Transitions to catalytically inactive conformations in EGFR kinase

Probing conformational dynamics of EGFR mutants via SEIRA spectroscopy: potential implications for tyrosine kinase inhibitor design

Missense mutations in EGFR's catalytic domain alter its function, promoting cancer. SEIRA spectroscopy, supported by MD simulations, reveals structural differences in the compactness and hydration of helical motifs between active and inactive EGFR conformations models. These findings provide novel insights into the biophysical mechanisms driving EGFR activation and drug resistance, offering a robust method for studying emerging EGFR mutations and their structural impacts on TKIs' efficacy.

Evolutionary sequence and structural basis for the distinct conformational landscapes of Tyr and Ser/Thr kinases

Protein kinases are molecular machines with rich sequence variation that distinguishes the two main evolutionary branches - tyrosine kinases (TKs) from serine/threonine kinases (STKs). Using a sequence co-variation Potts statistical energy model we previously concluded that TK catalytic domains are more likely than STKs to adopt an inactive conformation with the activation loop in an autoinhibitory folded conformation, due to intrinsic sequence effects. Here we investigate the structural basis for this phenomenon by integrating the sequence-based model with structure-based molecular dynamics (MD) to determine the effects of mutations on the free energy difference between active and inactive conformations, using a thermodynamic cycle involving many (n = 108) protein-mutation free energy perturbation (FEP) simulations in the active and inactive conformations. The sequence and structure-based results are consistent and support the hypothesis that the inactive conformation DFG-out Activation Loop Folded, is a functional regulatory state that has been stabilized in TKs relative to STKs over the course of their evolution via the accumulation of residue substitutions in the activation loop and catalytic loop that facilitate distinct substrate binding modes in trans and additional modes of regulation in cis for TKs.

Computational Analysis of Activation of Dimerized Epidermal Growth Factor Receptor Kinase Using the String Method and Markov State Model

Epidermal growth factor receptor (EGFR) activation is accompanied by dimerization. During the activation of the intracellular kinase domain, two EGFR kinases form an asymmetric dimer, and one side of the dimer (receiver) is activated. Using the string method and Markov state model (MSM), we performed a computational analysis of the structural changes in the activation of the EGFR dimer in this study. The string method reveals the minimum free-energy pathway (MFEP) from the inactive to active structure. The MSM was constructed from numerous trajectories of molecular dynamics simulations around the MFEP, which revealed the free-energy map of structural changes. In the activation of the receiver kinase, the unfolding of the activation loop (A-loop) is followed by the rearrangement of the C-helix, as observed in other kinases. However, unlike other kinases, the free-energy map of EGFR at the asymmetric dimer showed that the active state yielded the highest stability and revealed how interactions at the dimer interface induced receiver activation. As the H-helix of the activator approaches the C-helix of the receiver during activation, the A-loop unfolds. Subsequently, L782 of the receiver enters the pocket between the G- and H-helices of the activator, leading to a rearrangement of the hydrophobic residues around L782 of the receiver, which constitutes a structural rearrangement of the C-helix of the receiver from an outward to an inner position. The MSM analysis revealed long-time scale trajectories via kinetic Monte Carlo.

Influence of different pH milieu on the structure and function of human Aurora kinase B protein (AURK-B): Amalgamation of both spectroscopic and computational approach

Aurora kinase B (AURK-B) is a serine/threonine kinase protein that plays an essential role in chromosomal separation during the cell cycle event. AURK-B is highly expressed in various types of cancer such as human seminoma, thyroid carcinoma, non-small cell lung carcinoma (NSCLC), oral carcinoma, and gastric cancer. Hence, it is a potential therapeutic target in the treatment of various cancers. The structure of AURK-B in complex with one of its substrate inner centromeric protein (INCENP) is present, but the structural and functional characterization of native AURK-B at different pH environment is still unexplored.This study determines the effect of different pH milieu on the structure and function of AURK-B protein wherein the influence of pH on the protein conformation was probed using Circular dichroism (CD) and fluorescence spectroscopy. The structural studies were further combined with functional activity assay to observe the change in kinase activity at various pH milieu (2.0-11.0). CD and fluorescence spectroscopy experiments dictate that at high acidic conditions (pH 2.0 - 5.0), the secondary and tertiary structures of AURK-B become distorted, leading to diminished activity. The protein, however, was observed to stabilize towards pH 7.0 - 8.0 with minimal structure alteration over the basic pH range (pH 9.0 -11.0). The measured spectroscopic structural features were found to be in-line with obtained experimental kinase activity assays. Further, in-vitro experiments indicate that the enzyme is maximally active at pH 8.0. More ordered conformation and compact structure was observed at this pH (pH 8.0) as compared to other pH values through molecular dynamics simulation studies (MDS). As AURK-B localizes itself in the intracellular compartment, this study may provide a clue about the role of different pH environments in enhancing cancer growth, proliferation, and invasion.

Evolutionary sequence and structural basis for the distinct conformational landscapes of Tyr and Ser/Thr kinases

Protein kinases are molecular machines with rich sequence variation that distinguishes the two main evolutionary branches - tyrosine kinases (TKs) from serine/threonine kinases (STKs). Using a sequence co-variation Potts statistical energy model we previously concluded that TK catalytic domains are more likely than STKs to adopt an inactive conformation with the activation loop in an autoinhibitory "folded" conformation, due to intrinsic sequence effects. Here we investigated the structural basis for this phenomenon by integrating the sequence-based model with structure-based molecular dynamics (MD) to determine the effects of mutations on the free energy difference between active and inactive conformations, using a novel thermodynamic cycle involving many (n=108) protein-mutation free energy perturbation (FEP) simulations in the active and inactive conformations. The sequence and structure-based results are consistent and support the hypothesis that the inactive conformation "DFG-out Activation Loop Folded", is a functional regulatory state that has been stabilized in TKs relative to STKs over the course of their evolution via the accumulation of residue substitutions in the activation loop and catalytic loop that facilitate distinct substrate binding modes in trans and additional modes of regulation in cis for TKs.

