Structure-based network analysis of activation mechanisms in the ErbB family of receptor tyrosine kinases: the regulatory spine residues are global mediators of structural stability and allosteric interactions

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.

Deciphering the conformational transitions of LIMK2 active and inactive states to ponder specific druggable states through microsecond scale molecular dynamics simulation

LIMK2 inhibitors are one of the potential therapeutic modalities for treating various diseases. In the current scenario, there is a paucity of effective LIMK inhibitors that are highly specific with minimal off-target effects. To date, the conformational transitions of LIMK2 from DFG_inαC_in (CIDI) (active) to DFG_outαC_out (CODO) (inactive) states are yet to be probed and are essential for capturing the unique, druggable conformations. Therefore, this study was intended to capture the diverse conformational states of LIMK2 for accelerating the rational identification of conformation specific inhibitors through high-end structural bioinformatics protocols. Hence, in this study, molecular modelling followed by an extensive microsecond timescale of molecular dynamics simulation was performed encompassing perturbation response scanning, metapath, and community analysis towards the conformational sampling of LIMK2. Overall this study precisely identifies the conformational ensemble of LIMK2 the intermediate inactive states namely, CIDO, CinterDinter, CIDinter, CinterDI, CinterDO, CODI, CODinter apart from CIDI and CODO. This also facilitated observing that β8 preceding XDFG, αC (F373, L374), and αD (L413) as the major effectors that may facilitate the regulation of varying conformational transitions among the states. Additionally, the conserved β sheets and the loops namely, C.l, b.l, and G/P.loop were observed to be involved in the metapath for allosteric communication among the intermediates with CIDI and CODO state. Moreover, only the CODO state was observed to have closed type A.l, while the CIDI and other intermediate states except for CIDO were observed to have open-DFG out type A.l, thereby enabling the binding of substrate. Apart from these, the druggable site analysis inferred that the CIDI and CODO states harbor prominent druggable sites spanning the conserved N-lobe, while the intermediates were observed to have unraveled allosteric druggable sites distal from the ATP binding site, majorly spanning the C-lobe of LIMK2. Thus, this study provides potential insights into the intermediate conformational druggable states of LIMK2 and also the druggable conformations, especially the inactive states of LIMK2, as a specific therapeutic targeting mode. Thus, providing a widened avenue to ponder the allosteric sites or the isoform selectivity conformations for targeting LIMK2 in various disease conditions.

In silico studies of interactions of peptide-conjugated cholesterol metabolites and betulinic acid with EGFR, LDR, and N-terminal fragment of CCKA receptors

In this work, we designed three new ligands by conjugating cholesterol metabolites 3-hydroxy-5-cholestenoic acid (3-HC) and 3-oxo-4-cholestenoic acid (3-OC) and the natural tri-terpenoid betulinic acid with the tumor-targeting peptide YHWYGYTPQNVI. Molecular interactions with the unconjugated peptide and the conjugates were examined with three receptors that are commonly overexpressed in pancreatic adenocarcinoma cells using ligand docking and molecular dynamics. This study demonstrated the utility of the designed conjugates as a valuable scaffold for potentially targeting EGFR and LDLR receptors. Our results indicate that the conjugates showed strong binding affinities and formation of stable complexes with EGFR, while the unconjugated peptide, BT-peptide conjugate, an 3-HC-peptide conjugate showed the formation of fairly stable complexes with LDLR receptor. For EGFR, two receptor kinase domains were explored. Interactions with the N-terminal domain of CCKA-R were relatively weaker. For LDLR, binding occurred in the beta-propeller region. For the N-terminal fragment of CCKA-R, the conjugates induced significant conformational changes in the receptor. The molecular dynamic simulations for 100 ns demonstrate that BT-peptide conjugates and the unconjugated peptide had the highest binding and formed the most stable complexes with EGFR. RMSD and trajectory analyses indicate that these molecules transit to a dynamically stable configuration in most cases within 60 ns. NMA analysis indicated that amongst the conjugates that showed relatively higher interactions with the respective receptors, the highest potential for deformability was seen for the N-terminal-47 amino acid region of the CCKA-R receptor with and the lowest for the LDLR-receptor. Thus, the newly designed compounds may be evaluated in the future toward developing drug delivery materials for targeting tumor cells overexpressing LDLR or EGFR.

