A Practical Perspective: The Effect of Ligand Conformers on the Negative Image-Based Screening

Building shape-focused pharmacophore models for effective docking screening

The performance of molecular docking can be improved by comparing the shape similarity of the flexibly sampled poses against the target proteins' inverted binding cavities. The effectiveness of these pseudo-ligands or negative image-based models in docking rescoring is boosted further by performing enrichment-driven optimization. Here, we introduce a novel shape-focused pharmacophore modeling algorithm O-LAP that generates a new class of cavity-filling models by clumping together overlapping atomic content via pairwise distance graph clustering. Top-ranked poses of flexibly docked active ligands were used as the modeling input and multiple alternative clustering settings were benchmark-tested thoroughly with five demanding drug targets using random training/test divisions. In docking rescoring, the O-LAP modeling typically improved massively on the default docking enrichment; furthermore, the results indicate that the clustered models work well in rigid docking. The C+ +/Qt5-based algorithm O-LAP is released under the GNU General Public License v3.0 via GitHub ( https://github.com/jvlehtonen/overlap-toolkit ). SCIENTIFIC CONTRIBUTION: This study introduces O-LAP, a C++/Qt5-based graph clustering software for generating new type of shape-focused pharmacophore models. In the O-LAP modeling, the target protein cavity is filled with flexibly docked active ligands, the overlapping ligand atoms are clustered, and the shape/electrostatic potential of the resulting model is compared against the flexibly sampled molecular docking poses. The O-LAP modeling is shown to ensure high enrichment in both docking rescoring and rigid docking based on comprehensive benchmark-testing.

Virtual Screening Strategy to Identify Retinoic Acid-Related Orphan Receptor γt Modulators

Molecular docking is a key method used in virtual screening (VS) campaigns to identify small-molecule ligands for drug discovery targets. While docking provides a tangible way to understand and predict the protein-ligand complex formation, the docking algorithms are often unable to separate active ligands from inactive molecules in practical VS usage. Here, a novel docking and shape-focused pharmacophore VS protocol is demonstrated for facilitating effective hit discovery using retinoic acid receptor-related orphan receptor gamma t (RORγt) as a case study. RORγt is a prospective target for treating inflammatory diseases such as psoriasis and multiple sclerosis. First, a commercial molecular database was flexibly docked. Second, the alternative docking poses were rescored against the shape/electrostatic potential of negative image-based (NIB) models that mirror the target's binding cavity. The compositions of the NIB models were optimized via iterative trimming and benchmarking using a greedy search-driven algorithm or brute force NIB optimization. Third, a pharmacophore point-based filtering was performed to focus the hit identification on the known RORγt activity hotspots. Fourth, free energy binding affinity evaluation was performed on the remaining molecules. Finally, twenty-eight compounds were selected for in vitro testing and eight compounds were determined to be low μM range RORγt inhibitors, thereby showing that the introduced VS protocol generated an effective hit rate of ~29%.

Identification of Potential Cytochrome P450 3A5 Inhibitors: An Extensive Virtual Screening through Molecular Docking, Negative Image-Based Screening, Machine Learning and Molecular Dynamics Simulation Studies

Cytochrome P450 3A5 (CYP3A5) is one of the crucial CYP family members and has already proven to be an important drug target for cardiovascular diseases. In the current study, the PubChem database was screened through molecular docking and high-affinity molecules were adopted for further assessment. A negative image-based (NIB) model was used for a similarity search by considering the complementary shape and electrostatics of the target and small molecules. Further, the molecules were segregated into active and inactive groups through six machine learning (ML) matrices. The active molecules found in each ML model were used for in silico pharmacokinetics and toxicity assessments. A total of five molecules followed the acceptable pharmacokinetics and toxicity profiles. Several potential binding interactions between the proposed molecules and CYP3A5 were observed. The dynamic behavior of the selected molecules in the CYP3A5 was explored through a molecular dynamics (MD) simulation study. Several parameters obtained from the MD simulation trajectory explained the stability of the protein–ligand complexes in dynamic states. The high binding affinity of each molecule was revealed by the binding free energy calculation through the MM-GBSA methods. Therefore, it can be concluded that the proposed molecules might be potential CYP3A5 molecules for therapeutic application in cardiovascular diseases subjected to in vitro/in vivo validations.

Ligand-Enhanced Negative Images Optimized for Docking Rescoring

Despite the pivotal role of molecular docking in modern drug discovery, the default docking scoring functions often fail to recognize active ligands in virtual screening campaigns. Negative image-based rescoring improves docking enrichment by comparing the shape/electrostatic potential (ESP) of the flexible docking poses against the target protein’s inverted cavity volume. By optimizing these negative image-based (NIB) models using a greedy search, the docking rescoring yield can be improved massively and consistently. Here, a fundamental modification is implemented to this shape-focused pharmacophore modelling approach—actual ligand 3D coordinates are incorporated into the NIB models for the optimization. This hybrid approach, labelled as ligand-enhanced brute-force negative image-based optimization (LBR-NiB), takes the best from both worlds, i.e., the all-roundedness of the NIB models and the difficult to emulate atomic arrangements of actual protein-bound small-molecule ligands. Thorough benchmarking, focused on proinflammatory targets, shows that the LBR-NiB routinely improves the docking enrichment over prior iterations of the R-NiB methodology. This boost can be massive, if the added ligand information provides truly essential binding information that was lacking or completely missing from the cavity-based NIB model. On a practical level, the results indicate that the LBR-NiB typically works well when the added ligand 3D data originates from a high-quality source, such as X-ray crystallography, and, yet, the NIB model compositions can also sometimes be improved by fusing into them, for example, with flexibly docked solvent molecules. In short, the study demonstrates that the protein-bound ligands can be used to improve the shape/ESP features of the negative images for effective docking rescoring use in virtual screening.

