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Artificial neural networks enable genome-scale simulations of intracellular signaling

ABSTRACT: Mammalian cells adapt their functional state in response to external signals in form of ligands that bind receptors on the cell-surface. Mechanistically, this involves signal-processing through a complex network of molecular interactions that govern transcription factor activity patterns. Computer simulations of the information flow through this network could help predict cellular responses in health and disease. Here we develop a recurrent neural network framework constrained by prior knowledge of the signaling network with ligand-concentrations as input and transcription factor -activity as output. Applied to synthetic data, it predicts unseen test-data (Pearson correlation r=0.98) and the effects of gene knockouts (r=0.8). We stimulate macrophages with 59 different ligands, with and without addition of lipopolysaccharide, and collect transcriptomics data. The framework predicts this data under cross-validation (r=0.8) and knockout simulations suggest a role for RIPK1 in modulating the lipopolysaccharide response. This work demonstrates the feasibility of genome-scale simulations of intracellular signaling.

ORGANISM(S): Homo sapiens

PROVIDER: GSE202515 | GEO | 2022/05/09

REPOSITORIES: GEO

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Project description:Targeted protein degradation (TPD) has emerged as a powerful approach for removing (rather than inhibiting) proteins implicated in diseases. A key step in TPD is the formation of an induced proximity complex where a degrader molecule recruits an E3 ligase to the protein of interest (POI), facilitating the transfer of ubiquitin to the POI and initiating the proteasomal degradation process. Here, we address three critical aspects of the TPD process using atomistic simulations: 1) formation of the ternary complex induced by a degrader molecule, 2) conformational heterogeneity of the ternary complex, and 3) degradation efficiency via the full Cullin Ring Ligase (CRL) macromolecular assembly. We combine experimental biophysical data---in this case hydrogen-deuterium exchange mass spectrometry (HDX-MS, which measures the solvent exposure of protein residues)---with molecular dynamics (MD) simulations aided by enhanced sampling techniques to accurately predict ternary complex structures at atomic resolution. We demonstrate improved efficiency, accuracy, and reliability in the ternary structure predictions of the bromodomain of the cancer target SMARCA2 with the E3 ligase VHL mediated by three different degrader molecules. The simulations accurately reproduce X-ray crystal structures -- including a new structure that we determined in this work (PDB ID: 7S4E) -- with root mean square deviations (RMSD) of 1.1 to 1.6 \r{A}. The simulations also reveal a structural ensemble of low-energy conformations of the ternary complex. Snapshots from these simulations are used as seeds for additional simulations, where we perform 7.1 milliseconds of aggregate simulation time using Folding@home. The detailed free energy surface captures the crystal structure conformation within a low-energy basin and is consistent with solution-phase experimental data (HDX-MS and SAXS). Finally, we graft a structural ensemble of the ternary complexes onto the full CRL and perform enhanced sampling simulations, which suggest that differences in degradation efficiency may be related to the proximity distribution of lysine residues on the POI relative to the E2-loaded ubiquitin. Several of the predicted ubiquitinated lysine residues have been validated through a ubiquitin mapping proteomics experiment. DOI 10.1038/s41467-022-33575-4

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Artificial neural networks enable genome-scale simulations of intracellular signaling

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