A Review of HER4 (ErbB4) Kinase, Its Impact on Cancer, and Its Inhibitors

HER4 is a receptor tyrosine kinase that is required for the evolution of normal body systems such as cardiovascular, nervous, and endocrine systems, especially the mammary glands. It is activated through ligand binding and activates MAPKs and PI3K/AKT pathways. HER4 is commonly expressed in many human tissues, both adult and fetal. It is important to understand the role of HER4 in the treatment of many disorders. Many studies were also conducted on the role of HER4 in tumors and its tumor suppressor function. Mostly, overexpression of HER4 kinase results in cancer development. In the present article, we reviewed the structure, location, ligands, physiological functions of HER4, and its relationship to different cancer types. HER4 inhibitors reported mainly from 2016 to the present were reviewed as well.

A rotamer relay information system in the epidermal growth factor receptor-drug complexes reveals clues to new paradigm in protein conformational change

Cancer cells can escape the effects of chemotherapy through mutations and upregulation of a tyrosine kinase protein called the epidermal growth factor receptor (EGFR). In the past two decades, four generations of tyrosine kinase inhibitors targeting EGFR have been developed. Using comparative structure analysis of 116 EGFR-drug complex crystal structures, cluster analysis produces two clans of 73 and 43 structures, respectively. The first clan of 73 structures is larger and is comprised mostly of the C-helix-IN conformation while the second clan of 43 structures correlates with the C-helix-OUT conformation. A deep rotamer analysis identifies 43 residues (18%) of the total of 237 residues spanning the kinase structures under investigation with significant rotamer variations between the C-helix-IN and C-helix-OUT clans. The locations of these rotamer variations take on the appearance of side chain conformational relays extending out from points of EGFR mutation to different regions of the EGFR kinase. Accordingly, we propose that key EGFR mutations act singly or together to induce drug resistant conformational changes in EGFR that are communicated via these side chain conformational relays. Accordingly, these side chain conformational relays appear to play a significant role in the development of tumour resistance. This phenomenon also suggests a new paradigm in protein conformational change that is mediated by supportive relays of rotamers on the protein surface, rather than through conventional backbone movements.

Sequence Characterization and Molecular Modeling of Clinically Relevant Variants of the SARS-CoV-2 Main Protease

The SARS-CoV-2 main protease (Mpro) is essential to viral replication and cleaves highly specific substrate sequences, making it an obvious target for inhibitor design. However, as for any virus, SARS-CoV-2 is subject to constant neutral drift and selection pressure, with new Mpro mutations arising over time. Identification and structural characterization of Mpro variants is thus critical for robust inhibitor design. Here we report sequence analysis, structure predictions, and molecular modeling for seventy-nine Mpro variants, constituting all clinically observed mutations in this protein as of April 29, 2020. Residue substitution is widely distributed, with some tendency toward larger and more hydrophobic residues. Modeling and protein structure network analysis suggest differences in cohesion and active site flexibility, revealing patterns in viral evolution that have relevance for drug discovery.