Identification of bio-active food compounds as potential SARS-CoV-2 PLpro inhibitors-modulators via negative image-based screening and computational simulations

Despite significant studies on the COVID-19 pandemic, scientists around the world are still battling to find a definitive therapy against the ongoing severe global health crisis. In this study, advanced computational approaches have been employed to identify bioactive food constituents as potential SARS-CoV-2 PLpro inhibitors-modulators. As a validated antiviral drug target, PLpro has gained tremendous attention for therapeutics developments. Therefore, targeting the multifunctional SARS-CoV-2 PLpro protein, ∼1039 bioactive dietary compounds have been screened extensively through novel techniques like negative image-based (NIB) screening and molecular docking approaches. In particular, the three different models of NIB screening have been generated and used to re-score the dietary compounds based on the negative image which is created by reversing the shape and electrostatics features of PLpro protein's ligand-binding cavity. Further, 100 ns molecular dynamics simulation has been performed and MM-GBSA based binding free energies have been estimated for the final proposed four dietary compounds (PC000550, PC000361, PC000558, and PC000573) as potential inhibitors/modulators of SARS-CoV-2 PLpro protein. Employed computational study outcome also has been compared with respect to the earlier experimentally investigated compound GRL0617 against SARS-CoV-2 PLpro protein, which suggests much greater interaction potential in terms of binding affinity and other energetic contributions for the proposed dietary compounds. Hence, the present study suggests that proposed dietary compounds can be suitable chemical entities for modulating the activity of PLpro protein or can be further utilized for optimizing or screening of novel chemical surrogates, however also needs experimental evaluation for entry in clinical studies for better assessment.

Optimization of Cavity-Based Negative Images to Boost Docking Enrichment in Virtual Screening

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.

Negative Image-Based Rescoring: Using Cavity Information to Improve Docking Screening

Molecular docking produces often lackluster results in real-life virtual screening assays that aim to discover novel drug candidates or hit compounds. The problem lies in the inability of the default docking scoring to properly estimate the Gibbs free energy of binding, which impairs the recognition of the best binding poses and the separation of active ligands from inactive compounds. Negative image-based rescoring (R-NiB) provides both effective and efficient way for re-ranking the outputted flexible docking poses to improve the virtual screening yield. Importantly, R-NiB has been shown to work with multiple genuine drug targets and six popular docking algorithms using demanding benchmark test sets. The effectiveness of the R-NiB methodology relies on the shape/electrostatics similarity between the target protein's ligand-binding cavity and the docked ligand poses. In this chapter, the R-NiB method is described with practical usability in mind.

Negative Image-Based Screening: Rigid Docking Using Cavity Information

Rational drug discovery relies heavily on molecular docking-based virtual screening, which samples flexibly the ligand binding poses against the target protein's structure. The upside of flexible docking is that the geometries of the generated docking poses are adjusted to match the residue alignment inside the target protein's ligand-binding pocket. The downside is that the flexible docking requires plenty of computing resources and, regardless, acquiring a decent level of enrichment typically demands further rescoring or post-processing. Negative image-based screening is a rigid docking technique that is ultrafast and computationally light but also effective as proven by vast benchmarking and screening experiments. In the NIB screening, the target protein cavity's shape/electrostatics is aligned and compared against ab initio-generated ligand 3D conformers. In this chapter, the NIB methodology is explained at the practical level and both its weaknesses and strengths are discussed candidly.

Getting Docking into Shape Using Negative Image-Based Rescoring

The failure of default scoring functions to ensure virtual screening enrichment is a persistent problem for the molecular docking algorithms used in structure-based drug discovery. To remedy this problem, elaborate rescoring and postprocessing schemes have been developed with a varying degree of success, specificity, and cost. The negative image-based rescoring (R-NiB) has been shown to improve the flexible docking performance markedly with a variety of drug targets. The yield improvement is achieved by comparing the alternative docking poses against the negative image of the target protein's ligand-binding cavity. In other words, the shape and electrostatics of the binding pocket is directly used in the similarity comparison to rank the explicit docking poses. Here, the PANTHER/ShaEP-based R-NiB methodology is tested with six popular docking softwares, including GLIDE, PLANTS, GOLD, DOCK, AUTODOCK, and AUTODOCK VINA, using five validated benchmark sets. Overall, the results indicate that R-NiB outperforms the default docking scoring consistently and inexpensively, demonstrating that the methodology is ready for wide-scale virtual screening usage.

Fragment- and negative image-based screening of phosphodiesterase 10A inhibitors

A novel virtual screening methodology called fragment- and negative image-based (F-NiB) screening is introduced and tested experimentally using phosphodiesterase 10A (PDE10A) as a case study. Potent PDE10A-specific small-molecule inhibitors are actively sought after for their antipsychotic and neuroprotective effects. The F-NiB combines features from both fragment-based drug discovery and negative image-based (NIB) screening methodologies to facilitate rational drug discovery. The selected structural parts of protein-bound ligand(s) are seamlessly combined with the negative image of the target's ligand-binding cavity. This cavity- and fragment-based hybrid model, namely its shape and electrostatics, is used directly in the rigid docking of ab initio generated ligand 3D conformers. In total, 14 compounds were acquired using the F-NiB methodology, 3D quantitative structure-activity relationship modeling, and pharmacophore modeling. Three of the small molecules inhibited PDE10A at ~27 to ~67 μM range in a radiometric assay. In a larger context, the study shows that the F-NiB provides a flexible way to incorporate small-molecule fragments into the drug discovery.