Molecular systems biology of ErbB1 signaling: bridging the gap through multiscale modeling and high-performance computing

Membrane signalosome: where biophysics meets systems biology

We opine on the recent advances in experiments and modeling of modular signaling complexes assembled on mammalian cell membranes (membrane signalosomes) in the context of several applications including intracellular trafficking, cell migration, and immune response. Characterizing the individual components of the membrane assemblies at the nanoscale, ranging from protein-lipid and protein-protein interactions, to membrane morphology, and the energetics of emergent assemblies at the subcellular to cellular scales pose significant challenges. Overcoming these challenges through the iterative coupling of multiscale modeling and experiment can be transformative in terms of addressing the gaps between structural biology and super-resolution microscopy, as it holds the key to the discovery of fundamental mechanisms behind the emergence of function in the membrane signalosome.

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.

Link synthetic lethality to drug sensitivity of cancer cells

Synthetic lethal (SL) interactions occur when alterations in two genes lead to cell death but alteration in only one of them is not lethal. SL interactions provide a new strategy for molecular-targeted cancer therapy. Currently, there are few drugs targeting SL interactions that entered into clinical trials. Therefore, it is necessary to investigate the link between SL interactions and drug sensitivity of cancer cells systematically for drug development purpose. We identified SL interactions by integrating the high-throughput data from The Cancer Genome Atlas, small hairpin RNA data and genetic interactions of yeast. By integrating SL interactions from other studies, we tested whether the SL pairs that consist of drug target genes and the genes with genomic alterations are related with drug sensitivity of cancer cells. We found that only 6.26%∼34.61% of SL interactions showed the expected significant drug sensitivity using the pooled cancer cell line data from different tissues, but the proportion increased significantly to approximately 90% using the cancer cell line data for each specific tissue. From an independent pharmacogenomics data of 41 breast cancer cell lines, we found three SL interactions (ABL1-IFI16, ABL1-SLC50A1 and ABL1-SYT11) showed significantly better prognosis for the patients with both genes being altered than the patients with only one gene being altered, which partially supports the SL effect between the gene pairs. Our study not only provides a new way for unraveling the complex mechanisms of drug sensitivity but also suggests numerous potentially important drug targets for cancer therapy.

Computational algorithms for in silico profiling of activating mutations in cancer

Methods to catalog and computationally assess the mutational landscape of proteins in human cancers are desirable. One approach is to adapt evolutionary or data-driven methods developed for predicting whether a single-nucleotide polymorphism (SNP) is deleterious to protein structure and function. In cases where understanding the mechanism of protein activation and regulation is desired, an alternative approach is to employ structure-based computational approaches to predict the effects of point mutations. Through a case study of mutations in kinase domains of three proteins, namely, the anaplastic lymphoma kinase (ALK) in pediatric neuroblastoma patients, serine/threonine-protein kinase B-Raf (BRAF) in melanoma patients, and erythroblastic oncogene B 2 (ErbB2 or HER2) in breast cancer patients, we compare the two approaches above. We find that the structure-based method is most appropriate for developing a binary classification of several different mutations, especially infrequently occurring ones, concerning the activation status of the given target protein. This approach is especially useful if the effects of mutations on the interactions of inhibitors with the target proteins are being sought. However, many patients will present with mutations spread across different target proteins, making structure-based models computationally demanding to implement and execute. In this situation, data-driven methods-including those based on machine learning techniques and evolutionary methods-are most appropriate for recognizing and illuminate mutational patterns. We show, however, that, in the present status of the field, the two methods have very different accuracies and confidence values, and hence, the optimal choice of their deployment is context-dependent.

Personalized medicine approaches for colon cancer driven by genomics and systems biology: OncoTrack

The post-genomic era promises to pave the way to a personalized understanding of disease processes, with technological and analytical advances helping to solve some of the world's health challenges. Despite extraordinary progress in our understanding of cancer pathogenesis, the disease remains one of the world's major medical problems. New therapies and diagnostic procedures to guide their clinical application are urgently required. OncoTrack, a consortium between industry and academia, supported by the Innovative Medicines Initiative, signifies a new era in personalized medicine, which synthesizes current technological advances in omics techniques, systems biology approaches, and mathematical modeling. A truly personalized molecular imprint of the tumor micro-environment and subsequent diagnostic and therapeutic insight is gained, with the ultimate goal of matching the "right" patient to the "right" drug and identifying predictive biomarkers for clinical application. This comprehensive mapping of the colon cancer molecular landscape in tandem with crucial, clinical functional annotation for systems biology analysis provides unprecedented insight and predictive power for colon cancer management. Overall, we show that major biotechnological developments in tandem with changes in clinical thinking have laid the foundations for the OncoTrack approach and the future clinical application of a truly personalized approach to colon cancer theranostics.

