De Novo Design of Ligands Using Computational Methods

Exploring the Binding Pathway of Novel Nonpeptidomimetic Plasmepsin V Inhibitors

Predicting the interaction modes and binding affinities of virtual compound libraries is of great interest in drug development. It reduces the cost and time of lead compound identification and selection. Here we apply path-based metadynamics simulations to characterize the binding of potential inhibitors to the Plasmodium falciparum aspartic protease plasmepsin V (plm V), a validated antimalarial drug target that has a highly mobile binding site. The potential plm V binders were identified in a high-throughput virtual screening (HTVS) campaign and were experimentally verified in a fluorescence resonance energy transfer (FRET) assay. Our simulations allowed us to estimate compound binding energies and revealed relevant states along binding/unbinding pathways in atomistic resolution. We believe that the method described allows the prioritization of compounds for synthesis and enables rational structure-based drug design for targets that undergo considerable conformational changes upon inhibitor binding.

Converting peptides into drugs targeting intracellular protein-protein interactions

Peptides are gaining increasing attention as therapeutics to target intracellular protein-protein interactions that are involved in disease progression. In this review, we discuss how peptides that are able to bind and inhibit a therapeutic target can be translated into drug leads. We discuss the advantages of using peptides as therapeutics to target intracellular protein-protein interactions, chemical strategies to generate macrocyclic peptides that are resistant to proteolytic enzymes, high-throughput screening approaches to identify peptides that have high affinity for therapeutic targets, strategies that permit these peptides to cross cell membranes and so reach intracellular targets, and the importance of investigating their mode-of-action in guiding the development of novel therapeutics.

Molegro Virtual Docker for Docking

Molegro Virtual Docker is a protein-ligand docking simulation program that allows us to carry out docking simulations in a fully integrated computational package. MVD has been successfully applied to hundreds of different proteins, with docking performance similar to other docking programs such as AutoDock4 and AutoDock Vina. The program MVD has four search algorithms and four native scoring functions. Considering that we may have water molecules or not in the docking simulations, we have a total of 32 docking protocols. The integration of the programs SAnDReS ( https://github.com/azevedolab/sandres ) and MVD opens the possibility to carry out a detailed statistical analysis of docking results, which adds to the native capabilities of the program MVD. In this chapter, we describe a tutorial to carry out docking simulations with MVD and how to perform a statistical analysis of the docking results with the program SAnDReS. To illustrate the integration of both programs, we describe the redocking simulation focused the cyclin-dependent kinase 2 in complex with a competitive inhibitor.