Allosteric Regulation at the Crossroads of New Technologies: Multiscale Modeling, Networks, and Machine Learning

Allosteric regulation is a common mechanism employed by complex biomolecular systems for regulation of activity and adaptability in the cellular environment, serving as an effective molecular tool for cellular communication. As an intrinsic but elusive property, allostery is a ubiquitous phenomenon where binding or disturbing of a distal site in a protein can functionally control its activity and is considered as the "second secret of life." The fundamental biological importance and complexity of these processes require a multi-faceted platform of synergistically integrated approaches for prediction and characterization of allosteric functional states, atomistic reconstruction of allosteric regulatory mechanisms and discovery of allosteric modulators. The unifying theme and overarching goal of allosteric regulation studies in recent years have been integration between emerging experiment and computational approaches and technologies to advance quantitative characterization of allosteric mechanisms in proteins. Despite significant advances, the quantitative characterization and reliable prediction of functional allosteric states, interactions, and mechanisms continue to present highly challenging problems in the field. In this review, we discuss simulation-based multiscale approaches, experiment-informed Markovian models, and network modeling of allostery and information-theoretical approaches that can describe the thermodynamics and hierarchy allosteric states and the molecular basis of allosteric mechanisms. The wealth of structural and functional information along with diversity and complexity of allosteric mechanisms in therapeutically important protein families have provided a well-suited platform for development of data-driven research strategies. Data-centric integration of chemistry, biology and computer science using artificial intelligence technologies has gained a significant momentum and at the forefront of many cross-disciplinary efforts. We discuss new developments in the machine learning field and the emergence of deep learning and deep reinforcement learning applications in modeling of molecular mechanisms and allosteric proteins. The experiment-guided integrated approaches empowered by recent advances in multiscale modeling, network science, and machine learning can lead to more reliable prediction of allosteric regulatory mechanisms and discovery of allosteric modulators for therapeutically important protein targets.

Data-driven computational analysis of allosteric proteins by exploring protein dynamics, residue coevolution and residue interaction networks

BACKGROUND

Computational studies of allosteric interactions have witnessed a recent renaissance fueled by the growing interest in modeling of the complex molecular assemblies and biological networks. Allosteric interactions in protein structures allow for molecular communication in signal transduction networks.

METHODS

In this work, we performed a large scale comprehensive and multi-faceted analysis of >300 diverse allosteric proteins and complexes with allosteric modulators. By modeling and exploring coarse-grained dynamics, residue coevolution, and residue interaction networks for allosteric proteins, we have determined unifying molecular signatures shared by allosteric systems.

RESULTS

The results of this study have suggested that allosteric inhibitors and allosteric activators may differentially affect global dynamics and network organization of protein systems, leading to diverse allosteric mechanisms. By using structural and functional data on protein kinases, we present a detailed case study that that included atomic-level analysis of coevolutionary networks in kinases bound with allosteric inhibitors and activators.

CONCLUSIONS

We have found that coevolutionary networks can form direct communication pathways connecting functional regions and can recapitulate key regulatory sites and interactions responsible for allosteric signaling in the studied protein systems. The results of this computational investigation are compared with the experimental studies and reveal molecular signatures of known regulatory hotspots in protein kinases.

GENERAL SIGNIFICANCE

This study has shown that allosteric inhibitors and allosteric activators can have a different effect on residue interaction networks and can exploit distinct regulatory mechanisms, which could open up opportunities for probing allostery and new drug combinations with broad range of activities.

Integration of Random Forest Classifiers and Deep Convolutional Neural Networks for Classification and Biomolecular Modeling of Cancer Driver Mutations

Development of machine learning solutions for prediction of functional and clinical significance of cancer driver genes and mutations are paramount in modern biomedical research and have gained a significant momentum in a recent decade. In this work, we integrate different machine learning approaches, including tree based methods, random forest and gradient boosted tree (GBT) classifiers along with deep convolutional neural networks (CNN) for prediction of cancer driver mutations in the genomic datasets. The feasibility of CNN in using raw nucleotide sequences for classification of cancer driver mutations was initially explored by employing label encoding, one hot encoding, and embedding to preprocess the DNA information. These classifiers were benchmarked against their tree-based alternatives in order to evaluate the performance on a relative scale. We then integrated DNA-based scores generated by CNN with various categories of conservational, evolutionary and functional features into a generalized random forest classifier. The results of this study have demonstrated that CNN can learn high level features from genomic information that are complementary to the ensemble-based predictors often employed for classification of cancer mutations. By combining deep learning-generated score with only two main ensemble-based functional features, we can achieve a superior performance of various machine learning classifiers. Our findings have also suggested that synergy of nucleotide-based deep learning scores and integrated metrics derived from protein sequence conservation scores can allow for robust classification of cancer driver mutations with a limited number of highly informative features. Machine learning predictions are leveraged in molecular simulations, protein stability, and network-based analysis of cancer mutations in the protein kinase genes to obtain insights about molecular signatures of driver mutations and enhance the interpretability of cancer-specific classification models.

