OLAV-PMF: A Novel Scoring Scheme for High-Throughput Peptide Mass Fingerprinting

We propose a new type of probabilistic scoring scheme framework for protein identification from peptide masses. We first introduce the framework itself and explain its requirements. In a second part, we describe a particular implementation and test it on a data set of more than 8000 MALDI-TOF spectra with known contents. Doing so, we also compare its performance to two widely used scoring schemes, thereby demonstrating the potential of the proposed approach. Keywords: peptide mass fingerprint • protein identification • algorithm • scorin

X-Rank: A Robust Algorithm for Small Molecule Identification Using Tandem Mass Spectrometry

The diversity of experimental workflows involving LC−MS/MS and the extended range of mass spectrometers tend to produce extremely variable spectra. Variability reduces the accuracy of compound identification produced by commonly available software for a spectral library search. We introduce here a new algorithm that successfully matches MS/MS spectra generated by a range of instruments, acquired under different conditions. Our algorithm called X-Rank first sorts peak intensities of a spectrum and second establishes a correlation between two sorted spectra. X-Rank then computes the probability that a rank from an experimental spectrum matches a rank from a reference library spectrum. In a training step, characteristic parameter values are generated for a given data set. We compared the efficiency of the X-Rank algorithm with the dot-product algorithm implemented by MS Search from the National Institute of Standards and Technology (NIST) on two test sets produced with different instruments. Overall the X-Rank algorithm accurately discriminates correct from wrong matches and detects more correct substances than the MS Search. Furthermore, X-Rank could correctly identify and top rank eight chemical compounds in a commercially available test mix. This confirms the ability of the algorithm to perform both a straight single-platform identification and a cross-platform library search in comparison to other tools. It also opens the possibility for efficient general unknown screening (GUS) against large compound libraries

Proteomic Analyses Identify a Novel Role for EZH2 in the Initiation of Cancer Cell Drug Tolerance

Acquisition of drug resistance remains a chief impediment to successful cancer therapy, and we previously described a transient drug-tolerant cancer cell population (DTPs) whose survival is in part dependent on the activities of the histone methyltransferases G9a/EHMT2 and EZH2, the latter being the catalytic component of the polycomb repressive complex 2 (PRC2). Here, we apply multiple proteomic techniques to better understand the role of these histone methyltransferases (HMTs) in the establishment of the DTP state. Proteome-wide comparisons of lysine methylation patterns reveal that DTPs display an increase in methylation on K116 of PRC member Jarid2, an event that helps stabilize and recruit PRC2 to chromatin. We also find that EZH2, in addition to methylating histone H3K27, also can methylate G9a at K185, and that methylated G9a better recruits repressive complexes to chromatin. These complexes are similar to complexes recruited by histone H3 methylated at K9. Finally, a detailed histone post-translational modification (PTM) analysis shows that EZH2, either directly or through its ability to methylate G9a, alters H3K9 methylation in the context of H3 serine 10 phosphorylation, primarily in a cancer cell subpopulation that serves as DTP precursors. We also show that combinations of histone PTMs recruit a different set of complexes to chromatin, shedding light on the temporal mechanisms that contribute to drug tolerance