c-Src binds to the cancer drug imatinib with an inactive Abl/c-Kit conformation and a distributed thermodynamic penalty

Validation of ion mobility spectrometry - mass spectrometry as a screening tool to identify type II kinase inhibitors of FGFR1 kinase

RATIONALE

The protein kinase FGFR1 regulates cellular processes in human development. As over-activity of FGFR1 is implicated with cancer, effective inhibitors are in demand. Type I inhibitors, which bind to the active form of FGFR1, are less effective than type II inhibitors, which bind to the inactive form. Screening to distinguish between type I and type II inhibitors is required.

METHODS

X-ray crystallography was used to indicate whether a range of potential inhibitors bind to the active or inactive FGFR1 kinase conformation. The binding affinity of each ligand to FGFR1 was measured using biochemical methods. Electrospray ionisation - ion mobility spectrometry - mass spectrometry (ESI-IMS-MS) in conjunction with collision-induced protein unfolding generated a conformational profile of each FGFR1-ligand complex. The results indicate that the protein's conformational profile depends on whether the inhibitor is type I or type II.

RESULTS

X-ray crystallography confirmed which of the kinase inhibitors bind to the active or inactive form of FGFR1 kinase. Collision-induced unfolding combined with ESI-IMS-MS showed distinct differences in the FGFR1 folding landscape for type I and type II inhibitors. Biochemical studies indicated a similar range of FGFR1 affinities for both types of inhibitors, thus providing confidence that the conformational variations detected using ESI-IMS-MS can be interpretated unequivocally and that this is an effective screening method.

CONCLUSIONS

A robust ESI-IMS-MS method has been implemented to distinguish between the binding mode of type I and type II inhibitors by monitoring the conformational unfolding profile of FGFR1. This rapid method requires low sample concentrations and could be used as a high-throughput screening technique for the characterisation of novel kinase inhibitors.

Path sampling challenges in large biomolecular systems: RETIS and REPPTIS for ABL-imatinib kinetics

Predicting the kinetics of drug-protein interactions is crucial for understanding drug efficacy, particularly in personalized medicine, where protein mutations can significantly alter drug residence times. This study applies replica exchange transition interface sampling and its partial path variant to investigate the dissociation kinetics of imatinib from Abelson nonreceptor tyrosine kinase (ABL) and mutants relevant to chronic myeloid leukemia therapy. These path sampling methods offer a bias-free alternative to conventional approaches requiring qualitative predefined reaction coordinates. Nevertheless, the complex free energy landscape of ABL-imatinib dissociation presents significant challenges. Multiple metastable states and orthogonal barriers lead to parallel unbinding pathways, complicating convergence in transition interface sampling-based methods. Despite employing computational efficiency strategies such as asynchronous replica exchange, full convergence remained elusive. This work provides a critical assessment of path sampling in high-dimensional biological systems, discussing the need for enhanced initialization strategies, advanced Monte Carlo path generation moves, and machine learning-derived reaction coordinates to improve kinetic predictions of drug dissociation with minimal prior knowledge.

Estimation of Absolute Binding Free Energies for Drugs That Bind Multiple Proteins

The Gibbs energy of binding (absolute binding free energy, ABFE) of a drug to proteins in the body determines the drug's affinity to its molecular target and its selectivity. ABFE is challenging to measure, and experimental values are not available for many proteins together with potential drugs and other molecules that bind them. Accurate means of calculating such values are, therefore, highly in demand. Realizing that toxicity and side effects are closely related to off-target binding, here we calculate the ABFE of two drugs, each to multiple proteins, in order to examine whether it is possible to carry out such calculations and achieve the required accuracy. The methods that were used were free energy perturbation with replica exchange molecular dynamics (FEP/REMD) and density functional theory (DFT) with a cluster approach and a simplified model. DFT calculations were supplemented with energy decomposition analysis (EDA). The accuracy of each method is discussed, and suggestions are made for the approach toward better ABFE calculations.

Advancing Src kinase inhibition: From structural design to therapeutic innovation - A comprehensive review

Src kinase, a non-receptor tyrosine kinase implicated in cellular signaling networks, plays a pivotal role in tumor progression and therapeutic resistance. Despite intensive research efforts spanning decades, no Src-selective kinase inhibitors have yet entered clinical use, highlighting the challenges in developing targeted therapeutics. Here we review recent advances in small-molecule Src inhibitor development, focusing on structural design strategies, binding mechanisms, and therapeutic applications. We analyze emerging approaches including fragment-based drug design, allosteric targeting, and substrate-competitive inhibition that have yielded promising new scaffold classes. Special attention is given to innovations in achieving isozyme selectivity, particularly through exploitation of non-ATP binding pockets and covalent inhibition strategies. Integration of artificial intelligence, living organoid platforms, and targeted protein degradation technologies is accelerating inhibitor optimization. We discuss key challenges in Src inhibitor development, including the need for enhanced selectivity, reduced off-target effects, and improved resistance profiles. Our analysis reveals promising directions for future therapeutic development, emphasizing the importance of rational design principles guided by structural insights and emerging technologies. These findings provide a framework for developing next-generation Src inhibitors with improved clinical potential.

The conformation of the nSrc specificity-determining loop in the Src SH3 domain is modulated by a WX conserved sequence motif found in SH3 domains

Cellular signaling networks are modulated by multiple protein-protein interaction domains that coordinate extracellular inputs and processes to regulate cellular processes. Several of these domains recognize short linear motifs, or SLiMs, which are often highly conserved and are closely regulated. One such domain, the Src homology 3 (SH3) domain, typically recognizes proline-rich SLiMs and is one of the most abundant SLiM-binding domains in the human proteome. These domains are often described as quite versatile, and indeed, SH3 domains can bind ligands in opposite orientations dependent on target sequence. Furthermore, recent work has identified diverse modes of binding for SH3 domains and a wide variety of sequence motifs that are recognized by various domains. Specificity is often attributed to the RT and nSrc loops near the peptide-binding cleft in this domain family, particularly for Class I binding, which is defined as RT and nSrc loop interactions with the N-terminus of the ligand. Here, we used the Src and Abl SH3 domains as a model to further investigate the role of the RT and nSrc loops in SH3 specificity. We created chimeric domains with both the RT and nSrc loop sequences swapped between these SH3 domains, and used fluorescence anisotropy assays to test how relative binding affinities were affected for Src SH3- and Abl SH3-specific ligands. We also used Alphafold-Multimer to model our SH3:peptide complexes in combination with molecular dynamics simulations. We identified a position that contributes to the nSrc loop conformation in Src SH3, the amino acid immediately following a highly conserved Trp that creates a hydrophobic pocket critical for SH3 ligand recognition. We defined this as the WX motif, where X = Trp for Src and Cys for Abl. A broad importance of this position for modulating nSrc loop conformation in SH3 domains is suggested by analyses of previously deposited SH3 structures, multiple sequence alignment of SH3 domains in the human proteome, and our biochemical and computational data of mutant Src and Abl SH3 domains. Overall, our work uses experimental approaches and structural modeling to better understand specificity determinants in SH3 domains.

