Association rate constants of ras-effector interactions are evolutionarily conserved

Functional and structural insights into RAS effector proteins

RAS proteins are conserved guanosine triphosphate (GTP) hydrolases (GTPases) that act as molecular binary switches and play vital roles in numerous cellular processes. Upon GTP binding, RAS GTPases adopt an active conformation and interact with specific proteins termed RAS effectors that contain a conserved ubiquitin-like domain, thereby facilitating downstream signaling. Over 50 effector proteins have been identified in the human proteome, and many have been studied as potential mediators of RAS-dependent signaling pathways. Biochemical and structural analyses have provided mechanistic insights into these effectors, and studies using model organisms have complemented our understanding of their role in physiology and disease. Yet, many critical aspects regarding the dynamics and biological function of RAS-effector complexes remain to be elucidated. In this review, we discuss the mechanisms and functions of known RAS effector proteins, provide structural perspectives on RAS-effector interactions, evaluate their significance in RAS-mediated signaling, and explore their potential as therapeutic targets.

Modeling cell line-specific recruitment of signaling proteins to the insulin-like growth factor 1 receptor

Receptor tyrosine kinases (RTKs) typically contain multiple autophosphorylation sites in their cytoplasmic domains. Once activated, these autophosphorylation sites can recruit downstream signaling proteins containing Src homology 2 (SH2) and phosphotyrosine-binding (PTB) domains, which recognize phosphotyrosine-containing short linear motifs (SLiMs). These domains and SLiMs have polyspecific or promiscuous binding activities. Thus, multiple signaling proteins may compete for binding to a common SLiM and vice versa. To investigate the effects of competition on RTK signaling, we used a rule-based modeling approach to develop and analyze models for ligand-induced recruitment of SH2/PTB domain-containing proteins to autophosphorylation sites in the insulin-like growth factor 1 (IGF1) receptor (IGF1R). Models were parameterized using published datasets reporting protein copy numbers and site-specific binding affinities. Simulations were facilitated by a novel application of model restructuration, to reduce redundancy in rule-derived equations. We compare predictions obtained via numerical simulation of the model to those obtained through simple prediction methods, such as through an analytical approximation, or ranking by copy number and/or K_D value, and find that the simple methods are unable to recapitulate the predictions of numerical simulations. We created 45 cell line-specific models that demonstrate how early events in IGF1R signaling depend on the protein abundance profile of a cell. Simulations, facilitated by model restructuration, identified pairs of IGF1R binding partners that are recruited in anti-correlated and correlated fashions, despite no inclusion of cooperativity in our models. This work shows that the outcome of competition depends on the physicochemical parameters that characterize pairwise interactions, as well as network properties, including network connectivity and the relative abundances of competitors.

Cells rely on networks of interacting biomolecules to sense and respond to environmental perturbations and signals. However, it is unclear how information is processed to generate appropriate and specific responses to signals, especially given that these networks tend to share many components. For example, receptors that detect distinct ligands and regulate distinct cellular activities commonly interact with overlapping sets of downstream signaling proteins. Here, to investigate the downstream signaling of a well-studied receptor tyrosine kinase (RTK), the insulin-like growth factor 1 (IGF1) receptor (IGF1R), we formulated and analyzed 45 cell line-specific mathematical models, which account for recruitment of 18 different binding partners to six sites of receptor autophosphorylation in IGF1R. The models were parameterized using available protein copy number and site-specific affinity measurements, and restructured to allow for network generation. We find that recruitment is influenced by the protein abundance profile of a cell, with different patterns of recruitment in different cell lines. Furthermore, in a given cell line, we find that pairs of IGF1R binding partners may be recruited in a correlated or anti-correlated fashion. We demonstrate that the simulations of the model have greater predictive power than protein copy number and/or binding affinity data, and that even a simple analytical model cannot reproduce the predicted recruitment ranking obtained via simulations. These findings represent testable predictions and indicate that the outputs of IGF1R signaling depend on cell line-specific properties in addition to the properties that are intrinsic to the biomolecules involved.

