MaXIC-Q Web: a fully automated web service using statistical and computational methods for protein quantitation based on stable isotope labeling and LC-MS

Multi-omics tools for studying microbial biofilms: current perspectives and future directions

The advent of omics technologies has greatly improved our understanding of microbial biology, particularly in the last two decades. The field of microbial biofilms is, however, relatively new, consolidated in the 1980s. The morphogenic switching by microbes from planktonic to biofilm phenotype confers numerous survival advantages such as resistance to desiccation, antibiotics, biocides, ultraviolet radiation, and host immune responses, thereby complicating treatment strategies for pathogenic microorganisms. Hence, understanding the mechanisms governing the biofilm phenotype can result in efficient treatment strategies directed specifically against molecular markers mediating this process. The application of omics technologies for studying microbial biofilms is relatively less explored and holds great promise in furthering our understanding of biofilm biology. In this review, we provide an overview of the application of omics tools such as transcriptomics, proteomics, and metabolomics as well as multi-omics approaches for studying microbial biofilms in the current literature. We also highlight how the use of omics tools directed at various stages of the biological information flow, from genes to metabolites, can be integrated via multi-omics platforms to provide a holistic view of biofilm biology. Following this, we propose a future artificial intelligence-based multi-omics platform that can predict the pathways associated with different biofilm phenotypes.

Target identification and mechanism of action in chemical biology and drug discovery

Target-identification and mechanism-of-action studies have important roles in small-molecule probe and drug discovery. Biological and technological advances have resulted in the increasing use of cell-based assays to discover new biologically active small molecules. Such studies allow small-molecule action to be tested in a more disease-relevant setting at the outset, but they require follow-up studies to determine the precise protein target or targets responsible for the observed phenotype. Target identification can be approached by direct biochemical methods, genetic interactions or computational inference. In many cases, however, combinations of approaches may be required to fully characterize on-target and off-target effects and to understand mechanisms of small-molecule action.

A critical appraisal of techniques, software packages, and standards for quantitative proteomic analysis

New methods for performing quantitative proteome analyses based on differential labeling protocols or label-free techniques are reported in the literature on an almost monthly basis. In parallel, a correspondingly vast number of software tools for the analysis of quantitative proteomics data has also been described in the literature and produced by private companies. In this article we focus on the review of some of the most popular techniques in the field and present a critical appraisal of several software packages available to process and analyze the data produced. We also describe the importance of community standards to support the wide range of software, which may assist researchers in the analysis of data using different platforms and protocols. It is intended that this review will serve bench scientists both as a useful reference and a guide to the selection and use of different pipelines to perform quantitative proteomics data analysis. We have produced a web-based tool ( http://www.proteosuite.org/?q=other_resources ) to help researchers find appropriate software for their local instrumentation, available file formats, and quantitative methodology.

SILACtor: software to enable dynamic SILAC studies

Stable isotope labeling by amino acids in cell culture (SILAC) is a versatile tool in proteomics that has been used to explore protein turnover on a large scale. However, these studies pose a significant undertaking that can be greatly simplified through the use of computational tools that automate the data analysis. While SILAC technology has enjoyed rapid adoption through the availability of several software tools, algorithms do not exist for the automated analysis of protein turnover data generated using SILAC technology. Presented here is a software tool, SILACtor, designed to trace and compare SILAC-labeled peptides across multiple time points. SILACtor is used to profile protein turnover rates for more than 500 HeLa cell proteins using a SILAC label-chase approach. Additionally, SILACtor contains a method for the automated generation of accurate mass and retention time inclusion lists that target peptides of interest showing fast or slow turnover rates relative to the other peptides observed in the samples. SILACtor enables improved protein turnover studies using SILAC technology and also provides a framework for features extensible to comparative SILAC analyses and targeted methods.