Modeling and simulation of aggregation of membrane protein LAT with molecular variability in the number of binding sites for cytosolic Grb2-SOS1-Grb2

Identification of SH2 domain-containing proteins and motifs prediction by a deep learning method

The Src Homology 2 (SH2) domain plays an important role in the signal transmission mechanism in organisms. It mediates the protein-protein interactions based on the combination between phosphotyrosine and motifs in SH2 domain. In this study, we designed a method to identify SH2 domain-containing proteins and non-SH2 domain-containing proteins through deep learning technology. Firstly, we collected SH2 and non-SH2 domain-containing protein sequences including multiple species. We built six deep learning models through DeepBIO after data preprocessing and compared their performance. Secondly, we selected the model with the strongest comprehensive ability to conduct training and test separately again, and analyze the results visually. It was found that 288-dimensional (288D) feature could effectively identify two types of proteins. Finally, motifs analysis discovered the specific motif YKIR and revealed its function in signal transduction. In summary, we successfully identified SH2 domain and non-SH2 domain proteins through deep learning method, and obtained 288D features that perform best. In addition, we found a new motif YKIR in SH2 domain, and analyzed its function which helps to further understand the signaling mechanisms within the organism.

Kinetic frustration by limited bond availability controls the LAT protein condensation phase transition on membranes

LAT is a membrane-linked scaffold protein that undergoes a phase transition to form a two-dimensional protein condensate on the membrane during T cell activation. Governed by tyrosine phosphorylation, LAT recruits various proteins that ultimately enable condensation through a percolation network of discrete and selective protein-protein interactions. Here, we describe detailed kinetic measurements of the phase transition, along with coarse-grained model simulations, that reveal that LAT condensation is kinetically frustrated by the availability of bonds to form the network. Unlike typical miscibility transitions in which compact domains may coexist at equilibrium, the LAT condensates are dynamically arrested in extended states, kinetically trapped out of equilibrium. Modeling identifies the structural basis for this kinetic arrest as the formation of spindle arrangements, favored by limited multivalent binding interactions along the flexible, intrinsically disordered LAT protein. These results reveal how local factors controlling the kinetics of LAT condensation enable formation of different, stable condensates, which may ultimately coexist within the cell.

The intramolecular allostery of GRB2 governing its interaction with SOS1 is modulated by phosphotyrosine ligands

Growth factor receptor-bound protein 2 (GRB2) is a trivalent adaptor protein and a key element in signal transduction. It interacts via its flanking nSH3 and cSH3 domains with the proline-rich domain (PRD) of the RAS activator SOS1 and via its central SH2 domain with phosphorylated tyrosine residues of receptor tyrosine kinases (RTKs; e.g. HER2). The elucidation of structural organization and mechanistic insights into GRB2 interactions, however, remain challenging due to their inherent flexibility. This study represents an important advance in our mechanistic understanding of how GRB2 links RTKs to SOS1. Accordingly, it can be proposed that (1) HER2 pYP-bound SH2 potentiates GRB2 SH3 domain interactions with SOS1 (an allosteric mechanism); (2) the SH2 domain blocks cSH3, enabling nSH3 to bind SOS1 first before cSH3 follows (an avidity-based mechanism); and (3) the allosteric behavior of cSH3 to other domains appears to be unidirectional, although there is an allosteric effect between the SH2 and SH3 domains.

PLCγ1 promotes phase separation of T cell signaling components

The T cell receptor (TCR) pathway receives, processes, and amplifies the signal from pathogenic antigens to the activation of T cells. Although major components in this pathway have been identified, the knowledge on how individual components cooperate to effectively transduce signals remains limited. Phase separation emerges as a biophysical principle in organizing signaling molecules into liquid-like condensates. Here, we report that phospholipase Cγ1 (PLCγ1) promotes phase separation of LAT, a key adaptor protein in the TCR pathway. PLCγ1 directly cross-links LAT through its two SH2 domains. PLCγ1 also protects LAT from dephosphorylation by the phosphatase CD45 and promotes LAT-dependent ERK activation and SLP76 phosphorylation. Intriguingly, a nonmonotonic effect of PLCγ1 on LAT clustering was discovered. Computer simulations, based on patchy particles, revealed how the cluster size is regulated by protein compositions. Together, these results define a critical function of PLCγ1 in promoting phase separation of the LAT complex and TCR signal transduction.

