Abstract
Protein interaction pathways and networks are critically-required for a vast range of biological processes. Improved discovery of candidate druggable proteins within specific cell, tissue and disease contexts will aid development of new treatments. Predicting protein interaction networks from gene expression data can provide valuable insights into normal and disease biology. For example, the resulting protein networks can be used to identify potentially druggable targets and drug candidates for testing in cell and animal disease models. The advent of whole-transcriptome expression profiling techniques—that catalogue protein-coding genes expressed within cells and tissues—has enabled development of individual algorithms for particular tasks. For example,: (i) gene ontology algorithms that predict gene/protein subsets involved in related cell processes; (ii) algorithms that predict intracellular protein interaction pathways; and (iii) algorithms that correlate druggable protein targets with known drugs and/or drug candidates. This review examines approaches, advantages and disadvantages of existing gene expression, gene ontology, and protein network prediction algorithms. Using this framework, we examine current efforts to combine these algorithms into pipelines to enable identification of druggable targets, and associated known drugs, using gene expression datasets. In doing so, new opportunities are identified for development of powerful algorithm pipelines, suitable for wide use by non-bioinformaticians, that can predict protein interaction networks, druggable proteins, and related drugs from user gene expression datasets.
Highlights
One aim of transcriptomic analyses is to accurately predict candidate druggable targets for subsequent—and more laborious—testing of unapproved, approved or repurposed drug candidates within cell and/or animal disease models (1)
gene ontology (GO), protein-protein interaction (PPI), and other genomic association algorithms have been developed to identify druggable targets and associated drugs that might be useful for altering the biology of a cell population
Given the capabilities and limitations with the abovementioned algorithms, there is a need and opportunity to develop an algorithm pipeline that: (i) enables users to upload gene expression data as an input; (ii) groups genes based on their GO terms; (iii) creates additional outputs for each gene consisting of visualised PPI networks that include identified druggable targets and associated drugs; and (iv) has the potential to identify new pathways, and druggable target discovery, through modification of PPI network prediction parameters
Summary
One aim of transcriptomic analyses is to accurately predict candidate druggable targets for subsequent—and more laborious—testing of unapproved, approved or repurposed drug candidates within cell and/or animal disease models (1). Broad algorithm categories include algorithms for: gene/protein classification via gene ontology (GO) terms; prediction of protein-protein interaction (PPI) networks; and establishment of existing drug/drug target databases These tools are often designed to analyse gene or protein lists obtained from high-resolution and/or high-throughput transcriptomic or proteomic data. This includes a review of gene enrichment analysis algorithms, GO, PPI, and drug/target algorithms This creates a conceptual framework for understanding current—and future potential—algorithm pipelines for predicting protein networks and druggable targets from gene expression data. Outputs from Gene Enrichment Analysis include information such as GO terms and associated gene groupings, as well as related p-values derived by comparing the frequency of the GO term genes in the input list with their frequency in the genome. Terms or genes without strong inter-relationships could be left out from the analysis
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