Hares and tortoises: the high- versus low-throughput proteomic race

Extracting Coevolutionary Features from Protein Sequences for Predicting Protein-Protein Interactions

Knowing the ways proteins interact with each other are crucial to our understanding of the functional mechanisms of proteins. It is for this reason that different approaches have been developed in attempts to predict protein-protein interactions (PPIs) computationally. Among them, the sequence-based approaches are preferred to the others as they do not require any information about protein properties to perform their tasks. Instead, most sequence-based approaches make use of feature extraction methods to extract features directly from protein sequences so that for each protein sequence, we can construct a feature vector. The feature vectors of every pair of proteins are then concatenated to form two classes of interacting and non-interacting proteins. The prediction of whether or not two proteins interact with each other is then formulated as a classification problem. How accurate PPI predictions can be made therefore depends on how good the features are that can be extracted from the protein sequences to allow interacting or non-interacting to be best distinguished. To do so, instead of extracting such features from individual protein sequences independently of the other protein in the same pair, we propose to jointly consider features from both sequences in a protein pair during the feature extraction process through using a novel coevolutionary feature extraction approach called CoFex. Coevolutionary features extracted by CoFex refer to the covariations found at coevolving positions. Based on the presence and absence of these coevolutionary features in the sequences of two proteins, feature vectors can be composed for pairs of proteins rather than individual proteins. The experiment results show that CoFex is a promising feature extraction approach and can improve the performance of PPI prediction.

Toward a systems-level view of dynamic phosphorylation networks

To better understand how cells sense and respond to their environment, it is important to understand the organization and regulation of the phosphorylation networks that underlie most cellular signal transduction pathways. These networks, which are composed of protein kinases, protein phosphatases and their respective cellular targets, are highly dynamic. Importantly, to achieve signaling specificity, phosphorylation networks must be regulated at several levels, including at the level of protein expression, substrate recognition, and spatiotemporal modulation of enzymatic activity. Here, we briefly summarize some of the traditional methods used to study the phosphorylation status of cellular proteins before focusing our attention on several recent technological advances, such as protein microarrays, quantitative mass spectrometry, and genetically-targetable fluorescent biosensors, that are offering new insights into the organization and regulation of cellular phosphorylation networks. Together, these approaches promise to lead to a systems-level view of dynamic phosphorylation networks.

Employing TMT quantification in shotgun-MS proteomic analysis: a focus on skeletal muscle

Quantify proteins present in a sample is the ultimate goal of a proteomic experiment. Although it might reveal countless physiological and /or pathological process on a studied model, it is still a daunting task to perform. Of the protein present in a sample, only a small number can be identified and even a lesser number quantified, each with its own weaknesses and strengths. Presented here are the "tandem mass tags" isobaric labels (TMT) and a protocol for its utilization in the quantification of skeletal muscle, which might be adapted also to other animal tissues.

Analysis of the salivary proteome in gingivitis patients

BACKGROUND AND OBJECTIVE

Gingivitis is a disease that is characterized by inflammation of the gingival tissue, which can progress to periodontitis and tooth loss. Although many studies have attempted to identify salivary proteins that are associated with the disease, this is the first study to use a proteomic approach to analyze and compare the proteomic profile of whole saliva from gingivitis patients and healthy controls.

MATERIAL AND METHOD

To analyze the saliva proteome, two-dimensional gel electrophoresis and liquid chromatography were used, followed by mass spectrometry.

RESULTS

The analyses showed that gingival inflammation was associated with increased amounts of blood proteins (serum albumin and hemoglobin), immunoglobulin peptides and keratins. In the control group, salivary cystatins, which were detected using capillary Liquid Chromatography on line to electrospray ionization Quadrupole Time-of-flight mass spectrometry, appeared to be more abundant.

CONCLUSION

This approach provides novel insight into profiles of the salivary proteome during gingival inflammation, which may contribute to improvements in diagnosis.

Proteomics data mining

Marc Wilkins completed his undergraduate and doctoral studies at Macquarie University, Sydney, Australia. During his doctoral studies, he defined the concept of the proteome and coined the term. After postdoctoral studies in Geneva, Switzerland, during which he co-edited the first book on proteomics, he returned to Australia, where he cofounded the company Proteome Systems. More recently, Marc took a position as Professor of Systems Biology at the University of New South Wales. He has established and directs the NSW Systems Biology Initiative, and is currently researching the role that protein post-translational modifications play in the regulation of protein-interaction networks.