Evolutionary sequence and structural basis for the distinct conformational landscapes of Tyr and Ser/Thr kinases

Protein kinases are molecular machines with rich sequence variation that distinguishes the two main evolutionary branches - tyrosine kinases (TKs) from serine/threonine kinases (STKs). Using a sequence co-variation Potts statistical energy model we previously concluded that TK catalytic domains are more likely than STKs to adopt an inactive conformation with the activation loop in an autoinhibitory "folded" conformation, due to intrinsic sequence effects. Here we investigated the structural basis for this phenomenon by integrating the sequence-based model with structure-based molecular dynamics (MD) to determine the effects of mutations on the free energy difference between active and inactive conformations, using a novel thermodynamic cycle involving many (n=108) protein-mutation free energy perturbation (FEP) simulations in the active and inactive conformations. The sequence and structure-based results are consistent and support the hypothesis that the inactive conformation "DFG-out Activation Loop Folded", is a functional regulatory state that has been stabilized in TKs relative to STKs over the course of their evolution via the accumulation of residue substitutions in the activation loop and catalytic loop that facilitate distinct substrate binding modes in trans and additional modes of regulation in cis for TKs.

Predicting the functional state of protein kinases using interpretable graph neural networks from sequence and structural data

Protein kinases are central to cellular activities and are actively pursued as drug targets for several conditions including cancer and autoimmune diseases. Despite the availability of a large structural database for kinases, methodologies to elucidate the structure-function relationship of these proteins (without manual intervention) are lacking. Such techniques are essential in structural biology and to accelerate drug discovery efforts. Here, we implement an interpretable graph neural network (GNN) framework for classifying the functionally active and inactive states of a large set of protein kinases by only using their tertiary structure and amino acid sequence. We show that the GNN models can classify kinase structures with high accuracy (>97%). We implement the Gradient-weighted Class Activation Mapping for graphs (Graph Grad-CAM) to automatically identify structurally important residues and residue-residue contacts of the kinases without any a priori input. We show that the motifs identified through the Graph Grad-CAM methodology are functionally critical, consistent with the existing kinase literature. Notably, the highly conserved DFG and HRD motifs of the well-known hydrophobic spine are identified by the interpretable framework in addition to some of the lesser known motifs. Further, using Grad-CAM maps as the vector embedding of the protein structures, we identify the subtle differences in the crystal structures among different sub-classes of kinases in the Protein Data Bank (PDB). Frameworks such as the one implemented here, for high-throughput identification of protein structure-function relationships are essential in designing targeted small molecules therapies as well as in engineering new proteins for novel applications.

Molecular mechanisms underlying the regulation of tumour suppressor genes in lung cancer

Tumour suppressor genes play a cardinal role in the development of a large array of human cancers, including lung cancer, which is one of the most frequently diagnosed cancers worldwide. Therefore, extensive studies have been committed to deciphering the underlying mechanisms of alterations of tumour suppressor genes in governing tumourigenesis, as well as resistance to cancer therapies. In spite of the encouraging clinical outcomes demonstrated by lung cancer patients on initial treatment, the subsequent unresponsiveness to first-line treatments manifested by virtually all the patients is inherently a contentious issue. In light of the aforementioned concerns, this review compiles the current knowledge on the molecular mechanisms of some of the tumour suppressor genes implicated in lung cancer that are either frequently mutated and/or are located on the chromosomal arms having high LOH rates (1p, 3p, 9p, 10q, 13q, and 17p). Our study identifies specific genomic loci prone to LOH, revealing a recurrent pattern in lung cancer cases. These loci, including 3p14.2 (FHIT), 9p21.3 (p16INK4a), 10q23 (PTEN), 17p13 (TP53), exhibit a higher susceptibility to LOH due to environmental factors such as exposure to DNA-damaging agents (carcinogens in cigarette smoke) and genetic factors such as chromosomal instability, genetic mutations, DNA replication errors, and genetic predisposition. Furthermore, this review summarizes the current treatment landscape and advancements for lung cancers, including the challenges and endeavours to overcome it. This review envisages inspired researchers to embark on a journey of discovery to add to the list of what was known in hopes of prompting the development of effective therapeutic strategies for lung cancer.

Approximating Projections of Conformational Boltzmann Distributions with AlphaFold2 Predictions: Opportunities and Limitations

Protein thermodynamics is intimately tied to biological function and can enable processes such as signal transduction, enzyme catalysis, and molecular recognition. The relative free energies of conformations that contribute to these functional equilibria evolved for the physiology of the organism. Despite the importance of these equilibria for understanding biological function and developing treatments for disease, computational and experimental methods capable of quantifying the energetic determinants of these equilibria are limited to systems of modest size. Recently, it has been demonstrated that the artificial intelligence system AlphaFold2 can be manipulated to produce structurally valid protein conformational ensembles. Here, we extend these studies and explore the extent to which AlphaFold2 contact distance distributions can approximate projections of the conformational Boltzmann distributions. For this purpose, we examine the joint probability distributions of inter-residue contact distances along functionally relevant collective variables of several protein systems. Our studies suggest that AlphaFold2 normalized contact distance distributions can correlate with conformation probabilities obtained with other methods but that they suffer from peak broadening. We also find that the AlphaFold2 contact distance distributions can be sensitive to point mutations. Overall, we anticipate that our findings will be valuable as the community seeks to model the thermodynamics of conformational changes in large biomolecular systems.

Development of new thieno[2,3-d]pyrimidines as dual EGFR and STAT3 inhibitors endowed with anticancer and pro-apoptotic activities

In part due to the resilience of cellular feedback pathways that develop therapeutic resistance to targeting the EGFR alone, using EGFR inhibitors alone was demonstrated to be unsuccessful in clinical trials. The over-activation of the signal transducer/activator of transcription 3 (STAT3) during the administration of an EGFR inhibitor is expected to play a substantial part in the failure and resistance of EGFR inhibitor treatment. Therein, we proposed a hypothesis that induced STAT3-mediated resistance to EGFR inhibition therapy could be addressed by a dual inhibition of EGFR and STAT3 method. To this end, we tried to discover new thieno[2,3-d]pyrimidine derivatives "5a-o". Results from the screening on A549 and MCF7 cancer cell lines revealed that compounds 5j and 5k showed two-digit nanomolar with appropriate safety towards the WI-38 cell line. The best molecules, 5j and 5k, were subjected to γ-radiation, and their cytotoxic efficacy didn't change after irradiation, demonstrating that not having to use it avoided its side effects. Compounds 5j and 5k demonstrated the highest inhibition when their potency was tested as dual inhibitors on EGFR 67 and 41 nM, respectively, and STAT3 5.52 and 3.34 nM, respectively, proved with in silico molecular docking and dynamic simulation. In light of the results presented above, the capacity of both powerful compounds to alter the cell cycle and initiate the apoptotic process in breast cancer MCF7 cells was investigated. Caspase-8, Bcl-2, Bax and Caspase-9 apoptotic indicators were studied.