Computational delineation of tyrosyl-substrate recognition and catalytic landscapes by the epidermal growth factor receptor tyrosine kinase domain

The epidermal growth factor receptor (EGFR) is a receptor tyrosine kinase (RTK), which catalyzes protein phosphorylation reactions by transferring the γ-phosphoryl group from an ATP molecule to the hydroxyl group of tyrosine residues in protein substrates. EGFR is an important drug target in the treatment of cancers and a better understanding of the receptor function is critical to discern cancer mechanisms. We employ a suite of molecular simulation methods to explore the mechanism of substrate recognition and to delineate the catalytic landscape of the phosphoryl transfer reaction. Based on our results, we propose that a highly conserved region corresponding to Val852-Pro853-Ile854-Lys855-Trp856 in the EGFR tyrosine kinase domain (TKD) is essential for substrate binding. We also provide a possible explanation for the established experimental observation that protein tyrosine kinases (including EGFR) select substrates with a glutamic acid at the P - 1 position and a large hydrophobic amino acid at the P + 1 position. Furthermore, our mixed quantum mechanics/molecular mechanics (QM/MM) simulations show that the EGFR protein kinase favors the dissociative mechanism, although an alternative channel through the formation of an associative transition state is also possible. Our simulations establish some key molecular rules in the operation for substrate-recognition and for phosphoryl transfer in the EGFR TKD.

Dynamic transition states of ErbB1 phosphorylation predicted by spatial stochastic modeling

ErbB1 overexpression is strongly linked to carcinogenesis, motivating better understanding of erbB1 dimerization and activation. Recent single-particle-tracking data have provided improved measures of dimer lifetimes and strong evidence that transient receptor coconfinement promotes repeated interactions between erbB1 monomers. Here, spatial stochastic simulations explore the potential impact of these parameters on erbB1 phosphorylation kinetics. This rule-based mathematical model incorporates structural evidence for conformational flux of the erbB1 extracellular domains, as well as asymmetrical orientation of erbB1 cytoplasmic kinase domains during dimerization. The asymmetric dimer model considers the theoretical consequences of restricted transactivation of erbB1 receptors within a dimer, where the N-lobe of one monomer docks with the C-lobe of the second monomer and triggers its catalytic activity. The dynamic nature of the erbB1 phosphorylation state is shown by monitoring activation states of individual monomers as they diffuse, bind, and rebind after ligand addition. The model reveals the complex interplay between interacting liganded and nonliganded species and the influence of their distribution and abundance within features of the membrane landscape.

Molecular modeling of ErbB4/HER4 kinase in the context of the HER4 signaling network helps rationalize the effects of clinically identified HER4 somatic mutations on the cell phenotype

In the ErbB/HER family of receptor tyrosine kinases, the deregulation of the EGFR/ErbB1/HER1, HER2/ErbB2, and HER3/ErbB3 kinases is associated with several cancers, while the HER4/ErbB4 kinase has been shown to play an anti-carcinogenic role in certain tumors. We present molecular and network models of HER4/ErbB4 activation and signaling in order to elucidate molecular mechanisms of activation and rationalize the effects of the clinically identified HER4 somatic mutants. Our molecular-scale simulations identify the important role played by the interactions within the juxtamembrane region during the activation process. Our results also support the hypothesis that the HER4 mutants may heterodimerize but not activate, resulting in blockage of the HER4-STAT5 differentiation pathway, in favor of the proliferative PI3K/AKT pathway. Translating our molecular simulation results into a cellular pathway model of wild type versus mutant HER4 signaling, we are able to recapitulate the major features of the PI3K/AKT and JAK/STAT activation downstream of HER4. Our model predicts that the signaling downstream of the wild type HER4 is enriched for the JAK-STAT pathway, whereas downstream of the mutant HER4 is enriched for the PI3K/AKT pathway. HER4 mutations may hence constitute a cellular shift from a program of differentiation to that of proliferation.