Atomistic Modeling of the ABL Kinase Regulation by Allosteric Modulators Using Structural Perturbation Analysis and Community-Based Network Reconstruction of Allosteric Communications

In this work, we have examined the molecular mechanisms of allosteric regulation of the ABL tyrosine kinase at the atomic level. Atomistic modeling of the ABL complexes with a panel of allosteric modulators has been performed using a combination of molecular dynamics simulations, structural residue perturbation scanning, and a novel community analysis of the residue interaction networks. Our results have indicated that allosteric inhibitors and activators may exert a differential control on allosteric signaling between the kinase binding sites and functional regions. While the inhibitor binding can strengthen the closed ABL state and induce allosteric communications directed from the allosteric pocket to the ATP binding site, the DPH activator may induce a more dynamic open form and activate allosteric couplings between the ATP and substrate binding sites. By leveraging a network-centric theoretical framework, we have introduced a novel community analysis method and global topological parameters that have unveiled the hierarchical modularity and the intercommunity bridging sites in the residue interaction network. We have found that allosteric functional hotspots responsible for the kinase regulation may serve the intermodular bridges in the global interaction network. The central conclusion from this analysis is that the regulatory switch centers play a fundamental role in the modular network organization of ABL as the unique intercommunity bridges that connect the SH2 and SH3 domains with the catalytic core into a functional kinase assembly. The hierarchy of network organization in the ABL regulatory complexes may allow for the synergistic action of dense intercommunity links required for the robust signal transfer in the catalytic core and sparse network bridges acting as the regulatory control points that orchestrate allosteric transitions between the inhibited and active kinase forms.

Structural Insights into Characterizing Binding Sites in Epidermal Growth Factor Receptor Kinase Mutants

Over the last two decades epidermal growth factor receptor (EGFR) kinase has become an important target to treat nonsmall cell lung cancer (NSCLC). Currently, three generations of EGFR kinase-targeted small molecule drugs have been FDA approved. They nominally produce a response at the start of treatment and lead to a substantial survival benefit for patients. However, long-term treatment results in acquired drug resistance and further vulnerability to NSCLC. Therefore, novel EGFR kinase inhibitors that specially overcome acquired mutations are urgently needed. To this end, we carried out a comprehensive study of different EGFR kinase mutants using a structural systems pharmacology strategy. Our analysis shows that both wild-type and mutated structures exhibit multiple conformational states that have not been observed in solved crystal structures. We show that this conformational flexibility accommodates diverse types of ligands with multiple types of binding modes. These results provide insights for designing a new generation of EGFR kinase inhibitor that combats acquired drug-resistant mutations through a multiconformation-based drug design strategy.

