Landscape of drug-resistance mutations in kinase regulatory hotspots

Overcoming Secondary Mutations of Type II Kinase Inhibitors

Type II kinase inhibitors bind in the "DFG-out" kinase conformation and are generally considered to be more potent and selective than type I inhibitors, which target a DFG-in conformation. Nine type II inhibitors are currently clinically approved, with more undergoing clinical development. Resistance-conferring secondary mutations emerged with the first series of type II inhibitors, most commonly at residues within the kinase activation loop and at the "gatekeeper" position. Recently, new inhibitors have been developed to overcome such mutations; however, mutations activating other pathways (and/or other targets) have subsequently emerged on occasion. Here, we systematically summarize the secondary mutations that confer resistance to type II inhibitors, the structural basis for resistance, newer inhibitors designed to overcome resistance, as well as the challenges and opportunities for the development of new inhibitors to overcome secondary kinase domain mutations.

Unraveling Extremely Damaging IRAK4 Variants and Their Potential Implications for IRAK4 Inhibitor Efficacy

Interleukin-1-receptor-associated kinase 4 (IRAK4) possesses a crucial function in the toll-like receptor (TLR) signaling pathway, and the dysfunction of this molecule could lead to various infectious and immune-related diseases in addition to cancers. IRAK4 genetic variants have been linked to various types of diseases. Therefore, we conducted a comprehensive analysis to recognize the missense variants with the most damaging impacts on IRAK4 with the employment of diverse bioinformatics tools to study single-nucleotide polymorphisms' effects on function, stability, secondary structures, and 3D structure. The residues' location on the protein domain and their conservation status were investigated as well. Moreover, docking tools along with structural biology were engaged in analyzing the SNPs' effects on one of the developed IRAK4 inhibitors. By analyzing IRAK4 gene SNPs, the analysis distinguished ten variants as the most detrimental missense variants. All variants were situated in highly conserved positions on an important protein domain. L318S and L318F mutations were linked to changes in IRAK4 secondary structures. Eight SNPs were revealed to have a decreasing effect on the stability of IRAK4 via both I-Mutant 2.0 and Mu-Pro tools, while Mu-Pro tool identified a decreasing effect for the G198E SNP. In addition, detrimental effects on the 3D structure of IRAK4 were also discovered for the selected variants. Molecular modeling studies highlighted the detrimental impact of these identified SNP mutant residues on the druggability of the IRAK4 ATP-binding site towards the known target inhibitor, HG-12-6, as compared to the native protein. The loss of important ligand residue-wise contacts, altered protein global flexibility, increased steric clashes, and even electronic penalties at the ligand-binding site interfaces were all suggested to be associated with SNP models for hampering the HG-12-6 affinity towards IRAK4 target protein. This given model lays the foundation for the better prediction of various disorders relevant to IRAK4 malfunction and sheds light on the impact of deleterious IRAK4 variants on IRAK4 inhibitor efficacy.

Prediction of Amino Acid Substitutions in ABL1 Protein Leading to Tumor Drug Resistance Based on "Structure-Property" Relationship Classification Models

Drug resistance to anticancer drugs is a serious complication in patients with cancer. Typically, drug resistance occurs due to amino acid substitutions (AAS) in drug target proteins. The study aimed at developing and validating a new approach to the creation of structure-property relationships (SPR) classification models to predict AASs leading to drug resistance to inhibitors of tyrosine-protein kinase ABL1. The approach was based on the representation of AASs as peptides described in terms of structural formulas. The data on drug-resistant and non-resistant variants of AAS for two isoforms of ABL1 were extracted from the COSMIC database. The given training sets (approximately 700 missense variants) were used for the creation of SPR models in MultiPASS software based on substructural atom-centric multiple neighborhoods of atom (MNA) descriptors for the description of the structural formula of protein fragments and a Bayesian-like algorithm for revealing structure-property relationships. It was found that MNA descriptors of the 6th level and peptides from 11 amino acid residues were the best combination for ABL1 isoform 1 with the prediction accuracy (AUC) of resistance to imatinib (0.897) and dasatinib (0.996). For ABL1 isoform 2 (resistance to imatinib), the best combination was MNA descriptors of the 6th level, peptides form 15 amino acids (AUC value was 0.909). The prediction of possible drug-resistant AASs was made for dbSNP and gnomAD data. The six selected most probable imatinib-resistant AASs were additionally validated by molecular modeling and docking, which confirmed the possibility of resistance for the E334V and T392I variants.

