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Title

Leave-one-out-analysis (LOOA): web-based tool to predict influential proteins and interactions in aggregate-crosslinking proteomic data

 

Authors

Nirjal Mainali*1, Meenakshisundaram Balasubramaniam2, Jay Johnson1, Srinivas Ayyadevara2,3 & Robert J. Shmookler Reis*2,3

 

Affiliation

1Bioinformatics Program, University of Arkansas for Medical Sciences and University of Arkansas at Little Rock, Little Rock, AR, 72205, USA; 2Department of Geriatrics, Reynolds Institute on Aging, University of Arkansas for Medical Sciences, Little Rock, AR, 72205, USA; 3McClellan Veterans Medical Center, Central Arkansas Veterans Healthcare Service, Little Rock, AR, 72205, USA

 

Email

Nirjal Mainali E-mail : nmainali@uams.edu

Meenakshisundaram Balasubramaniam - E-mail : mbalasubramaniam@uams.edu

Jay Johnson - E-mail : jrjohnson@ualr.edu

Srinivas Ayyadevara - E-mail : ayyadevarasrinivas@uams.edu

Robert J. Shmookler Reis E-mail: rjsr@uams.edu

 

Article Type

Research Article

 

Date

Received January 01, 2024; Revised January 31, 2024; Accepted January 31, 2024, Published January 31, 2024

 

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.

 

Keywords

Leave-one-out-analysis (LOOA), web-based tool, influential proteins, interactions, aggregate-cross-linking proteomic data

 

Citation

Mainali et al. Bioinformation 20(1): 4-10 (2024)

 

Edited by

P Kangueane

 

ISSN

0973-2063

 

Publisher

Biomedical Informatics

 

License

This is an Open Access article which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. This is distributed under the terms of the Creative Commons Attribution License.