[No abstract]

A current challenge is to compute the native structures of proteins from their amino acid sequences. A main approach of bioinformatics is threading, in which a protein to be predicted is computationally threaded onto protein fragments of similar sequence having an already known structure. However, ∼15% of proteins cannot be folded in this way; this has been called the glass ceiling, and the proteins are called nonthreadables. For these, physical molecular dynamics (MD) modeling is promising because it does not require templates. We find that MD, when used with an accelerator called MELD, can fold many nonthreadables. For 41 nonthreadable proteins with fewer than 125 residues, MELD-accelerated MD (MELD × MD) folds 20 of them to better than 4 Å error. In 10 cases, MELD × MD succeeds even when the force field does not properly encode the native state. In 11 cases, MELD × MD foretells its own success; seeing large Boltzmann populations in the simulations predicts it has converged to the correct native state. MELD × MD acceleration can be applied to a broad physical protein modeling range.

We describe the performance of MELD-accelerated molecular dynamics (MELDxMD) in determining protein structures in the NMR-data-assisted category in CASP13. Seeded from web server predictions, MELDxMD was found best in the NMR category, over 17 targets, outperforming the next-best groups by a factor of ~4 in z-score. MELDxMD gives ensembles, not single structures; succeeds on a 326-mer, near the current upper limit for NMR structures; and predicts structures that match experimental residual dipolar couplings even though the only NMR-derived data used in the simulations was NOE-based ambiguous atom-atom contacts and backbone dihedrals. MELD can use noisy and ambiguous experimental information to reduce the MD search space. We believe MELDxMD is a promising method for determining protein structures from NMR data.

The Salmonella Syst-OMICS consortium is sequencing 4,500 Salmonella genomes and building an analysis pipeline for the study of Salmonella genome evolution, antibiotic resistance and virulence genes. Metadata, including phenotypic as well as genomic data, for isolates of the collection are provided through the Salmonella Foodborne Syst-OMICS database (SalFoS), at https://salfos.ibis.ulaval.ca/. Here, we present our strategy and the analysis of the first 3,377 genomes. Our data will be used to draw potential links between strains found in fresh produce, humans, animals and the environment. The ultimate goals are to understand how Salmonella evolves over time, improve the accuracy of diagnostic methods, develop control methods in the field, and identify prognostic markers for evidence-based decisions in epidemiology and surveillance.