Bioinformatics toolbox for exploring target mutation-induced drug resistance

Drug resistance is increasingly among the main issues affecting human health and threatening agriculture and food security. In particular, developing approaches to overcome target mutation-induced drug resistance has long been an essential part of biological research. During the past decade, many bioinformatics tools have been developed to explore this type of drug resistance, and they have become popular for elucidating drug resistance mechanisms in a low cost, fast and effective way. However, these resources are scattered and underutilized, and their strengths and limitations have not been systematically analyzed and compared. Here, we systematically surveyed 59 freely available bioinformatics tools for exploring target mutation-induced drug resistance. We analyzed and summarized these resources based on their functionality, data volume, data source, operating principle, performance, etc. And we concisely discussed the strengths, limitations and application examples of these tools. Specifically, we tested some predictive tools and offered some thoughts from the clinician's perspective. Hopefully, this work will provide a useful toolbox for researchers working in the biomedical, pesticide, bioinformatics and pharmaceutical engineering fields, and a good platform for non-specialists to quickly understand drug resistance prediction.

Arf GTPase activates the WAVE regulatory complex through a distinct binding site

Cross-talk between Rho- and Arf-family guanosine triphosphatases (GTPases) plays an important role in linking the actin cytoskeleton to membrane protrusions, organelle morphology, and vesicle trafficking. The central actin regulator, WAVE regulatory complex (WRC), integrates Rac1 (a Rho-family GTPase) and Arf signaling to promote Arp2/3-mediated actin polymerization in many processes, but how WRC senses Arf signaling is unknown. Here, we have reconstituted a direct interaction between Arf and WRC. This interaction is greatly enhanced by Rac1 binding to the D site of WRC. Arf1 binds to a previously unidentified, conserved surface on the Sra1 subunit of WRC, which, in turn, drives WRC activation using a mechanism distinct from that of Rac1. Mutating the Arf binding site abolishes Arf1-WRC interaction, disrupts Arf1-mediated WRC activation, and impairs lamellipodia formation and cell migration. This work uncovers a new mechanism underlying WRC activation and provides a mechanistic foundation for studying how WRC-mediated actin polymerization links Arf and Rac signaling in cells.

Targeting Gatekeeper Mutations for Kinase Drug Discovery

Clinically acquired resistance is a major challenge in cancer therapies with small-molecule kinase inhibitors (SMKIs). Gatekeeper mutations in the ATP-binding pocket of kinases are the most common mutations leading to acquired resistance. To date, seven new-generation kinase inhibitors targeting gatekeeper mutations have been approved by the FDA; however, the clinical need is still unmet. Here, we systematically summarize the types of gatekeeper mutations across the kinase family, the structural basis for acquired resistance, and newly developed SMKIs targeting gatekeeper mutations as well as highlight the opportunities and challenges of kinase drug discovery for targeting gatekeeper mutations.

The protein kinase CK1: Inhibition, activation, and possible allosteric modulation

Protein kinases play a vital role in biology and deregulation of kinases is implicated in numerous diseases ranging from cancer to neurodegenerative diseases, making them a major target class for the pharmaceutical industry. However, the high degree of conservation that exists between ATP-binding sites among kinases makes it difficult for current inhibitors to be highly specific. In the context of neurodegeneration, several groups including ours, have linked different kinases such as CK1 and Alzheimer’s disease for example. Strictly CK1-isoform specific regulators do not exist and known CK1 inhibitors are inhibiting the enzymatic activity, targeting the ATP-binding site. Here we review compounds known to target CK1, as well as other inhibitory types that could benefit CK1. We introduce the DNA-encoded library (DEL) technology that might represent an interesting approach to uncover allosteric modulators instead of ATP competitors. Such a strategy, taking into account known allosteric inhibitors and mechanisms, might help designing modulators that are more specific towards a specific kinase, and in the case of CK1, toward specific isoforms.

