Abstract
Accurate identification of drug targets is a crucial part of any drug development program. We mined the human proteome to discover properties of proteins that may be important in determining their suitability for pharmaceutical modulation. Data was gathered concerning each protein’s sequence, post-translational modifications, secondary structure, germline variants, expression profile and drug target status. The data was then analysed to determine features for which the target and non-target proteins had significantly different values. This analysis was repeated for subsets of the proteome consisting of all G-protein coupled receptors, ion channels, kinases and proteases, as well as proteins that are implicated in cancer. Machine learning was used to quantify the proteins in each dataset in terms of their potential to serve as a drug target. This was accomplished by first inducing a random forest that could distinguish between its targets and non-targets, and then using the random forest to quantify the drug target likeness of the non-targets. The properties that can best differentiate targets from non-targets were primarily those that are directly related to a protein’s sequence (e.g. secondary structure). Germline variants, expression levels and interactions between proteins had minimal discriminative power. Overall, the best indicators of drug target likeness were found to be the proteins’ hydrophobicities, in vivo half-lives, propensity for being membrane bound and the fraction of non-polar amino acids in their sequences. In terms of predicting potential targets, datasets of proteases, ion channels and cancer proteins were able to induce random forests that were highly capable of distinguishing between targets and non-targets. The non-target proteins predicted to be targets by these random forests comprise the set of the most suitable potential future drug targets, and should therefore be prioritised when building a drug development programme.
Highlights
The vast majority of the targets of approved drugs are proteins [1,2]
As a lower sequence identity threshold causes there to be a greater difference between the original dataset and the non-redundant one generated from it, using a range of thresholds enables classifier capability to be evaluated when the redundancy removal has different levels of influence on the dataset used for training
This enables a Random forests (RFs) induced using a non-redundant dataset to be evaluated in terms of its capability of generalising to the entire dataset, and allows the loss of information about the distribution of the proteins in the feature space, caused by the redundancy removal, to be assessed
Summary
The vast majority of the targets of approved drugs are proteins [1,2]. Knowledge of which proteins are the targets of approved drugs enables the division of the human proteome into two classes: approved drug targets and non-targets. A protein is an approved drug target if it is the target of an approved drug, and a non-target otherwise. In order for a protein to have any potential as a drug target it must be druggable. A druggable protein is one that possesses folds that favour interactions with small drug-like molecules, PLOS ONE | DOI:10.1371/journal.pone.0117955. A druggable protein is one that possesses folds that favour interactions with small drug-like molecules, PLOS ONE | DOI:10.1371/journal.pone.0117955 March 30, 2015
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