From laptop to benchtop to bedside: structure-based drug design on protein targets

Abstract

As an important aspect of computer-aided drug design, structure-based drug design brought a new horizon to pharmaceutical development. This in silico method permeates all aspects of drug discovery today, including lead identification, lead optimization, ADMET prediction and drug repurposing. Structure-based drug design has resulted in fruitful successes drug discovery targeting proteinligand and protein-protein interactions. Meanwhile, challenges, noted by low accuracy and combinatoric issues, may also cause failures. In this review, state-of-the-art techniques for protein modeling (e.g. structure prediction, modeling protein flexibility, etc.), hit identification/ optimization (e.g. molecular docking, focused library design, fragment-based design, molecular dynamic, etc.), and polypharmacology design will be discussed. We will explore how structure-based techniques can facilitate the drug discovery process and interplay with other experimental approaches.

Fig. 1. Structure-based pharmacophore approaches in drug development

1a. Workflow of prediction of the metabolism sites used by MetaSite. GRID-MIF of CYP is generated by chemotype probing technique. Then, similarity between GRID-MIF of CYPs and fingerprints of substrate is calculated to determine the substrate-specific CYPs. The site of metabolism is predicted as the degree of correlation based on the hypothesis that the distance between the reactive center on CYP and GRID-MIF points should correlate with the distance between the reactive center of the substrate and the positions of different chemotypes in the substrate. 1b. FLAP generation. Descriptors and fingerprints for both ligand and residues in the binding pockets are binned into FLAP fingerprints. Chemgenomic space is mined by machine learning method. 1c. Pharm-IF fingerprint generation. Only ligand atoms that participate in protein-ligand interaction are involved in fingerprints calculation. Pharm-IF fingerprints are derived from the distances of different pharmacophoric atoms. Target-specific Pharm-IF can be used to build pharmacophore models by machine learning method or similarity search.

Fig. 2

Example compounds that target protein-ligand interaction resulting from in silico structure-based design

Fig. 3. Successful protein-protein interaction targets for therapeutic developments

3a. Four hot-spot residues of p53 (magenta)-MDM2 (yellow) complex and two inhibitors (MI-219 and Nutlin-3). Complex structure was got from (PDB ID: 1YCR). 3b. Interactions between Bcl-X_L and Bad (PDB ID: 1G5J) and structures of two inhibitors (ABT-263 and HA14-1). Peptide of Bad are colored in yellow, and the electrostatic potential surface of Bcl-X_L is displayed. 3c. Hydrophobic groove of C-terminal heptad repeats from HIV-1 gp41 trimer (PDB ID: 1AIK). Two representatives of inhibitors (ADS-J1 and 2-aryl 5-(4-oxo-3-phenethyl-2-thioxothiazolidinylidenemethyl) furan derivatives) are in right side. All critical residues are displayed in sticks.