Suppression of human T cell activation by derivatives of glycerol monolaurate

Glycerol monolaurate (GML), a naturally occurring monoglyceride, is widely used commercially for its antimicrobial properties. Interestingly, several studies have shown that GML not only has antimicrobial properties but is also an anti-inflammatory agent. GML inhibits peripheral blood mononuclear cell proliferation and inhibits T cell receptor (TCR)-induced signaling events. In this study, we perform an extensive structure activity relationship analysis to investigate the structural components of GML necessary for its suppression of human T cell activation. Human T cells were treated with analogs of GML, differing in acyl chain length, head group, linkage of acyl chain, and number of laurate groups. Treated cells were then tested for changes in membrane dynamics, LAT clustering, calcium signaling, and cytokine production. We found that an acyl chain with 12-14 carbons, a polar head group, an ester linkage, and a single laurate group at any position are all necessary for GML to inhibit protein clustering, calcium signaling, and cytokine production. Removing the glycerol head group or replacing the ester linkage with a nitrogen prevented derivative-mediated inhibition of protein cluster formation and calcium signaling, while still inhibiting TCR-induced cytokine production. These findings expand our current understanding of the mechanisms of action of GML and the of GML needed to function as a novel immunosuppressant.

How the T cell signaling network processes information to discriminate between self and agonist ligands

T cells exhibit remarkable sensitivity and selectivity in detecting and responding to agonist peptides (p) bound to MHC molecules in a sea of self pMHC molecules. Despite much work, understanding of the underlying mechanisms of distinguishing such ligands remains incomplete. Here, we quantify T cell discriminatory capacity using channel capacity, a direct measure of the signaling network's ability to discriminate between antigen-presenting cells (APCs) displaying either self ligands or a mixture of self and agonist ligands. This metric shows how differences in information content between these two types of peptidomes are decoded by the topology and rates of kinetic proofreading signaling steps inside T cells. Using channel capacity, we constructed numerically substantiated hypotheses to explain the discriminatory role of a recently identified slow LAT Y132 phosphorylation step. Our results revealed that in addition to the number and kinetics of sequential signaling steps, a key determinant of discriminatory capability is spatial localization of a minimum number of these steps to the engaged TCR. Biochemical and imaging experiments support these findings. Our results also reveal the discriminatory role of early negative feedback and necessary amplification conferred by late positive feedback.

Understanding the Dynamics of T-Cell Activation in Health and Disease Through the Lens of Computational Modeling

T cells in the immune system are activated by binding to foreign peptides (from an external pathogen) or mutant peptide (derived from endogenous proteins) displayed on the surface of a diseased cell. This triggers a series of intracellular signaling pathways, which ultimately dictate the response of the T cell. The insights from computational models have greatly improved our understanding of the mechanisms that control T-cell activation. In this review, we focus on the use of ordinary differential equation–based mechanistic models to study T-cell activation. We highlight several examples that demonstrate the models’ utility in answering specific questions related to T-cell activation signaling, from antigen discrimination to the feedback mechanisms that initiate transcription factor activation. In addition, we describe other modeling approaches that can be combined with mechanistic models to bridge time scales and better understand how intracellular signaling events, which occur on the order of seconds to minutes, influence phenotypic responses of T-cell activation, which occur on the order of hours to days. Overall, through concrete examples, we emphasize how computational modeling can be used to enable the rational design and optimization of immunotherapies.

Regulation of Transmembrane Signaling by Phase Separation

Cell surface transmembrane receptors often form nanometer- to micrometer-scale clusters to initiate signal transduction in response to environmental cues. Extracellular ligand oligomerization, domain-domain interactions, and binding to multivalent proteins all contribute to cluster formation. Here we review the current understanding of mechanisms driving cluster formation in a series of representative receptor systems: glycosylated receptors, immune receptors, cell adhesion receptors, Wnt receptors, and receptor tyrosine kinases. We suggest that these clusters share properties of systems that undergo liquid-liquid phase separation and could be investigated in this light.