Predicting the mutation effects of protein–ligand interactions via end-point binding free energy calculations: strategies and analyses

Protein mutations occur frequently in biological systems, which may impact, for example, the binding of drugs to their targets through impairing the critical H-bonds, changing the hydrophobic interactions, etc. Thus, accurately predicting the effects of mutations on biological systems is of great interests to various fields. Unfortunately, it is still unavailable to conduct large-scale wet-lab mutation experiments because of the unaffordable experimental time and financial costs. Alternatively, in silico computation can serve as a pioneer to guide the experiments. In fact, numerous pioneering works have been conducted from computationally cheaper machine-learning (ML) methods to the more expensive alchemical methods with the purpose to accurately predict the mutation effects. However, these methods usually either cannot result in a physically understandable model (ML-based methods) or work with huge computational resources (alchemical methods). Thus, compromised methods with good physical characteristics and high computational efficiency are expected. Therefore, here, we conducted a comprehensive investigation on the mutation issues of biological systems with the famous end-point binding free energy calculation methods represented by MM/GBSA and MM/PBSA. Different computational strategies considering different length of MD simulations, different value of dielectric constants and whether to incorporate entropy effects to the predicted total binding affinities were investigated to provide a more accurate way for predicting the energetic change upon protein mutations. Overall, our result shows that a relatively long MD simulation (e.g. 100 ns) benefits the prediction accuracy for both MM/GBSA and MM/PBSA (with the best Pearson correlation coefficient between the predicted ∆∆G and the experimental data of ~ 0.44 for a challenging dataset). Further analyses shows that systems involving large perturbations (e.g. multiple mutations and large number of atoms change in the mutation site) are much easier to be accurately predicted since the algorithm works more sensitively to the large change of the systems. Besides, system-specific investigation reveals that conformational adjustment is needed to refine the micro-environment of the manually mutated systems and thus lead one to understand why longer MD simulation is necessary to improve the predicting result. The proposed strategy is expected to be applied in large-scale mutation effects investigation with interpretation.

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.

Structure-guided machine learning prediction of drug resistance mutations in Abelson 1 kinase

Kinases play crucial roles in cellular signalling and biological processes with their dysregulation associated with diseases, including cancers. Kinase inhibitors, most notably those targeting ABeLson 1 (ABL1) kinase in chronic myeloid leukemia, have had a significant impact on cancer survival, yet emergence of resistance mutations can reduce their effectiveness, leading to therapeutic failure. Limited effort, however, has been devoted to developing tools to accurately identify ABL1 resistance mutations, as well as providing insights into their molecular mechanisms. Here we investigated the structural basis of ABL1 mutations modulating binding affinity of eight FDA-approved drugs. We found mutations impair affinity of type I and type II inhibitors differently and used this insight to developed a novel web-based diagnostic tool, SUSPECT-ABL, to pre-emptively predict resistance profiles and binding free-energy changes (ΔΔG) of all possible ABL1 mutations against inhibitors with different binding modes. Resistance mutations in ABL1 were successfully identified, achieving a Matthew's Correlation Coefficient of up to 0.73 and the resulting change in ligand binding affinity with a Pearson's correlation of up to 0.77, with performances consistent across non-redundant blind tests. Through an in silico saturation mutagenesis, our tool has identified possibly emerging resistance mutations, which offers opportunities for in vivo experimental validation. We believe SUSPECT-ABL will be an important tool not just for improving precision medicine efforts, but for facilitating the development of next-generation inhibitors that are less prone to resistance. We have made our tool freely available at http://biosig.unimelb.edu.au/suspect_abl/.