Dynamics fingerprints of active conformers of epidermal growth factor receptor kinase

Epidermal growth factor receptor (EGFR) is a prototypical cell-surface receptor that plays a key role in the regulation of cellular signaling, proliferation and differentiation. Mutations of its kinase domain have been associated with the development of a variety of cancers and, therefore, it has been the target of drug design. Single amino acid substitutions (SASs) in this domain have been proven to alter the equilibrium of pre-existing conformer populations. Despite the advances in structural descriptions of its so-called active and inactive conformations, the associated dynamics aspects that characterize them have not been thoroughly studied yet. As the dynamic behaviors and molecular motions of proteins are important for a complete understanding of their structure-function relationships we present a novel procedure, using (or based on) normal mode analysis, to identify the collective dynamics shared among different conformers in EGFR kinase. The method allows the comparison of patterns of low-frequency vibrational modes defining representative directions of motions. Our procedure is able to emphasize the main similarities and differences between the collective dynamics of different conformers. In the case of EGFR kinase, two representative directions of motions have been found as dynamics fingerprints of the active conformers. Protein motion along both directions reveals to have a significant impact on the cavity volume of the main pocket of the active site. Otherwise, the inactive conformers exhibit a more heterogeneous distribution of collective motions. © 2018 Wiley Periodicals, Inc.

"Infostery" analysis of short molecular dynamics simulations identifies highly sensitive residues and predicts deleterious mutations

Characterizing a protein mutational landscape is a very challenging problem in Biology. Many disease-associated mutations do not seem to produce any effect on the global shape nor motions of the protein. Here, we use relatively short all-atom biomolecular simulations to predict mutational outcomes and we quantitatively assess the predictions on several hundreds of mutants. We perform simulations of the wild type and 175 mutants of PSD95’s third PDZ domain in complex with its cognate ligand. By recording residue displacements correlations and interactions, we identify “communication pathways” and quantify them to predict the severity of the mutations. Moreover, we show that by exploiting simulations of the wild type, one can detect 80% of the positions highly sensitive to mutations with a precision of 89%. Importantly, our analysis describes the role of these positions in the inter-residue communication and dynamical architecture of the complex. We assess our approach on three different systems using data from deep mutational scanning experiments and high-throughput exome sequencing. We refer to our analysis as “infostery”, from “info” - information - and “steric” - arrangement of residues in space. We provide a fully automated tool, COMMA2 (www.lcqb.upmc.fr/COMMA2), that can be used to guide medicinal research by selecting important positions/mutations.

Pockets as structural descriptors of EGFR kinase conformations

Epidermal Growth Factor Receptor (EGFR), a tyrosine kinase receptor, is one of the main tumor markers in different types of cancers. The kinase native state is mainly composed of two populations of conformers: active and inactive. Several sequence variations in EGFR kinase region promote the differential enrichment of conformers with higher activity. Some structural characteristics have been proposed to differentiate kinase conformations, but these considerations could lead to ambiguous classifications. We present a structural characterisation of EGFR kinase conformers, focused on active site pocket comparisons, and the mapping of known pathological sequence variations. A structural based clustering of this pocket accurately discriminates active from inactive, well-characterised conformations. Furthermore, this main pocket contains, or is in close contact with, ≈65% of cancer-related variation positions. Although the relevance of protein dynamics to explain biological function has been extensively recognised, the usage of the ensemble of conformations in dynamic equilibrium to represent the functional state of proteins and the importance of pockets, cavities and/or tunnels was often neglected in previous studies. These functional structures and the equilibrium between them could be structurally analysed in wild type as well as in sequence variants. Our results indicate that biologically important pockets, as well as their shape and dynamics, are central to understanding protein function in wild-type, polymorphic or disease-related variations.

P2X7 receptor antagonism: Implications in diabetic retinopathy

Diabetic retinopathy (DR) is the most frequent complication of diabetes and one of leading causes of blindness worldwide. Early phases of DR are characterized by retinal pericyte loss mainly related to concurrent inflammatory process. Recently, an important link between P2X7 receptor (P2X7R) and inflammation has been demonstrated indicating this receptor as potential pharmacological target in DR. Here we first carried out an in silico molecular modeling study in order to characterize the allosteric pocket in P2X7R, and identify a suitable P2X7R antagonist through molecular docking. JNJ47965567 was identified as the hit compound in docking calculations, as well as for its absorption, distribution, metabolism and excretion (ADME) profile. As an in vitro model of early diabetic retinopathy, human retinal pericytes were exposed to high glucose (25mM, 48h) that caused a significant (p<0.05) release of IL-1β and LDH. The block of P2X7R by JNJ47965567 significantly (p<0.05) reverted the damage elicited by high glucose, detected as IL-1β and LDH release. Overall, our findings suggest that the P2X7R represents an attractive pharmacological target to manage the early phase of diabetic retinopathy, and the compound JNJ47965567 is a good template to discover other P2X7R selective antagonists.

