Alignment-free estimation of sequence conservation for identifying functional sites using protein sequence embeddings.

Protein language modeling is a fast-emerging deep learning method in bioinformatics with diverse applications such as structure prediction and protein design. However, application toward estimating sequence conservation for functional site prediction has not been systematically explored. Here, we present a method for the alignment-free estimation of sequence conservation using sequence embeddings generated from protein language models. Comprehensive benchmarks across publicly available protein language models reveal that ESM2 models provide the best performance to computational cost ratio for conservation estimation. Applying our method to full-length protein sequences, we demonstrate that embedding-based methods are not sensitive to the order of conserved elements-conservation scores can be calculated for multidomain proteins in a single run, without the need to separate individual domains. Our method can also identify conserved functional sites within fast-evolving sequence regions (such as domain inserts), which we demonstrate through the identification of conserved phosphorylation motifs in variable insert segments in protein kinases. Overall, embedding-based conservation analysis is a broadly applicable method for identifying potential functional sites in any full-length protein sequence and estimating conservation in an alignment-free manner. To run this on your protein sequence of interest, try our scripts at https://github.com/esbgkannan/kibby.

Tree visualizations of protein sequence embedding space enable improved functional clustering of diverse protein superfamilies.

Protein language models, trained on millions of biologically observed sequences, generate feature-rich numerical representations of protein sequences. These representations, called sequence embeddings, can infer structure-functional properties, despite protein language models being trained on primary sequence alone. While sequence embeddings have been applied toward tasks such as structure and function prediction, applications toward alignment-free sequence classification have been hindered by the lack of studies to derive, quantify and evaluate relationships between protein sequence embeddings. Here, we develop workflows and visualization methods for the classification of protein families using sequence embedding derived from protein language models. A benchmark of manifold visualization methods reveals that Neighbor Joining (NJ) embedding trees are highly effective in capturing global structure while achieving similar performance in capturing local structure compared with popular dimensionality reduction techniques such as t-SNE and UMAP. The statistical significance of hierarchical clusters on a tree is evaluated by resampling embeddings using a variational autoencoder (VAE). We demonstrate the application of our methods in the classification of two well-studied enzyme superfamilies, phosphatases and protein kinases. Our embedding-based classifications remain consistent with and extend upon previously published sequence alignment-based classifications. We also propose a new hierarchical classification for the S-Adenosyl-L-Methionine (SAM) enzyme superfamily which has been difficult to classify using traditional alignment-based approaches. Beyond applications in sequence classification, our results further suggest NJ trees are a promising general method for visualizing high-dimensional data sets.

Using explainable machine learning to uncover the kinase-substrate interaction landscape.

MOTIVATION: Phosphorylation, a post-translational modification regulated by protein kinase enzymes, plays an essential role in almost all cellular processes. Understanding how each of the nearly 500 human protein kinases selectively phosphorylates their substrates is a foundational challenge in bioinformatics and cell signaling. Although deep learning models have been a popular means to predict kinase-substrate relationships, existing models often lack interpretability and are trained on datasets skewed toward a subset of well-studied kinases. RESULTS: Here we leverage recent peptide library datasets generated to determine substrate specificity profiles of 300 serine/threonine kinases to develop an explainable Transformer model for kinase-peptide interaction prediction. The model, trained solely on primary sequences, achieved state-of-the-art performance. Its unique multitask learning paradigm built within the model enables predictions on virtually any kinase-peptide pair, including predictions on 139 kinases not used in peptide library screens. Furthermore, we employed explainable machine learning methods to elucidate the model's inner workings. Through analysis of learned embeddings at different training stages, we demonstrate that the model employs a unique strategy of substrate prediction considering both substrate motif patterns and kinase evolutionary features. SHapley Additive exPlanation (SHAP) analysis reveals key specificity determining residues in the peptide sequence. Finally, we provide a web interface for predicting kinase-substrate associations for user-defined sequences and a resource for visualizing the learned kinase-substrate associations. AVAILABILITY AND IMPLEMENTATION: All code and data are available at https://github.com/esbgkannan/Phosformer-ST. Web server is available at https://phosformer.netlify.app.

Cancer-associated mutations in protein kinase C theta are loss-of-function.

