Towards a reproducible interactome: semantic-based detection of redundancies to unify protein-protein interaction databases

Role of death-associated protein kinase 1 (DAPK1) in retinal degenerative diseases: an in-silico approach towards therapeutic intervention

The Death-associated protein kinase 1 (DAPK1) has emerged as a crucial player in the pathogenesis of degenerative diseases. As a serine/threonine kinase family member, DAPK1 regulates critical signaling pathways, such as apoptosis and autophagy. In this study, we comprehensively analyzed DAPK1 interactors and enriched molecular functions, biological processes, phenotypic expression, disease associations, and aging signatures to elucidate the molecular networks of DAPK1. Furthermore, we employed a structure-based virtual screening approach using the PubChem database, which enabled the identification of potential bioactive compounds capable of inhibiting DAPK1, including caspase inhibitors and synthetic analogs. Three selected compounds, CID24602687, CID8843795, and CID110869998, exhibited high docking affinity and selectivity towards DAPK1, which were further investigated using molecular dynamics simulations to understand their binding patterns. Our findings establish a connection between DAPK1 and retinal degenerative diseases and highlight the potential of these selected compounds for the development of novel therapeutic strategies. This study provides valuable insights into the molecular mechanisms underlying DAPK1-related diseases, and offers new opportunities for the discovery of effective treatments for retinal degeneration.Communicated by Ramaswamy H. Sarma.

PhyloString: A web server designed to identify, visualize, and evaluate functional relationships between orthologous protein groups across different phylogenetic lineages

Proteins are biological units whose essence is defined by their functional relationships with other proteins or biomolecules such as RNA, DNA, lipids, or carbohydrates. These functions encompass enzymatic, structural, regulatory, or physical interaction roles. The STRING database (Nucleic Acids Research, 8 Jan 2021;49(D1): D605-12) provides an index that defines the functional interaction networks between proteins in model organisms. To facilitate the identification, visualization, and evaluation of potential functional networks across organisms from different phylogenetic lineages, we have developed PhyloString (https://biocomputo.ibt.unam.mx/phylostring/), a web server that utilizes the indices of the STRING database. PhyloString decomposes these functional networks into modules, representing cohesive units of proteins grouped based on their similarity of STRING values and the phylogenetic origins of their respective organisms. This study presents and thoroughly discusses examples of such functional networks and their modules identified using PhyloString.

Genome-wide screening and identification of potential kinases involved in endoplasmic reticulum stress responses

AIM

This study aims to identify endoplasmic reticulum stress response elements (ERSE) in the human genome to explore potentially regulated genes, including kinases and transcription factors, involved in the endoplasmic reticulum (ER) stress and its related diseases.

MATERIALS AND METHODS

Python-based whole genome screening of ERSE was performed using the Amazon Web Services elastic computing system. The Kinome database was used to filter out the kinases from the extracted list of ERSE-related genes. Additionally, network analysis and genome enrichment were achieved using NDEx, the Network and Data Exchange software, and web-based computational tools. To validate the gene expression, quantitative RT-PCR was performed for selected kinases from the list by exposing the HeLa cells to tunicamycin and brefeldin, ER stress inducers, for various time points.

KEY FINDINGS

The overall number of ERSE-associated genes follows a similar pattern in humans, mice, and rats, demonstrating the ERSE's conservation in mammals. A total of 2705 ERSE sequences were discovered in the human genome (GRCh38.p14), from which we identified 36 kinases encoding genes. Gene expression analysis has shown a significant change in the expression of selected genes under ER stress conditions in HeLa cells, supporting our finding.

SIGNIFICANCE

In this study, we have introduced a rapid method using Amazon cloud-based services for genome-wide screening of ERSE sequences from both positive and negative strands, which covers the entire genome reference sequences. Approximately 10 % of human protein-protein interactomes were found to be associated with ERSE-related genes. Our study also provides a rich resource of human ER stress-response-based protein networks and transcription factor interactions and a reference point for future research aiming at targeted therapeutics.