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Erickson KE, Rukhlenko OS, Shahinuzzaman M, Slavkova KP, Lin YT, Suderman R, Stites EC, Anghel M, Posner RG, Barua D, Kholodenko BN, Hlavacek WS. Modeling cell line-specific recruitment of signaling proteins to the insulin-like growth factor 1 receptor. PLoS Comput Biol 2019; 15:e1006706. [PMID: 30653502 PMCID: PMC6353226 DOI: 10.1371/journal.pcbi.1006706] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2018] [Revised: 01/30/2019] [Accepted: 12/09/2018] [Indexed: 12/27/2022] Open
Abstract
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 KD 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.
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Affiliation(s)
- Keesha E. Erickson
- Theoretical Biology and Biophysics Group, Theoretical Division, Los Alamos National Laboratory, Los Alamos, New Mexico, United States of America
| | | | - Md Shahinuzzaman
- Department of Chemical and Biochemical Engineering, University of Missouri Science and Technology, Rolla, Missouri, United States of America
| | - Kalina P. Slavkova
- Theoretical Biology and Biophysics Group, Theoretical Division, Los Alamos National Laboratory, Los Alamos, New Mexico, United States of America
- Center for Nonlinear Studies, Los Alamos National Laboratory, Los Alamos, New Mexico, United States of America
| | - Yen Ting Lin
- Theoretical Biology and Biophysics Group, Theoretical Division, Los Alamos National Laboratory, Los Alamos, New Mexico, United States of America
- Center for Nonlinear Studies, Los Alamos National Laboratory, Los Alamos, New Mexico, United States of America
| | - Ryan Suderman
- Theoretical Biology and Biophysics Group, Theoretical Division, Los Alamos National Laboratory, Los Alamos, New Mexico, United States of America
- Center for Nonlinear Studies, Los Alamos National Laboratory, Los Alamos, New Mexico, United States of America
| | - Edward C. Stites
- The Salk Institute for Biological Studies, La Jolla, California, United States of America
| | - Marian Anghel
- Information Sciences Group, Computer, Computational and Statistical Sciences Division, Los Alamos National Laboratory, Los Alamos, New Mexico, United States of America
| | - Richard G. Posner
- Department of Biological Sciences, Northern Arizona University, Flagstaff, Arizona, United States of America
| | - Dipak Barua
- Department of Chemical and Biochemical Engineering, University of Missouri Science and Technology, Rolla, Missouri, United States of America
| | - Boris N. Kholodenko
- Systems Biology Ireland, University College Dublin, Belfield, Dublin, Ireland
- School of Medicine and Medical Science and Conway Institute of Biomolecular and Biomedical Research, University College Dublin, Belfield, Dublin, Ireland
| | - William S. Hlavacek
- Theoretical Biology and Biophysics Group, Theoretical Division, Los Alamos National Laboratory, Los Alamos, New Mexico, United States of America
- Center for Nonlinear Studies, Los Alamos National Laboratory, Los Alamos, New Mexico, United States of America
- * E-mail:
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Jadwin JA, Curran TG, Lafontaine AT, White FM, Mayer BJ. Src homology 2 domains enhance tyrosine phosphorylation in vivo by protecting binding sites in their target proteins from dephosphorylation. J Biol Chem 2017; 293:623-637. [PMID: 29162725 DOI: 10.1074/jbc.m117.794412] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2017] [Revised: 11/17/2017] [Indexed: 02/03/2023] Open
Abstract
Phosphotyrosine (pTyr)-dependent signaling is critical for many cellular processes. It is highly dynamic, as signal output depends not only on phosphorylation and dephosphorylation rates but also on the rates of binding and dissociation of effectors containing phosphotyrosine-dependent binding modules such as Src homology 2 (SH2) and phosphotyrosine-binding (PTB) domains. Previous in vitro studies suggested that binding of SH2 and PTB domains can enhance protein phosphorylation by protecting the sites bound by these domains from phosphatase-mediated dephosphorylation. To test whether this occurs in vivo, we used the binding of growth factor receptor bound 2 (GRB2) to phosphorylated epidermal growth factor receptor (EGFR) as a model system. We analyzed the effects of SH2 domain overexpression on protein tyrosine phosphorylation by quantitative Western and far-Western blotting, mass spectrometry, and computational modeling. We found that SH2 overexpression results in a significant, dose-dependent increase in EGFR tyrosine phosphorylation, particularly of sites corresponding to the binding specificity of the overexpressed SH2 domain. Computational models using experimentally determined EGFR phosphorylation and dephosphorylation rates, and pTyr-EGFR and GRB2 concentrations, recapitulated the experimental findings. Surprisingly, both modeling and biochemical analyses suggested that SH2 domain overexpression does not result in a major decrease in the number of unbound phosphorylated SH2 domain-binding sites. Our results suggest that signaling via SH2 domain binding is buffered over a relatively wide range of effector concentrations and that SH2 domain proteins with overlapping binding specificities are unlikely to compete with one another for phosphosites in vivo.