The Ca2+-independent, but diacylglycerol-regulated, novel protein kinase C (PKC) theta (θ) is highly expressed in hematopoietic cells where it participates in immune signaling and platelet function. Mounting evidence suggests that PKCθ may be involved in cancer, particularly blood cancers, breast cancer, and gastrointestinal stromal tumors, yet how to target this kinase (as an oncogene or as a tumor suppressor) has not been established. Here, we examine the effect of four cancer-associated mutations, R145H/C in the autoinhibitory pseudosubstrate, E161K in the regulatory C1A domain, and R635W in the regulatory C-terminal tail, on the cellular activity and stability of PKCθ. Live-cell imaging studies using the genetically-encoded fluorescence resonance energy transfer-based reporter for PKC activity, C kinase activity reporter 2 (CKAR2), revealed that the pseudosubstrate and C1A domain mutations impaired autoinhibition to increase basal signaling. This impaired autoinhibition resulted in decreased stability of the protein, consistent with the well-characterized behavior of Ca2+-regulated PKC isozymes wherein mutations that impair autoinhibition are paradoxically loss-of-function because the mutant protein is degraded. In marked contrast, the C-terminal tail mutation resulted in enhanced autoinhibition and enhanced stability. Thus, the examined mutations were loss-of-function by different mechanisms: mutations that impaired autoinhibition promoted the degradation of PKC, and those that enhanced autoinhibition stabilized an inactive PKC. Supporting a general loss-of-function of PKCθ in cancer, bioinformatics analysis revealed that protein levels of PKCθ are reduced in diverse cancers, including lung, renal, head and neck, and pancreatic. Our results reveal that PKCθ function is lost in cancer.

Phosformer: an explainable transformer model for protein kinase-specific phosphorylation predictions.

MOTIVATION: The human genome encodes over 500 distinct protein kinases which regulate nearly all cellular processes by the specific phosphorylation of protein substrates. While advances in mass spectrometry and proteomics studies have identified thousands of phosphorylation sites across species, information on the specific kinases that phosphorylate these sites is currently lacking for the vast majority of phosphosites. Recently, there has been a major focus on the development of computational models for predicting kinase-substrate associations. However, most current models only allow predictions on a subset of well-studied kinases. Furthermore, the utilization of hand-curated features and imbalances in training and testing datasets pose unique challenges in the development of accurate predictive models for kinase-specific phosphorylation prediction. Motivated by the recent development of universal protein language models which automatically generate context-aware features from primary sequence information, we sought to develop a unified framework for kinase-specific phosphosite prediction, allowing for greater investigative utility and enabling substrate predictions at the whole kinome level. RESULTS: We present a deep learning model for kinase-specific phosphosite prediction, termed Phosformer, which predicts the probability of phosphorylation given an arbitrary pair of unaligned kinase and substrate peptide sequences. We demonstrate that Phosformer implicitly learns evolutionary and functional features during training, removing the need for feature curation and engineering. Further analyses reveal that Phosformer also learns substrate specificity motifs and is able to distinguish between functionally distinct kinase families. Benchmarks indicate that Phosformer exhibits significant improvements compared to the state-of-the-art models, while also presenting a more generalized, unified, and interpretable predictive framework. AVAILABILITY AND IMPLEMENTATION: Code and data are available at https://github.com/esbgkannan/phosformer. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Cerebellar Ataxia and Coenzyme Q Deficiency through Loss of Unorthodox Kinase Activity.

The UbiB protein kinase-like (PKL) family is widespread, comprising one-quarter of microbial PKLs and five human homologs, yet its biochemical activities remain obscure. COQ8A (ADCK3) is a mammalian UbiB protein associated with ubiquinone (CoQ) biosynthesis and an ataxia (ARCA2) through unclear means. We show that mice lacking COQ8A develop a slowly progressive cerebellar ataxia linked to Purkinje cell dysfunction and mild exercise intolerance, recapitulating ARCA2. Interspecies biochemical analyses show that COQ8A and yeast Coq8p specifically stabilize a CoQ biosynthesis complex through unorthodox PKL functions. Although COQ8 was predicted to be a protein kinase, we demonstrate that it lacks canonical protein kinase activity in trans. Instead, COQ8 has ATPase activity and interacts with lipid CoQ intermediates, functions that are likely conserved across all domains of life. Collectively, our results lend insight into the molecular activities of the ancient UbiB family and elucidate the biochemical underpinnings of a human disease.

Evolutionary and cellular analysis of the 'dark' pseudokinase PSKH2.