Novel 4-(2-arylidenehydrazineyl)thienopyrimidine derivatives as anticancer EGFR inhibitors: Design, synthesis, biological evaluation, kinome selectivity and in silico insights

The current study discovered fifteen new thieno[2,3-d]pyrimidine derivatives with potential anticancer action, including 5a-l, 6, and 7a-b. Results from the NCI screening revealed that compounds 5f-i and 7a significantly inhibited the proliferation of MDA-MB-468 cells at mean GI% and GI50 levels. Compared to staurosporine, these compounds (5f-i and 7a) demonstrated better safety towards typical WI-38 cells. Compounds 5g and 7a demonstrated the highest inhibition (two-digit nanomolar) when compared to erlotinib when their potency was tested on EGFR kinase. Considering the outcomes above, 5g was examined for its ability to disrupt the cell cycle with trigger apoptosis in breast cancer MDA-MB-468 cell lines. The apoptosis markers Bax, Bcl-2, Caspase-8, and Caspase-9, were detected. In silico molecular docking and dynamic simulation were used to explainthe biological activities of the most potent compound.

Architecture of the MKK6-p38α complex defines the basis of MAPK specificity and activation

The mitogen-activated protein kinase (MAPK) p38α is a central component of signaling in inflammation and the immune response and is, therefore, an important drug target. Little is known about the molecular mechanism of its activation by double phosphorylation from MAPK kinases (MAP2Ks), because of the challenge of trapping a transient and dynamic heterokinase complex. We applied a multidisciplinary approach to generate a structural model of p38α in complex with its MAP2K, MKK6, and to understand the activation mechanism. Integrating cryo-electron microscopy with molecular dynamics simulations, hydrogen-deuterium exchange mass spectrometry, and experiments in cells, we demonstrate a dynamic, multistep phosphorylation mechanism, identify catalytically relevant interactions, and show that MAP2K-disordered amino termini determine pathway specificity. Our work captures a fundamental step of cell signaling: a kinase phosphorylating its downstream target kinase.

Molecular Dynamics and Machine Learning Give Insights on the Flexibility-Activity Relationships in Tyrosine Kinome

Tyrosine kinases are a subfamily of kinases with critical roles in cellular machinery. Dysregulation of their active or inactive forms is associated with diseases like cancer. This study aimed to holistically understand their flexibility-activity relationships, focusing on pockets and fluctuations. We studied 43 different tyrosine kinases by collecting 120 μs of molecular dynamics simulations, pocket and residue fluctuation analysis, and a complementary machine learning approach. We found that the inactive forms often have increased flexibility, particularly at the DFG motif level. Noteworthy, thanks to these long simulations combined with a decision tree, we identified a semiquantitative fluctuation threshold of the DGF+3 residue over which the kinase has a higher probability to be in the inactive form.

Approximating conformational Boltzmann distributions with AlphaFold2 predictions

Protein dynamics are intimately tied to biological function and can enable processes such as signal transduction, enzyme catalysis, and molecular recognition. The relative free energies of conformations that contribute to these functional equilibria are evolved for the physiology of the organism. Despite the importance of these equilibria for understanding biological function and developing treatments for disease, the computational and experimental methods capable of quantifying them are limited to systems of modest size. Here, we demonstrate that AlphaFold2 contact distance distributions can approximate conformational Boltzmann distributions, which we evaluate through examination of the joint probability distributions of inter-residue contact distances along functionally relevant collective variables of several protein systems. Further, we show that contact distance probability distributions generated by AlphaFold2 are sensitive to points mutations thus AF2 can predict the structural effects of mutations in some systems. We anticipate that our approach will be a valuable tool to model the thermodynamics of conformational changes in large biomolecular systems.

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.

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.

Efficient Interrogation of the Kinetic Barriers Demarcating Catalytic States of a Tyrosine Kinase with Optimal Physical Descriptors and Mixture Models

Computer simulations are increasingly used to access thermo-kinetic information underlying structural transformation of protein kinases. Such information are necessary to probe their roles in disease progression and interactions with drug targets. However, the investigations are frequently challenged by forbiddingly high computational expense, and by the lack of standard protocols for the design of low dimensional physical descriptors that encode system features important for transitions. Here, we consider the demarcating characteristics of the different states of Abelson tyrosine kinase associated with distinct catalytic activity to construct a set of physically meaningful, orthogonal collective variables that preserve the slow modes of the system. Independent sampling of each metastable state is followed by the estimation of global partition function along the appropriate physical descriptors using the modified Expectation Maximized Molecular Dynamics method. The resultant free energy barriers are in excellent agreement with experimentally known rate-limiting dynamics and activation energy computed with conventional enhanced sampling methods. We discuss possible directions for further development and applications.

Structure-Guided Strategies of Targeted Therapies for Patients with EGFR-Mutant Non-Small Cell Lung Cancer

Oncogenic mutations within the EGFR kinase domain are well-established driver mutations in non-small cell lung cancer (NSCLC). Small-molecule tyrosine kinase inhibitors (TKIs) specifically targeting these mutations have improved treatment outcomes for patients with this subtype of NSCLC. The selectivity of these targeted agents is based on the location of the mutations within the exons of the EGFR gene, and grouping mutations based on structural similarities has proved a useful tool for conceptualizing the heterogeneity of TKI response. Structure-based analysis of EGFR mutations has influenced TKI development, and improved structural understanding will inform continued therapeutic development and further improve patient outcomes. In this review, we summarize recent progress on targeted therapy strategies for patients with EGFR-mutant NSCLC based on structure and function analysis.

