Iterative Conversion of Cyclin Binding Groove Peptides into Drug like CDK Inhibitors with Antitumor Activity

The cyclin groove is an important recognition site for substrates of the cell cycle cyclin dependent kinases and provides an opportunity for highly selective inhibition of kinase activity through a non-ATP competitive mechanism. The key peptide residues of the cyclin binding motif have been studied in order to precisely define the structureactivity relationship for CDK kinase inhibition. Through this information, new insights into the interactions of peptide CDK inhibitors with key subsites of the cyclin binding groove provide for the replacement of binding determinants with more druglike functionality through REPLACE, a strategy for the iterative conversion of peptidic blockers of proteinprotein interactions into pharmaceutically relevant compounds. As a result, REPLACE is further exemplified in combining optimized peptidic sequences with effective N-terminal capping groups to generate more stable compounds possessing antitumor activity consistent with on-target inhibition of cell cycle CDKs. The compounds described here represent prototypes for a next generation of kinase therapeutics with high efficacy and kinome selectivity, thus avoiding problems observed with first generation CDK inhibitors

Mapping of Protein-Protein Interactions: Web-Based Resources for Revealing Interactomes

Background: The significant number of protein-protein interactions (PPIs) discovered by harnessing concomitant advances in the fields of sequencing, crystallography, spectrometry and two-hybrid screening suggests astonishing prospects for remodelling drug discovery. The PPI space which includes up to 650 000 entities is a remarkable reservoir of potential therapeutic targets for every human disease. In order to allow modern drug discovery programs to leverage this, we should be able to discern complete PPI maps associated with a specific disorder and corresponding normal physiology. Objective: Here, we will review community available computational programs for predicting PPIs and web-based resources for storing experimentally annotated interactions. Methods: We compared the capacities of prediction tools: iLoops, Struck2Net, HOMCOS, COTH, PrePPI, InterPreTS and PRISM to predict recently discovered protein interactions. Results: We described sequence-based and structure-based PPI prediction tools and addressed their peculiarities. Additionally, since the usefulness of prediction algorithms critically depends on the quality and quantity of the experimental data they are built on; we extensively discussed community resources for protein interactions. We focused on the active and recently updated primary and secondary PPI databases, repositories specialized to the subject or species, as well as databases that include both experimental and predicted PPIs. Conclusion: PPI complexes are the basis of important physiological processes and therefore, possible targets for cell-penetrating ligands. Reliable computational PPI predictions can speed up new target discoveries through prioritization of therapeutically relevant protein–protein complexes for experimental studies