Deep-learning protein structure predictions suggest likely molecular functions for three uncharacterised polytopic membrane proteins from theP. falciparumapicoplast

Abstract

AbstractMalaria is a burdensome disease to humanity caused chiefly by the still poorly understood parasite genusPlasmodium. Much of the pathogenic success of these and other related parasites is due to the presence of the apicoplast, a comparatively poorly characterised biosynthetic organelle containing many proteins of unknown function. Here we present AlphaFold2 protein structure predictions together with furtherin silicoanalyses to infer molecular functions for the three uncharacterised transmembrane apicoplast proteins PF3D7_0622700, PF3D7_0908100 and PF3D7_1021300. The targets PF3D7_0622700 and PF3D7_0908100 are shown herein to belong to the polytopic Major Facilitator and Cation-Proton Antiporter and Anion Transporter superfamilies respectively, confirming previous suspicions for PF3D7_0908100 of a transporter function. Importantly, our docking screens further suggest pyridoxal-5-phosphate may be transported by PF3D7_0622700, and PF3D7_0908100 likely transports a larger negatively charged metabolite. These findings will help direct experimental assays to confirm what apicoplast metabolites these proteins may transport. PF3D7_1021300 is proposed to possess a six transmembrane alpha-helix domain of a currently unknown fold which may also possess a transporter molecular function. This work highlights the power of high accuracy protein structure predictions to illuminate proteins of unknown structure and function.