The linker for activation of T cells (LAT) signaling hub: from signaling complexes to microclusters

Since the cloning of the critical adapter, LAT (linker for activation of T cells), more than 15 years ago, a combination of multiple scientific approaches and techniques continues to provide valuable insights into the formation, composition, regulation, dynamics, and function of LAT-based signaling complexes. In this review, we will summarize current views on the assembly of signaling complexes nucleated by LAT. LAT forms numerous interactions with other signaling molecules, leading to cooperativity in the system. Furthermore, oligomerization of LAT by adapter complexes enhances intracellular signaling and is physiologically relevant. These results will be related to data from super-resolution microscopy studies that have revealed the smallest LAT-based signaling units and nanostructure.

An Interaction Library for the FcεRI Signaling Network

Antigen receptors play a central role in adaptive immune responses. Although the molecular networks associated with these receptors have been extensively studied, we currently lack a systems-level understanding of how combinations of non-covalent interactions and post-translational modifications are regulated during signaling to impact cellular decision-making. To fill this knowledge gap, it will be necessary to formalize and piece together information about individual molecular mechanisms to form large-scale computational models of signaling networks. To this end, we have developed an interaction library for signaling by the high-affinity IgE receptor, FcεRI. The library consists of executable rules for protein–protein and protein–lipid interactions. This library extends earlier models for FcεRI signaling and introduces new interactions that have not previously been considered in a model. Thus, this interaction library is a toolkit with which existing models can be expanded and from which new models can be built. As an example, we present models of branching pathways from the adaptor protein Lat, which influence production of the phospholipid PIP₃ at the plasma membrane and the soluble second messenger IP₃. We find that inclusion of a positive feedback loop gives rise to a bistable switch, which may ensure robust responses to stimulation above a threshold level. In addition, the library is visualized to facilitate understanding of network circuitry and identification of network motifs.

Extracellular matrix density promotes EMT by weakening cell–cell adhesions

This paper probes the influence of extracellular matrix density on cell–cell adhesion and its relevance to EMT.

Rule-based modeling: a computational approach for studying biomolecular site dynamics in cell signaling systems

Rule-based modeling was developed to address the limitations of traditional approaches for modeling chemical kinetics in cell signaling systems. These systems consist of multiple interacting biomolecules (e.g., proteins), which themselves consist of multiple parts (e.g., domains, linear motifs, and sites of phosphorylation). Consequently, biomolecules that mediate information processing generally have the potential to interact in multiple ways, with the number of possible complexes and posttranslational modification states tending to grow exponentially with the number of binary interactions considered. As a result, only large reaction networks capture all possible consequences of the molecular interactions that occur in a cell signaling system, which is problematic because traditional modeling approaches for chemical kinetics (e.g., ordinary differential equations) require explicit network specification. This problem is circumvented through representation of interactions in terms of local rules. With this approach, network specification is implicit and model specification is concise. Concise representation results in a coarse graining of chemical kinetics, which is introduced because all reactions implied by a rule inherit the rate law associated with that rule. Coarse graining can be appropriate if interactions are modular, and the coarseness of a model can be adjusted as needed. Rules can be specified using specialized model-specification languages, and recently developed tools designed for specification of rule-based models allow one to leverage powerful software engineering capabilities. A rule-based model comprises a set of rules, which can be processed by general-purpose simulation and analysis tools to achieve different objectives (e.g., to perform either a deterministic or stochastic simulation).

