Methods in molecular biology
-
Kinases play a pivotal role in propagating the phosphorylation-mediated signaling networks in living cells. With the overwhelming quantities of phosphoproteomics data being generated, the number of identified phosphorylation sites (phosphosites) is ever increasing. Often, proteomics investigations aim to understand the global signaling modulation that takes place in different biological conditions investigated. ⋯ These algorithms employ different approaches to predict kinase consensus sequence motifs, mostly based on large scale in vivo and in vitro experiments. The context of the kinase and the phosphorylated proteins in a biological system is equally important for predicting association between the enzymes and substrates, an aspect that is also being tackled with available bioinformatics tools. This chapter summarizes the use of the larger phosphorylation databases, and approaches that can be applied to predict kinases that phosphorylate individual sites or that are globally modulated in phosphoproteomics datasets.
-
The goals of this chapter are to provide an introduction into the variety of animal models available for studying traumatic brain injury (TBI) and to provide a concise systematic review of the general materials and methods involved in each model. Materials and methods were obtained from a literature search of relevant peer-reviewed articles. Strengths and weaknesses of each animal choice were presented to include relative cost, anatomical and physiological features, and mechanism of injury desired. ⋯ Therefore, this chapter reflects a representative sampling of the TBI animal models available and is not an exhaustive comparison of every possible model and associated parameters. Throughout this chapter, special considerations for animal choice and TBI animal model classification are discussed. Criteria central to choosing appropriate animal models of TBI include ethics, funding, complexity (ease of use, safety, and controlled access requirements), type of model, model characteristics, and range of control (scope).
-
Advances in mass spectrometric instrumentation in the past 15 years have resulted in an explosion in the raw data yield from typical phosphoproteomics workflows. This poses the challenge of confidently identifying peptide sequences, localizing phosphosites to proteins and quantifying these from the vast amounts of raw data. This task is tackled by computational tools implementing algorithms that match the experimental data to databases, providing the user with lists for downstream analysis. ⋯ Equally critical for generating highly confident output datasets is the application of sound statistical criteria to limit the inclusion of incorrect peptide identifications from database searches. Additionally, careful filtering and use of appropriate statistical tests on the output datasets affects the quality of all downstream analyses and interpretation of the data. Our considerations and general practices on these aspects of phosphoproteomics data processing are presented here.
-
Global phosphoproteomics investigations yield overwhelming datasets with up to tens of thousands of quantified phosphosites. The main challenge after acquiring such large-scale data is to extract the biological meaning and relate this to the experimental question at hand. ⋯ The use of these tools requires careful consideration with regard to the input data, and the interpretation demands a critical approach. This chapter provides a summary of the most appropriate tools for systems analysis of phosphoproteomics datasets, and the considerations that are critical for acquiring meaningful output.
-
The bioluminescent resonance energy transfer (BRET) assay has been extensively used in cell-based and in vivo imaging systems for detecting protein-protein interactions in the native environment of living cells. These protein-protein interactions are essential for the functional response of many signaling pathways to environmental chemicals. ⋯ Here we review the construction of BRET fusion proteins, describe the BRET methodology, and outline strategies to overcome obstacles that may arise. Furthermore, we describe the advantage of BRET over other resonance energy transfer methods for monitoring protein-protein interactions.