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.

Publication: TagGraph reveals vast protein modification landscapes from large tandem mass spectrometry datasets
People:  Arun Devabhaktuni, Sam Pearlman, Sarah Lin

Although mass spectrometry is well suited to identifying thousands of potential protein post-translational modifications (PTMs), it has historically been biased towards just a few. To measure the entire set of PTMs across diverse proteomes, software must overcome the dual challenges of covering enormous search spaces and distinguishing correct from incorrect spectrum interpretations. Here, we describe TagGraph, a computational tool that overcomes both challenges with an unrestricted string-based search method that is as much as 350-fold faster than existing approaches, and a probabilistic validation model that we optimized for PTM assignments. We applied TagGraph to a published human proteomic dataset of 25 million mass spectra and tripled confident spectrum identifications compared to its original analysis. We identified thousands of modification types on almost 1 million sites in the proteome. We show alternative contexts for highly abundant yet understudied PTMs such as proline hydroxylation, and its unexpected association with cancer mutations. By enabling broad characterization of PTMs, TagGraph informs as to how their functions and regulation intersect.

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.