Computational Methods for the Analysis and Prediction of EGFR-Mutated Lung Cancer Drug Resistance: Recent Advances in Drug Design, Challenges and Future Prospects

Lung cancer is a major cause of cancer deaths worldwide, and has a very low survival rate. Non-small cell lung cancer (NSCLC) is the largest subset of lung cancers, which accounts for about 85% of all cases. It has been well established that a mutation in the epidermal growth factor receptor (EGFR) can lead to lung cancer. EGFR Tyrosine Kinase Inhibitors (TKIs) are developed to target the kinase domain of EGFR. These TKIs produce promising results at the initial stage of therapy, but the efficacy becomes limited due to the development of drug resistance. In this paper, we provide a comprehensive overview of computational methods, for understanding drug resistance mechanisms. The important EGFR mutants and the different generations of EGFR-TKIs, with the survival and response rates are discussed. Next, we evaluate the role of important EGFR parameters in drug resistance mechanism, including structural dynamics, hydrogen bonds, stability, dimerization, binding free energies, and signaling pathways. Personalized drug resistance prediction models, drug response curve, drug synergy, and other data-driven methods are also discussed. Recent advancements in deep learning; such as AlphaFold2, deep generative models, big data analytics, and the applications of statistics and permutation are also highlighted. We explore limitations in the current methodologies, and discuss strategies to overcome them. We believe this review will serve as a reference for researchers; to apply computational techniques for precision medicine, analyzing structures of protein-drug complexes, drug discovery, and understanding the drug response and resistance mechanisms in lung cancer patients.

Evolutionary divergence in the conformational landscapes of tyrosine vs serine/threonine kinases

Inactive conformations of protein kinase catalytic domains where the DFG motif has a "DFG-out" orientation and the activation loop is folded present a druggable binding pocket that is targeted by FDA-approved 'type-II inhibitors' in the treatment of cancers. Tyrosine kinases (TKs) typically show strong binding affinity with a wide spectrum of type-II inhibitors while serine/threonine kinases (STKs) usually bind more weakly which we suggest here is due to differences in the folded to extended conformational equilibrium of the activation loop between TKs vs. STKs. To investigate this, we use sequence covariation analysis with a Potts Hamiltonian statistical energy model to guide absolute binding free-energy molecular dynamics simulations of 74 protein-ligand complexes. Using the calculated binding free energies together with experimental values, we estimated free-energy costs for the large-scale (~17-20 Å) conformational change of the activation loop by an indirect approach, circumventing the very challenging problem of simulating the conformational change directly. We also used the Potts statistical potential to thread large sequence ensembles over active and inactive kinase states. The structure-based and sequence-based analyses are consistent; together they suggest TKs evolved to have free-energy penalties for the classical 'folded activation loop' DFG-out conformation relative to the active conformation, that is, on average, 4-6 kcal/mol smaller than the corresponding values for STKs. Potts statistical energy analysis suggests a molecular basis for this observation, wherein the activation loops of TKs are more weakly 'anchored' against the catalytic loop motif in the active conformation and form more stable substrate-mimicking interactions in the inactive conformation. These results provide insights into the molecular basis for the divergent functional properties of TKs and STKs, and have pharmacological implications for the target selectivity of type-II inhibitors.

Finding inhibitors and deciphering inhibitor-induced conformational plasticity in the Janus kinase via multiscale simulations

The Janus kinase (JAK) is a master regulator of the JAK/STAT pathway. Dysregulation of this signalling cascade causes neuroinflammation and autoimmune disorders. Therefore, JAKs have been characterized as an attractive target for developing anti-inflammatory drugs. Nowadays, designing efficient, effective, and specific targeted therapeutics without being cytotoxic has gained interest. We performed the virtual screening of natural products in combination with pharmacological analyses. Subsequently, we performed molecular dynamics simulations to study the stability of the ligand-bound complexes and ligand-induced inactive conformations. Notably, inactive kinases display remarkable conformational plasticity; however, ligand-induced molecular mechanisms of these conformations are still poorly understood. Herein, we performed a free energy landscape analysis to explore the conformational plasticity of the JAK1 kinase. Leonurine, STOCK1N-68642, STOCK1N-82656, and STOCK1N-85809 bound JAK1 exhibited a smooth transition from an active (αC-in) to a completely inactive conformation (αC-out). Ligand binding induces disorders in the αC-helix. Molecular mechanics Poisson Boltzmann surface area (MM/PBSA) calculation suggested three phytochemicals, namely STOCK1N-68642, Epicatechin, and STOCK1N-98615, have higher binding affinity compared to other ligand molecules. The ligand-induced conformational plasticity revealed by our simulations differs significantly from the available crystal structures, which might help in designing allosteric drugs.

Allele-specific activation, enzyme kinetics, and inhibitor sensitivities of EGFR exon 19 deletion mutations in lung cancer

Epidermal growth factor receptor (EGFR) mutations are detected in approximately 30% of all lung adenocarcinomas, and the most common EGFR mutation occurring in ∼50% of patients is termed “exon 19 deletion” (ex19del). Despite the existence of dozens of different genomic variants comprising what is generically referred to clinically as ex19del, clinicians currently do not distinguish between ex19del variants in considering treatment options, and differences between ex19del variants are largely unstudied in the broader scientific community. Herein, we describe functional differences between distinct EGFR ex19del variants attributable to the structural features of each variant. These findings suggest a possible explanation for observed differences in patient outcomes stratified by ex19del subtype and reinforce the need for allele-specific considerations in clinical treatment decision-making.

Oncogenic mutations within the epidermal growth factor receptor (EGFR) are found in 15 to 30% of all non–small-cell lung carcinomas. The term exon 19 deletion (ex19del) is collectively used to refer to more than 20 distinct genomic alterations within exon 19 that comprise the most common EGFR mutation subtype in lung cancer. Despite this heterogeneity, clinical treatment decisions are made irrespective of which EGFR ex19del variant is present within the tumor, and there is a paucity of information regarding how individual ex19del variants influence protein structure and function. Herein, we identified allele-specific functional differences among ex19del variants attributable to recurring sequence and structure motifs. We built all-atom structural models of 60 ex19del variants identified in patients and combined molecular dynamics simulations with biochemical and biophysical experiments to analyze three ex19del mutations (E746_A750, E746_S752 > V, and L747_A750 > P). We demonstrate that sequence variation in ex19del alters oncogenic cell growth, dimerization propensity, enzyme kinetics, and tyrosine kinase inhibitor (TKI) sensitivity. We show that in contrast to E746_A750 and E746_S752 > V, the L747_A750 > P variant forms highly active ligand-independent dimers. Enzyme kinetic analysis and TKI inhibition experiments suggest that E746_S752 > V and L747_A750 > P display reduced TKI sensitivity due to decreased adenosine 5′-triphosphate K_m. Through these analyses, we propose an expanded framework for interpreting ex19del variants and considerations for therapeutic intervention.