Models of Models: A Translational Route for Cancer Treatment and Drug Development

Every patient and every disease is different. Each patient therefore requires a personalized treatment approach. For technical reasons, a personalized approach is feasible for treatment strategies such as surgery, but not for drug-based therapy or drug development. The development of individual mechanistic models of the disease process in every patient offers the possibility of attaining truly personalized drug-based therapy and prevention. The concept of virtual clinical trials and the integrated use of in silico, in vitro, and in vivo models in preclinical development could lead to significant gains in efficiency and order of magnitude increases in the cost effectiveness of drug development and approval. We have developed mechanistic computational models of large-scale cellular signal transduction networks for prediction of drug effects and functional responses, based on patient-specific multi-level omics profiles. However, a major barrier to the use of such models in a clinical and developmental context is the reliability of predictions. Here we detail how the approach of using “models of models” has the potential to impact cancer treatment and drug development. We describe the iterative refinement process that leverages the flexibility of experimental systems to generate highly dimensional data, which can be used to train and validate computational model parameters and improve model predictions. In this way, highly optimized computational models with robust predictive capacity can be generated. Such models open up a number of opportunities for cancer drug treatment and development, from enhancing the design of experimental studies, reducing costs, and improving animal welfare, to increasing the translational value of results generated.

Kinetic characterization of apoptotic Ras signaling through Nore1-MST1 complex formation

Ras-mediated apoptotic signaling is expected to be mediated via Rassf-MST complexes, but the system has been poorly characterized in vitro until now. Here we demonstrate that active H-Ras, Nore1A and MST1 form a stable ternary complex in vitro without other external factors, Nore1A interacting simultaneously with H-Ras and MST1 via its RBD and SARAH domain, respectively. Moreover, our data show for the first time that the SARAH domain of Nore1A plays a role in the Nore1A binding to H-Ras. Finally, we analyze the relation between the electrostatic and hydrophobic forces and kinetic constants of the Nore1A - H-Ras complex.

The efficacy of Raf kinase recruitment to the GTPase H-ras depends on H-ras membrane conformer-specific nanoclustering

Solution structures and biochemical data have provided a wealth of mechanistic insight into Ras GTPases. However, information on how much the membrane organization of these lipid-modified proteins impacts on their signaling is still scarce. Ras proteins are organized into membrane nanoclusters, which are necessary for Ras-MAPK signaling. Using quantitative conventional and super-resolution fluorescence methods, as well as mathematical modeling, we investigated nanoclustering of H-ras helix α4 and hypervariable region mutants that have different bona fide conformations on the membrane. By following the emergence of conformer-specific nanoclusters in the plasma membrane of mammalian cells, we found that conformers impart distinct nanoclustering responses depending on the cytoplasmic levels of the nanocluster scaffold galectin-1. Computational modeling revealed that complexes containing H-ras conformers and galectin-1 affect both the number and lifetime of nanoclusters and thus determine the specific Raf effector recruitment. Our results show that mutations in Ras can affect its nanoclustering response and thus allosterically effector recruitment and downstream signaling. We postulate that cancer- and developmental disease-linked mutations that are associated with the Ras membrane conformation may exhibit so far unrecognized Ras nanoclustering and therefore signaling alterations.

Modeling intracellular signaling underlying striatal function in health and disease

Striatum, which is the input nucleus of the basal ganglia, integrates cortical and thalamic glutamatergic inputs with dopaminergic afferents from the substantia nigra pars compacta. The combination of dopamine and glutamate strongly modulates molecular and cellular properties of striatal neurons and the strength of corticostriatal synapses. These actions are performed via intracellular signaling networks, containing several intertwined feedback loops. Understanding the role of dopamine and other neuromodulators requires the development of quantitative dynamical models for describing the intracellular signaling, in order to provide precise unambiguous descriptions and quantitative predictions. Building such models requires integration of data from multiple data sources containing information regarding the molecular interactions, the strength of these interactions, and the subcellular localization of the molecules. Due to the uncertainty, variability, and sparseness of these data, parameter estimation techniques are critical for inferring or constraining the unknown parameters, and sensitivity analysis evaluates which parameters are most critical for a given observed macroscopic behavior. Here, we briefly review the modeling approaches and tools that have been used to investigate biochemical signaling in the striatum, along with some of the models built around striatum. We also suggest a future direction for the development of such models from the, now becoming abundant, high-throughput data.