Elucidating molecular mechanism and chemical space of chalcones through biological networks and machine learning approaches

We developed a bio-cheminformatics method, exploring disease inhibition mechanisms using machine learning-enhanced quantitative structure-activity relationship (ML-QSAR) models and knowledge-driven neural networks. ML-QSAR models were developed using molecular fingerprint descriptors and the Random Forest algorithm to explore the chemical spaces of Chalcones inhibitors against diverse disease properties, including antifungal, anti-inflammatory, anticancer, antimicrobial, and antiviral effects. We generated and validated robust machine learning-based bioactivity prediction models (https://github.com/RatulChemoinformatics/QSAR) for the top genes. These models underwent ROC and applicability domain analysis, followed by molecular docking studies to elucidate the molecular mechanisms of the molecules. Through comprehensive neural network analysis, crucial genes such as AKT1, HSP90AA1, SRC, and STAT3 were identified. The PubChem fingerprint-based model revealed key descriptors: PubchemFP521 for AKT1, PubchemFP180 for SRC, PubchemFP633 for HSP90AA1, and PubchemFP145 and PubchemFP338 for STAT3, consistently contributing to bioactivity across targets. Notably, chalcone derivatives demonstrated significant bioactivity against target genes, with compound RA1 displaying a predictive pIC50 value of 5.76 against HSP90AA1 and strong binding affinities across other targets. Compounds RA5 to RA7 also exhibited high binding affinity scores comparable to or exceeding existing drugs. These findings emphasize the importance of knowledge-based neural network-based research for developing effective drugs against diverse disease properties. These interactions warrant further in vitro and in vivo investigations to elucidate their potential in rational drug design. The presented models provide valuable insights for inhibitor design and hold promise for drug development. Future research will prioritize investigating these molecules for mycobacterium tuberculosis, enhancing the comprehension of effectiveness in addressing infectious diseases.

A comprehensive exploration of the druggable conformational space of protein kinases using AI-predicted structures

Protein kinase function and interactions with drugs are controlled in part by the movement of the DFG and ɑC-Helix motifs that are related to the catalytic activity of the kinase. Small molecule ligands elicit therapeutic effects with distinct selectivity profiles and residence times that often depend on the active or inactive kinase conformation(s) they bind. Modern AI-based structural modeling methods have the potential to expand upon the limited availability of experimentally determined kinase structures in inactive states. Here, we first explored the conformational space of kinases in the PDB and models generated by AlphaFold2 (AF2) and ESMFold, two prominent AI-based protein structure prediction methods. Our investigation of AF2's ability to explore the conformational diversity of the kinome at various multiple sequence alignment (MSA) depths showed a bias within the predicted structures of kinases in DFG-in conformations, particularly those controlled by the DFG motif, based on their overabundance in the PDB. We demonstrate that predicting kinase structures using AF2 at lower MSA depths explored these alternative conformations more extensively, including identifying previously unobserved conformations for 398 kinases. Ligand enrichment analyses for 23 kinases showed that, on average, docked models distinguished between active molecules and decoys better than random (average AUC (avgAUC) of 64.58), but select models perform well (e.g., avgAUCs for PTK2 and JAK2 were 79.28 and 80.16, respectively). Further analysis explained the ligand enrichment discrepancy between low- and high-performing kinase models as binding site occlusions that would preclude docking. The overall results of our analyses suggested that, although AF2 explored previously uncharted regions of the kinase conformational space and select models exhibited enrichment scores suitable for rational drug discovery, rigorous refinement of AF2 models is likely still necessary for drug discovery campaigns.

Binding Affinity Determination in Drug Design: Insights from Lock and Key, Induced Fit, Conformational Selection, and Inhibitor Trapping Models

Binding affinity is a fundamental parameter in drug design, describing the strength of the interaction between a molecule and its target protein. Accurately predicting binding affinity is crucial for the rapid development of novel therapeutics, the prioritization of promising candidates, and the optimization of their properties through rational design strategies. Binding affinity is determined by the mechanism of recognition between proteins and ligands. Various models, including the lock and key, induced fit, and conformational selection, have been proposed to explain this recognition process. However, current computational strategies to predict binding affinity, which are based on these models, have yet to produce satisfactory results. This article explores the connection between binding affinity and these protein-ligand interaction models, highlighting that they offer an incomplete picture of the mechanism governing binding affinity. Specifically, current models primarily center on the binding of the ligand and do not address its dissociation. In this context, the concept of ligand trapping is introduced, which models the mechanisms of dissociation. When combined with the current models, this concept can provide a unified theoretical framework that may allow for the accurate determination of the ligands' binding affinity.

TPX2 overexpression promotes sensitivity to dasatinib in breast cancer by activating YAP transcriptional signaling

Chromosomal instability (CIN) is a hallmark of cancer aggressiveness, providing genetic plasticity and tumor heterogeneity that allows the tumor to evolve and adapt to stress conditions. CIN is considered a cancer therapeutic biomarker because healthy cells do not exhibit CIN. Despite recent efforts to identify therapeutic strategies related to CIN, the results obtained have been very limited. CIN is characterized by a genetic signature where a collection of genes, mostly mitotic regulators, are overexpressed in CIN-positive tumors, providing aggressiveness and poor prognosis. We attempted to identify new therapeutic strategies related to CIN genes by performing a drug screen, using cells that individually express CIN-associated genes in an inducible manner. We find that the overexpression of targeting protein for Xklp2 (TPX2) enhances sensitivity to the proto-oncogene c-Src (SRC) inhibitor dasatinib due to activation of the Yes-associated protein 1 (YAP) pathway. Furthermore, using breast cancer data from The Cancer Genome Atlas (TCGA) and a cohort of cancer-derived patient samples, we find that both TPX2 overexpression and YAP activation are present in a significant percentage of cancer tumor samples and are associated with poor prognosis; therefore, they are putative biomarkers for selection for dasatinib therapy.

Clinical Insights into Structure, Regulation, and Targeting of ABL Kinases in Human Leukemia

Chronic myeloid leukemia is a multistep, multi-lineage myeloproliferative disease that originates from a translocation event between chromosome 9 and chromosome 22 within the hematopoietic stem cell compartment. The resultant fusion protein BCR::ABL1 is a constitutively active tyrosine kinase that can phosphorylate multiple downstream signaling molecules to promote cellular survival and inhibit apoptosis. Currently, tyrosine kinase inhibitors (TKIs), which impair ABL1 kinase activity by preventing ATP entry, are widely used as a successful therapeutic in CML treatment. However, disease relapses and the emergence of resistant clones have become a critical issue for CML therapeutics. Two main reasons behind the persisting obstacles to treatment are the acquired mutations in the ABL1 kinase domain and the presence of quiescent CML leukemia stem cells (LSCs) in the bone marrow, both of which can confer resistance to TKI therapy. In this article, we systemically review the structural and molecular properties of the critical domains of BCR::ABL1 and how understanding the essential role of BCR::ABL1 kinase activity has provided a solid foundation for the successful development of molecularly targeted therapy in CML. Comparison of responses and resistance to multiple BCR::ABL1 TKIs in clinical studies and current combination treatment strategies are also extensively discussed in this article.