Localisation Microscopy of Breast Epithelial ErbB-2 Receptors and Gap Junctions: Trafficking after γ-Irradiation, Neuregulin-1β, and Trastuzumab Application

In cancer, vulnerable breast epithelium malignance tendency correlates with number and activation of ErbB receptor tyrosine kinases. In the presented work, we observe ErbB receptors activated by irradiation-induced DNA injury or neuregulin-1β application, or alternatively, attenuated by a therapeutic antibody using high resolution fluorescence localization microscopy. The gap junction turnover coinciding with ErbB receptor activation and co-transport is simultaneously recorded. DNA injury caused by 4 Gray of 6 MeV photon γ-irradiation or alternatively neuregulin-1β application mobilized ErbB receptors in a nucleograde fashion—a process attenuated by trastuzumab antibody application. This was accompanied by increased receptor density, indicating packing into transport units. Factors mobilizing ErbB receptors also mobilized plasma membrane resident gap junction channels. The time course of ErbB receptor activation and gap junction mobilization recapitulates the time course of non-homologous end-joining DNA repair. We explain our findings under terms of DNA injury-induced membrane receptor tyrosine kinase activation and retrograde trafficking. In addition, we interpret the phenomenon of retrograde co-trafficking of gap junction connexons stimulated by ErbB receptor activation.

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

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

Design, Synthesis, and Evaluation of Dasatinib-Amino Acid and Dasatinib-Fatty Acid Conjugates as Protein Tyrosine Kinase Inhibitors

Derivatives of the tyrosine kinase inhibitor dasatinib were synthesized by esterification with 25 carboxylic acids, including amino acids and fatty acids, thereby extending the drug to interact with more diverse sites and to improve specificity. The dasatinib-l-arginine derivative (Das-R, 7) was found to be the most potent of the inhibitors tested, with IC50 values of 4.4, <0.25, and <0.45 nm against Csk, Src, and Abl kinases, respectively. The highest selectivity ratio obtained in our study, 91.4 Csk/Src, belonged to compound 18 (Das-C10 ) with an IC50 value of 3.2 μm for Csk compared with 35 nm for Src. Furthermore, many compounds displayed increased selectivity toward Src over Abl. Compounds 15 (Das-glutamic acid) and 13 (Das-cysteine) demonstrated the largest gains (10.2 and 10.3 Abl/Src IC50 ratios). Das-R (IC50 =2.06 μm) was significantly more potent than the parent dasatinib (IC50 =26.3 μm) against Panc-1 cells, whereas both compounds showed IC50 <51.2 pm against BV-173 and K562 cells. Molecular modeling and binding free energy simulations revealed good agreements with the experimental results and rationalized the differences in selectivity among the studied compounds. Integration of experimental and computational approaches in the design and biochemical screening of dasatinib derivatives facilitated rational engineering and diversification of the dasatinib scaffold, providing useful insight into mechanisms of kinase selectivity.

Network-based modelling and percolation analysis of conformational dynamics and activation in the CDK2 and CDK4 proteins: dynamic and energetic polarization of the kinase lobes may determine divergence of the regulatory mechanisms

Network modeling and percolation analysis of conformational dynamics and energetics of regulatory mechanisms in cyclin-dependent kinases.

