PRASA: An integrated web server that analyzes protein interaction types

This work presents the Protein Association Analyzer (PRASA) (http://zoro.ee.ncku.edu.tw/prasa/) that predicts protein interactions as well as interaction types. Protein interactions are essential to most biological functions. The existence of diverse interaction types, such as physically contacted or functionally related interactions, makes protein interactions complex. Different interaction types are distinct and should not be confused. However, most existing tools focus on a specific interaction type or mix different interaction types. This work collected 7234058 associations with experimentally verified interaction types from five databases and compiled individual probabilistic models for different interaction types. The PRASA result page shows predicted associations and their related references by interaction type. Experimental results demonstrate the performance difference when distinguishing between different interaction types. The PRASA provides a centralized and organized platform for easy browsing, downloading and comparing of interaction types, which helps reveal insights into the complex roles that proteins play in organisms.     

Identifying significant genes and functionally enriched pathways in familial hypercholesterolemia using integrated gene co-expression network analysis

Familial hypercholesterolemia (FH) is a monogenic lipid disorder which promotes atherosclerosis and cardiovascular diseases. Owing to the lack of sufficient published information, this study aims to identify the potential genetic biomarkers for FH by studying the global gene expression profile of blood cells. The microarray expression data of FH patients and controls was analyzed by different computational biology methods like differential expression analysis, protein network mapping, hub gene identification, functional enrichment of biological pathways, and immune cell restriction analysis. Our results showed the dysregulated expression of 115 genes connected to lipid homeostasis, immune responses, cell adhesion molecules, canonical Wnt signaling, mucin type O-glycan biosynthesis pathways in FH patients. The findings from expanded protein interaction network construction with known FH genes and subsequent Gene Ontology (GO) annotations have also supported the above findings, in addition to identifying the involvement of dysregulated thyroid hormone and ErbB signaling pathways in FH patients. The genes like CSNK1A1, JAK3, PLCG2, RALA, and ZEB2 were found to be enriched under all GO annotation categories. The subsequent phenotype ontology results have revealed JAK3I, PLCG2, and ZEB2 as key hub genes contributing to the inflammation underlying cardiovascular and immune response related phenotypes. Immune cell restriction findings show that above three genes are highly expressed by T-follicular helper CD4⁺ T cells, naïve B cells, and monocytes, respectively. These findings not only provide a theoretical basis to understand the role of immune dysregulations underlying the atherosclerosis among FH patients but may also pave the way to develop genomic medicine for cardiovascular diseases.     

Gene microarray analysis of expression profiles in Suberoyllanilide hyroxamic acid-treated Dendritic cells

Objective

The purpose of this study is to provide a further theoretical basis for the role of Suberoyllanilide hyroxamic acid (SAHA) affect on Dendritic cells (DCs).

Gene co-expression network analysis for identifying genetic markers in Parkinson's disease - a three-way comparative approach

Parkinson's disease (PD) is a neurodegenerative disorder involving progressive deterioration of dopaminergic neurons. Although few genetic markers for familial PD are known, the etiology of sporadic PD remains poorly understood. Microarray data was analysed for induced pluripotent stem cells (iPSCs) derived from PD patients and mature neuronal cells (mDA) differentiated from these iPSCs. Combining expression and semantic similarity, a highly-correlated PD interactome was constructed that included interactions of established Parkinson's disease marker genes. A novel three-way comparative approach was employed, delineating topologically and functionally important genes. These genes showed involvement in pathways like Parkin-ubiquitin proteosomal system (UPS), immune associated biological processes and apoptosis. Of interest are three genes, eEF1A1, CASK, and PSMD6 that are linked to PARK2 activity in the cell and thereby form attractive candidate genes for understanding PD. Network biology approach delineated in this study can be applied to other neurodegenerative disorders for identification of important genetic regulators.     

Genome-wide Protein-protein Interaction Screening by Protein-fragment Complementation Assay (PCA) in Living Cells

Proteins are the building blocks, effectors and signal mediators of cellular processes. A protein’s function, regulation and localization often depend on its interactions with other proteins. Here, we describe a protocol for the yeast protein-fragment complementation assay (PCA), a powerful method to detect direct and proximal associations between proteins in living cells. The interaction between two proteins, each fused to a dihydrofolate reductase (DHFR) protein fragment, translates into growth of yeast strains in presence of the drug methotrexate (MTX). Differential fitness, resulting from different amounts of reconstituted DHFR enzyme, can be quantified on high-density colony arrays, allowing to differentiate interacting from non-interacting bait-prey pairs. The high-throughput protocol presented here is performed using a robotic platform that parallelizes mating of bait and prey strains carrying complementary DHFR-fragment fusion proteins and the survival assay on MTX. This protocol allows to systematically test for thousands of protein-protein interactions (PPIs) involving bait proteins of interest and offers several advantages over other PPI detection assays, including the study of proteins expressed from their endogenous promoters without the need for modifying protein localization and for the assembly of complex reporter constructs.     

