e Protein kinase inhibitors have emerged as new drugs in various therapeutic areas, including leishmaniasis, an important parasitic disease. Members of the Leishmania casein kinase 1 (CK1) family represent promising therapeutic targets. Leishmania casein kinase 1 isoform 2 (CK1.2) has been identified as an exokinase capable of phosphorylating host proteins, thus exerting a potential immune-suppressive action on infected host cells. Moreover, its inhibition reduces promastigote growth. Despite these important properties, its requirement for intracellular infection and its chemical validation as a therapeutic target in the diseaserelevant amastigote stage remain to be established. In this study, we used a multidisciplinary approach combining bioinformatics, biochemical, and pharmacological analyses with a macrophage infection assay to characterize and define Leishmania CK1.2 as a valid drug target. We show that recombinant and transgenic Leishmania CK1.2 (i) can phosphorylate CK1-specific substrates, (ii) is sensitive to temperature, and (iii) is susceptible to CK1-specific inhibitors. CK1.2 is constitutively expressed at both the promastigote insect stage and the vertebrate amastigote stage. We further demonstrated that reduction of CK1 activity by specific inhibitors, such as D4476, blocks promastigote growth, strongly compromises axenic amastigote viability, and decreases the number of intracellular Leishmania donovani and L. amazonensis amastigotes in infected macrophages. These results underline the potential role of CK1 kinases in intracellular survival. The identification of differences in structure and inhibition profiles compared to those of mammalian CK1 kinases opens new opportunities for Leishmania CK1.2 antileishmanial drug development. Our report provides the first chemical validation of Leishmania CK1 protein kinases, required for amastigote intracellular survival, as therapeutic targets.

Predicting protein functional classes such as localization sites and modifications plays a crucial role in function annotation. Given a tremendous amount of sequence data yielded from high-throughput sequencing experiments, the need of efficient and interpretable prediction strategies has been rapidly amplified. Our previous approach for subcellular localization prediction, PSLDoc, archives high overall accuracy for Gram-negative bacteria. However, PSLDoc is computational intensive due to incorporation of homology extension in feature extraction and probabilistic latent semantic analysis in feature reduction. Besides, prediction results generated by support vector machines are accurate but generally difficult to interpret. In this work, we incorporate three new techniques to improve efficiency and interpretability. First, homology extension is performed against a compact non-redundant database using a fast search model to reduce running time. Second, correspondence analysis (CA) is incorporated as an efficient feature reduction to generate a clear visual separation of different protein classes. Finally, functional classes are predicted by a combination of accurate compact set (CS) relation and interpretable one-nearest neighbor (1-NN) algorithm. Besides localization data sets, we also apply a human protein kinase set to validate generality of our proposed method. Experiment results demonstrate that our method make accurate prediction in a more efficient and interpretable manner. First, homology extension using a fast search on a compact database can greatly accelerate traditional running time up to twenty-five times faster without sacrificing prediction performance. This suggests that computational costs of many other predictors that also incorporate homology information can be largely reduced. In addition, CA can not only efficiently identify discriminative features but also provide a clear visualization of different functional classes. Moreover, predictions based on CS achieve 100% precision. When combined with 1-NN on unpredicted targets by CS, our method attains slightly better or comparable performance compared with the state-of-the-art systems.