Targeted proteomics-driven computational modeling of the mouse macrophage toll-like receptor signaling pathway

Abstract

Abstract The Toll-like receptor (TLR) signaling pathway is crucial for the initiation of effective immune responses. Computational modeling of biological pathways can test mechanistic hypotheses about how regulation is achieved and why it sometimes fails, causing disease (e.g., sepsis). In this investigation, mass spectrometry (LC-MS) and computational modeling are being integrated to develop a model of the TLR4 pathway. TLR4 pathway proteins within mouse macrophages were quantitated using targeted LC-MS and stable-isotope labeled internal (phospho)peptide standards (136 unmodified peptides and 29 phosphopeptides for 54 (phospho)proteins). AlphaFold-Multimer was used to predict protein complex structures, Rosetta was used to idealize and relax the structures, and Simulation of Diffusional Association (SDA) and TransComp were used to estimate protein-protein association rates. Simmune was used to perform rule-based pathway modeling, simulation, and training. LPS stimulation altered proteins known to be affected by TLR4 pathway activation (e.g., phosphorylated ERK1 increased from 30,000 to 250,000 copies per cell). Hundreds of protein-protein association rates were predicted. The (phospho)protein absolute abundance values and protein-protein association rates are being used as parameters for TLR4 pathway models. The parameter space is being explored to identify parameter sets that accurately reproduce the experimental data. Experimental and computational techniques are being integrated to generate a strongly data driven model of the TLR pathway. This work was supported by the Intramural Research Program of NIAID, NIH. This work was supported by the Intramural Research Program of NIAID, NIH.