Structural systems biology and multiscale signaling models

We review current advances in experimental as well as computational modeling and simulation approaches to structural systems biology, whose overall aim is to build quantitative models of signaling networks while retaining the crucial elements of molecular specificity. We briefly discuss the current and emerging experimental and computational methods, particularly focusing on hybrid and multiscale methods, and highlight several applications in cell signaling with quantitative and predictive capabilities. The scope of such models range from delineating protein-protein interactions to describing clinical implications.

Multiscale cancer modeling

Simulating cancer behavior across multiple biological scales in space and time, i.e., multiscale cancer modeling, is increasingly being recognized as a powerful tool to refine hypotheses, focus experiments, and enable more accurate predictions. A growing number of examples illustrate the value of this approach in providing quantitative insights in the initiation, progression, and treatment of cancer. In this review, we introduce the most recent and important multiscale cancer modeling works that have successfully established a mechanistic link between different biological scales. Biophysical, biochemical, and biomechanical factors are considered in these models. We also discuss innovative, cutting-edge modeling methods that are moving predictive multiscale cancer modeling toward clinical application. Furthermore, because the development of multiscale cancer models requires a new level of collaboration among scientists from a variety of fields such as biology, medicine, physics, mathematics, engineering, and computer science, an innovative Web-based infrastructure is needed to support this growing community.

A multiscale modeling approach to investigate molecular mechanisms of pseudokinase activation and drug resistance in the HER3/ErbB3 receptor tyrosine kinase signaling network

Multiscale modeling provides a powerful and quantitative platform for investigating the complexity inherent in intracellular signaling pathways and rationalizing the effects of molecular perturbations on downstream signaling events and ultimately, on the cell phenotype. Here we describe the application of a multiscale modeling scheme to the HER3/ErbB3 receptor tyrosine kinase (RTK) signaling network, which regulates critical cellular processes including proliferation, migration and differentiation. The HER3 kinase is a topic of current interest and investigation, as it has been implicated in mechanisms of resistance to tyrosine kinase inhibition (TKI) of EGFR and HER2 in the treatment of many human malignancies. Moreover, the commonly regarded status of HER3 as a catalytically inactive 'pseudokinase' has recently been challenged by our previous study, which demonstrated robust residual kinase activity for HER3. Through our multiscale model, we investigate the most significant molecular interactions that contribute to potential mechanisms of HER3 activity and the physiological relevance of this activity to mechanisms of drug resistance in an ErbB-driven tumor cell in silico. The results of our molecular-scale simulations support the characterization of HER3 as a weakly active kinase that, in contrast to its fully-active ErbB family members, depends upon a unique hydrophobic interface to coordinate the alignment of specific catalytic residues required for its activity. Translating our molecular simulation results of the uniquely active behavior of the HER3 kinase into a physiologically relevant environment, our HER3 signaling model demonstrates that even a weak level of HER3 activity may be sufficient to induce AKT signaling and TKI resistance in the context of an ErbB signaling-dependent tumor cell, and therefore therapeutic targeting of HER3 may represent a superior treatment strategy for specific ErbB-driven cancers.

Molecular dynamics analysis of conserved hydrophobic and hydrophilic bond-interaction networks in ErbB family kinases

The EGFR (epidermal growth factor receptor)/ErbB/HER (human EGFR) family of kinases contains four homologous receptor tyrosine kinases that are important regulatory elements in key signalling pathways. To elucidate the atomistic mechanisms of dimerization-dependent activation in the ErbB family, we have performed molecular dynamics simulations of the intracellular kinase domains of three members of the ErbB family (those with known kinase activity), namely EGFR, ErbB2 (HER2) and ErbB4 (HER4), in different molecular contexts: monomer against dimer and wild-type against mutant. Using bioinformatics and fluctuation analyses of the molecular dynamics trajectories, we relate sequence similarities to correspondence of specific bond-interaction networks and collective dynamical modes. We find that in the active conformation of the ErbB kinases, key subdomain motions are co-ordinated through conserved hydrophilic interactions: activating bond-networks consisting of hydrogen bonds and salt bridges. The inactive conformations also demonstrate conserved bonding patterns (albeit less extensive) that sequester key residues and disrupt the activating bond network. Both conformational states have distinct hydrophobic advantages through context-specific hydrophobic interactions. We show that the functional (activating) asymmetric kinase dimer interface forces a corresponding change in the hydrophobic and hydrophilic interactions that characterize the inactivating bond network, resulting in motion of the αC-helix through allostery. Several of the clinically identified activating kinase mutations of EGFR act in a similar fashion to disrupt the inactivating bond network. The present molecular dynamics study reveals a fundamental difference in the sequence of events in EGFR activation compared with that described for the Src kinase Hck.