Lessons from computer simulations of Ras proteins in solution and in membrane

BACKGROUND

A great deal has been learned over the last several decades about the function of Ras proteins in solution and membrane environments. While much of this knowledge has been derived from a plethora of experimental techniques, computer simulations have also played a substantial role.

SCOPE OF REVIEW

Our goal here is to summarize the contribution of molecular simulations to our current understanding of normal and aberrant Ras function. We focus on lessons from molecular dynamics simulations in aqueous and membrane environments.

MAJOR CONCLUSIONS

The central message is that a close interaction between theory and simulation on the one hand and cell-biological, spectroscopic and other experimental approaches on the other has played, and will likely continue to play, a vital role in Ras research.

GENERAL SIGNIFICANCE

Atomistic insights emerging from detailed simulations of Ras in solution and in bilayers may be the key to unlock the secret that to date prevented development of selective anti-Ras inhibitors for cancer therapy.

Positively selected sites in cetacean myoglobins contribute to protein stability

Since divergence ∼50 Ma ago from their terrestrial ancestors, cetaceans underwent a series of adaptations such as a ∼10-20 fold increase in myoglobin (Mb) concentration in skeletal muscle, critical for increasing oxygen storage capacity and prolonging dive time. Whereas the O2-binding affinity of Mbs is not significantly different among mammals (with typical oxygenation constants of ∼0.8-1.2 µM(-1)), folding stabilities of cetacean Mbs are ∼2-4 kcal/mol higher than for terrestrial Mbs. Using ancestral sequence reconstruction, maximum likelihood and bayesian tests to describe the evolution of cetacean Mbs, and experimentally calibrated computation of stability effects of mutations, we observe accelerated evolution in cetaceans and identify seven positively selected sites in Mb. Overall, these sites contribute to Mb stabilization with a conditional probability of 0.8. We observe a correlation between Mb folding stability and protein abundance, suggesting that a selection pressure for stability acts proportionally to higher expression. We also identify a major divergence event leading to the common ancestor of whales, during which major stabilization occurred. Most of the positively selected sites that occur later act against other destabilizing mutations to maintain stability across the clade, except for the shallow divers, where late stability relaxation occurs, probably due to the shorter aerobic dive limits of these species. The three main positively selected sites 66, 5, and 35 undergo changes that favor hydrophobic folding, structural integrity, and intra-helical hydrogen bonds.

Oligomerization of rab/effector complexes in the regulation of vesicle trafficking

Rabs comprise the largest member of the Ras superfamily of small GTPases with over 60 proteins in mammals and 11 proteins in yeast. Like all small GTPases, Rabs oscillate between an inactive GDP-bound conformation and an active GTP-bound state that is tethered to lipid membranes via a C-terminal prenylation site on conserved cysteine residues. In their active state, Rabs regulate various aspects of membrane trafficking, including vesicle formation, transport, docking, and fusion. The critical element of biological activity is the recruitment of cytosolic effector proteins to specific endomembranes by active Rabs. The importance of Rabs in cellular processes is apparent from their links to genetic disorders, immunodeficiency, cancer, and pathogen invasion. During the last decade, numerous structures of complexes have shed light on the molecular basis for Rab/effector specificity and their topological organization on subcellular membranes. Here, I review the known structures of Rab/effector complexes and their modes of oligomerization. This is followed by a brief discussion on the thermodynamics of effector recruitment, which has not been documented sufficiently in previous reviews. A summary of diseases associated with Rab/effector trafficking pathways concludes this chapter.