The Conformational Transition Pathways and Hidden Intermediates in DFG-Flip Process of c-Met Kinase Revealed by Metadynamics Simulations

Protein kinases intrinsically translate their conformations between active and inactive states, which is key to their enzymatic activities. The conformational flipping of the three-residue conservative motif, Asp-Phe-Gly (DFG), is crucial for many kinases' biological functions. Obtaining a detailed demonstration of the DFG flipping process and its corresponding dynamical and thermodynamical features could broaden our understanding of kinases' conformation-activity relationship. In this study, we employed metadynamics simulation, a widely used enhanced sampling technique, to analyze the conformational transition pathways of the DFG flipping for the c-Met kinase. The corresponding free energy landscape suggested two distinct transition pathways between the "DFG-in" and "DFG-out" states of the DFG-flip from c-Met. On the basis of the orientation direction of the F1223 residue, we correspondingly named the two pathways the "DFG-up" path, featuring forming a commonly discovered "DFG-up" transition state, and the "DFG-down" path, a unique transition pathway with F1223 rotating along the opposite direction away from the hydrophobic cavity. The free energies along the two pathways were then calculated using the Path Collective Variable (PCV) metadynamics simulation. The simulation results showed that, though having similar free energy barriers, the free energy cuve for the DFG-down path suggested a two-step conformational transition mechanism, while that for the DFG-up path showed the one-step transition feature. The c-Met DFG flipping mechanism and the new intermediate state discovered in this work could provide a deeper understanding of the conformation-activity relationship for c-Met and, possibly, reveal a new conformational state as the drug target for c-Met and other similar kinases.

Uncovering the Molecular Basis for the Better Gefitinib Sensitivity of EGFR with Complex Mutations over Single Rare Mutation: Insights from Molecular Simulations

Epidermal growth factor receptor (EGFR) is an intensively focused target for anti-tumor compounds used in non-small cell lung cancer (NSCLC) therapy. Compared to the classical activating mutations, there are still many uncommon EGFR mutations associated with poorer responses to EGFR inhibitors. A detailed understanding of the molecular basis for multiple EGFR mutants exhibiting diverse responses to inhibitors is of critical importance for related drug development. Herein, we explored the molecular determinants contributing to the distinct responses of EGFR with a single rare mutation (G719S) or combined mutations (G719S/L858R and G719S/l861Q) to Gefitinib (IRE). Our results indicated that interactions, formed within the tetrad of residues S768 (in the αC-helix), D770 (in the αC-β4 loop), Y827 (in the αE-helix), and R831 (in the catalytic loop), play an important role in the stability of αC-helix and the maintenance of K745–E762 salt bridge in the absence of IRE, which are weakened in the EGFR^G719S system and enhanced in the EGFR^G719S/L858R system upon IRE binding. Besides, the introduced hydrogen bonds by the co-occurring mutation partner also contribute to the stability of αC-helix. The work done for inhibitor dissociation suggests that IRE exhibits a stronger binding affinity to EGFR^G719S/L858R mutant. Together, these findings provide a deeper understanding of minor mutations, which is essential for drug development targeting EGFR with less common mutations.

Computational Modeling of Molecular Structures Guided by Hydrogen-Exchange Data

Data produced by hydrogen-exchange monitoring experiments have been used in structural studies of molecules for several decades. Despite uncertainties about the structural determinants of hydrogen exchange itself, such data have successfully helped guide the structural modeling of challenging molecular systems, such as membrane proteins or large macromolecular complexes. As hydrogen-exchange monitoring provides information on the dynamics of molecules in solution, it can complement other experimental techniques in so-called integrative modeling approaches. However, hydrogen-exchange data have often only been used to qualitatively assess molecular structures produced by computational modeling tools. In this paper, we look beyond qualitative approaches and survey the various paradigms under which hydrogen-exchange data have been used to quantitatively guide the computational modeling of molecular structures. Although numerous prediction models have been proposed to link molecular structure and hydrogen exchange, none of them has been widely accepted by the structural biology community. Here, we present as many hydrogen-exchange prediction models as we could find in the literature, with the aim of providing the first exhaustive list of its kind. From purely structure-based models to so-called fractional-population models or knowledge-based models, the field is quite vast. We aspire for this paper to become a resource for practitioners to gain a broader perspective on the field and guide research toward the definition of better prediction models. This will eventually improve synergies between hydrogen-exchange monitoring and molecular modeling.

Analysis of changes of cavity volumes in predefined directions of protein motions and cavity flexibility

Dynamics of protein cavities associated with protein fluctuations and conformational plasticity is essential for their biological function. NMR ensembles, molecular dynamics (MD) simulations, and normal mode analysis (NMA) provide appropriate frameworks to explore functionally relevant protein dynamics and cavity changes relationships. Within this context, we have recently developed analysis of null areas (ANA), an efficient method to calculate cavity volumes. ANA is based on a combination of algorithms that guarantees its robustness against numerical differentiations. This is a unique feature with respect to other methods. Herein, we present an updated and improved version that expands it use to quantify changes in cavity features, like volume and flexibility, due to protein structural distortions performed on predefined biologically relevant directions, for example, directions of largest contribution to protein fluctuations (principal component analysis [PCA modes]) obtained by MD simulations or ensembles of NMR structures, collective NMA modes or any other direction of motion associated with specific conformational changes. A web page has been developed where its facilities are explained in detail. First, we show that ANA can be useful to explore gradual changes of cavity volume and flexibility associated with protein ligand binding. Secondly, we perform a comparison study of the extent of variability between protein backbone structural distortions, and changes in cavity volumes and flexibilities evaluated for an ensemble of NMR active and inactive conformers of the epidermal growth factor receptor structures. Finally, we compare changes in size and flexibility between sets of NMR structures for different homologous chains of dynein.