Exploring Molecular Mechanisms of Paradoxical Activation in the BRAF Kinase Dimers: Atomistic Simulations of Conformational Dynamics and Modeling of Allosteric Communication Networks and Signaling Pathways

The recent studies have revealed that most BRAF inhibitors can paradoxically induce kinase activation by promoting dimerization and enzyme transactivation. Despite rapidly growing number of structural and functional studies about the BRAF dimer complexes, the molecular basis of paradoxical activation phenomenon is poorly understood and remains largely hypothetical. In this work, we have explored the relationships between inhibitor binding, protein dynamics and allosteric signaling in the BRAF dimers using a network-centric approach. Using this theoretical framework, we have combined molecular dynamics simulations with coevolutionary analysis and modeling of the residue interaction networks to determine molecular determinants of paradoxical activation. We have investigated functional effects produced by paradox inducer inhibitors PLX4720, Dabrafenib, Vemurafenib and a paradox breaker inhibitor PLX7904. Functional dynamics and binding free energy analyses of the BRAF dimer complexes have suggested that negative cooperativity effect and dimer-promoting potential of the inhibitors could be important drivers of paradoxical activation. We have introduced a protein structure network model in which coevolutionary residue dependencies and dynamic maps of residue correlations are integrated in the construction and analysis of the residue interaction networks. The results have shown that coevolutionary residues in the BRAF structures could assemble into independent structural modules and form a global interaction network that may promote dimerization. We have also found that BRAF inhibitors could modulate centrality and communication propensities of global mediating centers in the residue interaction networks. By simulating allosteric communication pathways in the BRAF structures, we have determined that paradox inducer and breaker inhibitors may activate specific signaling routes that correlate with the extent of paradoxical activation. While paradox inducer inhibitors may facilitate a rapid and efficient communication via an optimal single pathway, the paradox breaker may induce a broader ensemble of suboptimal and less efficient communication routes. The central finding of our study is that paradox breaker PLX7904 could mimic structural, dynamic and network features of the inactive BRAF-WT monomer that may be required for evading paradoxical activation. The results of this study rationalize the existing structure-functional experiments by offering a network-centric rationale of the paradoxical activation phenomenon. We argue that BRAF inhibitors that amplify dynamic features of the inactive BRAF-WT monomer and intervene with the allosteric interaction networks may serve as effective paradox breakers in cellular environment.

Dynamic Allostery Mediated by a Conserved Tryptophan in the Tec Family Kinases

Bruton’s tyrosine kinase (Btk) is a Tec family non-receptor tyrosine kinase that plays a critical role in immune signaling and is associated with the immunological disorder X-linked agammaglobulinemia (XLA). Our previous findings showed that the Tec kinases are allosterically activated by the adjacent N-terminal linker. A single tryptophan residue in the N-terminal 17-residue linker mediates allosteric activation, and its mutation to alanine leads to the complete loss of activity. Guided by hydrogen/deuterium exchange mass spectrometry results, we have employed Molecular Dynamics simulations, Principal Component Analysis, Community Analysis and measures of node centrality to understand the details of how a single tryptophan mediates allostery in Btk. A specific tryptophan side chain rotamer promotes the functional dynamic allostery by inducing coordinated motions that spread across the kinase domain. Either a shift in the rotamer population, or a loss of the tryptophan side chain by mutation, drastically changes the coordinated motions and dynamically isolates catalytically important regions of the kinase domain. This work also identifies a new set of residues in the Btk kinase domain with high node centrality values indicating their importance in transmission of dynamics essential for kinase activation. Structurally, these node residues appear in both lobes of the kinase domain. In the N-lobe, high centrality residues wrap around the ATP binding pocket connecting previously described Catalytic-spine residues. In the C-lobe, two high centrality node residues connect the base of the R- and C-spines on the αF-helix. We suggest that the bridging residues that connect the catalytic and regulatory architecture within the kinase domain may be a crucial element in transmitting information about regulatory spine assembly to the catalytic machinery of the catalytic spine and active site.

