic spine called the regulatory (R) spine, which consists of four residues, two in the N-lobe and two in the C-lobe. RS1 is in the catalytic loop, RS2 is the Phe in the DFG motif, RS3 is at the C-terminus of the αC-Helix, and RS4 is at the beginning of β4. Assembly of the R-spine is typically facilitated by phosphorylation of the Activation Loop. The assembled R-spine brings together all of the functional motifs that are essential for transferring the phosphate from ATP to a tethered protein substrate. This includes the G-Loop, which anchors the ATP, the catalytic loop, the DFG motif fused to the Activation Loop, and the αC-Helix. We focus here on the properties of the αC-Helix showing 1) how residues communicate with different parts of the molecule, 2) how it is recruited to the active site as a consequence of assembling of the R-spine, and 3) how it is regulated by linkers/motifs/proteins that lie outside the conserved kinase core. This article is part of a Special Issue entitled: Inhibitors of Protein Kinases.