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Affiliation(s)
- Joshua A Jadwin
- From the Raymond and Beverly Sackler Laboratory of Molecular Medicine, Department of Genetics and Genome Sciences, and the Richard D. Berlin Center for Cell Analysis and Modeling, University of Connecticut School of Medicine, Farmington, Connecticut 06030 and
| | - Timothy G Curran
- the Department of Biological Engineering and Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139
| | - Adam T Lafontaine
- From the Raymond and Beverly Sackler Laboratory of Molecular Medicine, Department of Genetics and Genome Sciences, and the Richard D. Berlin Center for Cell Analysis and Modeling, University of Connecticut School of Medicine, Farmington, Connecticut 06030 and
| | - Forest M White
- the Department of Biological Engineering and Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139
| | - Bruce J Mayer
- From the Raymond and Beverly Sackler Laboratory of Molecular Medicine, Department of Genetics and Genome Sciences, and the Richard D. Berlin Center for Cell Analysis and Modeling, University of Connecticut School of Medicine, Farmington, Connecticut 06030 and
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Stites EC, Aziz M, Creamer MS, Von Hoff DD, Posner RG, Hlavacek WS. Use of mechanistic models to integrate and analyze multiple proteomic datasets. Biophys J 2016; 108:1819-1829. [PMID: 25863072 DOI: 10.1016/j.bpj.2015.02.030] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2014] [Revised: 02/18/2015] [Accepted: 02/24/2015] [Indexed: 11/30/2022] Open
Abstract
Proteins in cell signaling networks tend to interact promiscuously through low-affinity interactions. Consequently, evaluating the physiological importance of mapped interactions can be difficult. Attempts to do so have tended to focus on single, measurable physicochemical factors, such as affinity or abundance. For example, interaction importance has been assessed on the basis of the relative affinities of binding partners for a protein of interest, such as a receptor. However, multiple factors can be expected to simultaneously influence the recruitment of proteins to a receptor (and the potential of these proteins to contribute to receptor signaling), including affinity, abundance, and competition, which is a network property. Here, we demonstrate that measurements of protein copy numbers and binding affinities can be integrated within the framework of a mechanistic, computational model that accounts for mass action and competition. We use cell line-specific models to rank the relative importance of protein-protein interactions in the epidermal growth factor receptor (EGFR) signaling network for 11 different cell lines. Each model accounts for experimentally characterized interactions of six autophosphorylation sites in EGFR with proteins containing a Src homology 2 and/or phosphotyrosine-binding domain. We measure importance as the predicted maximal extent of recruitment of a protein to EGFR following ligand-stimulated activation of EGFR signaling. We find that interactions ranked highly by this metric include experimentally detected interactions. Proteins with high importance rank in multiple cell lines include proteins with recognized, well-characterized roles in EGFR signaling, such as GRB2 and SHC1, as well as a protein with a less well-defined role, YES1. Our results reveal potential cell line-specific differences in recruitment.
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Affiliation(s)
- Edward C Stites
- Clinical Translational Research Division, Translational Genomics Research Institute, Phoenix, Arizona; Department of Pathology & Immunology, Washington University School of Medicine, St. Louis, Missouri.
| | - Meraj Aziz
- Clinical Translational Research Division, Translational Genomics Research Institute, Phoenix, Arizona
| | - Matthew S Creamer
- Clinical Translational Research Division, Translational Genomics Research Institute, Phoenix, Arizona; Department of Molecular, Cellular, and Developmental Biology, Yale University, New Haven, Connecticut
| | - Daniel D Von Hoff
- Clinical Translational Research Division, Translational Genomics Research Institute, Phoenix, Arizona
| | - Richard G Posner
- Clinical Translational Research Division, Translational Genomics Research Institute, Phoenix, Arizona; Department of Biological Sciences, Northern Arizona University, Flagstaff, Arizona.
| | - William S Hlavacek
- Clinical Translational Research Division, Translational Genomics Research Institute, Phoenix, Arizona; Theoretical Biology and Biophysics Group, Theoretical Division, Los Alamos National Laboratory, Los Alamos, New Mexico.
