No abstract

CS23D (chemical shift to 3D structure) is a web server for rapidly generating accurate 3D protein structures using only assigned nuclear magnetic resonance (NMR) chemical shifts and sequence data as input. Unlike conventional NMR methods, CS23D requires no NOE and/or J-coupling data to perform its calculations. CS23D accepts chemical shift files in either SHIFTY or BMRB formats, and produces a set of PDB coordinates for the protein in about 10–15 min. CS23D uses a pipeline of several preexisting programs or servers to calculate the actual protein structure. Depending on the sequence similarity (or lack thereof) CS23D uses either (i) maximal subfragment assembly (a form of homology modeling), (ii) chemical shift threading or (iii) shift-aided de novo structure prediction (via Rosetta) followed by chemical shift refinement to generate and/or refine protein coordinates. Tests conducted on more than 100 proteins from the BioMagResBank indicate that CS23D converges (i.e. finds a solution) for >95% of protein queries. These chemical shift generated structures were found to be within 0.2–2.8 Å RMSD of the NMR structure generated using conventional NOE-base NMR methods or conventional X-ray methods. The performance of CS23D is dependent on the completeness of the chemical shift assignments and the similarity of the query protein to known 3D folds. CS23D is accessible at http://www.cs23d.ca.

Identification of unknown metabolites is a major challenge in metabolomics. Without the identities of the metabolites, the metabolome data generated from a biological sample cannot be readily linked with the proteomic and genomic information for studies in systems biology and medicine. We have developed a web-based metabolite identification tool ( http://www.mycompoundid.org ) that allows searching and interpreting mass spectrometry (MS) data against a newly constructed metabolome library composed of 8,021 known human endogenous metabolites and their predicted metabolic products (375,809 compounds from one metabolic reaction and 10,583,901 from two reactions). As an example, in the analysis of a simple extract of human urine or plasma and the whole human urine by liquid chromatography-mass spectrometry and MS/MS, we are able to identify at least two times more metabolites in these samples than by using a standard human metabolome library. In addition, it is shown that the evidence-based metabolome library (EML) provides a much superior performance in identifying putative metabolites from a human urine sample, compared to the use of the ChemPub and KEGG libraries.