Pseudokinases, so named because they lack one or more conserved canonical amino acids that define their catalytically active relatives, have evolved a variety of biological functions in both prokaryotic and eukaryotic organisms. Human PSKH2 is closely related to the canonical kinase PSKH1, which maps to the CAMK family of protein kinases. Primates encode PSKH2 in the form of a pseudokinase, which is predicted to be catalytically inactive due to loss of the invariant catalytic Asp residue. Although the biological role(s) of vertebrate PSKH2 proteins remains unclear, we previously identified species-level adaptions in PSKH2 that have led to the appearance of kinase or pseudokinase variants in vertebrate genomes alongside a canonical PSKH1 paralog. In this paper we confirm that, as predicted, PSKH2 lacks detectable protein phosphotransferase activity, and exploit structural informatics, biochemistry and cellular proteomics to begin to characterise vertebrate PSKH2 orthologues. AlphaFold 2-based structural analysis predicts functional roles for both the PSKH2 N- and C-regions that flank the pseudokinase domain core, and cellular truncation analysis confirms that the N-terminal domain, which contains a conserved myristoylation site, is required for both stable human PSKH2 expression and localisation to a membrane-rich subcellular fraction containing mitochondrial proteins. Using mass spectrometry-based proteomics, we confirm that human PSKH2 is part of a cellular mitochondrial protein network, and that its expression is regulated through client-status within the HSP90/Cdc37 molecular chaperone system. HSP90 interactions are mediated through binding to the PSKH2 C-terminal tail, leading us to predict that this region might act as both a cis and trans regulatory element, driving outputs linked to the PSKH2 pseudokinase domain that are important for functional signalling.

Modularity of the hydrophobic core and evolution of functional diversity in fold A glycosyltransferases.

Hydrophobic cores are fundamental structural properties of proteins typically associated with protein folding and stability; however, how the hydrophobic core shapes protein evolution and function is poorly understood. Here, we investigated the role of conserved hydrophobic cores in fold-A glycosyltransferases (GT-As), a large superfamily of enzymes that catalyze formation of glycosidic linkages between diverse donor and acceptor substrates through distinct catalytic mechanisms (inverting versus retaining). Using hidden Markov models and protein structural alignments, we identify similarities in the phosphate-binding cassette (PBC) of GT-As and unrelated nucleotide-binding proteins, such as UDP-sugar pyrophosphorylases. We demonstrate that GT-As have diverged from other nucleotide-binding proteins through structural elaboration of the PBC and its unique hydrophobic tethering to the F-helix, which harbors the catalytic base (xED-Asp). While the hydrophobic tethering is conserved across diverse GT-A fold enzymes, some families, such as B3GNT2, display variations in tethering interactions and core packing. We evaluated the structural and functional impact of these core variations through experimental mutational analysis and molecular dynamics simulations and find that some of the core mutations (T336I in B3GNT2) increase catalytic efficiency by modulating the conformational occupancy of the catalytic base between "D-in" and acceptor-accessible "D-out" conformation. Taken together, our studies support a model of evolution in which the GT-A core evolved progressively through elaboration upon an ancient PBC found in diverse nucleotide-binding proteins, and malleability of this core provided the structural framework for evolving new catalytic and substrate-binding functions in extant GT-A fold enzymes.

Rethinking pseudokinases.

Pseudokinases lack conservation of one or more of the catalytic residues in the kinase core and as a consequence are typically thought to be catalytically inactive. New work by Mukherjee et al. (2008) challenges this assumption. They show that the pseudokinase domain of CASK (Ca2+/calmodulin activated serine-threonine kinase) adopts an active conformation and displays catalytic activity in vivo.

Cancer progression by reprogrammed BCAA metabolism in myeloid leukaemia.

Reprogrammed cellular metabolism is a common characteristic observed in various cancers. However, whether metabolic changes directly regulate cancer development and progression remains poorly understood. Here we show that BCAT1, a cytosolic aminotransferase for branched-chain amino acids (BCAAs), is aberrantly activated and functionally required for chronic myeloid leukaemia (CML) in humans and in mouse models of CML. BCAT1 is upregulated during progression of CML and promotes BCAA production in leukaemia cells by aminating the branched-chain keto acids. Blocking BCAT1 gene expression or enzymatic activity induces cellular differentiation and impairs the propagation of blast crisis CML both in vitro and in vivo. Stable-isotope tracer experiments combined with nuclear magnetic resonance-based metabolic analysis demonstrate the intracellular production of BCAAs by BCAT1. Direct supplementation with BCAAs ameliorates the defects caused by BCAT1 knockdown, indicating that BCAT1 exerts its oncogenic function through BCAA production in blast crisis CML cells. Importantly, BCAT1 expression not only is activated in human blast crisis CML and de novo acute myeloid leukaemia, but also predicts disease outcome in patients. As an upstream regulator of BCAT1 expression, we identified Musashi2 (MSI2), an oncogenic RNA binding protein that is required for blast crisis CML. MSI2 is physically associated with the BCAT1 transcript and positively regulates its protein expression in leukaemia. Taken together, this work reveals that altered BCAA metabolism activated through the MSI2-BCAT1 axis drives cancer progression in myeloid leukaemia.

