Molecular dynamics simulation reveals structural and thermodynamic features of kinase activation by cancer mutations within the epidermal growth factor receptor

High-Throughput Prediction of the Impact of Genetic Variability on Drug Sensitivity and Resistance Patterns for Clinically Relevant Epidermal Growth Factor Receptor Mutations from Atomistic Simulations

Mutations in the kinase domain of the epidermal growth factor receptor (EGFR) can be drivers of cancer and also trigger drug resistance in patients receiving chemotherapy treatment based on kinase inhibitors. A priori knowledge of the impact of EGFR variants on drug sensitivity would help to optimize chemotherapy and design new drugs that are effective against resistant variants before they emerge in clinical trials. To this end, we explored a variety of in silico methods, from sequence-based to "state-of-the-art" atomistic simulations. We did not find any sequence signal that can provide clues on when a drug-related mutation appears or the impact of such mutations on drug activity. Low-level simulation methods provide limited qualitative information on regions where mutations are likely to cause alterations in drug activity, and they can predict around 70% of the impact of mutations on drug efficiency. High-level simulations based on nonequilibrium alchemical free energy calculations show predictive power. The integration of these "state-of-the-art" methods into a workflow implementing an interface for parallel distribution of the calculations allows its automatic and high-throughput use, even for researchers with moderate experience in molecular simulations.

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.

Machine learning based personalized drug response prediction for lung cancer patients

Lung cancers with a mutated epidermal growth factor receptor (EGFR) are a major contributor to cancer fatalities globally. Targeted tyrosine kinase inhibitors (TKIs) have been developed against EGFR and show encouraging results for survival rate and quality of life. However, drug resistance may affect treatment plans and treatment efficacy may be lost after about a year. Predicting the response to EGFR-TKIs for EGFR-mutated lung cancer patients is a key research area. In this study, we propose a personalized drug response prediction model (PDRP), based on molecular dynamics simulations and machine learning, to predict the response of first generation FDA-approved small molecule EGFR-TKIs, Gefitinib/Erlotinib, in lung cancer patients. The patient's mutation status is taken into consideration in molecular dynamics (MD) simulation. Each patient's unique mutation status was modeled considering MD simulation to extract molecular-level geometric features. Moreover, additional clinical features were incorporated into machine learning model for drug response prediction. The complete feature set includes demographic and clinical information (DCI), geometrical properties of the drug-target binding site, and the binding free energy of the drug-target complex from the MD simulation. PDRP incorporates an XGBoost classifier, which achieves state-of-the-art performance with 97.5% accuracy, 93% recall, 96.5% precision, and 94% F1-score, for a 4-class drug response prediction task. We found that modeling the geometry of the binding pocket combined with binding free energy is a good predictor for drug response. However, we observed that clinical information had a little impact on the performance of the model. The proposed model could be tested on other types of cancers. We believe PDRP will support the planning of effective treatment regimes based on clinical-genomic information. The source code and related files are available on GitHub at:   https://github.com/rizwanqureshi123/PDRP/ .

Structural insight into the binding mechanism of ATP to EGFR and L858R, and T790M and L858R/T790 mutants

The L858R mutation in EGFR is particularly responsive to small tyrosine kinase inhibitors (TKIs) such as gefitinib and erlotinib. This efficacy decreases due to drug resistance conferred by a second mutation, T790M, which subsequently produces a double mutant, L858R/T790M. Although this resistance was initially attributed to steric blocking by the T790M mutation, experimental studies have demonstrated that differences in the binding affinities of TKIs to T790M and L858R/T790M mutants are more a result of the increased sensitivity of these mutants to ATP than to a decrease in the affinity to TKIs. Regrettably, detailed information at the atomic level on the origins of the increased binding affinity of mutants for ATP is lacking. In this study, we have combined structural data and molecular dynamics simulations with the MMGBSA approach to determine how the L858R, T790M and L858R/T790 mutations impact the binding mechanism of ATP with respect to wild-type EGFR. Structural and energetic analyses provided novel information that helps to explain the increased affinity of ATP to T790M and L858R/T790 mutants with respect to L858R and wild-type systems. In addition, it was observed that dimerization of the wild-type and mutant systems exerts dissimilar effects on the ATP binding affinity characteristic of negative cooperativity. Communicated by Ramaswamy H. Sarma.