The ability of Sos1 to oligomerize the adaptor protein LAT is separable from its guanine nucleotide exchange activity in vivo

The activation of the small guanosine triphosphatase Ras by the guanine nucleotide exchange factor (GEF) Sos1 (Son of Sevenless 1) is a central feature of many receptor-stimulated signaling pathways. In developing T cells (thymocytes), Sos1-dependent activation of extracellular signal-regulated kinase (ERK) is required to stimulate cellular proliferation and differentiation. We showed that in addition to its GEF activity, Sos1 acted as a scaffold to nucleate oligomerization of the T cell adaptor protein LAT (linker for activation of T cells) in vivo. The scaffold function of Sos1 depended on its ability to bind to the adaptor protein Grb2. Furthermore, the GEF activity of Sos1 and the Sos1-dependent oligomerization of LAT were separable functions in vivo. Whereas the GEF activity of Sos1 was required for optimal ERK phosphorylation in response to T cell receptor (TCR) stimulation, the Sos1-dependent oligomerization of LAT was required for maximal TCR-dependent phosphorylation and activation of phospholipase C-γ1 and Ca(2+) signaling. Finally, both of these Sos1 functions were required for early thymocyte proliferation. Whereas transgenic restoration of either the GEF activity or the LAT oligomerization functions of Sos1 alone failed to rescue thymocyte development in Sos1-deficient mice, simultaneous reconstitution of these two signals in the same cell restored normal T cell development. This ability of Sos1 to act both as a RasGEF and as a scaffold to nucleate Grb2-dependent adaptor oligomerization may also occur in other Grb2-dependent pathways, such as those activated by growth factor receptors.

The adaptor protein LAT serves as an integration node for signaling pathways that drive T cell activation

T cells are essential for the adaptive immune response to pathogens. However, dysfunctional T cell activity has been implicated in numerous diseases, including the failure of organ transplants, allergic reactions, asthma, autoimmune disorders, and coronary artery disease. T cell responses to pathogens require the induction of the primary activating receptor, the T cell receptor (TCR), along with other costimulatory and adhesion receptors. Signal transduction pathways activated downstream of these receptors drive T cell responses required for the immune response and disease progression. A key question in our understanding of the mechanism of T cell activation is how signaling pathways emanating from multiple receptors integrate together to alter T cell effector functions. One integration node for intracellular signaling is the membrane-associated adaptor protein linker for the activation of T cells or LAT. Upon stimulation of the TCR and other receptors, LAT is phosphorylated at several tyrosines residues on its cytoplasmic tail. This leads to the binding of SH2 domain-containing proteins and their associated molecules and the formation of large multiprotein complexes. These dynamic and highly regulated signaling complexes facilitate the production of second messengers, activate downstream pathways, induce actin cytoskeleton polymerization, and stimulate the activity of multiple transcription factors. Thus, signaling pathways from several receptors feed into LAT, which then integrates this information and selectively induces pathways critical for T cell activation and the adaptive immune response.

Specification, annotation, visualization and simulation of a large rule-based model for ERBB receptor signaling

BACKGROUND

Mathematical/computational models are needed to understand cell signaling networks, which are complex. Signaling proteins contain multiple functional components and multiple sites of post-translational modification. The multiplicity of components and sites of modification ensures that interactions among signaling proteins have the potential to generate myriad protein complexes and post-translational modification states. As a result, the number of chemical species that can be populated in a cell signaling network, and hence the number of equations in an ordinary differential equation model required to capture the dynamics of these species, is prohibitively large. To overcome this problem, the rule-based modeling approach has been developed for representing interactions within signaling networks efficiently and compactly through coarse-graining of the chemical kinetics of molecular interactions.

RESULTS

Here, we provide a demonstration that the rule-based modeling approach can be used to specify and simulate a large model for ERBB receptor signaling that accounts for site-specific details of protein-protein interactions. The model is considered large because it corresponds to a reaction network containing more reactions than can be practically enumerated. The model encompasses activation of ERK and Akt, and it can be simulated using a network-free simulator, such as NFsim, to generate time courses of phosphorylation for 55 individual serine, threonine, and tyrosine residues. The model is annotated and visualized in the form of an extended contact map.

CONCLUSIONS

With the development of software that implements novel computational methods for calculating the dynamics of large-scale rule-based representations of cellular signaling networks, it is now possible to build and analyze models that include a significant fraction of the protein interactions that comprise a signaling network, with incorporation of the site-specific details of the interactions. Modeling at this level of detail is important for understanding cellular signaling.