Mitochondrial ADCK3 employs an atypical protein kinase-like fold to enable coenzyme Q biosynthesis.

The ancient UbiB protein kinase-like family is involved in isoprenoid lipid biosynthesis and is implicated in human diseases, but demonstration of UbiB kinase activity has remained elusive for unknown reasons. Here, we quantitatively define UbiB-specific sequence motifs and reveal their positions within the crystal structure of a UbiB protein, ADCK3. We find that multiple UbiB-specific features are poised to inhibit protein kinase activity, including an N-terminal domain that occupies the typical substrate binding pocket and a unique A-rich loop that limits ATP binding by establishing an unusual selectivity for ADP. A single alanine-to-glycine mutation of this loop flips this coenzyme selectivity and enables autophosphorylation but inhibits coenzyme Q biosynthesis in vivo, demonstrating functional relevance for this unique feature. Our work provides mechanistic insight into UbiB enzyme activity and establishes a molecular foundation for further investigation of how UbiB family proteins affect diseases and diverse biological pathways.

Peters plus syndrome mutations affect the function and stability of human ß1,3-glucosyltransferase.

Peters Plus Syndrome (PTRPLS OMIM #261540) is a severe congenital disorder of glycosylation where patients have multiple structural anomalies, including Peters anomaly of the eye (anterior segment dysgenesis), disproportionate short stature, brachydactyly, dysmorphic facial features, developmental delay, and variable additional abnormalities. PTRPLS patients and some Peters Plus-like (PTRPLS-like) patients (who only have a subset of PTRPLS phenotypes) have mutations in the gene encoding ß1,3-glucosyltransferase (B3GLCT). B3GLCT catalyzes the transfer of glucose to O-linked fucose on thrombospondin type-1 repeats. Most B3GLCT substrate proteins belong to the ADAMTS superfamily and play critical roles in extracellular matrix. We sought to determine whether the PTRPLS or PTRPLS-like mutations abrogated B3GLCT activity. B3GLCT has two putative active sites, one in the N-terminal region and the other in the C-terminal glycosyltransferase domain. Using sequence analysis and in vitro activity assays, we demonstrated that the C-terminal domain catalyzes transfer of glucose to O-linked fucose. We also generated a homology model of B3GLCT and identified D421 as the catalytic base. PTRPLS and PTRPLS-like mutations were individually introduced into B3GLCT, and the mutated enzymes were evaluated using in vitro enzyme assays and cell-based functional assays. Our results demonstrated that PTRPLS mutations caused loss of B3GLCT enzymatic activity and/or significantly reduced protein stability. In contrast, B3GLCT with PTRPLS-like mutations retained enzymatic activity, although some showed a minor destabilizing effect. Overall, our data supports the hypothesis that loss of glucose from B3GLCT substrate proteins is responsible for the defects observed in PTRPLS patients, but not for those observed in PTRPLS-like patients.

Evolution of Functional Diversity in the Holozoan Tyrosine Kinome.

The emergence of multicellularity is strongly correlated with the expansion of tyrosine kinases, a conserved family of signaling enzymes that regulates pathways essential for cell-to-cell communication. Although tyrosine kinases have been classified from several model organisms, a molecular-level understanding of tyrosine kinase evolution across all holozoans is currently lacking. Using a hierarchical sequence constraint-based classification of diverse holozoan tyrosine kinases, we construct a new phylogenetic tree that identifies two ancient clades of cytoplasmic and receptor tyrosine kinases separated by the presence of an extended insert segment in the kinase domain connecting the D and E-helices. Present in nearly all receptor tyrosine kinases, this fast-evolving insertion imparts diverse functionalities, such as post-translational modification sites and regulatory interactions. Eph and EGFR receptor tyrosine kinases are two exceptions which lack this insert, each forming an independent lineage characterized by unique functional features. We also identify common constraints shared across multiple tyrosine kinase families which warrant the designation of three new subgroups: Src module (SrcM), insulin receptor kinase-like (IRKL), and fibroblast, platelet-derived, vascular, and growth factor receptors (FPVR). Subgroup-specific constraints reflect shared autoinhibitory interactions involved in kinase conformational regulation. Conservation analyses describe how diverse tyrosine kinase signaling functions arose through the addition of family-specific motifs upon subgroup-specific features and coevolving protein domains. We propose the oldest tyrosine kinases, IRKL, SrcM, and Csk, originated from unicellular premetazoans and were coopted for complex multicellular functions. The increased frequency of oncogenic variants in more recent tyrosine kinases suggests that lineage-specific functionalities are selectively altered in human cancers.