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

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

An allosteric switch between the activation loop and a c-terminal palindromic phospho-motif controls c-Src function

Autophosphorylation controls the transition between discrete functional and conformational states in protein kinases, yet the structural and molecular determinants underlying this fundamental process remain unclear. Here we show that c-terminal Tyr 530 is a de facto c-Src autophosphorylation site with slow time-resolution kinetics and a strong intermolecular component. On the contrary, activation-loop Tyr 419 undergoes faster kinetics and a cis-to-trans phosphorylation switch that controls c-terminal Tyr 530 autophosphorylation, enzyme specificity, and strikingly, c-Src non-catalytic function as a substrate. In line with this, we visualize by X-ray crystallography a snapshot of Tyr 530 intermolecular autophosphorylation. In an asymmetric arrangement of both catalytic domains, a c-terminal palindromic phospho-motif flanking Tyr 530 on the substrate molecule engages the G-loop of the active kinase adopting a position ready for entry into the catalytic cleft. Perturbation of the phospho-motif accounts for c-Src dysfunction as indicated by viral and colorectal cancer (CRC)-associated c-terminal deleted variants. We show that c-terminal residues 531 to 536 are required for c-Src Tyr 530 autophosphorylation, and such a detrimental effect is caused by the substrate molecule inhibiting allosterically the active kinase. Our work reveals a crosstalk between the activation and c-terminal segments that control the allosteric interplay between substrate- and enzyme-acting kinases during autophosphorylation.

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

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

Significance Statement

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

Molecular Simulations of Conformational Transitions within the Insulin Receptor Kinase Reveal Consensus Features in a Multistep Activation Pathway

Modulating the transitions between active and inactive conformations of protein kinases is the primary means of regulating their catalytic activity, achieved by phosphorylation of the activation loop (A-loop). To elucidate the mechanism of this conformational activation, we applied the string method to determine the conformational transition path of insulin receptor kinase between the active and inactive conformations and the corresponding free-energy profiles with and without A-loop phosphorylation. The conformational change was found to proceed in three sequential steps: first, the flipping of the DFG motif of the active site; second, rotation of the A-loop; finally, the inward movement of the αC helix. The main energetic bottleneck corresponds to the conformational change in the A-loop, while changes in the DFG motif and αC helix occur before and after A-loop conformational change, respectively. In accordance with this, two intermediate states are identified, the first state just after the DFG flipping and the second state after the A-loop rotation. These intermediates exhibit structural features characteristic of the corresponding inactive and active conformations of other protein kinases. To understand the impact of A-loop phosphorylation on kinase conformation, the free energies of A-loop phosphorylation were determined at several states along the conformational transition path using the free-energy perturbation simulations. The calculated free energies reveal that while the unphosphorylated kinase interconverts between the inactive and active conformations, A-loop phosphorylation restricts access to the inactive conformation, thereby increasing the active conformation population. Overall, this study suggests a consensus mechanism of conformational activation between different protein kinases.

Structure and RAF family kinase isoform selectivity of type II RAF inhibitors tovorafenib and naporafenib

Upon activation by RAS, RAF family kinases initiate signaling through the MAP kinase cascade to control cell growth, proliferation, and differentiation. Among RAF isoforms (ARAF, BRAF, and CRAF), oncogenic mutations are by far most frequent in BRAF. The BRAFV600E mutation drives more than half of all malignant melanoma and is also found in many other cancers. Selective inhibitors of BRAFV600E (vemurafenib, dabrafenib, encorafenib) are used clinically for these indications, but they are not effective inhibitors in the context of oncogenic RAS, which drives dimerization and activation of RAF, nor for malignancies driven by aberrantly dimerized truncation/fusion variants of BRAF. By contrast, a number of "type II" RAF inhibitors have been developed as potent inhibitors of RAF dimers. Here, we compare potency of type II inhibitors tovorafenib (TAK-580) and naporafenib (LHX254) in biochemical assays against the three RAF isoforms and describe crystal structures of both compounds in complex with BRAF. We find that tovorafenib and naporafenib are most potent against CRAF but markedly less potent against ARAF. Crystal structures of both compounds with BRAFV600E or WT BRAF reveal the details of their molecular interactions, including the expected type II-binding mode, with full occupancy of both subunits of the BRAF dimer. Our findings have important clinical ramifications. Type II RAF inhibitors are generally regarded as pan-RAF inhibitors, but our studies of these two agents, together with recent work with type II inhibitors belvarafenib and naporafenib, indicate that relative sparing of ARAF may be a property of multiple drugs of this class.

Structural mechanism of a drug-binding process involving a large conformational change of the protein target

Proteins often undergo large conformational changes when binding small molecules, but atomic-level descriptions of such events have been elusive. Here, we report unguided molecular dynamics simulations of Abl kinase binding to the cancer drug imatinib. In the simulations, imatinib first selectively engages Abl kinase in its autoinhibitory conformation. Consistent with inferences drawn from previous experimental studies, imatinib then induces a large conformational change of the protein to reach a bound complex that closely resembles published crystal structures. Moreover, the simulations reveal a surprising local structural instability in the C-terminal lobe of Abl kinase during binding. The unstable region includes a number of residues that, when mutated, confer imatinib resistance by an unknown mechanism. Based on the simulations, NMR spectra, hydrogen-deuterium exchange measurements, and thermostability measurements and estimates, we suggest that these mutations confer imatinib resistance by exacerbating structural instability in the C-terminal lobe, rendering the imatinib-bound state energetically unfavorable.

Probing conformational landscapes and mechanisms of allosteric communication in the functional states of the ABL kinase domain using multiscale simulations and network-based mutational profiling of allosteric residue potentials

In the current study, multiscale simulation approaches and dynamic network methods are employed to examine the dynamic and energetic details of conformational landscapes and allosteric interactions in the ABL kinase domain that determine the kinase functions. Using a plethora of synergistic computational approaches, we elucidate how conformational transitions between the active and inactive ABL states can employ allosteric regulatory switches to modulate intramolecular communication networks between the ATP site, the substrate binding region, and the allosteric binding pocket. A perturbation-based network approach that implements mutational profiling of allosteric residue propensities and communications in the ABL states is proposed. Consistent with biophysical experiments, the results reveal functionally significant shifts of the allosteric interaction networks in which preferential communication paths between the ATP binding site and substrate regions in the active ABL state become suppressed in the closed inactive ABL form, which in turn features favorable allosteric coupling between the ATP site and the allosteric binding pocket. By integrating the results of atomistic simulations with dimensionality reduction methods and Markov state models, we analyze the mechanistic role of macrostates and characterize kinetic transitions between the ABL conformational states. Using network-based mutational scanning of allosteric residue propensities, this study provides a comprehensive computational analysis of long-range communications in the ABL kinase domain and identifies conserved regulatory hotspots that modulate kinase activity and allosteric crosstalk between the allosteric pocket, ATP binding site, and substrate binding regions.