Analysis of Somatic Mutations in Cancer: Molecular Mechanisms of Activation in the ErbB Family of Receptor Tyrosine Kinases

The ErbB/EGFR/HER family of kinases consists of four homologous receptor tyrosine kinases which are important regulatory elements in many cellular processes, including cell proliferation, differentiation, and migration. Somatic mutations in, or over-expression of, the ErbB family is found in many cancers and is correlated with a poor prognosis; particularly, clinically identified mutations found in non-small-cell lung cancer (NSCLC) of ErbB1 have been shown to increase its basal kinase activity and patients carrying these mutations respond remarkably to the small tyrosine kinase inhibitor gefitinib. Here, we analyze the potential effects of the currently catalogued clinically identified mutations in the ErbB family kinase domains on the molecular mechanisms of kinase activation. Recently, we identified conserved networks of hydrophilic and hydrophobic interactions characteristic to the active and inactive conformation, respectively. Here, we show that the clinically identified mutants influence the kinase activity in distinctive fashion by affecting the characteristic interaction networks.

Investigating Molecular Mechanisms of Activation and Mutation of the HER2 Receptor Tyrosine Kinase through Computational Modeling and Simulation

Human epidermal growth factor receptor 2 (HER2)/ErbB2 is a receptor tyrosine kinase belonging to the EGFR/ErbB family and is overexpressed in 20-30% of human breast cancers. Since there is a growing effort to develop pharmacological inhibitors of the HER2 kinase for the treatment of breast cancer, it is clinically valuable to rationalize how specific mutations impact the molecular mechanism of receptor activation. Although several crystal structures of the ErbB kinases have been solved, the precise mechanism of HER2 activation remains unknown, and it has been suggested that HER2 is unique in its requirement for phosphorylation of Y877, a key tyrosine residue located in the activation loop (A-loop). In our studies, discussed here, we have investigated the mechanisms that are important in HER2 kinase domain regulation and compared them with the other ErbB family members, namely EGFR and ErbB4, to determine the molecular basis for HER2's unique mode of activation. We apply computational simulation techniques at the atomic level and at the electronic structure (quantum mechanical) level to elucidate details of the mechanisms governing the kinase domains of these ErbB members. Through analysis of our simulation results, we have discovered potential regulatory mechanisms common to EGFR, HER2, and ErbB4, including a tight coupling between the A-loop and catalytic loop that may contribute to alignment of residues required for catalysis in the active kinase. We further postulate an autoinhibitory mechanism whereby the inactive kinase is stabilized through sequestration of catalytic residues. In HER2, we also predict a role for phosphorylated Y877 in bridging a network of hydrogen bonds that fasten the A-loop in its active conformation, suggesting that HER2 may be unique among the ErbB members in requiring A-loop tyrosine phosphorylation for functionality. In EGFR, HER2, and ErbB4, we discuss the possible effects of activating mutations. Delineation of the activation mechanism of HER2 in the context of the other ErbB members is crucial for understanding how the activated kinase might interact with downstream molecules and couple to signaling cascades that promote cancer. Our comparative analysis furthers insight into the mechanics of activation of the HER2 kinase and enables us to predict the effect of an identified insertion mutation on HER2 activation. Further understanding of the mechanism of HER2 kinase activation at the atomic scale and how it couples to downstream signaling at the cellular scale will elucidate predictive molecular phenotypes that may indicate likelihood of response to specific therapies for HER2-mediated cancers.

Cancer Cell: Linking Oncogenic Signaling to Molecular Structure

A multiscale strategy is presented for constructing models of intracellular signaling networks in which the oncogenic behavior of the network is encoded through alternate parameterization of the kinetic and structural properties of mutant oncoproteins. The approach uses molecular dynamics and docking simulations to quantify altered topologies of interactions as well as to provide the missing parameters for network models of both wild-type and oncogenic signaling. Through simulation of the resulting signaling networks, the global behavior of these networks may then be compared and functional roles may be assigned to the mutant oncoproteins. An example of this approach is presented in which structural alterations found in a mutant form of the epidermal growth factor receptor are represented as kinetic perturbations in a model of growth factor signaling. Based on network parameters estimated from molecular-level simulations, simulations at the network level show that small perturbations in molecular structure can lead to profoundly altered cellular phenotype.