Lipid-targeting pleckstrin homology domain turns its autoinhibitory face toward the TEC kinases.

The pleckstrin homology (PH) domain is well known for its phospholipid targeting function. The PH-TEC homology (PHTH) domain within the TEC family of tyrosine kinases is also a crucial component of the autoinhibitory apparatus. The autoinhibitory surface on the PHTH domain has been previously defined, and biochemical investigations have shown that PHTH-mediated inhibition is mutually exclusive with phosphatidylinositol binding. Here we use hydrogen/deuterium exchange mass spectrometry, nuclear magnetic resonance (NMR), and evolutionary sequence comparisons to map where and how the PHTH domain affects the Bruton's tyrosine kinase (BTK) domain. The data map a PHTH-binding site on the activation loop face of the kinase C lobe, suggesting that the PHTH domain masks the activation loop and the substrate-docking site. Moreover, localized NMR spectral changes are observed for non-surface-exposed residues in the active site and on the distal side of the kinase domain. These data suggest that the association of PHTH induces allosteric conformational shifts in regions of the kinase domain that are critical for catalysis. Through statistical comparisons of diverse tyrosine kinase sequences, we identify residues unique to BTK that coincide with the experimentally determined PHTH-binding surface on the kinase domain. Our data provide a more complete picture of the autoinhibitory conformation adopted by full-length TEC kinases, creating opportunities to target the regulatory domains to control the function of these kinases in a biological setting.

LF4/MOK and a CDK-related kinase regulate the number and length of cilia in Tetrahymena.

The length of cilia is controlled by a poorly understood mechanism that involves members of the conserved RCK kinase group, and among them, the LF4/MOK kinases. The multiciliated protist model, Tetrahymena, carries two types of cilia (oral and locomotory) and the length of the locomotory cilia is dependent on their position with the cell. In Tetrahymena, loss of an LF4/MOK ortholog, LF4A, lengthened the locomotory cilia, but also reduced their number. Without LF4A, cilia assembled faster and showed signs of increased intraflagellar transport (IFT). Consistently, overproduced LF4A shortened cilia and downregulated IFT. GFP-tagged LF4A, expressed in the native locus and imaged by total internal reflection microscopy, was enriched at the basal bodies and distributed along the shafts of cilia. Within cilia, most LF4A-GFP particles were immobile and a few either diffused or moved by IFT. We suggest that the distribution of LF4/MOK along the cilium delivers a uniform dose of inhibition to IFT trains that travel from the base to the tip. In a longer cilium, the IFT machinery may experience a higher cumulative dose of inhibition by LF4/MOK. Thus, LF4/MOK activity could be a readout of cilium length that helps to balance the rate of IFT-driven assembly with the rate of disassembly at steady state. We used a forward genetic screen to identify a CDK-related kinase, CDKR1, whose loss-of-function suppressed the shortening of cilia caused by overexpression of LF4A, by reducing its kinase activity. Loss of CDKR1 alone lengthened both the locomotory and oral cilia. CDKR1 resembles other known ciliary CDK-related kinases: LF2 of Chlamydomonas, mammalian CCRK and DYF-18 of C. elegans, in lacking the cyclin-binding motif and acting upstream of RCKs. The new genetic tools we developed here for Tetrahymena have potential for further dissection of the principles of cilia length regulation in multiciliated cells.

KinOrtho: a method for mapping human kinase orthologs across the tree of life and illuminating understudied kinases.