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.

Binding mechanism of kinase inhibitors to EGFR and T790M, L858R and L858R/T790M mutants through structural and energetic analysis

Experimental studies have demonstrated that L858R mutation in the EGF receptor (EGFR) confers tumor sensitivity whereas T790M and L858R/T790M mutations cause resistance to tyrosine kinase inhibitors in patients with non-small cell lung cancer. Theoretical studies have been carried out to try to clarify the structural and energetic details linked to acquired resistance to Gefitinib, Erlotinib or Lapatinib, however, some of these studies are contradictory with each other and with experimental reports and did not mention whether the study was performed by considering the inactive or active EGFR states. In this study, we combined structural data and molecular dynamic simulations coupled to a molecular mechanics generalized Born surface area approach to provide insight into the binding mechanism between three FDA-approved drugs (Erlotinib, Gefitinib and Lapatinib) that target the wild-type and T790M, L858R and L858R/T790M mutants of EGFR. Structural analysis showed that the drugs impact differently the conformational space of active and inactive EGFR. Energetic analysis pointed out that some ligands have better affinity for the inactive EGFR than the active EGFR state. Comparative analysis of the molecular recognition of Gefitinib, Erlotinib and Lapatinib provided insight into the drug sensitivity or resistance observed for the three FDA-approved drugs evaluated.

Differential protein stability of EGFR mutants determines responsiveness to tyrosine kinase inhibitors

Non-small cell lung cancer (NSCLC) patients carrying specific EGFR kinase activating mutations (L858R, delE746-A750) respond well to tyrosine kinase inhibitors (TKIs). However, drug resistance develops within a year. In about 50% of such patients, acquired drug resistance is attributed to the enrichment of a constitutively active point mutation within the EGFR kinase domain (T790M). To date, differential drug-binding and altered ATP affinities by EGFR mutants have been shown to be responsible for differential TKI response. As it has been reported that EGFR stability plays a role in the survival of EGFR driven cancers, we hypothesized that differential TKI-induced receptor degradation between the sensitive L858R and delE746-A750 and the resistant T790M may also play a role in drug responsiveness. To explore this, we have utilized an EGFR-null CHO overexpression system as well as NSCLC cell lines expressing various EGFR mutants and determined the effects of erlotinib treatment. We found that erlotinib inhibits EGFR phosphorylation in both TKI sensitive and resistant cells, but the protein half-lives of L858R and delE746-A750 were significantly shorter than L858R/T790M. Third generation EGFR kinase inhibitor (AZD9291) inhibits the growth of L858R/T790M-EGFR driven cells and also induces EGFR degradation. Erlotinib treatment induced polyubiquitination and proteasomal degradation, primarily in a c-CBL-independent manner, in TKI sensitive L858R and delE746-A750 mutants when compared to the L858R/T790M mutant, which correlated with drug sensitivity. These data suggest an additional mechanism of TKI resistance, and we postulate that agents that degrade L858R/T790M-EGFR protein may overcome TKI resistance.

The molecular mechanism behind resistance of the kinase FLT3 to the inhibitor quizartinib

Fms-like tyrosine kinase 3 (FLT3) is a receptor tyrosine kinase that is a drug target for leukemias. Several potent inhibitors of FLT3 exist, and bind to the inactive form of the enzyme. Unfortunately, resistance due to mutations in the kinase domain of FLT3 limits the therapeutic effects of these inhibitors. As in many other cases, it is not straightforward to explain why certain mutations lead to drug resistance. Extensive fully atomistic molecular dynamics (MD) simulations of FLT3 were carried out with an inhibited form (FLT-quizartinib complex), a free (apo) form, and an active conformation. In all cases, both the wild type (wt) proteins and two resistant mutants (D835F and Y842H) were studied. Analysis of the simulations revealed that impairment of protein-drug interactions cannot explain the resistance mutations in question. Rather, it appears that the active state of the mutant forms is perturbed by the mutations. It is therefore likely that perturbation of deactivation of the protein (which is necessary for drug binding) is responsible for the reduced affinity of the drug to the mutants. Importantly, this study suggests that it is possible to explain the source of resistance by mutations in FLT3 by an analysis of unbiased MD simulations.