Computational Design of a PAK1 Binding Protein

We describe a computational protocol, called DDMI, for redesigning scaffold proteins to bind to a specified region on a target protein. The DDMI protocol is implemented within the Rosetta molecular modeling program and uses rigid-body docking, sequence design, and gradient-based minimization of backbone and side-chain torsion angles to design low-energy interfaces between the scaffold and target protein. Iterative rounds of sequence design and conformational optimization were needed to produce models that have calculated binding energies that are similar to binding energies calculated for native complexes. We also show that additional conformation sampling with molecular dynamics can be iterated with sequence design to further lower the computed energy of the designed complexes. To experimentally test the DDMI protocol, we redesigned the human hyperplastic discs protein to bind to the kinase domain of p21-activated kinase 1 (PAK1). Six designs were experimentally characterized. Two of the designs aggregated and were not characterized further. Of the remaining four designs, three bound to the PAK1 with affinities tighter than 350 μM. The tightest binding design, named Spider Roll, bound with an affinity of 100 μM. NMR-based structure prediction of Spider Roll based on backbone and ¹³C^β chemical shifts using the program CS-ROSETTA indicated that the architecture of human hyperplastic discs protein is preserved. Mutagenesis studies confirmed that Spider Roll binds the target patch on PAK1. Additionally, Spider Roll binds to full-length PAK1 in its activated state but does not bind PAK1 when it forms an auto-inhibited conformation that blocks the Spider Roll target site. Subsequent NMR characterization of the binding of Spider Roll to PAK1 revealed a comparably small binding ‘on-rate’ constant (? 10⁵ M^− 1 s^− 1). The ability to rationally design the site of novel protein-protein interactions is an important step towards creating new proteins that are useful as therapeutics or molecular probes.     

Systematic identification of common functional modules related to heart failure with different etiologies

The development of heart failure (HF) is a complex process that can be initiated by multiple etiologies. Identifying common functional modules associated with HF is a challenging task. Here, we developed a systems method to identify these common functional modules by integrating multiple expression profiles, protein interactions from four species, gene function annotations, and text information. We identified 1439 consistently differentially expressed genes (CDEGs) across HF with different etiologies by applying three meta-analysis methods to multiple HF-related expression profiles. Using a weighted human interaction network constructed by combining interaction data from multiple species, we extracted 60 candidate CDEG modules. We further evaluated the functional relevance of each module by using expression, interaction network, functional annotations, and text information together. Finally, five functional modules with significant biological relevance were identified. We found that almost half of the genes in these modules are hubs in the weighted network, and that these modules can accurately classify HF patients from healthy subjects. We also identified many significantly enriched biological processes that contribute to the pathophysiology of HF, including two new ones, RNA splicing and vesicle-mediated protein transport. In summary, we proposed a novel framework to analyze common functional modules related to HF with different etiologies. Our findings provide important insights into the complex mechanism of HF. Further biological experimentations should be required to validate these novel biological processes.     

Deciphering Spatial Protein–Protein Interactions in Brain Using Proximity Labeling

Cellular biomolecular complexes including protein–protein, protein–RNA, and protein–DNA interactions regulate and execute most biological functions. In particular in brain, protein–protein interactions (PPIs) mediate or regulate virtually all nerve cell functions, such as neurotransmission, cell–cell communication, neurogenesis, synaptogenesis, and synaptic plasticity. Perturbations of PPIs in specific subsets of neurons and glia are thought to underly a majority of neurobiological disorders. Therefore, understanding biological functions at a cellular level requires a reasonably complete catalog of all physical interactions between proteins. An enzyme-catalyzed method to biotinylate proximal interacting proteins within 10 to 300 nm of each other is being increasingly used to characterize the spatiotemporal features of complex PPIs in brain. Thus, proximity labeling has emerged recently as a powerful tool to identify proteomes in distinct cell types in brain as well as proteomes and PPIs in structures difficult to isolate, such as the synaptic cleft, axonal projections, or astrocyte–neuron junctions. In this review, we summarize recent advances in proximity labeling methods and their application to neurobiology.