BACKGROUND: Protein kinases are among the largest druggable family of signaling proteins, involved in various human diseases, including cancers and neurodegenerative disorders. Despite their clinical relevance, nearly 30% of the 545 human protein kinases remain highly understudied. Comparative genomics is a powerful approach for predicting and investigating the functions of understudied kinases. However, an incomplete knowledge of kinase orthologs across fully sequenced kinomes severely limits the application of comparative genomics approaches for illuminating understudied kinases. Here, we introduce KinOrtho, a query- and graph-based orthology inference method that combines full-length and domain-based approaches to map one-to-one kinase orthologs across 17 thousand species. RESULTS: Using multiple metrics, we show that KinOrtho performed better than existing methods in identifying kinase orthologs across evolutionarily divergent species and eliminated potential false positives by flagging sequences without a proper kinase domain for further evaluation. We demonstrate the advantage of using domain-based approaches for identifying domain fusion events, highlighting a case between an understudied serine/threonine kinase TAOK1 and a metabolic kinase PIK3C2A with high co-expression in human cells. We also identify evolutionary fission events involving the understudied OBSCN kinase domains, further highlighting the value of domain-based orthology inference approaches. Using KinOrtho-defined orthologs, Gene Ontology annotations, and machine learning, we propose putative biological functions of several understudied kinases, including the role of TP53RK in cell cycle checkpoint(s), the involvement of TSSK3 and TSSK6 in acrosomal vesicle localization, and potential functions for the ULK4 pseudokinase in neuronal development. CONCLUSIONS: In sum, KinOrtho presents a novel query-based tool to identify one-to-one orthologous relationships across thousands of proteomes that can be applied to any protein family of interest. We exploit KinOrtho here to identify kinase orthologs and show that its well-curated kinome ortholog set can serve as a valuable resource for illuminating understudied kinases, and the KinOrtho framework can be extended to any protein-family of interest.

GTXplorer: A portal to navigate and visualize the evolutionary information encoded in fold A glycosyltransferases.

Glycosyltransferases (GTs) play a central role in sustaining all forms of life through the biosynthesis of complex carbohydrates. Despite significant strides made in recent years to establish computational resources, databases and tools to understand the nature and role of carbohydrates and related glycoenzymes, a data analytics framework that connects the sequence-structure-function relationships to the evolution of GTs is currently lacking. This hinders the characterization of understudied GTs and the synthetic design of GTs for medical and biotechnology applications. Here, we present GTXplorer as an integrated platform that presents evolutionary information of GTs adopting a GT-A fold in an intuitive format enabling in silico investigation through comparative sequence analysis to derive informed hypotheses about their function. The tree view mode provides an overview of the evolutionary relationships of GT-A families and allows users to select phylogenetically relevant families for comparisons. The selected families can then be compared in the alignment view at the residue level using annotated weblogo stacks of the GT-A core specific to the selected clade, family, or subfamily. All data are easily accessible and can be downloaded for further analysis. GTXplorer can be accessed at https://vulcan.cs.uga.edu/gtxplorer/ or from GitHub at https://github.com/esbgkannan/GTxplorer to deploy locally. By packaging multiple data streams into an accessible, user-friendly format, GTXplorer presents the first evolutionary data analytics platform for comparative glycomics.

Altered conformational landscape and dimerization dependency underpins the activation of EGFR by αC-ß4 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-ß4 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.

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.

The crystal structure of the protein kinase HIPK2 reveals a unique architecture of its CMGC-insert region.

The homeodomain-interacting protein kinase (HIPK) family is comprised of four nuclear protein kinases, HIPK1-4. HIPK proteins phosphorylate a diverse range of transcription factors involved in cell proliferation, differentiation, and apoptosis. HIPK2, thus far the best-characterized member of this largely understudied family of protein kinases, plays a role in the activation of p53 in response to DNA damage. Despite this tumor-suppressor function, HIPK2 is also found overexpressed in several cancers, and its hyperactivation causes chronic fibrosis. There are currently no structures of HIPK2 or of any other HIPK kinase. Here, we report the crystal structure of HIPK2's kinase domain bound to CX-4945, a casein kinase 2α (CK2α) inhibitor currently in clinical trials against several cancers. The structure, determined at 2.2 Å resolution, revealed that CX-4945 engages the HIPK2 active site in a hybrid binding mode between that seen in structures of CK2α and Pim1 kinases. The HIPK2 kinase domain crystallized in the active conformation, which was stabilized by phosphorylation of the activation loop. We noted that the overall kinase domain fold of HIPK2 closely resembles that of evolutionarily related dual-specificity tyrosine-regulated kinases (DYRKs). Most significant structural differences between HIPK2 and DYRKs included an absence of the regulatory N-terminal domain and a unique conformation of the CMGC-insert region and of a newly defined insert segment in the αC-ß4 loop. This first crystal structure of HIPK2 paves the way for characterizing the understudied members of the HIPK family and for developing HIPK2-directed therapies for managing cancer and fibrosis.