Anti-neoplastic characteristics and potential targets of calycosin against bisphenol A-related osteosarcoma: bioinformatics analysis

Environmentally, bisphenol A (BPA) is a well-known pollutant caused human health risk, including osteosarcoma (OS). OS, a deadly bone neoplasia, may occur in children and adults. However, the anti-OS pharmacotherapy prescribes limitedly in clinical practice. Interestingly, previous experimental evidences indicate calycosin-exerting potential anti-OS actions. Thus, in this report, we aimed to further characterize and detail the therapeutic targets and molecular mechanisms of calycosin-anti-BPA-related OS by using network pharmacology and molecular docking analyses. In results, the bioinformatics data disclosed all mapped, core targets, biological functions, molecular pathways of calycosin to treat BPA-related OS. The computational analysis using molecular docking indicated that potential binding ability of core targets in calycosin to treat BPA-related OS was identified. Moreover, detailed biological functions and optimal pathways of calycosin-anti-BPA-related OS were revealed, as shown in integrated network maps. Taken together, these network pharmacology and structural biology findings illustrate the core biotargets, pharmacological functions and pathways of calycosin-anti-BPA-related OS. Potentially, these core targets identified by molecular docking may attribute to the potential clinical application of calycosin against BPA-related OS.

KinaseMD: kinase mutations and drug response database

Mutations in kinases are abundant and critical to study signaling pathways and regulatory roles in human disease, especially in cancer. Somatic mutations in kinase genes can affect drug treatment, both sensitivity and resistance, to clinically used kinase inhibitors. Here, we present a newly constructed database, KinaseMD (kinase mutations and drug response), to structurally and functionally annotate kinase mutations. KinaseMD integrates 679 374 somatic mutations, 251 522 network-rewiring events, and 390 460 drug response records curated from various sources for 547 kinases. We uniquely annotate the mutations and kinase inhibitor response in four types of protein substructures (gatekeeper, A-loop, G-loop and αC-helix) that are linked to kinase inhibitor resistance in literature. In addition, we annotate functional mutations that may rewire kinase regulatory network and report four phosphorylation signals (gain, loss, up-regulation and down-regulation). Overall, KinaseMD provides the most updated information on mutations, unique annotations of drug response especially drug resistance and functional sites of kinases. KinaseMD is accessible at https://bioinfo.uth.edu/kmd/, having functions for searching, browsing and downloading data. To our knowledge, there has been no systematic annotation of these structural mutations linking to kinase inhibitor response. In summary, KinaseMD is a centralized database for kinase mutations and drug response.

Transmembrane Polar Relay Drives the Allosteric Regulation for ABCG5/G8 Sterol Transporter

The heterodimeric ATP-binding cassette (ABC) sterol transporter, ABCG5/G8, is responsible for the biliary and transintestinal secretion of cholesterol and dietary plant sterols. Missense mutations of ABCG5/G8 can cause sitosterolemia, a loss-of-function disorder characterized by plant sterol accumulation and premature atherosclerosis. A new molecular framework was recently established by a crystal structure of human ABCG5/G8 and reveals a network of polar and charged amino acids in the core of the transmembrane domains, namely, a polar relay. In this study, we utilize genetic variants to dissect the mechanistic role of this transmembrane polar relay in controlling ABCG5/G8 function. We demonstrated a sterol-coupled ATPase activity of ABCG5/G8 by cholesteryl hemisuccinate (CHS), a relatively water-soluble cholesterol memetic, and characterized CHS-coupled ATPase activity of three loss-of-function missense variants, R543S, E146Q, and A540F, which are respectively within, in contact with, and distant from the polar relay. The results established an in vitro phenotype of the loss-of-function and missense mutations of ABCG5/G8, showing significantly impaired ATPase activity and loss of energy sufficient to weaken the signal transmission from the transmembrane domains. Our data provide a biochemical evidence underlying the importance of the polar relay and its network in regulating the catalytic activity of ABCG5/G8 sterol transporter.