Regulation of hSos1 activity is a system-level property generated by its multi-domain structure

The multi-domain protein hSos1 plays a major role in cell growth and differentiation through its Ras-specific guanine nucleotide exchange domain whose complex regulation involves intra-molecular, inter-domain rearrangements. We present a stochastic mathematical model describing intra-molecular regulation of hSos1 activity. The population macroscopic effect is reproduced through a Monte-Carlo approach. Key model parameters have been experimentally determined by BIAcore analysis. Complementation experiments of a Saccharomyces cerevisiae cdc25(ts) strain with Sos deletion mutants provided a comprehensive data set for estimation of unknown parameters and model validation. The model is robust against parameter alteration and describes both the behavior of Sos deletion mutants and modulation of activity of the full length molecule under physiological conditions. By incorporating the calculated effect of amino acid changes at an inter-domain interface, the behavior of a mutant correlating with a developmental syndrome could be simulated, further validating the model. The activation state of Ras-specific guanine nucleotide exchange domain of hSos1 arises as an "emergent property" of its multi-domain structure that allows multi-level integration of a complex network of intra- and inter-molecular signals.

PLC regulation: emerging pictures for molecular mechanisms

Phosphoinositide-specific phospholipase C (PLC) enzymes are common signalling components linked to the activation of most cellular receptors. All PLC families are complex, modular, multi-domain proteins and together cover a broad spectrum of regulatory interactions, including direct binding to G protein subunits, small GTPases from Rho and Ras families, receptor and non-receptor tyrosine kinases and lipid components of cellular membranes. Recent structural determinations of PLC components and their complexes with regulatory proteins and direct mechanistic studies, together with earlier work, have provided the foundation to propose molecular mechanisms that stringently regulate PLC activity.

Cell type-specific importance of ras-c-raf complex association rate constants for MAPK signaling

We generated 17 c-Raf (RAF proto-oncogene serine-threonine protein kinase) mutants with altered Ras-Raf association and dissociation rates to investigate the role of electrostatically driven Ras-Raf association rates on epidermal growth factor (EGF)-activated mitogen-activated protein kinase (MAPK) signal transduction. Some of these mutants had compensating changes in association and dissociation rates, enabling the effects of changes in association rate to be distinguished from those of changes in affinity. In rabbit kidney (RK13) cells, these mutants affected downstream signaling, with changes in Ras-c-Raf association rates having a greater effect on MAPK signaling than did similar changes in dissociation rates. Mutants with compensating decreases in both association and dissociation rates stimulated less extracellular signal-regulated kinase (ERK)-dependent reporter activity than did wild-type c-Raf, whereas the converse was true for mutants with increased association and dissociation rates. In marked contrast, the mutants had little or no effect on signaling in human embryonic kidney (HEK) 293 cells. These two cell lines also showed distinct patterns of EGF-dependent ERK phosphorylation and signaling: ERK activation and signaling were transient in HEK293 cells and sustained in RK13 cells, with the difference resulting from the lack of negative feedback from ERK to Sos (Son of Sevenless) in the latter. Computer simulation revealed that, in the presence of negative feedback, changes in the rate of Ras-c-Raf binding have little effect on ERK activation. Thus, EGF-MAPK activation kinetics and feedback regulation is cell type specific and depends on the network topology.

Molecular networks and system-level properties

Molecular systems biology aims to describe the functions of complex biological processes through recursive integration of molecular analysis, modeling, simulation and theory. It focuses on networks that originate from interconnection of genes, proteins and metabolites whose dynamic interactions generate, as an emergent property of the system, the corresponding function. Although evolutionary optimized, intracellular biochemical parameters, such as the expression level of gene products or the affinity between two or more proteins, must have a permissible range that gives robustness against perturbations to the system. Using the yeast G(1)-to-S transition network as an example we show that sophisticated relations exist among network structure, emergent property and robustness. Different emergent properties are generated from the same network by changing the strength of its interactions, not only by altering expression level, but also through mono and multi-site phosphorylation/dephosphorylation. Besides, multi-site protein phosphorylation modules, widespread in cell cycle, may ensure robust and coherent timing of cell cycle transitions as it happens for the onset of DNA replication. In conclusion, the modulation of biological function/emergent property by modifying interaction strength provides an efficient, highly tunable device to regulate biological processes. Furthermore, the principles outlined herein may provide new insight to network analysis in drug discovery.