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%.

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.

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.

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.

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

Negative image-based (NIB) screening is a rigid molecular docking methodology that can also be employed in docking rescoring. During the NIB screening, a negative image is generated based on the target protein's ligand-binding cavity by inverting its shape and electrostatics. The resulting NIB model is a drug-like entity or pseudo-ligand that is compared directly against ligand 3D conformers, as is done with a template compound in the ligand-based screening. This cavity-based rigid docking has been demonstrated to work with genuine drug targets in both benchmark testing and drug candidate/lead discovery. Firstly, the study explores in-depth the applicability of different ligand 3D conformer generation software for acquiring the best NIB screening results using cyclooxygenase-2 (COX-2) as the example system. Secondly, the entire NIB workflow from the protein structure preparation, model build-up, and ligand conformer generation to the similarity comparison is performed for COX-2. Accordingly, hands-on instructions are provided on how to employ the NIB methodology from start to finish, both with the rigid docking and docking rescoring using noncommercial software. The practical aspects of the NIB methodology, especially the effect of ligand conformers, are discussed thoroughly, thus, making the methodology accessible for new users.

Detection of Binding Sites on SARS-CoV-2 Spike Protein Receptor-Binding Domain by Molecular Dynamics Simulations in Mixed Solvents.

The novel SARS-CoV-2 uses ACE2 (Angiotensin-Converting Enzyme 2) receptor as an entry point. Insights on S protein receptor-binding domain (RBD) interaction with ACE2 receptor and drug repurposing has accelerated drug discovery for the novel SARS-CoV-2 infection. Finding small molecule binding sites in S protein and ACE2 interface is crucial in search of effective drugs to prevent viral entry. In this study, we employed molecular dynamics simulations in mixed solvents together with virtual screening to identify small molecules that could be potential inhibitors of S protein -ACE2 interaction. Observation of organic probe molecule localization during the simulations revealed multiple sites at the S protein surface related to small molecule, antibody, and ACE2 binding. In addition, a novel conformation of the S protein was discovered that could be stabilized by small molecules to inhibit attachment to ACE2. The most promising binding site on RBD-ACE2 interface was targeted with virtual screening and top-ranked compounds (DB08248, DB02651, DB03714, and DB14826) are suggested for experimental testing. The protocol described here offers an extremely fast method for characterizing key proteins of a novel pathogen and for the identification of compounds that could inhibit or accelerate spreading of the disease.

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.

Screening of Natural Products Targeting SARS-CoV-2-ACE2 Receptor Interface - A MixMD Based HTVS Pipeline.

The COVID-19 pandemic, caused by novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is a severe global health crisis now. SARS-CoV-2 utilizes its Spike protein receptor-binding domain (S-protein) to invade human cell through binding to Angiotensin-Converting Enzyme 2 receptor (ACE2). S-protein is the key target for many therapeutics and vaccines. Potential S-protein-ACE2 fusion inhibitor is expected to block the virus entry into the host cell. In many countries, traditional practices, based on natural products (NPs) have been in use to slow down COVID-19 infection. In this study, a protocol was applied that combines mixed solvent molecular dynamics simulations (MixMD) with high-throughput virtual screening (HTVS) to search NPs to block SARS-CoV-2 entry into the human cell. MixMD simulations were employed to discover the most promising stable binding conformations of drug-like probes in the S-protein-ACE2 interface. Detected stable sites were used for HTVs of 612093 NPs to identify molecules that could interfere with the S-protein-ACE2 interaction. In total, 19 NPs were selected with rescoring model. These top-ranked NP-S-protein complexes were subjected to classical MD simulations for 300 ns (3 replicates of 100 ns) to estimate the stability and affinity of binding. Three compounds, ZINC000002128789, ZINC000002159944 and SN00059335, showed better stability in all MD runs, of which ZINC000002128789 was predicted to have the highest binding affinity, suggesting that it could be effective modulator in RBD-ACE2 interface to prevent SARS-CoV-2 infection. Our results support that NPs may provide tools to fight COVID-19.

Improving Docking Performance Using Negative Image-Based Rescoring.

Despite the large computational costs of molecular docking, the default scoring functions are often unable to recognize the active hits from the inactive molecules in large-scale virtual screening experiments. Thus, even though a correct binding pose might be sampled during the docking, the active compound or its biologically relevant pose is not necessarily given high enough score to arouse the attention. Various rescoring and post-processing approaches have emerged for improving the docking performance. Here, it is shown that the very early enrichment (number of actives scored higher than 1% of the highest ranked decoys) can be improved on average 2.5-fold or even 8.7-fold by comparing the docking-based ligand conformers directly against the target protein's cavity shape and electrostatics. The similarity comparison of the conformers is performed without geometry optimization against the negative image of the target protein's ligand-binding cavity using the negative image-based (NIB) screening protocol. The viability of the NIB rescoring or the R-NiB, pioneered in this study, was tested with 11 target proteins using benchmark libraries. By focusing on the shape/electrostatics complementarity of the ligand-receptor association, the R-NiB is able to improve the early enrichment of docking essentially without adding to the computing cost. By implementing consensus scoring, in which the R-NiB and the original docking scoring are weighted for optimal outcome, the early enrichment is improved to a level that facilitates effective drug discovery. Moreover, the use of equal weight from the original docking scoring and the R-NiB scoring improves the yield in most cases.