Utilizing knowledge base of amino acids structural neighborhoods to predict protein-protein interaction sites

Protein–Protein Interaction Methods and Protein Phase Separation

In the last decade, newly developed experimental methods have made it possible to highlight that macromolecules in the cell milieu physically interact to support physiology. This has shifted the problem of protein–protein interaction from a microscopic, electron-density scale to a mesoscopic one. Further, nowadays there is increasing evidence that proteins in the nucleus and in the cytoplasm can aggregate in membraneless organelles for different physiological reasons. In this scenario, it is urgent to face the problem of biomolecule functional annotation with efficient computational methods, suited to extract knowledge from reliable data and transfer information across different domains of investigation. Here, we revise the present state of the art of our knowledge of protein–protein interaction and the computational methods that differently implement it. Furthermore, we explore experimental and computational features of a set of proteins involved in phase separation.

Development of New Methods Needs Proper Evaluation-Benchmarking Sets for Machine Learning Experiments for Class A GPCRs

New computational approaches for virtual screening applications are constantly being developed. However, before a particular tool is used to search for new active compounds, its effectiveness in the type of task must be examined. In this study, we conducted a detailed analysis of various aspects of preparation of respective data sets for such an evaluation. We propose a protocol for fetching data from the ChEMBL database, examine various compound representations in terms of the possible bias resulting from the way they are generated, and define a new metric for comparing the structural similarity of compounds, which is in line with chemical intuition. The newly developed method is also used for the evaluation of various approaches for division of the data set into training and test set parts, which are also examined in detail in terms of being the source of possible results bias. Finally, machine learning methods are applied in cross-validation studies of data sets constructed within the paper, constituting benchmarks for the assessment of computational methods developed for virtual screening tasks. Additionally, analogous data sets for class A G protein-coupled receptors (100 targets with the highest number of records) were prepared. They are available at http://gmum.net/benchmarks/ , together with script enabling reproduction of all results available at https://github.com/lesniak43/ananas .

ITRAQ-based proteomic analysis reveals possible target-related proteins in human adrenocortical adenomas

Background

Adrenocortical adenomas (ACAs) can lead to the autonomous secretion of aldosterone responsible for primary aldosteronism (PA), which is the most common form of secondary arterial hypertension. However, the authentic fundamental mechanisms underlying ACAs remain unclear.

Objective

Isobaric tags for relative and absolute quantitation (iTRAQ)-based proteomics and bioinformatics analyses from etiological studies of ACAs were performed to screen the differentially expressed proteins (DEPs) and investigate the relevant mechanisms of their occurrence and development. Results could help determine therapeutic targets of clinical significance.

Methods

In the present study, iTRAQ-based proteomics was applied to analyze ACA tissue samples from normal adrenal cortex tissues adjacent to the tumor. Using proteins extracted from a panel of four pairs of ACA samples, we identified some upregulated proteins and other downregulated proteins in all four pairs of ACA samples compared with adjacent normal tissue. Subsequently, we predicted protein–protein interaction networks of three DEPs to determine the authentic functional factors in ACA.

Results

A total of 753 DEPs were identified, including 347 upregulated and 406 downregulated proteins. The expression of three upregulated proteins (E2F3, KRT6A, and ALDH1A2) was validated by Western blot in 24 ACA samples. Our data suggested that some DEPs might be important hallmarks during the development of ACA.

Conclusions

This study is the first proteomic research to investigate alterations in protein levels and affected pathways in ACA using the iTRAQ technique. Thus, this study not only provides a comprehensive dataset on overall protein changes but also sheds light on its potential molecular mechanism in human ACAs.

Electronic supplementary material

The online version of this article (10.1186/s12864-019-6030-5) contains supplementary material, which is available to authorized users.