Computational Core

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Project Summary – Computational Core The Computational Core at the West Coast Metabolomics Center for Compound Identification (WCMC) is committed to the overall goals of the NIH Common Fund Metabolomics Initiative and specifically aims to greatly improve small molecule identifications. Computational Core leader Dr. Kind and quantum chemistry experts Prof. Tantillo and Prof. Wang will work with Program director Prof. Fiehn and computational experts and programmers on the following specific aims. 1) The generation of large in-silico mass spectral libraries for use in both GC-MS and LC-MS/MS applications to improve compound annotations in metabolomics. These in-silico spectral libraries will be created by using quantum chemistry including Born-Oppenheimer molecular dynamics and transition state reaction modelling. Inclusion of classical approaches such as heuristic and machine learning models using novel compounds that are derived from computationally predicted enzyme promiscuity databases. 2) The development of high accuracy in-silico fragmentation ensembles, for fast ranking of unknown mass spectra. Such an approach is advantageous in case no reference spectrum is available yet, but the compound is covered in existing chemical databases. This includes the development of a filtering pipeline with data obtained from the WCMC Experimental Core and inclusion of additional priori data and literature references. 3) The development of highly accurate retention prediction methods for a diverse and large set of compounds in GC-MS and LC-MS to be used for orthogonal filtering and refinement of compound identification reports. The software tools and databases will be independently validated and tested in close collaboration with the steering committee and the Administrative Core.