Label-assisted de novo peptide sequencing (LADS)
Publication: Application of de Novo Sequencing to Large-Scale Complex Proteomics Data Sets
People: Arun Devabhaktuni, Sam Pearlman, Sarah Lin

Computational tools that search databases of known proteins for the peptides that best match observed mass spectra make modern proteomics possible. However, for some applications where the underlying source proteome is either unknown or unwieldy, a better option is to identify peptide sequences directly from observed spectra, i.e., de novo. We have developed a computational strategy known as LADS that lets us discover peptides in this way, and are applying it to the discovery of antigenic MHC-presented peptides, and to the gut microbiome.

People:  Arun Devabhaktuni, Sam Pearlman, Sarah Lin

TagGraph is a sensitive, protease independent, blind modification search tool for large and small sequence databases.

People:  Xueheng Zhao

A length-independent computational tool for ab initio motif-discovery Amino acid motifs are the foundation for many protein-protein and protein-peptide interactions. Although such motifs may have a semi-variable structure (e.g., AxxBxxC and AxxxBxxxxC), most motif discovery algorithms endeavor to discover fixed-length motifs from fixed-length input sequences. Motif-Z adapts the motif discovery strategy employed in the Motif-X algorithm to return flexible-length motifs from a variety of input sequence lengths. Motif-Z is suited to discover motifs from peptide antigens presented by MHC-I complexes.