Blue Native PAGE and Mass Spectrometry as an Approach for the Investigation of Stable and Transient Protein-Protein Interactions

DWPPI: A Deep Learning Approach for Predicting Protein–Protein Interactions in Plants Based on Multi-Source Information With a Large-Scale Biological Network

The prediction of protein–protein interactions (PPIs) in plants is vital for probing the cell function. Although multiple high-throughput approaches in the biological domain have been developed to identify PPIs, with the increasing complexity of PPI network, these methods fall into laborious and time-consuming situations. Thus, it is essential to develop an effective and feasible computational method for the prediction of PPIs in plants. In this study, we present a network embedding-based method, called DWPPI, for predicting the interactions between different plant proteins based on multi-source information and combined with deep neural networks (DNN). The DWPPI model fuses the protein natural language sequence information (attribute information) and protein behavior information to represent plant proteins as feature vectors and finally sends these features to a deep learning–based classifier for prediction. To validate the prediction performance of DWPPI, we performed it on three model plant datasets: Arabidopsis thaliana (A. thaliana), mazie (Zea mays), and rice (Oryza sativa). The experimental results with the fivefold cross-validation technique demonstrated that DWPPI obtains great performance with the AUC (area under ROC curves) values of 0.9548, 0.9867, and 0.9213, respectively. To further verify the predictive capacity of DWPPI, we compared it with some different state-of-the-art machine learning classifiers. Moreover, case studies were performed with the AC149810.2_FGP003 protein. As a result, 14 of the top 20 PPI pairs identified by DWPPI with the highest scores were confirmed by the literature. These excellent results suggest that the DWPPI model can act as a promising tool for related plant molecular biology.

Mass Spectrometry- and Computational Structural Biology-Based Investigation of Proteins and Peptides

Recent developments of mass spectrometry (MS) allow us to identify, estimate, and characterize proteins and protein complexes. At the same time, structural biology helps to determine the protein structure and its structure-function relationship. Together, they aid to understand the protein structure, property, function, protein-complex assembly, protein-protein interaction, and dynamics. The present chapter is organized with illustrative results to demonstrate how experimental mass spectrometry can be combined with computational structural biology for detailed studies of protein's structures. We have used tumor differentiation factor protein/peptide as ligand and Hsp70/Hsp90 as receptor protein as examples to study ligand-protein interaction. To investigate possible protein conformation, we will describe two proteins-lysozyme and myoglobin. As an application of MS-based assignment of disulfide bridges, the case of the spider venom polypeptide Phα1β will also be discussed.

Using breast milk to assess breast cancer risk: the role of mass spectrometry-based proteomics

Although mammography and treatment advances have led to declines in breast cancer mortality in the United States, breast cancer remains a major cause of morbidity and mortality. Breast cancer in young women is associated with increased mortality and current methods of detecting breast cancers in this group of women have known limitations. Tools for accurately assessing personal breast cancer risk in young women are needed to identify those women who would benefit the most from earlier intervention. Proteomic analysis of breast milk could identify biomarkers of breast cancer risk and provide a tool for identifying women at increased risk. A preliminary analysis of milk from four women provides a proof of concept for using breast milk to assess breast cancer risk.

Protein-protein interactions: switch from classical methods to proteomics and bioinformatics-based approaches

Following the sequencing of the human genome and many other organisms, research on protein-coding genes and their functions (functional genomics) has intensified. Subsequently, with the observation that proteins are indeed the molecular effectors of most cellular processes, the discipline of proteomics was born. Clearly, proteins do not function as single entities but rather as a dynamic network of team players that have to communicate. Though genetic (yeast two-hybrid Y2H) and biochemical methods (co-immunoprecipitation Co-IP, affinity purification AP) were the methods of choice at the beginning of the study of protein-protein interactions (PPI), in more recent years there has been a shift towards proteomics-based methods and bioinformatics-based approaches. In this review, we first describe in depth PPIs and we make a strong case as to why unraveling the interactome is the next challenge in the field of proteomics. Furthermore, classical methods of investigation of PPIs and structure-based bioinformatics approaches are presented. The greatest emphasis is placed on proteomic methods, especially native techniques that were recently developed and that have been shown to be reliable. Finally, we point out the limitations of these methods and the need to set up a standard for the validation of PPI experiments.

Characterization of tumor differentiation factor (TDF) and its receptor (TDF-R)

Tumor differentiation factor (TDF) is an under-investigated protein produced by the pituitary with no definitive function. TDF is secreted into the bloodstream and targets the breast and prostate, suggesting that it has an endocrine function. Initially, TDF was indirectly discovered based on the differentiation effect of alkaline pituitary extracts of the mammosomatotropic tumor MtTWlO on MTW9/PI rat mammary tumor cells. Years later, the cDNA clone responsible for this differentiation activity was isolated from a human pituitary cDNA library using expression cloning. The cDNA encoded a 108-amino-acid polypeptide that had differentiation activity on MCF7 breast cancer cells and on DU145 prostate cancer cells in vitro and in vivo. Recently, our group focused on identification of the TDF receptor (TDF-R). As potential TDF-R candidates, we identified the members of the Heat Shock 70-kDa family of proteins (HSP70) in both MCF7 and BT-549 human breast cancer cells (HBCC) and PC3, DU145, and LNCaP human prostate cancer cells (HPCC), but not in HeLa cells, NG108 neuroblastoma, or HDF-a and BLK CL.4 cells fibroblasts or fibroblast-like cells. Here we review the current advances on TDF, with particular focus on the structural investigation of its receptor and on its functional effects on breast and prostate cells.