From pure FPP to mixed FPP and CAAX competitive inhibitors of farnesyl protein transferase

Pharmacological Inhibition of Protein Lipidation

Lipid modifications of mammalian proteins are widespread, modifying thousands of targets involved in all aspects of cellular physiology cellular physiology. Broadly, lipidations serve to increase protein hydrophobicity and association with cellular membranes. Often, these modifications are absolutely essential for protein stability and localization, and serve critical roles in dynamic regulation of protein function. A number of lipidated proteins are associated with diseases, including parasite infections, neurological diseases, diabetes, and cancer, suggesting that lipid modifications represent potentially attractive targets for pharmacological intervention. This review briefly describes the various types of posttranslational protein lipid modifications, proteins modified by them, and the enzymatic machinery associated with these. We then discuss several case studies demonstrating successful development of lipidation inhibitors of potential (and more rarely, realized) clinical value. Although this field remains in its infancy, we believe these examples demonstrate the potential utility of targeting protein lipidation as a viable strategy for inhibiting the function of pathogenic proteins.

Synthesis and biological evaluation of potential bisubstrate inhibitors of protein farnesyltransferase. Design and synthesis of functionalized imidazoles

A novel series of compounds, derived from 2,5-functionalized imidazoles, have been synthesized as potential bisubstrate inhibitors of protein farnesyltransferase (FTase) using structure-based design. These compounds have a 1,4-diacid chain and a tripeptide connected by an imidazole ring. The synthetic strategy relies on the functionalization at the C-2 position of the heterocycle with the diacid side chain and peptide coupling at the C-5 position. Several new compounds were synthesized in good yields. Kinetic experiments on the most active compounds revealed different binding modes depending on the diacid chain length.

Broad-Based Quantitative Structure−Activity Relationship Modeling of Potency and Selectivity of Farnesyltransferase Inhibitors Using a Bayesian Regularized Neural Network

Inhibitors of the enzyme farnesyltransferase show potential as novel anticancer agents. There are many known inhibitors, but efforts to build predictive SAR models have been hampered by the structural diversity and flexibility of inhibitors. We have undertaken for the first time a QSAR study of the potency and selectivity of a large, diverse data set of farnesyltransferase inhibitors. We used novel molecular descriptors based on binned atomic properties and invariants of molecular matrices and a robust, nonlinear QSAR mapping paradigm, the Bayesian regularized neural network. We have built robust QSAR models of farnesyltransferase inhibition, geranylgeranyltransferase inhibition, and in vivo data. We have derived a novel selectivity index that allows us to model potency and selectivity simultaneously and have built robust QSAR models using this index that have the potential to discover new potent and selective inhibitors.