Reply to Correspondence on "Synergy and Antagonism between Allosteric and Active-Site Inhibitors of Abl Tyrosine Kinase"

Manley and co-workers provide data demonstrating that, at super-pharmacological concentrations (300 μM), a ternary complex between Abl, asciminib, and ATP-competitive inhibitors is possible. The work in our manuscript concerns the interplay of asciminib (and GNF-2) with ATP-competitive inhibitors at pharmacologically relevant concentrations (Cmax =1.6-3.7 μM for asciminib). Manley and co-workers do not question any of the studies that we reported, nor do they provide explanations for how our work fits into their preferred model. Herein, we consider the data presented by Manley and co-workers. In addition, we provide new data supporting the findings in our Communication. Asciminib and ATP-competitive inhibitors do not simultaneously bind Abl at pharmacologically relevant concentrations unless the conformation selectivity for both ligands is matched.

Protein Flexibility and Dissociation Pathway Differentiation Can Explain Onset of Resistance Mutations in Kinases

Understanding how mutations render a drug ineffective is a problem of immense relevance. Often the mechanism through which mutations cause drug resistance can be explained purely through thermodynamics. However, the more perplexing situation is when two proteins have the same drug binding affinities but different residence times. In this work, we demonstrate how all-atom molecular dynamics simulations using recent developments grounded in statistical mechanics can provide a detailed mechanistic rationale for such variances. We discover dissociation mechanisms for the anti-cancer drug Imatinib (Gleevec) against wild-type and the N368S mutant of Abl kinase. We show how this point mutation triggers far-reaching changes in the protein's flexibility and leads to a different, much faster, drug dissociation pathway. We believe that this work marks an efficient and scalable approach to obtain mechanistic insight into resistance mutations in biomolecular receptors that are hard to explain using a structural perspective.

Validation of an allosteric binding site of Src kinase identified by unbiased ligand binding simulations

Allostery plays a primary role in regulating protein activity, making it an important mechanism in human disease and drug discovery. Identifying allosteric regulatory sites to explore their biological significance and therapeutic potential is invaluable to drug discovery; however, identification remains a challenge. Allosteric sites are often "cryptic" without clear geometric or chemical features. Since allosteric regulatory sites are often less conserved in protein kinases than the orthosteric ATP binding site, allosteric ligands are commonly more specific than ATP competitive inhibitors. We present a generalizable computational protocol to predict allosteric ligand binding sites based on unbiased ligand binding simulation trajectories. We demonstrate the feasibility of this protocol by revisiting our previously published ligand binding simulations using the first identified viral proto-oncogene, Src kinase, as a model system. The binding paths for kinase inhibitor PP1 uncovered three metastable intermediate states before binding the high-affinity ATP-binding pocket, revealing two previously known allosteric sites and one novel site. Herein, we validate the novel site using a combination of virtual screening and experimental assays to identify a v-type allosteric small-molecule inhibitor that targets this novel site with specificity for Src over closely related kinases. This study provides a proof-of-concept for employing unbiased ligand binding simulations to identify cryptic allosteric binding sites and is widely applicable to other protein-ligand systems.

Practical Protocols for Efficient Sampling of Kinase-Inhibitor Binding Pathways Using Two-Dimensional Replica-Exchange Molecular Dynamics

Molecular dynamics (MD) simulations are increasingly used to study various biological processes such as protein folding, conformational changes, and ligand binding. These processes generally involve slow dynamics that occur on the millisecond or longer timescale, which are difficult to simulate by conventional atomistic MD. Recently, we applied a two-dimensional (2D) replica-exchange MD (REMD) method, which combines the generalized replica exchange with solute tempering (gREST) with the replica-exchange umbrella sampling (REUS) in kinase-inhibitor binding simulations, and successfully observed multiple ligand binding/unbinding events. To efficiently apply the gREST/REUS method to other kinase-inhibitor systems, we establish modified, practical protocols with non-trivial simulation parameter tuning. The current gREST/REUS simulation protocols are tested for three kinase-inhibitor systems: c-Src kinase with PP1, c-Src kinase with Dasatinib, and c-Abl kinase with Imatinib. We optimized the definition of kinase-ligand distance as a collective variable (CV), the solute temperatures in gREST, and replica distributions and umbrella forces in the REUS simulations. Also, the initial structures of each replica in the 2D replica space were prepared carefully by pulling each ligand from and toward the protein binding sites for keeping stable kinase conformations. These optimizations were carried out individually in multiple short MD simulations. The current gREST/REUS simulation protocol ensures good random walks in 2D replica spaces, which are required for enhanced sampling of inhibitor dynamics around a target kinase.

A distributed residue network permits conformational binding specificity in a conserved family of actin remodelers

Metazoan proteomes contain many paralogous proteins that have evolved distinct functions. The Ena/VASP family of actin regulators consists of three members that share an EVH1 interaction domain with a 100 % conserved binding site. A proteome-wide screen revealed photoreceptor cilium actin regulator (PCARE) as a high-affinity ligand for ENAH EVH1. Here, we report the surprising observation that PCARE is ~100-fold specific for ENAH over paralogs VASP and EVL and can selectively bind ENAH and inhibit ENAH-dependent adhesion in cells. Specificity arises from a mechanism whereby PCARE stabilizes a conformation of the ENAH EVH1 domain that is inaccessible to family members VASP and EVL. Structure-based modeling rapidly identified seven residues distributed throughout EVL that are sufficient to differentiate binding by ENAH vs. EVL. By exploiting the ENAH-specific conformation, we rationally designed the tightest and most selective ENAH binder to date. Our work uncovers a conformational mechanism of interaction specificity that distinguishes highly similar paralogs and establishes tools for dissecting specific Ena/VASP functions in processes including cancer cell invasion.

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.

TYROSINE KINASES: COMPLEX MOLECULAR SYSTEMS CHALLENGING COMPUTATIONAL METHODOLOGIES

Classical molecular dynamics (MD) simulations based on atomic models play an increasingly important role in a wide range of applications in physics, biology, and chemistry. Nonetheless, generating genuine knowledge about biological systems using MD simulations remains challenging. Protein tyrosine kinases are important cellular signaling enzymes that regulate cell growth, proliferation, metabolism, differentiation, and migration. Due to the large conformational changes and long timescales involved in their function, these kinases present particularly challenging problems to modern computational and theoretical frameworks aimed at elucidating the dynamics of complex biomolecular systems. Markov state models have achieved limited success in tackling the broader conformational ensemble and biased methods are often employed to examine specific long timescale events. Recent advances in machine learning continue to push the limitations of current methodologies and provide notable improvements when integrated with the existing frameworks. A broad perspective is drawn from a critical review of recent studies.

Ensemble epistasis: thermodynamic origins of nonadditivity between mutations

Epistasis-when mutations combine nonadditively-is a profoundly important aspect of biology. It is often difficult to understand its mechanistic origins. Here, we show that epistasis can arise from the thermodynamic ensemble, or the set of interchanging conformations a protein adopts. Ensemble epistasis occurs because mutations can have different effects on different conformations of the same protein, leading to nonadditive effects on its average, observable properties. Using a simple analytical model, we found that ensemble epistasis arises when two conditions are met: (1) a protein populates at least three conformations and (2) mutations have differential effects on at least two conformations. To explore the relative magnitude of ensemble epistasis, we performed a virtual deep-mutational scan of the allosteric Ca2+ signaling protein S100A4. We found that 47% of mutation pairs exhibited ensemble epistasis with a magnitude on the order of thermal fluctuations. We observed many forms of epistasis: magnitude, sign, and reciprocal sign epistasis. The same mutation pair could even exhibit different forms of epistasis under different environmental conditions. The ubiquity of thermodynamic ensembles in biology and the pervasiveness of ensemble epistasis in our dataset suggests that it may be a common mechanism of epistasis in proteins and other macromolecules.