Tracking the ATP-binding response in adenylate kinase in real time

The biological function of proteins is critically dependent on dynamics inherent to the native structure. Such structural dynamics obey a predefined order and temporal timing to execute the specific reaction. Determination of the cooperativity of key structural rearrangements requires monitoring protein reactions in real time. In this work, we used time-resolved x-ray solution scattering (TR-XSS) to visualize structural changes in the Escherichia coli adenylate kinase (AdK) enzyme upon laser-induced activation of a protected ATP substrate. A 4.3-ms transient intermediate showed partial closing of both the ATP- and AMP-binding domains, which indicates a cooperative closing mechanism. The ATP-binding domain also showed local unfolding and breaking of an Arg¹³¹-Asp¹⁴⁶ salt bridge. Nuclear magnetic resonance spectroscopy data identified similar unfolding in an Arg¹³¹Ala AdK mutant, which refolded in a closed, substrate-binding conformation. The observed structural dynamics agree with a “cracking mechanism” proposed to underlie global structural transformation, such as allostery, in proteins.

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.

Structural basis of the effect of activating mutations on the EGF receptor

Mutations within the kinase domain of the epidermal growth factor receptor (EGFR) are common oncogenic driver events in non-small cell lung cancer. Although the activation of EGFR in normal cells is primarily driven by growth-factor-binding-induced dimerization, mutations on different exons of the kinase domain of the receptor have been found to affect the equilibrium between its active and inactive conformations giving rise to growth-factor-independent kinase activation. Using molecular dynamics simulations combined with enhanced sampling techniques, we compare here the conformational landscape of the monomers and homodimers of the wild-type and mutated forms of EGFR ΔELREA and L858R, as well as of two exon 20 insertions, D770-N771insNPG, and A763-Y764insFQEA. The differences in the conformational energy landscapes are consistent with multiple mechanisms of action including the regulation of the hinge motion, the stabilization of the dimeric interface, and local unfolding transitions. Overall, a combination of different effects is caused by the mutations and leads to the observed aberrant signaling.

Genotype-determined EGFR-RTK heterodimerization and its effects on drug resistance in lung Cancer treatment revealed by molecular dynamics simulations

BACKGROUND

Epidermal growth factor receptor (EGFR) and its signaling pathways play a vital role in pathogenesis of lung cancer. By disturbing EGFR signaling, mutations of EGFR may lead to progression of cancer or the emergence of resistance to EGFR-targeted drugs.

RESULTS

We investigated the correlation between EGFR mutations and EGFR-receptor tyrosine kinase (RTK) crosstalk in the signaling network, in order to uncover the drug resistance mechanism induced by EGFR mutations. For several EGFR wild type (WT) or mutated proteins, we measured the EGFR-RTK interactions using several computational methods based on molecular dynamics (MD) simulations, including geometrical characterization of the interfaces and conventional estimation of free energy of binding. Geometrical properties, namely the matching rate of atomic solid angles in the interfaces and center-of-mass distances between interacting atoms, were extracted relying on Alpha Shape modeling. For a couple of RTK partners (c-Met, ErbB2 and IGF-1R), results have shown a looser EGFR-RTK crosstalk for the drug-sensitive EGFR mutant while a tighter crosstalk for the drug-resistant mutant. It guarantees the genotype-determined EGFR-RTK crosstalk, and further proposes a potential drug resistance mechanism by amplified EGFR-RTK crosstalk induced by EGFR mutations.

CONCLUSIONS

This study will lead to a deeper understanding of EGFR mutation-induced drug resistance mechanisms and promote the design of innovative drugs.

The dynamics of subunit rotation in a eukaryotic ribosome

Protein synthesis by the ribosome is coordinated by an intricate series of large-scale conformational rearrangements. Structural studies can provide information about long-lived states, however biological kinetics are controlled by the intervening free-energy barriers. While there has been progress describing the energy landscapes of bacterial ribosomes, very little is known about the energetics of large-scale rearrangements in eukaryotic systems. To address this topic, we constructed an all-atom model with simplified energetics and performed simulations of subunit rotation in the yeast ribosome. In these simulations, the small subunit (SSU; ~1MDa) undergoes spontaneous and reversible rotations (~8°). By enabling the simulation of this rearrangement under equilibrium conditions, these calculations provide initial insights into the molecular factors that control dynamics in eukaryotic ribosomes. Through this, we are able to identify specific inter-subunit interactions that have a pronounced influence on the rate-limiting free-energy barrier. We also show that, as a result of changes in molecular flexibility, the thermodynamic balance between the rotated and unrotated states is temperature-dependent. This effect may be interpreted in terms of differential molecular flexibility within the rotated and unrotated states. Together, these calculations provide a foundation, upon which the field may begin to dissect the energetics of these complex molecular machines.

Untangling Dual-Targeting Therapeutic Mechanism of Epidermal Growth Factor Receptor (EGFR) Based on Reversed Allosteric Communication

Dual-targeting therapeutics by coadministration of allosteric and orthosteric drugs is drawing increased attention as a revolutionary strategy for overcoming the drug-resistance problems. It was further observed that the occupation of orthosteric sites by therapeutics agents has the potential to enhance allosteric ligand binding, which leads to improved potency of allosteric drugs. Epidermal growth factor receptor (EGFR), as one of the most critical anti-cancer targets belonging to the receptor tyrosine kinase family, represents a quintessential example. It was revealed that osimertinib, an ATP-competitive covalent EGFR inhibitor, remarkably enhanced the affinity of a recently developed allosteric inhibitor JBJ-04-125-02 for EGFRL858R/T790M. Here, we utilized extensive large-scale molecular dynamics simulations and the reversed allosteric communication to untangle the detailed molecular underpinning, in which occupation of osimertinib at the orthosteric site altered the overall conformational ensemble of EGFR mutant and reshaped the allosteric site via long-distance signaling. A unique intermediate state resembling the active conformation was identified, which was further stabilized by osimertinib loading. Based on the allosteric communication pathway, we predicted a novel allosteric site positioned around K867, E868, H893, and K960 within the intermediate state. Its correlation with the orthosteric site was validated by both structural and energetic analysis, and its low sequence conservation indicated the potential for selective targeting across the human kinome. Together, these findings not only provided a mechanistic basis for future clinical application of the dual-targeting therapeutics, but also explored an innovative perception of allosteric inhibition of tyrosine kinase signaling.