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Engelmann BW, Kim Y, Wang M, Peters B, Rock RS, Nash PD. The development and application of a quantitative peptide microarray based approach to protein interaction domain specificity space. Mol Cell Proteomics 2014; 13:3647-62. [PMID: 25135669 DOI: 10.1074/mcp.o114.038695] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023] Open
Abstract
Protein interaction domain (PID) linear peptide motif interactions direct diverse cellular processes in a specific and coordinated fashion. PID specificity, or the interaction selectivity derived from affinity preferences between possible PID-peptide pairs is the basis of this ability. Here, we develop an integrated experimental and computational cellulose peptide conjugate microarray (CPCMA) based approach for the high throughput analysis of PID specificity that provides unprecedented quantitative resolution and reproducibility. As a test system, we quantify the specificity preferences of four Src Homology 2 domains and 124 physiological phosphopeptides to produce a novel quantitative interactome. The quantitative data set covers a broad affinity range, is highly precise, and agrees well with orthogonal biophysical validation, in vivo interactions, and peptide library trained algorithm predictions. In contrast to preceding approaches, the CPCMAs proved capable of confidently assigning interactions into affinity categories, resolving the subtle affinity contributions of residue correlations, and yielded predictive peptide motif affinity matrices. Unique CPCMA enabled modes of systems level analysis reveal a physiological interactome with expected node degree value decreasing as a function of affinity, resulting in minimal high affinity binding overlap between domains; uncover that Src Homology 2 domains bind ligands with a similar average affinity yet strikingly different levels of promiscuity and binding dynamic range; and parse with unprecedented quantitative resolution contextual factors directing specificity. The CPCMA platform promises broad application within the fields of PID specificity, synthetic biology, specificity focused drug design, and network biology.
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Affiliation(s)
- Brett W Engelmann
- From the ‡The Department of Biochemistry and Molecular Biology, The University of Chicago, Chicago, Illinois 60637;
| | - Yohan Kim
- ¶The La Jolla Institute for Allergy and Immunology, La Jolla, California 92037
| | - Miaoyan Wang
- ‖The Department of Statistics, The University of Chicago, Chicago, Illinois 60637
| | - Bjoern Peters
- ¶The La Jolla Institute for Allergy and Immunology, La Jolla, California 92037
| | - Ronald S Rock
- From the ‡The Department of Biochemistry and Molecular Biology, The University of Chicago, Chicago, Illinois 60637
| | - Piers D Nash
- **The Ben May Department for Cancer Research, The University of Chicago, Chicago, Illinois 60637
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Leung KK, Hause RJ, Barkinge JL, Ciaccio MF, Chuu CP, Jones RB. Enhanced prediction of Src homology 2 (SH2) domain binding potentials using a fluorescence polarization-derived c-Met, c-Kit, ErbB, and androgen receptor interactome. Mol Cell Proteomics 2014; 13:1705-23. [PMID: 24728074 PMCID: PMC4083110 DOI: 10.1074/mcp.m113.034876] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022] Open
Abstract
Many human diseases are associated with aberrant regulation of phosphoprotein signaling networks. Src homology 2 (SH2) domains represent the major class of protein domains in metazoans that interact with proteins phosphorylated on the amino acid residue tyrosine. Although current SH2 domain prediction algorithms perform well at predicting the sequences of phosphorylated peptides that are likely to result in the highest possible interaction affinity in the context of random peptide library screens, these algorithms do poorly at predicting the interaction potential of SH2 domains with physiologically derived protein sequences. We employed a high throughput interaction assay system to empirically determine the affinity between 93 human SH2 domains and phosphopeptides abstracted from several receptor tyrosine kinases and signaling proteins. The resulting interaction experiments revealed over 1000 novel peptide-protein interactions and provided a glimpse into the common and specific interaction potentials of c-Met, c-Kit, GAB1, and the human androgen receptor. We used these data to build a permutation-based logistic regression classifier that performed considerably better than existing algorithms for predicting the interaction potential of several SH2 domains.
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Affiliation(s)
| | - Ronald J Hause
- ¶Committee on Genetics, Genomics, and Systems Biology, and
| | - John L Barkinge
- From the ‡Committee on Cancer Biology, ¶Committee on Genetics, Genomics, and Systems Biology, and ‡‡Committee on Cellular and Molecular Physiology, The Ben May Department for Cancer Research and the Institute for Genomics and Systems Biology, The Gwen and Jules Knapp Center for Biomedical Discovery, University of Chicago, Chicago, Illinois 60637
| | - Mark F Ciaccio
- ‡‡Committee on Cellular and Molecular Physiology, The Ben May Department for Cancer Research and the Institute for Genomics and Systems Biology, The Gwen and Jules Knapp Center for Biomedical Discovery, University of Chicago, Chicago, Illinois 60637
| | - Chih-Pin Chuu
- From the ‡Committee on Cancer Biology, ¶Committee on Genetics, Genomics, and Systems Biology, and ‡‡Committee on Cellular and Molecular Physiology, The Ben May Department for Cancer Research and the Institute for Genomics and Systems Biology, The Gwen and Jules Knapp Center for Biomedical Discovery, University of Chicago, Chicago, Illinois 60637
| | - Richard B Jones
- From the ‡Committee on Cancer Biology, ¶Committee on Genetics, Genomics, and Systems Biology, and
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