Computer Simulations of the Dissociation Mechanism of Gleevec from Abl Kinase with Milestoning

Gleevec (a.k.a., imatinib) is an important anticancer (e.g., chronic myeloid leukemia) chemotherapeutic drug due to its inhibitory interaction with the Abl kinase. Here, we use atomically detailed simulations within the Milestoning framework to study the molecular dissociation mechanism of Gleevec from Abl kinase. We compute the dissociation free energy profile, the mean first passage time for unbinding, and explore the transition state ensemble of conformations. The milestones form a multidimensional network with average connectivity of about 2.93, which is significantly higher than the connectivity for a one-dimensional reaction coordinate. The free energy barrier for Gleevec dissociation is estimated to be ∼10 kcal/mol, and the exit time is ∼55 ms. We examined the transition state conformations using both, the committor and transition function. We show that near the transition state the highly conserved salt bridge K217 and E286 is transiently broken. Together with the calculated free energy profile, these calculations can advance the understanding of the molecular interaction mechanisms between Gleevec and Abl kinase and play a role in future drug design and optimization studies.

Development of a TSR-Based Method for Protein 3-D Structural Comparison With Its Applications to Protein Classification and Motif Discovery

Development of protein 3-D structural comparison methods is important in understanding protein functions. At the same time, developing such a method is very challenging. In the last 40 years, ever since the development of the first automated structural method, ~200 papers were published using different representations of structures. The existing methods can be divided into five categories: sequence-, distance-, secondary structure-, geometry-based, and network-based structural comparisons. Each has its uniqueness, but also limitations. We have developed a novel method where the 3-D structure of a protein is modeled using the concept of Triangular Spatial Relationship (TSR), where triangles are constructed with the C_α atoms of a protein as vertices. Every triangle is represented using an integer, which we denote as “key,” A key is computed using the length, angle, and vertex labels based on a rule-based formula, which ensures assignment of the same key to identical TSRs across proteins. A structure is thereby represented by a vector of integers. Our method is able to accurately quantify similarity of structure or substructure by matching numbers of identical keys between two proteins. The uniqueness of our method includes: (i) a unique way to represent structures to avoid performing structural superimposition; (ii) use of triangles to represent substructures as it is the simplest primitive to capture shape; (iii) complex structure comparison is achieved by matching integers corresponding to multiple TSRs. Every substructure of one protein is compared to every other substructure in a different protein. The method is used in the studies of proteases and kinases because they play essential roles in cell signaling, and a majority of these constitute drug targets. The new motifs or substructures we identified specifically for proteases and kinases provide a deeper insight into their structural relations. Furthermore, the method provides a unique way to study protein conformational changes. In addition, the results from CATH and SCOP data sets clearly demonstrate that our method can distinguish alpha helices from beta pleated sheets and vice versa. Our method has the potential to be developed into a powerful tool for efficient structure-BLAST search and comparison, just as BLAST is for sequence search and alignment.

Selective targeting of the inactive state of hematopoietic cell kinase (Hck) with a stable curcumin derivative

Hck, a Src family nonreceptor tyrosine kinase (SFK), has recently been established as an attractive pharmacological target to improve pulmonary function in COVID-19 patients. Hck inhibitors are also well known for their regulatory role in various malignancies and autoimmune diseases. Curcumin has been previously identified as an excellent DYRK-2 inhibitor, but curcumin's fate is tainted by its instability in the cellular environment. Besides, small molecules targeting the inactive states of a kinase are desirable to reduce promiscuity. Here, we show that functionalization of the 4-arylidene position of the fluorescent curcumin scaffold with an aryl nitrogen mustard provides a stable Hck inhibitor (Kd = 50 ± 10 nM). The mustard curcumin derivative preferentially interacts with the inactive conformation of Hck, similar to type-II kinase inhibitors that are less promiscuous. Moreover, the lead compound showed no inhibitory effect on three other kinases (DYRK2, Src, and Abl). We demonstrate that the cytotoxicity may be mediated via inhibition of the SFK signaling pathway in triple-negative breast cancer and murine macrophage cells. Our data suggest that curcumin is a modifiable fluorescent scaffold to develop selective kinase inhibitors by remodeling its target affinity and cellular stability.

Is Structure-Based Drug Design Ready for Selectivity Optimization?

Alchemical free-energy calculations are now widely used to drive or maintain potency in small-molecule lead optimization with a roughly 1 kcal/mol accuracy. Despite this, the potential to use free-energy calculations to drive optimization of compound selectivity among two similar targets has been relatively unexplored in published studies. In the most optimistic scenario, the similarity of binding sites might lead to a fortuitous cancellation of errors and allow selectivity to be predicted more accurately than affinity. Here, we assess the accuracy with which selectivity can be predicted in the context of small-molecule kinase inhibitors, considering the very similar binding sites of human kinases CDK2 and CDK9 as well as another series of ligands attempting to achieve selectivity between the more distantly related kinases CDK2 and ERK2. Using a Bayesian analysis approach, we separate systematic from statistical errors and quantify the correlation in systematic errors between selectivity targets. We find that, in the CDK2/CDK9 case, a high correlation in systematic errors suggests that free-energy calculations can have significant impact in aiding chemists in achieving selectivity, while in more distantly related kinases (CDK2/ERK2), the correlation in systematic error suggests that fortuitous cancellation may even occur between systems that are not as closely related. In both cases, the correlation in systematic error suggests that longer simulations are beneficial to properly balance statistical error with systematic error to take full advantage of the increase in apparent free-energy calculation accuracy in selectivity prediction.

Impact of imatinib or dasatinib coadministration on in vitro preantral follicle development and oocyte acquisition in cyclophosphamide-treated mice

OBJECTIVE

We investigated the impact of tyrosine kinase inhibitor (imatinib or dasatinib) coadministration with cyclophosphamide (Cp) on preantral follicle development in an in vitro mouse model.

METHODS

Seventy-three female BDF1 mice were allocated into four experimental groups: group A, saline; group B, Cp (25 mg/kg); group C, Cp (25 mg/kg) and imatinib (7.5 mg/kg); and group D, Cp (25 mg/kg) and dasatinib (7.5 mg/kg). Preantral follicles were isolated and cultured in vitro up to 12 days. Final oocyte acquisition and spindle integrity of metaphase II (MII) oocytes were assessed. Levels of 17β-estradiol and anti-Müllerian hormone (AMH) in the final spent media were measured by enzyme-linked immunosorbent assays, and the mRNA levels of Star, Sod1, Mapk3, and Casp3 in the final follicular cells were quantified by real-time polymerase chain reaction.