Protein Loop Conformational Free Energy Changes via an Alchemical Path without Reaction Coordinates

We introduce a method called restrain-free energy perturbation-release 2.0 (R-FEP-R 2.0) to estimate conformational free energy changes of protein loops via an alchemical path. R-FEP-R 2.0 is a generalization of the method called restrain-free energy perturbation-release (R-FEP-R) that can only estimate conformational free energy changes of protein side chains but not loops. The reorganization of protein loops is a central feature of many biological processes. Unlike other advanced sampling algorithms such as umbrella sampling and metadynamics, R-FEP-R and R-FEP-R 2.0 do not require predetermined collective coordinates and transition pathways that connect the two endpoint conformational states. The R-FEP-R 2.0 method was applied to estimate the conformational free energy change of a β-turn flip in the protein ubiquitin. The result obtained by R-FEP-R 2.0 agrees with the benchmarks very well. We also comment on problems commonly encountered when applying umbrella sampling to calculate protein conformational free energy changes.

Classical MD and metadynamics simulations on back-pocket binders of CDK2 and VEGFR2: a guidepost to design novel small-molecule dual inhibitors

Cyclin-Dependent Kinase 2 (CDK2) and Vascular-Endothelial Growth Factor Receptor 2 (VEGFR2) are promising targets for the design of novel inhibitors in anticancer therapeutics. In a recent work, our group designed a set of potential dual inhibitors predicted to occupy an allosteric back pocket near the active site of both enzymes, but their dynamic and unbinding behavior was unclear. Here, we used molecular dynamics (MD) and metadynamics (meta-D) simulations to study two of these virtual candidates (herein called IQ2 and IQ3). Their binding mode was predicted to be similar to that observed in LQ5 and BAX, well-known back-pocket binders of CDK2 and VEGFR2, respectively, including H-bonding with critical residues such as Leu83/Cys113 and Asp145/Asp190 (but excepting H-bonding with Glu51/Glu111) in CDK2/VEGFR2, correspondingly. Likewise, while LQ5 and BAX unbound through the allosteric channel as expected for type-IIA inhibitors, IQ2 and IQ3 unbound via the ATP channel (except for CDK2-IQ2) as expected for type-I½A inhibitors. Interestingly, a C-C single/double bond difference between IQ2/IQ3, respectively, resulted associated with differences in the AS/T loop flexibility observed for CDK2. These insights will help developing scaffold modifications during an optimization stage, serving as a starting point to develop dual kinase inhibitors in challenging biological targets with a promising anticancer potential.Communicated by Ramaswamy H. Sarma.

Binding of tyrosine kinase inhibitor to epidermal growth factor receptor: surface-enhanced infrared absorption microscopy reveals subtle protein secondary structure variations

Surface-Enhanced Infrared Absorption (SEIRA) has been proposed as a valuable tool for protein binding studies, but its performances have been often proven on model proteins undergoing severe secondary structure rearrangements, while ligand binding only marginally involves the protein backbone in the vast majority of the biologically relevant cases. In this study we demonstrate the potential of SEIRA microscopy for highlighting the very subtle secondary structure modifications associated with the binding of Lapatinib, a tyrosine kinase inhibitor (TKI), to epidermal growth factor receptor (EGFR), a well-known driver of tumorigenesis in pathological settings such as lung, breast and brain cancers. By boosting the performances of Mid-IR plasmonic devices based on nanoantennas cross-geometry, accustoming the protein purification protocols, carefully tuning the protein anchoring methodology and optimizing the data analysis, we were able to detect EGFR secondary structure modification associated with few amino acids. A nano-patterned platform with this kind of sensitivity bridges biophysical and structural characterization methods, thus opening new possibilities in studying of proteins of biomedical interest, particularly for drug-screening purposes.

Structural Basis for the Functional Changes by EGFR Exon 20 Insertion Mutations

Activating somatic mutations of the epidermal growth factor receptor (EGFR) are frequently implicated in non-small cell lung cancer (NSCLC). While L858R and exon 19 deletion mutations are most prevalent, exon 20 insertions are often observed in NSCLC. Here, we investigated the structural implications of two common EGFR exon 20 insertions in NSCLC, V769insASV and D770insNPG. The active and inactive conformations of wild-type, D770insNPG and V769insASV EGFRs were probed with molecular dynamics simulations to identify local and global alterations that the mutations exert on the EGFR kinase domain, highlighting mechanisms for increased enzymatic activity. In the active conformation, the mutations increase interactions that stabilize the αC helix that is essential for EGFR activity. Moreover, the key Lys745-Glu762 salt bridge was more conserved in the insertion mutations. The mutants also preserved the state of the structurally critical aspartate-phenylalanine-glycine (DFG)-motif and regulatory spine (R-spine), which were altered in wild-type EGFR. The insertions altered the structure near the ATP-binding pocket, e.g., the P-loop, which may be a factor for the clinically observed tyrosine kinase inhibitor (TKI) insensitivity by the insertion mutants. The inactive state simulations also showed that the insertions disrupt the Ala767-Arg776 interaction that is key for maintaining the "αC-out" inactive conformation, which could consequently fuel the transition from the inactive towards the active EGFR state.

QM/MM and molecular dynamics investigation of the mechanism of covalent inhibition of TAK1 kinase

TAK1 is a serine/threonine kinase which is involved in the moderation of cell survival and death via the TNFα signalling pathway. It is also implicated in a range of cancer and anti-inflammatory diseases. Drug discovery efforts on this target have focused on both traditional reversible ATP-binding site inhibitors and increasingly popular irreversible covalent binding inhibitors. Irreversible inhibitors can offer benefits in terms of potency, selectivity and PK/PD meaning they are increasingly pursued where the strategy exists. TAK1 kinase differs from the better-known kinase EGFR in that the reactive cysteine nucleophile targeted by electrophilic inhibitors is located towards the back of the ATP binding site, not at its mouth. While a wealth of structural and computational effort has been spent exploring EGFR, only limited studies on TAK1 have been reported. In this work we report the first QM/MM study on TAK1 aiming to better understand aspects of covalent adduct formation. Our goal is to identify the general base in the catalytic reaction, whether the process proceeds via a stepwise or concerted pathway, and how the highly flexible G-loop and A-loop affect the catalytic cysteine located nearby.

