Blind testing cross-linking/mass spectrometry under the auspices of the 11th critical assessment of methods of protein structure prediction (CASP11)

Determining the structure of a protein by any method requires various contributions from experimental and computational sides. In a recent study, high-density cross-linking/mass spectrometry (HD-CLMS) data in combination with ab initio structure prediction determined the structure of human serum albumin (HSA) domains, with an RMSD to X-ray structure of up to 2.5 Å, or 3.4 Å in the context of blood serum. This paper reports the blind test on the readiness of this technology through the help of Critical Assessment of protein Structure Prediction (CASP). We identified between 201-381 unique residue pairs at an estimated 5% FDR (at link level albeit with missing site assignment precision evaluation), for four target proteins. HD-CLMS proved reliable once crystal structures were released. However, improvements in structure prediction using cross-link data were slight. We identified two reasons for this. Spread of cross-links along the protein sequence and the tightness of the spatial constraints must be improved. However, for the selected targets even ideal contact data derived from crystal structures did not allow modellers to arrive at the observed structure. Consequently, the progress of HD-CLMS in conjunction with computational modeling methods as a structure determination method, depends on advances on both arms of this hybrid approach

Mechanism of gluconeogenesis inhibition in rat hepatocytes isolated after in vivo hypoxia.

International audienceGluconeogenesis was studied in hepatocytes isolated from fasted rats submitted to 24 h of hypoxic exposure (inspired O2 fraction 0.1) or to room air. Hepatocytes from hypoxic rats compared with controls exhibited a lower gluconeogenic rate with lactate (5.1 +/- 0.3 vs. 7.2 +/- 0.3 mumol.min-1.g dry cells-1, P < 0.001) but not with dihydroxyacetone (9.1 +/- 0.3 vs. 9.4 +/- 0.4 mumol.min-1.g dry cells-1), suggesting involvement of the phosphoenolpyruvate-pyruvate cycle. Experiments with perifused hepatocytes from hypoxic and control rats showed a single relationship between phosphoenolpyruvate and glucose flux (JGlc) but two different curves when cytosolic oxalacetate was plotted against JGlc. The decreased phosphoenolpyruvate carboxykinase (PEPCK) activity in the hypoxic group (9.0 +/- 0.9 vs. 16.2 +/- 1.9 nmol.min-1.mg protein-1, P < 001) without change in the Michaelis constant further settled the involvement of this step. The significant decrease in PEPCK mRNA levels in livers from hypoxic rats led us to propose that in vivo hypoxic exposure inhibits gluconeogenesis at the PEPCK level by decreasing PEPCK gene transcription

Computational and Experimental Investigation of the Structure of Peptide Monolayers on Gold Nanoparticles

The self-assembly and self-organization of small molecules on the surface of nanoparticles constitute a potential route toward the preparation of advanced proteinlike nanosystems. However, their structural characterization, critical to the design of bionanomaterials with well-defined biophysical and biochemical properties, remains highly challenging. Here, a computational model for peptide-capped gold nanoparticles (GNPs) is developed using experimentally characterized Cys-Ala-Leu-Asn-Asn (CALNN)- and Cys-Phe-Gly-Ala-Ile-Leu-Ser-Ser (CFGAILSS)-capped GNPs as a benchmark. The structure of CALNN and CFGAILSS monolayers is investigated using both structural biology techniques and molecular dynamics simulations. The calculations reproduce the experimentally observed dependence of the monolayer secondary structure on the peptide capping density and on the nanoparticle size, thus giving us confidence in the model. Furthermore, the computational results reveal a number of new features of peptide-capped monolayers, including the importance of sulfur movement for the formation of secondary structure motifs, the presence of water close to the gold surface even in tightly packed peptide monolayers, and the existence of extended 2D parallel β-sheet domains in CFGAILSS monolayers. The model developed here provides a predictive tool that may assist in the design of further bionanomaterials