RESULTS

The percentage of MII oocytes per initiated follicle, the proportion of MII oocytes with normal spindles, and the 17β-estradiol level were similar in all four groups. The median AMH level in group B (7.74 ng/mL) was significantly lower than that in group A (10.84 ng/mL). However, the median AMH levels in group C (9.96 ng/mL) and group D (9.71 ng/mL) were similar to that in group A. The mRNA expression levels of Star, Sod1, Mapk3, and Casp3 were similar in all four groups.

CONCLUSION

Coadministration of imatinib or dasatinib with Cp could preserve AMH production capacity in this in vitro mice preantral follicle culture model, and it did not affect MII oocyte acquisition.

Intermolecular Interactions in Crystal Structures of Imatinib-Containing Compounds

Imatinib, one of the most used therapeutic agents to treat leukemia, is an inhibitor that specifically blocks the activity of tyrosine kinases. The molecule of imatinib is flexible and contains several functional groups able to take part in H-bonding and hydrophobic interactions. Analysis of molecular conformations for this drug was carried out using density functional theory calculations of rotation potentials along single bonds and by analyzing crystal structures of imatinib-containing compounds taken from the Cambridge Structural Database and the Protein Data Bank. Rotation along the N-C bond in the region of the amide group was found to be the reason for two relatively stable molecular conformations, an extended and a folded one. The role of various types of intermolecular interactions in stabilization of the particular molecular conformation was studied in terms of (i) the likelihood of H-bond formation, and (ii) their contribution to the Voronoi molecular surface. It is shown that experimentally observed hydrogen bonds are in accord with the likelihood of their formation. The number of H-bonds in ligand-receptor complexes surpasses that in imatinib salts due to the large number of donors and acceptors of H-bonding within the binding pocket of tyrosine kinases. Contribution of hydrophilic intermolecular interactions to the Voronoi molecular surface is similar for both conformations, while π...π stacking is more typical for the folded conformation of imatinib.

Diversity of Long-Lived Intermediates along the Binding Pathway of Imatinib to Abl Kinase Revealed by MD Simulations

Imatinib, a drug used for the treatment of chronic myeloid leukemia and other cancers, works by blocking the catalytic site of pathological constitutively active Abl kinase. While the binding pose is known from X-ray crystallography, the different steps leading to the formation of the complex are not well understood. The results from extensive molecular dynamics simulations show that imatinib can primarily exit the known crystallographic binding pose through the cleft of the binding site or by sliding under the αC helix. Once displaced from the crystallographic binding pose, imatinib becomes trapped in intermediate states. These intermediates are characterized by a high diversity of ligand orientations and conformations, and relaxation timescales within this region may exceed 3-4 ms. Analysis indicates that the metastable intermediate states should be spectroscopically indistinguishable from the crystallographic binding pose, in agreement with tryptophan stopped-flow fluorescence experiments.

Three-Dimensional Interactions Analysis of the Anticancer Target c-Src Kinase with Its Inhibitors

Src family kinases (SFKs) constitute the biggest family of non-receptor tyrosine kinases considered as therapeutic targets for cancer therapy. An aberrant expression and/or activation of the proto-oncogene c-Src kinase, which is the oldest and most studied member of the family, has long been demonstrated to play a major role in the development, growth, progression and metastasis of numerous human cancers, including colon, breast, gastric, pancreatic, lung and brain carcinomas. For these reasons, the pharmacological inhibition of c-Src activity represents an effective anticancer strategy and a few compounds targeting c-Src, together with other kinases, have been approved as drugs for cancer therapy, while others are currently undergoing preclinical studies. Nevertheless, the development of potent and selective inhibitors of c-Src aimed at properly exploiting this biological target for the treatment of cancer still represents a growing field of study. In this review, the co-crystal structures of c-Src kinase in complex with inhibitors discovered in the past two decades have been described, highlighting the key ligand-protein interactions necessary to obtain high potency and the features to be exploited for addressing selectivity and drug resistance issues, thus providing useful information for the design of new and potent c-Src kinase inhibitors.

Cumulative mechanism of several major imatinib-resistant mutations in Abl kinase

Despite the outstanding success of the cancer drug imatinib, one obstacle in prolonged treatment is the emergence of resistance mutations within the kinase domain of its target, Abl. We noticed that many patient-resistance mutations occur in the dynamic hot spots recently identified to be responsible for imatinib's high selectivity toward Abl. In this study, we provide an experimental analysis of the mechanism underlying drug resistance for three major resistance mutations (G250E, Y253F, and F317L). Our data settle controversies, revealing unexpected resistance mechanisms. The mutations alter the energy landscape of Abl in complex ways: increased kinase activity, altered affinity, and cooperativity for the substrates, and, surprisingly, only a modestly decreased imatinib affinity. Only under cellular adenosine triphosphate (ATP) concentrations, these changes cumulate in an order of magnitude increase in imatinib's half-maximal inhibitory concentration (IC50). These results highlight the importance of characterizing energy landscapes of targets and its changes by drug binding and by resistance mutations developed by patients.

How ATP-Competitive Inhibitors Allosterically Modulate Tyrosine Kinases That Contain a Src-like Regulatory Architecture

Small molecule kinase inhibitors that stabilize distinct ATP binding site conformations can differentially modulate the global conformation of Src-family kinases (SFKs). However, it is unclear which specific ATP binding site contacts are responsible for modulating the global conformation of SFKs and whether these inhibitor-mediated allosteric effects generalize to other tyrosine kinases. Here, we describe the development of chemical probes that allow us to deconvolute which features in the ATP binding site are responsible for the allosteric modulation of the global conformation of Src. We find that the ability of an inhibitor to modulate the global conformation of Src's regulatory domain-catalytic domain module relies mainly on the influence it has on the conformation of a structural element called helix αC. Furthermore, by developing a set of orthogonal probes that target a drug-sensitized Src variant, we show that stabilizing Src's helix αC in an active conformation is sufficient to promote a Src-mediated, phosphotransferase-independent alteration in cell morphology. Finally, we report that ATP-competitive, conformation-selective inhibitors can influence the global conformation of tyrosine kinases beyond the SFKs, suggesting that the allosteric networks we observe in Src are conserved in kinases that have a similar regulatory architecture. Our study highlights that an ATP-competitive inhibitor's interactions with helix αC can have a major influence on the global conformation of some tyrosine kinases.

Ovarian damage from chemotherapy and current approaches to its protection

BACKGROUND

Anti-cancer therapy is often a cause of premature ovarian insufficiency and infertility since the ovarian follicle reserve is extremely sensitive to the effects of chemotherapy and radiotherapy. While oocyte, embryo and ovarian cortex cryopreservation can help some women with cancer-induced infertility achieve pregnancy, the development of effective methods to protect ovarian function during chemotherapy would be a significant advantage.

OBJECTIVE AND RATIONALE

This paper critically discusses the different damaging effects of the most common chemotherapeutic compounds on the ovary, in particular, the ovarian follicles and the molecular pathways that lead to that damage. The mechanisms through which fertility-protective agents might prevent chemotherapy drug-induced follicle loss are then reviewed.

SEARCH METHODS

Articles published in English were searched on PubMed up to March 2019 using the following terms: ovary, fertility preservation, chemotherapy, follicle death, adjuvant therapy, cyclophosphamide, cisplatin, doxorubicin. Inclusion and exclusion criteria were applied to the analysis of the protective agents.