Atomistic insights into regulatory mechanisms of the HER2 tyrosine kinase domain: a molecular dynamics study

HER2 (ErbB2/Neu) is a receptor tyrosine kinase belonging to the epidermal growth factor receptor (EGFR)/ErbB family and is overexpressed in 20-30% of human breast cancers. Although several crystal structures of ErbB kinases have been solved, the precise mechanism of HER2 activation remains unknown, and it has been suggested that HER2 is unique in its requirement for phosphorylation of Y877, a key tyrosine residue located in the activation loop. To elucidate mechanistic details of kinase domain regulation, we performed molecular dynamics simulations of a homology-modeled HER2 kinase structure in active and inactive conformations. Principal component analysis of the atomistic fluctuations reveals a tight coupling between the activation loop and catalytic loop that may contribute to alignment of residues required for catalysis in the active kinase. The free energy perturbation method is also employed to predict a role for phosphorylated Y877 in stabilizing the kinase conformations. Finally, simulation results are presented for a HER2/EGFR heterodimer and reveal that the dimeric interface induces a rearrangement of the alphaC helix toward the active conformation. Elucidation of the molecular regulatory mechanisms in HER2 will help establish structure-function relationships in the wild-type kinase, as well as predict mutations with a propensity for constitutive activation in HER2-mediated cancers.

Fast predictions of thermodynamics and kinetics of protein-protein recognition from structures: from molecular design to systems biology

The increasing call for an overall picture of the interactions between the components of a biological system that give rise to the observed function is often summarized by the expression systems biology. Both the interpretative and predictive capabilities of holistic models of biochemical systems, however, depend to a large extent on the level of physico-chemical knowledge of the individual molecular interactions making up the network. This review is focused on the structure-based quantitative characterization of protein-protein interactions, ubiquitous in any biochemical pathway. Recently developed, fast and effective computational methods are reviewed, which allow the assessment of kinetic and thermodynamic features of the association-dissociation processes of protein complexes, both in water soluble and membrane environments. The performance and the accuracy of fast and semi-empirical structure-based methods have reached comparable levels with respect to the classical and more elegant molecular simulations. Nevertheless, the broad accessibility and lower computational cost provide the former methods with the advantageous possibility to perform systems-level analyses including extensive in silico mutagenesis screenings and large-scale structural predictions of multiprotein complexes.

Role of network branching in eliciting differential short-term signaling responses in the hypersensitive epidermal growth factor receptor mutants implicated in lung cancer

We study the effects of EGFR inhibition in wild-type and mutant cell lines upon tyrosine kinase inhibitor TKI treatment through a systems level deterministic and spatially homogeneous model to help characterize the hypersensitive response of the cancer cell lines harboring constitutively active mutant kinases to inhibitor treatment. By introducing a molecularly resolved branched network systems model (the molecular resolution is introduced for EGFR reactions and interactions in order to distinguish differences in activation between wild-type and mutants), we are able to quantify differences in (1) short-term signaling in downstream ERK and Akt activation, (2) the changes in the cellular inhibition EC50 associated with receptor phosphorylation (i.e., 50% inhibition of receptor phosphorylation in the cellular context), and (3) EC50 for the inhibition of activated downstream markers ERK-(p) and Akt-(p), where (p) denotes phosphorylated, upon treatment with the inhibitors in cell lines carrying both wild-type and mutant forms of the receptor. Using the branched signaling model, we illustrate a possible mechanism for preferential Akt activation in the cell lines harboring the oncogenic mutants of EGFR implicated in non-small-cell lung cancer and the enhanced efficacy of the inhibitor erlotinib especially in ablating the cellular Akt-(p) response. Using a simple phenomenological model to describe the effect of Akt activation on cellular decisions, we discuss how this preferential Akt activation is conducive to cellular oncogene addiction and how its disruption can lead to dramatic apoptotic response and hence remarkable inhibitor efficacies. We also identify key network nodes of our branched signaling model through sensitivity analysis as those rendering the network hypersensitive to enhanced ERK-(p) and Akt-(p); intriguingly, the identified nodes have a strong correlation with species implicated in oncogenic transformations in human cancers as well as in drug resistance mechanisms identified for the inhibitors in non-small-cell lung cancer therapy.