Analyzing compound activity records and promiscuity degrees in light of publication statistics

Data-Independent Acquisition of HLA Class I Peptidomes on the Q Exactive Mass Spectrometer Platform

The characterization of peptides presented by human leukocyte antigen (HLA) class I molecules is crucial for understanding immune processes, biomarker discovery, and the development of novel immunotherapies or vaccines. Mass spectrometry allows the direct identification of thousands of HLA-bound peptides from cell lines, blood, or tissue. In recent years, data-independent acquisition (DIA) mass spectrometry methods have evolved, promising to increase reproducibility and sensitivity over classical data-dependent acquisition (DDA) workflows. Here, we describe a DIA setup on the Q Exactive mass spectrometer, optimized regarding the unique properties of HLA class I peptides. The methodology enables sensitive and highly reproducible characterization of HLA peptidomes from individual cell lines. From up to 16 DDA analyses of 100 million human cells, more than 10 000 peptides could be confidently identified, serving as basis for the generation of spectral libraries. This knowledge enabled the subsequent interrogation of DIA data, leading to the identification of peptide sets with >90% overlap between replicate samples, a prerequisite for the comparative study of closely related specimens. Furthermore, >3000 peptides could be identified from just one million cells after DIA analysis using a library generated from 300 million cells. The reduction in sample quantity and the high reproducibility of DIA-based HLA peptidome analysis should facilitate personalized medicine applications.

Entering the 'big data' era in medicinal chemistry: molecular promiscuity analysis revisited

The 'big data' concept plays an increasingly important role in many scientific fields. Big data involves more than unprecedentedly large volumes of data that become available. Different criteria characterizing big data must be carefully considered in computational data mining, as we discuss herein focusing on medicinal chemistry. This is a scientific discipline where big data is beginning to emerge and provide new opportunities. For example, the ability of many drugs to specifically interact with multiple targets, termed promiscuity, forms the molecular basis of polypharmacology, a hot topic in drug discovery. Compound promiscuity analysis is an area that is much influenced by big data phenomena. Different results are obtained depending on chosen data selection and confidence criteria, as we also demonstrate.

Do the complementarities of electrokinetic and chromatographic procedures represent the "Swiss knife" in proteomic investigation? An overview of the literature in the past decade

This report reviews the literature of the past decade dealing with the combination of electrokinetic and chromatographic strategies in the proteomic field. Aim of this article is to highlight how the application of complementary techniques may contribute to substantially improve protein identification. Several studies here considered demonstrate that exploring the combination of these approaches can be a strategy to enrich the extent of proteomic information achieved from a sample. The coupling of "top-down" and "bottom-up" proteomics may result in the generation of a hybrid analytical tool, very efficient not only for large-scale profiling of complex proteomes but also for studying specific subproteomes. The range of applications described, while evidencing a continuous boost in the imagination of researchers for developing new combinations of methods for protein separation, also underlines the adaptability of these techniques to a wide variety of samples. This report points out the general usefulness of combining different procedures for proteomic analysis, an approach that allows researchers to go deeper in the proteome of samples under investigation.