Bruton’s tyrosine kinase (Btk) belongs to the Tec family of protein tyrosine kinases, and plays a crucial role in the signaling pathway in B-cells. Alteration of Btk activity results in the serious immunological disorder, X-linked agammaglobulinemia. Btk is a multi-domain protein and the activity of the kinase domain is regulated by the adjacent non-catalytic domains, which mediate their effect by means of a conserved tryptophan residue. In this work, we have investigated the mechanism of regulation by this tryptophan residue, W395, in the linker preceding the Btk kinase domain. Using hydrogen-deuterium exchange mass spectrometry and molecular dynamics simulations we identify structural elements within the kinase domain that are required for function by transmitting the allosteric effects of W395. Molecular Dynamics simulations further guided us to delineate the kinase domain into dynamically correlated sets of residues using community analysis, thereby identifying the important communication nodes that connect the various elements of the kinase domain required for function. The analyses performed indicate clearly how the W395A mutant changes the communication pathway required for function.

Dynamics-Driven Allostery in Protein Kinases

Protein kinases have very dynamic structures and their functionality strongly depends on their dynamic state. Active kinases reveal a dynamic pattern with residues clustering into semirigid communities that move in μs-ms timescale. Previously detected hydrophobic spines serve as connectors between communities. Communities do not follow the traditional subdomain structure of the kinase core or its secondary structure elements. Instead they are organized around main functional units. Integration of the communities depends on the assembly of the hydrophobic spine and phosphorylation of the activation loop. Single mutations can significantly disrupt the dynamic infrastructure and thereby interfere with long-distance allosteric signaling that propagates throughout the whole molecule. Dynamics is proposed to be the underlying mechanism for allosteric regulation in protein kinases.

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

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

Molecular Determinants Underlying Binding Specificities of the ABL Kinase Inhibitors: Combining Alanine Scanning of Binding Hot Spots with Network Analysis of Residue Interactions and Coevolution

Quantifying binding specificity and drug resistance of protein kinase inhibitors is of fundamental importance and remains highly challenging due to complex interplay of structural and thermodynamic factors. In this work, molecular simulations and computational alanine scanning are combined with the network-based approaches to characterize molecular determinants underlying binding specificities of the ABL kinase inhibitors. The proposed theoretical framework unveiled a relationship between ligand binding and inhibitor-mediated changes in the residue interaction networks. By using topological parameters, we have described the organization of the residue interaction networks and networks of coevolving residues in the ABL kinase structures. This analysis has shown that functionally critical regulatory residues can simultaneously embody strong coevolutionary signal and high network centrality with a propensity to be energetic hot spots for drug binding. We have found that selective (Nilotinib) and promiscuous (Bosutinib, Dasatinib) kinase inhibitors can use their energetic hot spots to differentially modulate stability of the residue interaction networks, thus inhibiting or promoting conformational equilibrium between inactive and active states. According to our results, Nilotinib binding may induce a significant network-bridging effect and enhance centrality of the hot spot residues that stabilize structural environment favored by the specific kinase form. In contrast, Bosutinib and Dasatinib can incur modest changes in the residue interaction network in which ligand binding is primarily coupled only with the identity of the gate-keeper residue. These factors may promote structural adaptability of the active kinase states in binding with these promiscuous inhibitors. Our results have related ligand-induced changes in the residue interaction networks with drug resistance effects, showing that network robustness may be compromised by targeted mutations of key mediating residues. This study has outlined mechanisms by which inhibitor binding could modulate resilience and efficiency of allosteric interactions in the kinase structures, while preserving structural topology required for catalytic activity and regulation.

Small-world networks of residue interactions in the Abl kinase complexes with cancer drugs: topology of allosteric communication pathways can determine drug resistance effects

Computational modelling of efficiency and robustness of the residue interaction networks and allosteric pathways in kinase structures can characterize protein kinase sensitivity to drug binding and drug resistance effects.