Abstract
Molecular docking is a key in silico method used routinely in modern drug discovery projects. Although docking provides high-quality ligand binding predictions, it regularly fails to separate the active compounds from the inactive ones. In negative image-based rescoring (R-NiB), the shape/electrostatic potential (ESP) of docking poses is compared to the negative image of the protein’s ligand binding cavity. While R-NiB often improves the docking yield considerably, the cavity-based models do not reach their full potential without expert editing. Accordingly, a greedy search-driven methodology, brute force negative image-based optimization (BR-NiB), is presented for optimizing the models via iterative editing and benchmarking. Thorough and unbiased training, testing and stringent validation with a multitude of drug targets, and alternative docking software show that BR-NiB ensures excellent docking efficacy. BR-NiB can be considered as a new type of shape-focused pharmacophore modeling, where the optimized models contain only the most vital cavity information needed for effectively filtering docked actives from the inactive or decoy compounds. Finally, the BR-NiB code for performing the automated optimization is provided free-of-charge under MIT license via GitHub (https://github.com/jvlehtonen/brutenib) for boosting the success rates of docking-based virtual screening campaigns.
Highlights
Despite the pivotal role of molecular docking in protein structure-based drug discovery,[1−4] the docking-based screening often falls short of expectations
Target-tailored rescoring methods are frequently needed for improved molecular docking yields in virtual screening.[42]
We report a brute force negative image-based optimization (BR-NiB) (Figure 2; Videos S1 and S2) method that augments the composition of protein cavity or negative image-based (NIB) models (Figure S1; Table S10) for improved docking rescoring performance
Summary
Despite the pivotal role of molecular docking in protein structure-based drug discovery,[1−4] the docking-based screening often falls short of expectations. A potential solution to this persistent problem is to score accurately the shape complementarity between the docked ligand and its target protein’s binding cavity.[10−12] Negative image-based rescoring (R-NiB; Figure 1) is a cavity-based docking rescoring methodology that takes on this challenge by focusing squarely on the shape/electrostatic potential (ESP) complementarity.[13] R-NiB has been shown to improve the yields with several docking algorithms (e.g., DOCK,[14] GLIDE,[15,16] or PLANTS17) and multiple drug targets, such as neuraminidase (NEU) and retinoid X receptor alpha (RXRα; Figure 1A).[18]. The binding poses of ligands are sampled flexibly against the target’s cavity using a docking algorithm (Figure 1C,D)
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