Dynamic, structural and thermodynamic basis of insulin-like growth factor 1 kinase allostery mediated by activation loop phosphorylation

Despite the importance of kinases' catalytic activity regulation in cell signaling, detailed mechanisms underlying their activity regulation are poorly understood. Herein, using insulin-like growth factor 1 receptor kinase (IGF-1RK) as a model, the mechanisms of kinase regulation by its activation loop (A-loop) phosphorylation were investigated through molecular dynamics (MD) and alchemical free energy simulations. Analyses of the simulation results and free energy landscapes determined for the entire catalytic cycle of the kinase revealed that A-loop phosphorylation affects each step in the IGF-1RK catalytic cycle, including conformational change, substrate binding/product release and catalytic phosphoryl transfer. Specifically, the conformational equilibrium of the kinase is shifted by 13.2 kcal mol-1 to favor the active conformation after A-loop phosphorylation, which increases substrate binding affinity of the activated kinase. This free energy shift is achieved primarily via destabilization of the inactive conformation. The free energy of the catalytic reaction is also changed by 3.3 kcal mol-1 after the phosphorylation and in the end, facilitates product release. Analyses of MD simulations showed that A-loop phosphorylation produces these energetic effects by perturbing the side chain interactions around each A-loop tyrosine. These interaction changes are propagated to the remainder of the kinase to modify the orientations and dynamics of the αC-helix and A-loop, and together yield the observed free energy changes. Since many protein kinases share similar interactions identified in this work, the mechanisms of kinase allostery and catalysis unraveled here can be applicable to them.

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.

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.

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

The ErbB protein tyrosine kinases are among the most important cell signaling families and mutation-induced modulation of their activity is associated with diverse functions in biological networks and human disease. We have combined molecular dynamics simulations of the ErbB kinases with the protein structure network modeling to characterize the reorganization of the residue interaction networks during conformational equilibrium changes in the normal and oncogenic forms. Structural stability and network analyses have identified local communities integrated around high centrality sites that correspond to the regulatory spine residues. This analysis has provided a quantitative insight to the mechanism of mutation-induced “superacceptor” activity in oncogenic EGFR dimers. We have found that kinase activation may be determined by allosteric interactions between modules of structurally stable residues that synchronize the dynamics in the nucleotide binding site and the αC-helix with the collective motions of the integrating αF-helix and the substrate binding site. The results of this study have pointed to a central role of the conserved His-Arg-Asp (HRD) motif in the catalytic loop and the Asp-Phe-Gly (DFG) motif as key mediators of structural stability and allosteric communications in the ErbB kinases. We have determined that residues that are indispensable for kinase regulation and catalysis often corresponded to the high centrality nodes within the protein structure network and could be distinguished by their unique network signatures. The optimal communication pathways are also controlled by these nodes and may ensure efficient allosteric signaling in the functional kinase state. Structure-based network analysis has quantified subtle effects of ATP binding on conformational dynamics and stability of the EGFR structures. Consistent with the NMR studies, we have found that nucleotide-induced modulation of the residue interaction networks is not limited to the ATP site, and may enhance allosteric cooperativity with the substrate binding region by increasing communication capabilities of mediating residues.

Hotspot mutations in KIT receptor differentially modulate its allosterically coupled conformational dynamics: impact on activation and drug sensitivity

Receptor tyrosine kinase KIT controls many signal transduction pathways and represents a typical allosterically regulated protein. The mutation-induced deregulation of KIT activity impairs cellular physiological functions and causes serious human diseases. The impact of hotspots mutations (D816H/Y/N/V and V560G/D) localized in crucial regulatory segments, the juxtamembrane region (JMR) and the activation (A-) loop, on KIT internal dynamics was systematically studied by molecular dynamics simulations. The mutational outcomes predicted in silico were correlated with in vitro and in vivo activation rates and drug sensitivities of KIT mutants. The allosteric regulation of KIT in the native and mutated forms is described in terms of communication between the two remote segments, JMR and A-loop. A strong correlation between the communication profile and the structural and dynamical features of KIT in the native and mutated forms was established. Our results provide new insight on the determinants of receptor KIT constitutive activation by mutations and resistance of KIT mutants to inhibitors. Depiction of an intra-molecular component of the communication network constitutes a first step towards an integrated description of vast communication pathways established by KIT in physiopathological contexts.

