A Machine Learning Model for the Proteome-Wide Prediction of Lipid-Interacting Proteins

Lipids are essential
metabolites that play critical roles in multiple
cellular pathways. Like many primary metabolites, mutations that disrupt
lipid synthesis can be lethal. Proteins involved in lipid synthesis,
trafficking, and modification, are targets for therapeutic intervention
in infectious disease and metabolic disorders. The ability to rapidly
detect these proteins can accelerate their evaluation as targets for
deranged lipid pathologies. However, it remains challenging to identify
lipid binding motifs in proteins because the rules that govern protein
engagement with specific lipids are poorly understood. As such, new
bioinformatic tools that reveal conserved features in lipid binding
proteins are necessary. Here, we present Structure-based Lipid-interacting Pocket Predictor (SLiPP), an algorithm that
leverages machine learning to detect protein cavities capable of binding
to lipids in protein structures. SLiPP uses a Random Forest classifier
and operates at scale to predict lipid binding pockets with an accuracy
of 96.8% and an F1 score of 86.9% when testing against a set of 8,380
pockets embedded within proteins. Our analyses revealed that the algorithm
relies on hydrophobicity-related features to distinguish lipid binding
pockets from those that bind to other ligands. SLiPP is fast and does
not require substantial computational resources. Use of the algorithm
to detect lipid binding proteins in various proteomes produced hits
annotated or verified as bona fide lipid binding proteins. Additionally,
SLiPP identified many new putative lipid binders in well studied proteomes.
Because of its ability to identify novel lipid binding proteins, SLiPP
can spur the discovery of new and “targetable” lipid-sensitive
pathways.