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Disease Prediction and Drug Development
Published in Arvind Kumar Bansal, Javed Iqbal Khan, S. Kaisar Alam, Introduction to Computational Health Informatics, 2019
Arvind Kumar Bansal, Javed Iqbal Khan, S. Kaisar Alam
Computational prediction of protein–protein interaction network is based upon many techniques that involve: 1) the conservation of the proteins' proximity across multiple genomes; 2) protein-fusion in another genome and 3) a common subset of proteins involved in the network of multiple genomes. In all these cases, the assumption is that there is a natural pressure to conserve the common protein–protein interaction networks. Multiple machine learning techniques have been used to study protein–protein interactions such as clustering, support vector machine, Markov model, Bayesian classifiers and decision trees. Protein–protein interactions exhibit dependencies. The knowledge of these dependencies improves the modeling of interaction-networks.
Personalizing treatments for patients based on cardiovascular phenotyping
Published in Expert Review of Precision Medicine and Drug Development, 2022
Analysis of protein–protein interaction networks has been used to identify and repurpose drugs that may be effective in treating cardiovascular diseases [89]. A drug-disease proximity measure based on how close the target of a drug was located to a disease gene was used to quantify and predict the therapeutic effect of drugs. Examination of 78 diseases that had a minimum of 20 disease-related genes in the network found 238 drugs relevant for the diseases of interest. This analysis revealed that the drugs had an average of 3.5 targets in the network. The proximity measure was also used to investigate the relationship between drug targets and disease proteins. This identified 18,162 previously unknown drug-disease associations, which could represent new candidate drugs amenable for drug repurposing. This type of analysis can also give insight into drug mechanism of action as well as identify the mechanism underlying potential drug side effects. For example, two type 2 diabetes drugs are located proximal to cardiac arrhythmia in the disease network. Administration of these drugs is likely to affect disease genes related to arrhythmia and, therefore, explain some of the adverse cardiovascular events associated with these drugs [90].
Proximity labeling and other novel mass spectrometric approaches for spatiotemporal protein dynamics
Published in Expert Review of Proteomics, 2021
Lindsay Pino, Birgit Schilling
Innovative methodologies at the intersection of molecular biology, analytical chemistry, and bioinformatics are enabling four-dimensional protein network analysis encompassing not only protein identity and quantity but also spatial arrangement and temporal dynamics. A particularly exciting example of this is proximity labeling, which although requires effort to generate constructs in living animals, provides unique opportunities to detect and quantify protein interactions in proximity. The in-depth study of protein complexes as they vary across phenotypes and exhibit different biological function offer the opportunity to capture network analyses, even in mammalian tissues in vivo. The resulting protein–protein interaction networks not only generate new hypotheses but also uncover mechanisms of action and phenotypes. Temporal monitoring of biological conditions provides relevant information that can be investigated using protein turnover and/or PTM signaling. These spatiotemporal tools empower systems biology, with the goal of understanding biomolecules not only as their expression levels change but also within context-dependent protein networks that are dynamic across space and time. These properties are not static, and their study can provide insights into temporal protein changes within disease, in response to environmental stressors, or therapeutic interventions.
A combined microRNA and proteome profiling to investigate the effect of ZnO nanoparticles on neuronal cells
Published in Nanotoxicology, 2020
Ankur Kumar Srivastava, Smriti Singh Yadav, Saumya Mishra, Sanjeev Kumar Yadav, Devendra Parmar, Sanjay Yadav
Proteostasis of undifferentiated and differentiated PC12 cells exposed to ZnO NPs (8 μg/ml) for 72 h. (A) Volcano plot is plotted between log2 of the fold changes on the x-axis versus −log10 p-value on the y-axis. Boxes on the left side of the graph represents downregulated proteins, and boxes on the right side of the graph represents upregulated proteins with p-value < 0.05 and log2 fold change = 1. Data analyzed by Proteome Discoverer version 2.2 Thermo Fisher. (B) Comparative proteomics data analysis of undifferentiated and differentiated PC12 cells exposed to ZnO NPs. (C) Venn diagram of ZnO NPs regulated proteins in undifferentiated and differentiated PC12 cells have identified 39 common proteins. (D and E) Protein–protein interaction networks. The functional interaction network of ZnO NPs regulated proteins were created by the STRING algorithm in undifferentiated PC12 samples (D) and differentiated PC12 samples (E). Stronger interactions are represented by thicker lines and only high confidence interactions (score ≥ 0.7) are shown. The circle represents biological functions based on gene ontology annotation are also depicted in the figure.