AI Article Synopsis
- Many age-related diseases are linked to protein aggregation, but the details of these complex interactions still need clarity.
- The development of web-based software allows researchers to analyze and rank key proteins and their interactions within these aggregates.
- Utilizing techniques like Leave-One-Vertex-Out and Leave-One-Edge-Out with Principal Components Analysis, this tool helps streamline the identification of important proteins that could be targeted for therapeutic interventions.
Article Abstract
Many age-progressive diseases are accompanied by (and likely caused by) the presence of protein aggregation in affected tissues. Protein aggregates are conjoined by complex protein-protein interactions, which remain poorly understood. Knowledge of the proteins that comprise aggregates, and their adherent interfaces, can be useful to identify therapeutic targets to treat or prevent pathology, and to discover small molecules for disease interventions. We present web-based software to evaluate and rank influential proteins and protein-protein interactions based on graph modelling of the cross linked aggregate interactome. We have used two network-graph-based techniques: Leave-One-Vertex-Out (LOVO) and Leave-One-Edge-Out (LOEO), each followed by dimension reduction and calculation of influential vertices and edges using Principal Components Analysis (PCA) implemented as an R program. This method enables researchers to quickly and accurately determine influential proteins and protein-protein interactions present in their aggregate interactome data.
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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10859942 | PMC |
| http://dx.doi.org/10.6026/973206300200004 | DOI Listing |
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