Receptor tyrosine kinase KIT plays a crucial role in the regulation of cell signaling. This allosterically controlled activity may be affected by gain-of-function mutations that promote the development of several cancers. Identification of the molecular basis of KIT constitutive activation and allosteric regulation has inspired computational study of KIT hotspot mutations. In the present contribution, we investigated the mutation-induced effects on KIT conformational dynamics and intra-protein communication conditionally on the mutation location and the nature of the substituting amino acid. Our data elucidate that all studied mutations stabilize an inactive non-autoinhibited state of KIT over the inactive auto-inhibited state prevalent for the native protein. This shift in the protein conformational landscape promotes KIT constitutive activation. Our in silico analysis established correlations between the structural and dynamical effects induced by oncogenic mutations and the mutants auto-activation rates and drug sensitivities measured in vitro and in vivo. Particularly, the A-loop mutations stabilize the drug-resistant forms, while the JMR mutations may facilitate inhibitors binding to the active site. Cross-correlations established between local and long-range structural and dynamical effects demonstrate the allosteric character of the gain-of-function mutations mode of action.

Differential effects of CSF-1R D802V and KIT D816V homologous mutations on receptor tertiary structure and allosteric communication

The colony stimulating factor-1 receptor (CSF-1R) and the stem cell factor receptor KIT, type III receptor tyrosine kinases (RTKs), are important mediators of signal transduction. The normal functions of these receptors can be compromised by gain-of-function mutations associated with different physiopatological impacts. Whereas KIT D816V/H mutation is a well-characterized oncogenic event and principal cause of systemic mastocytosis, the homologous CSF-1R D802V has not been identified in human cancers. The KIT D816V oncogenic mutation triggers resistance to the RTK inhibitor Imatinib used as first line treatment against chronic myeloid leukemia and gastrointestinal tumors. CSF-1R is also sensitive to Imatinib and this sensitivity is altered by mutation D802V. Previous in silico characterization of the D816V mutation in KIT evidenced that the mutation caused a structure reorganization of the juxtamembrane region (JMR) and facilitated its departure from the kinase domain (KD). In this study, we showed that the equivalent CSF-1R D802V mutation does not promote such structural effects on the JMR despite of a reduction on some key H-bonds interactions controlling the JMR binding to the KD. In addition, this mutation disrupts the allosteric communication between two essential regulatory fragments of the receptors, the JMR and the A-loop. Nevertheless, the mutation-induced shift towards an active conformation observed in KIT D816V is not observed in CSF-1R D802V. The distinct impact of equivalent mutation in two homologous RTKs could be associated with the sequence difference between both receptors in the native form, particularly in the JMR region. A local mutation-induced perturbation on the A-loop structure observed in both receptors indicates the stabilization of an inactive non-inhibited form, which Imatinib cannot bind.

Ligand binding and dynamics of the monomeric epidermal growth factor receptor ectodomain

The ectodomain of the human epidermal growth factor receptor (hEGFR) controls input to several cell signalling networks via binding with extracellular growth factors. To gain insight into the dynamics and ligand binding of the ectodomain, the hEGFR monomer was subjected to molecular dynamics simulation. The monomer was found to be substantially more flexible than the ectodomain dimer studied previously. Simulations where the endogeneous ligand EGF binds to either Subdomain I or Subdomain III, or where hEGFR is unbound, show significant differences in dynamics. The molecular mechanics Poisson-Boltzmann surface area method has been used to derive relative free energies of ligand binding, and we find that the ligand is capable of binding either subdomain with a slight preference for III. Alanine-scanning calculations for the effect of selected ligand mutants on binding reproduce the trends of affinity measurements. Taken together, these results emphasize the possible role of the ectodomain monomer in the initial step of ligand binding, and add details to the static picture obtained from crystal structures.

Binding sensitivity of adefovir to the polymerase from different genotypes of HBV: molecular modeling, docking and dynamics simulation studies

AIM

To investigate the molecular mechanisms underlying the influence of DNA polymerase from different genotypes of hepatitis B virus (HBV) on the binding affinity of adefovir (ADV).

METHODS

Computational approaches, including homology modeling, docking, MD simulation and MM/PBSA free energy analyses were used.

