Computational Investigation of Protein Assemblies

Selective nitrosylation of glyceraldehyde 3-phosphate dehydrogenase (GAPDH) at Cys-247 affects gene regulation through the interferon-gamma (IFN- γ) activated inhibitor of translation (GAIT) complex. Oxidized low-density lipoprotein (LDLox) and INF-γ induce assembly of the nitrosylase complex composed of inducible nitric oxide synthase (iNOS), S100A8, and S100A9 proteins. Crystal structure of the complex of GAPDH and S100A8A9 is not known, structural prediction method were employed by protein-protein docking and binding energy calculation with PatchDock and FIREDock respectively. Candidate models were selected, based on a weight factor calculated, from the computational method developed from the artificial protease cleavage mapping Fe(III) (s)-1-(p- bromoacetamidobenzyl) EDTA to identify helical domains of GAPDH that may interact with S100A8. Models were also selected based on the Boltzmann distribution according to their binding energy. Interface residue analysis suggest that from the models that matched with experimental data, DCE-9 has highest weight factor of 1.68. Docking complexes without experimental bias has the highest binding energy of -76.04 kcal/mol when compared to other candidate models. Our analysis also suggests that complex that matched with experimental data are less likely to form as their binding energies were much lower when compared to the models that were not selected based on experimental data. It can be inferred from our analysis that artificial cleavage mapping may lead to artefacts and the CHARMM19 force field used in FIREDock may not accurately represent the true binding energy of these complexes

Protein Shape Sampled by Ion Mobility Mass Spectrometry Consistently Improves Protein Structure Prediction

Ion mobility (IM) mass spectrometry provides structural information about protein shape and size in the form of an orientationally-averaged collision cross-section (CCSIM). While IM data have been used with various computational methods, they have not yet been utilized to predict monomeric protein structure from sequence. Here, we show that IM data can significantly improve protein structure determination using the modelling suite Rosetta. We develop the Rosetta Projection Approximation using Rough Circular Shapes (PARCS) algorithm that allows for fast and accurate prediction of CCSIM from structure. Following successful testing of the PARCS algorithm, we use an integrative modelling approach to utilize IM data for protein structure prediction. Additionally, we propose a confidence metric that identifies near native models in the absence of a known structure. The results of this study demonstrate the ability of IM data to consistently improve protein structure prediction