OUTCOMES

Recent studies reveal how chemotherapeutic drugs can affect the different cellular components of the ovary, causing rapid depletion of the ovarian follicular reserve. The three most commonly used drugs, cyclophosphamide, cisplatin and doxorubicin, cause premature ovarian insufficiency by inducing death and/or accelerated activation of primordial follicles and increased atresia of growing follicles. They also cause an increase in damage to blood vessels and the stromal compartment and increment inflammation. In the past 20 years, many compounds have been investigated as potential protective agents to counteract these adverse effects. The interactions of recently described fertility-protective agents with these damage pathways are discussed.

WIDER IMPLICATIONS

Understanding the mechanisms underlying the action of chemotherapy compounds on the various components of the ovary is essential for the development of efficient and targeted pharmacological therapies that could protect and prolong female fertility. While there are increasing preclinical investigations of potential fertility preserving adjuvants, there remains a lack of approaches that are being developed and tested clinically.

Molecular Requirements for the Expression of Antiplatelet Effects by Synthetic Structural Optimized Analogues of the Anticancer Drugs Imatinib and Nilotinib

BACKGROUND

Platelets play important roles in cancer progression and metastasis, as well as in cancer-associated thrombosis (CAT). Tyrosine kinases are implicated in several intracellular signaling pathways involved in tumor biology, thus tyrosine kinase inhibitors (TKIs) represent an important class of anticancer drugs, based on the concept of targeted therapy.

PURPOSE

The objective of this study is the design and synthesis of analogues of the TKIs imatinib and nilotinib in order to develop tyrosine kinase inhibitors, by investigating their molecular requirements, which would express antiplatelet properties.

METHODS

Based on a recently described by us improved approach in the preparation of imatinib and/or nilotinib analogues, we designed and synthesized in five-step reaction sequences, 8 analogues of imatinib (I-IV), nilotinib (V, VI) and imatinib/nilotinib (VII, VIII). Their inhibitory effects on platelet aggregation and P-selectin membrane expression induced by arachidonic acid (AA), adenosine diphosphate (ADP) and thrombin receptor activating peptide-6 (TRAP-6), in vitro, were studied. Molecular docking studies and calculations were also performed.

RESULTS

The novel analogues V-VIII were well established with the aid of spectroscopic methods. Imatinib and nilotinib inhibited AA-induced platelet aggregation, exhibiting IC50 values of 13.30 μΜ and 3.91 μΜ, respectively. Analogues I and II exhibited an improved inhibitory activity compared with imatinib. Among the nilotinib analogues, V exhibited a 9-fold higher activity than nilotinib. All compounds were less efficient in inhibiting platelet aggregation towards ADP and TRAP-6. Similar results were obtained for the membrane expression of P-selectin. Molecular docking studies showed that the improved antiplatelet activity of nilotinib analogue V is primarily attributed to the number and the strength of hydrogen bonds.

CONCLUSION

Our results show that there is considerable potential to develop synthetic analogues of imatinib and nilotinib, as TKIs with antiplatelet properties and therefore being suitable to target cancer progression and metastasis, as well as CAT by inhibiting platelet activation.

Encounter complexes and hidden poses of kinase-inhibitor binding on the free-energy landscape

Modern drug discovery increasingly focuses on the drug-target binding kinetics which depend on drug (un)binding pathways. The conventional molecular dynamics simulation can observe only a few binding events even using the fastest supercomputer. Here, we develop 2D gREST/REUS simulation with enhanced flexibility of the ligand and the protein binding site. Simulation (43 μs in total) applied to an inhibitor binding to c-Src kinase covers 100 binding and unbinding events. On the statistically converged free-energy landscapes, we succeed in predicting the X-ray binding structure, including water positions. Furthermore, we characterize hidden semibound poses and transient encounter complexes on the free-energy landscapes. Regulatory residues distant from the catalytic core are responsible for the initial inhibitor uptake and regulation of subsequent bindings, which was unresolved by experiments. Stabilizing/blocking of either the semibound poses or the encounter complexes can be an effective strategy to optimize drug-target residence time.

Dynamic regulatory features of the protein tyrosine kinases

The SRC, Abelson murine leukemia viral oncogene homolog 1, TEC and C-terminal SRC Kinase families of non-receptor tyrosine kinases (collectively the Src module kinases) mediate an array of cellular signaling processes and are therapeutic targets in many disease states. Crystal structures of Src modules kinases provide valuable insights into the regulatory mechanisms that control activation and generate a framework from which drug discovery can advance. The conformational ensembles visited by these multidomain kinases in solution are also key features of the regulatory machinery controlling catalytic activity. Measurement of dynamic motions within kinases substantially augments information derived from crystal structures. In this review, we focus on a body of work that has transformed our understanding of non-receptor tyrosine kinase regulation from a static view to one that incorporates how fluctuations in conformational ensembles and dynamic motions influence activation status. Regulatory dynamic networks are often shared across and between kinase families while specific dynamic behavior distinguishes unique regulatory mechanisms for select kinases. Moreover, intrinsically dynamic regions of kinases likely play important regulatory roles that have only been partially explored. Since there is clear precedence that kinase inhibitors can exploit specific dynamic features, continued efforts to define conformational ensembles and dynamic allostery will be key to combating drug resistance and devising alternate treatments for kinase-associated diseases.

Integrated computational and Drosophila cancer model platform captures previously unappreciated chemicals perturbing a kinase network

Drosophila provides an inexpensive and quantitative platform for measuring whole animal drug response. A complementary approach is virtual screening, where chemical libraries can be efficiently screened against protein target(s). Here, we present a unique discovery platform integrating structure-based modeling with Drosophila biology and organic synthesis. We demonstrate this platform by developing chemicals targeting a Drosophila model of Medullary Thyroid Cancer (MTC) characterized by a transformation network activated by oncogenic dRet^M955T. Structural models for kinases relevant to MTC were generated for virtual screening to identify unique preliminary hits that suppressed dRet^M955T-induced transformation. We then combined features from our hits with those of known inhibitors to create a ‘hybrid’ molecule with improved suppression of dRet^M955T transformation. Our platform provides a framework to efficiently explore novel kinase inhibitors outside of explored inhibitor chemical space that are effective in inhibiting cancer networks while minimizing whole body toxicity.

Effective and safe treatment of multigenic diseases often involves drugs that address multiple points along disease networks, i.e., polypharmacology. Polypharmacology is increasingly appreciated as a potentially desirable property of kinase drugs. However, most known drugs that interact with multiple targets have been identified as such by chance and most polypharmacological compounds are not chemically unique, resembling structures of known kinase inhibitors. The fruit fly Drosophila provides an inexpensive, rapid, quantitative, whole animal screening platform that has the potential to complement computational approaches. We present a chemical genetics approach that efficiently combines Drosophila with structural prediction and virtual screening, creating a unique discovery platform. We demonstrate the utility of our approach by developing useful small molecules targeting a kinase network in a Drosophila model of Medullary Thyroid Cancer (MTC) driven by oncogenic dRet^M955T.