RESULTS

Sequence analyses revealed that residue 238 near the binding pocket was not only a polymorphic site but also a genotype-specific site (His238 in genotype B; Asn238 in genotype C). The calculated binding free-energy supported the hypothesis that the polymerase from HBV genotype C was more sensitive to ADV than that from genotype B. By using MD simulation trajectory analysis, binding free energy decomposition and alanine scanning, some energy variation in the residues around the binding pocket was observed. Both the alanine mutations at residues 236 and 238 led to an increase of the energy difference between genotypes C and B (ΔΔG(C-B)), suggesting that these residues contributed to the genotype-associated antiviral variability with regard to the interaction with ADV.

CONCLUSION

The results support the hypothesis that the HBV genotype C polymerase is more sensitive to ADV than that from genotype B. Moreover, residue N236 and the polymorphic site 238 play important roles in contributing to the higher sensitivity of genotype C over B in the interaction with ADV.

Computational study of EGFR inhibition: molecular dynamics studies on the active and inactive protein conformations

The structural diversity observed across protein kinases, resulting in subtly different active site cavities, is highly desirable in the pursuit of selective inhibitors, yet it can also be a hindrance from a structure-based design perspective. An important challenge in structure-based design is to better understand the dynamic nature of protein kinases and the underlying reasons for specific conformational preferences in the presence of different inhibitors. To investigate this issue, we performed molecular dynamics simulation on both the active and inactive wild type epidermal growth factor receptor (EGFR) protein with both type-I and type-II inhibitors. Our goal is to better understand the origin of the two distinct EGFR protein conformations, their dynamic differences, and their relative preference for Type-I inhibitors such as gefitinib and Type-II inhibitors such as lapatinib. We discuss the implications of protein dynamics from a structure-based design perspective.

From base pair to bedside: molecular simulation and the translation of genomics to personalized medicine

Despite the promises made that genomic sequencing would transform therapy by introducing a new era of personalized medicine, relatively few tangible breakthroughs have been made. This has led to the recognition that complex interactions at multiple spatial, temporal, and organizational levels may often combine to produce disease. Understanding this complexity requires that existing and future models are used and interpreted within a framework that incorporates knowledge derived from investigations at multiple levels of biological function. It also requires a computational infrastructure capable of dealing with the vast quantities of data generated by genomic approaches. In this review, we discuss the use of molecular modeling to generate quantitative and qualitative insights at the smallest scales of the systems biology hierarchy, how it can play an important role in the development of a systems understanding of disease and in the application of such knowledge to help discover new therapies and target existing ones on a personal level.

Mechanism of drug efficacy within the EGF receptor revealed by microsecond molecular dynamics simulation

The EGF receptor (EGFR) regulates important cellular processes including proliferation, differentiation, and apoptosis. EGFR is frequently overexpressed in a range of cancers and is associated with disease progression and treatment. Clinical studies have shown that EGFR mutations confer tumor sensitivity to tyrosine kinase inhibitors in patients with non-small cell lung cancer. In this study, we have conducted molecular dynamics simulations over several microseconds for wild-type and L858R mutant forms of EGFR in the ligand-free state. Close inspection of the conformations and interactions within the binding pocket reveals, converse to the wild type, that the mutant EGFR prefers to bind gefitinib, a targeted anticancer drug, rather than ATP, offering an explanation for why gefitinib is more effective in patients with EGFR mutations than those without.

Clinically driven design of multi-scale cancer models: the ContraCancrum project paradigm

The challenge of modelling cancer presents a major opportunity to improve our ability to reduce mortality from malignant neoplasms, improve treatments and meet the demands associated with the individualization of care needs. This is the central motivation behind the ContraCancrum project. By developing integrated multi-scale cancer models, ContraCancrum is expected to contribute to the advancement of in silico oncology through the optimization of cancer treatment in the patient-individualized context by simulating the response to various therapeutic regimens. The aim of the present paper is to describe a novel paradigm for designing clinically driven multi-scale cancer modelling by bringing together basic science and information technology modules. In addition, the integration of the multi-scale tumour modelling components has led to novel concepts of personalized clinical decision support in the context of predictive oncology, as is also discussed in the paper. Since clinical adaptation is an inelastic prerequisite, a long-term clinical adaptation procedure of the models has been initiated for two tumour types, namely non-small cell lung cancer and glioblastoma multiforme; its current status is briefly summarized.