Characterizing an allosteric inhibitor-induced inactive state in with-no-lysine kinase 1 using Gaussian accelerated molecular dynamics simulations

The binding of an allosteric inhibitor in WNK1 leads to the inactive state.

Parthenolide inhibits the growth of non-small cell lung cancer by targeting epidermal growth factor receptor

Background

EGFR tyrosine kinase inhibitors (TKIs) have been developed for the treatment of EGFR mutated NSCLC. Parthenolide, a natural product of parthenolide, which belongs to the sesquiterpene lactone family and has a variety of biological and therapeutic activities, including anti-cancer effects. However, its effect on non-small cell lung cancer is little known.

Methods

The CCK8 assay and colony formation assays were used to assess cell viability. Flow cytometry was used to measure the cell apoptosis. In silico molecular docking was used to evaluate the binding of parthenolide to EGFR. Network pharmacology analysis was was used to evaluate the key gene of parthenolide target NSCLC. Western blotting was used to evaluate the key proteins involved apoptosis and EGFR signalling. The effect of parthenolide treatment in vivo was determined by using a xenograft mouse model.

Results

In this study, parthenolide could induce apoptosis and growth inhibition in the EGFR mutated lung cancer cells. Parthenolide also reduces the phosphorylation of EGFR as well as its downstream signaling pathways MAPK/ERK and PI3K/Akt. Molecular docking analysis of EGFR binding site with parthenolide show that the anti-cancer effect of parthenolide against NSCLC is mediated by a strong binding to EGFR. Network pharmacology analysis show parthenolide suppresses NSCLC via inhibition of EGFR expression. In addition, parthenolide inhibits the growth of H1975 xenografts in nude mice, which is associated with the inhibition of the EGFR signaling pathway.

Conclusions

Taken together, these results demonstrate effective inhibition of parthenolide in NSCLC cell growth by targeting EGFR through downregulation of ERK and AKT expression, which could be promisingly used for patients carrying the EGFR mutation.

The next tier of EGFR resistance mutations in lung cancer

EGFR mutations account for the majority of druggable targets in lung adenocarcinoma. Over the past decades the optimization of EGFR inhibitors revolutionized the treatment options for patients suffering from this disease. The pace of this development was largely dictated by the inevitable emergence of resistance mutations during drug treatment. As a result, a rapid understanding of the structural and molecular biology of the individual mutations is the key for the development of next-generation inhibitors. Currently, the field faces an unprecedented number of combinations of activating mutations with distinct resistance mutations in parallel to the approval of osimertinib as a first-line drug for EGFR-mutant lung cancer. In this review, we present a survey of the diverse landscape of EGFR resistance mechanisms with a focus on new insights into on-target EGFR kinase mutations. We discuss array of mutations, their structural effects on the EGFR kinase domain as well as the most promising strategies to overcome the individual resistance profiles found in lung cancer patients.

ATP-Driven Nonequilibrium Activation of Kinase Clients by the Molecular Chaperone Hsp90

The molecular chaperone 90-kDa heat-shock protein (Hsp90) assists the late-stage folding and activation of diverse types of protein substrates (called clients), including many kinases. Previous studies have established that the Hsp90 homodimer undergoes an ATP-driven cycle through open and closed conformations. Here, I propose a model of client activation by Hsp90 that predicts that this cycle enables Hsp90 to use ATP energy to drive a client out of thermodynamic equilibrium toward its active conformation. My model assumes that an Hsp90-bound client can transition between a deactivating conformation and an activating conformation. It suggests that the cochaperone Cdc37 aids Hsp90 to activate kinase clients by differentiating between these two intermediate conformations. My model makes experimentally testable predictions, including how modulating the stepwise kinetics of the Hsp90 cycle-for example, by various cochaperones-affects the activation of different clients. My model may inform client-specific and cell-type-specific therapeutic intervention of Hsp90-mediated protein activation.

Making NSCLC Crystal Clear: How Kinase Structures Revolutionized Lung Cancer Treatment

The parallel advances of different scientific fields provide a contemporary scenario where collaboration is not a differential, but actually a requirement. In this context, crystallography has had a major contribution on the medical sciences, providing a “face” for targets of diseases that previously were known solely by name or sequence. Worldwide, cancer still leads the number of annual deaths, with 9.6 million associated deaths, with a major contribution from lung cancer and its 1.7 million deaths. Since the relationship between cancer and kinases was unraveled, these proteins have been extensively explored and became associated with drugs that later attained blockbuster status. Crystallographic structures of kinases related to lung cancer and their developed and marketed drugs provided insight on their conformation in the absence or presence of small molecules. Notwithstanding, these structures were also of service once the initially highly successful drugs started to lose their effectiveness in the emergence of mutations. This review focuses on a subclassification of lung cancer, non-small cell lung cancer (NSCLC), and major oncogenic driver mutations in kinases, and how crystallographic structures can be used, not only to provide awareness of the function and inhibition of these mutations, but also how these structures can be used in further computational studies aiming at addressing these novel mutations in the field of personalized medicine.

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.

Molecular Basis of the Mechanisms Controlling MASTL

The human MASTL (Microtubule-associated serine/threonine kinase-like) gene encodes an essential protein in the cell cycle. MASTL is a key factor preventing early dephosphorylation of M-phase targets of Cdk1/CycB. Little is known about the mechanism of MASTL activation and regulation. MASTL contains a non-conserved insertion of 550 residues within its activation loop, splitting the kinase domain, and making it unique. Here, we show that this non-conserved middle region (NCMR) of the protein is crucial for target specificity and activity. We performed a phosphoproteomic assay with different MASTL constructs identifying key phosphorylation sites for its activation and determining whether they arise from autophosphorylation or exogenous kinases, thus generating an activation model. Hydrogen/deuterium exchange data complements this analysis revealing that the C-lobe in full-length MASTL forms a stable structure, whereas the N-lobe is dynamic and the NCMR and C-tail contain few localized regions with higher-order structure. Our results indicate that truncated versions of MASTL conserving a cryptic C-Lobe in the NCMR, display catalytic activity and different targets, thus establishing a possible link with truncated mutations observed in cancer-related databases.