Electrostatic Complementarity as a Fast and Effective Tool to Optimize Binding and Selectivity of Protein-Ligand Complexes

Electrostatic interactions between small molecules and their respective receptors are essential for molecular recognition and are also key contributors to the binding free energy. Assessing the electrostatic match of protein-ligand complexes therefore provides important insights into why ligands bind and what can be changed to improve binding. Ideally, the ligand and protein electrostatic potentials at the protein-ligand interaction interface should maximize their complementarity while minimizing desolvation penalties. In this work, we present a fast and efficient tool to calculate and visualize the electrostatic complementarity (EC) of protein-ligand complexes. We compiled benchmark sets demonstrating electrostatically driven structure-activity relationships (SAR) from literature data, including kinase, protein-protein interaction, and GPCR targets, and used these to demonstrate that the EC method can visualize, rationalize, and predict electrostatically driven ligand affinity changes and help to predict compound selectivity. The methodology presented here for the analysis of EC is a powerful and versatile tool for drug design.

What Makes a Kinase Promiscuous for Inhibitors?

ATP-competitive kinase inhibitors often bind several kinases due to the high conservation of the ATP binding pocket. Through clustering analysis of a large kinome profiling dataset, we found a cluster of eight promiscuous kinases that on average bind more than five times more kinase inhibitors than the other 398 kinases in the dataset. To understand the structural basis of promiscuous inhibitor binding, we determined the co-crystal structure of the receptor tyrosine kinase DDR1 with the type I inhibitors dasatinib and VX-680. Surprisingly, we find that DDR1 binds these type I inhibitors in an inactive conformation typically reserved for type II inhibitors. Our computational and biochemical studies show that DDR1 is unusually stable in this inactive conformation, giving a mechanistic explanation for inhibitor promiscuity. This phenotypic clustering analysis provides a strategy to obtain functional insights not available by sequence comparison alone.

Understanding molecular mechanisms in cell signaling through natural and artificial sequence variation

The functionally tolerated sequence space of proteins can now be explored in an unprecedented way, owing to the expansion of genomic databases and the development of high-throughput methods to interrogate protein function. For signaling proteins, several recent studies have shown how the analysis of sequence variation leverages the available protein-structure information to provide new insights into specificity and allosteric regulation. In this Review, we discuss recent work that illustrates how this emerging approach is providing a deeper understanding of signaling proteins.

Synthesis, anticancer activity, toxicity evaluation and molecular docking studies of novel phenylaminopyrimidine-(thio)urea hybrids as potential kinase inhibitors

Thirty-two novel urea/thiourea compounds as potential kinase inhibitor were designed, synthesized and evaluated for their cytotoxic activity on breast (MCF7), colon (HCT116) and liver (Huh7) cancer cell lines. Compounds 10, 19 and 30 possessing anticancer activity with IC₅₀ values of 0.9, 0.8 and 1.6μM respectively on Huh7 cells were selected for further studies. These hit compounds were tested against liver carcinoma panel. Real time cell electronic sensing assay was used to evaluate the effects of the compounds 10, 19 and 30 on the growth pattern of liver cancer cells. Apoptotic cell death and cell cycle analysis upon treatment of liver carcinoma cells with hit compounds were determined. A significant apoptotic cell death was detected upon treatment of Huh7 and Mahlavu cells with compound 30 after 48 h of treatment. Additionally, compound 10 caused cell cycle arrest at G0/G1 phase. Mutagenicity of hit compounds was evaluated. Assertively, these compounds were not found to be mutagenic on Salmonella typhimurium strains TA98 and TA100. To understand the binding modes of the synthesized compounds, molecular docking studies were performed using the crystal data of VEGFR and Src-kinase enzymes in correlation with anticancer activities.

A Multiscale Simulation Approach to Modeling Drug-Protein Binding Kinetics

Drug-target binding kinetics has recently emerged as a sometimes critical determinant of in vivo efficacy and toxicity. Its rational optimization to improve potency or reduce side effects of drugs is, however, extremely difficult. Molecular simulations can play a crucial role in identifying features and properties of small ligands and their protein targets affecting the binding kinetics, but significant challenges include the long time scales involved in (un)binding events and the limited accuracy of empirical atomistic force fields (lacking, e.g., changes in electronic polarization). In an effort to overcome these hurdles, we propose a method that combines state-of-the-art enhanced sampling simulations and quantum mechanics/molecular mechanics (QM/MM) calculations at the BLYP/VDZ level to compute association free energy profiles and characterize the binding kinetics in terms of structure and dynamics of the transition state ensemble. We test our combined approach on the binding of the anticancer drug Imatinib to Src kinase, a well-characterized target for cancer therapy with a complex binding mechanism involving significant conformational changes. The results indicate significant changes in polarization along the binding pathways, which affect the predicted binding kinetics. This is likely to be of widespread importance in binding of ligands to protein targets.

Molecular dynamics simulations provide insights into the origin of gleevec's selectivity toward human tyrosine kinases

Protein kinases are critical drug targets against cancer. Since the discovery of Gleevec, a specific inhibitor of Abl kinase, the capability of this drug to distinguish between Abl and other tyrosine kinases, such as Src, has been intensely investigated but the origin of Gleevec's selectivity to Abl against Src is less studied. Here, we performed molecular dynamics (MD) simulations, dynamical cross-correlation matrices (DCCM), dynamical network analysis, and binding free energy calculations to explore Gleevec's selectivity based on the crystal structures of Abl, Src, and their common ancestors (ANC-AS) and the two constructed mutation systems (AS→Abl and AS→Src). MD simulations revealed that the conformation of the phosphate-binding loop (P-loop) was altered significantly in the AS→Abl system. DCCM results unraveled that mutations increased anticorrelated motions in the AS→Abl system. Community network analysis suggested that the P-loop established special contacts in the AS→Abl system that are devoid in the AS→Src system. The binding free energy calculations unveiled that the affinity of Gleevec to AS→Abl increased to near the Abl level, whereas its affinity to AS→Src decreased to near the Src level. Analysis of individual residue contributions showed that the differences were located mainly at the P-loop. This study is valuable for understanding the sensitivity of Gleevec to human tyrosine kinases. Communicated by Ramaswamy H. Sarma.

The Src module: an ancient scaffold in the evolution of cytoplasmic tyrosine kinases

Tyrosine kinases were first discovered as the protein products of viral oncogenes. We now know that this large family of metazoan enzymes includes nearly one hundred structurally diverse members. Tyrosine kinases are broadly classified into two groups: the transmembrane receptor tyrosine kinases, which sense extracellular stimuli, and the cytoplasmic tyrosine kinases, which contain modular ligand-binding domains and propagate intracellular signals. Several families of cytoplasmic tyrosine kinases have in common a core architecture, the "Src module," composed of a Src-homology 3 (SH3) domain, a Src-homology 2 (SH2) domain, and a kinase domain. Each of these families is defined by additional elaborations on this core architecture. Structural, functional, and evolutionary studies have revealed a unifying set of principles underlying the activity and regulation of tyrosine kinases built on the Src module. The discovery of these conserved properties has shaped our knowledge of the workings of protein kinases in general, and it has had important implications for our understanding of kinase dysregulation in disease and the development of effective kinase-targeted therapies.