A novel and efficient ligand-based virtual screening approach using the HWZ scoring function and an enhanced shape-density model

A New Class of Diaryl Ether Herbicides: Structure-Activity Relationship Studies Enabled by a Rapid Scaffold Hopping Approach

We report on the development of a novel class of diaryl ether herbicides. After the discovery of a phenoxybenzoic acid with modest herbicidal activity, optimization led to several molecules with improved control of broadleaf and grass weeds. To facilitate this process, we first employed a three-step combinatorial approach, then pivoted to a one-step Ullmann-type coupling that provided faster access to new analogs. After determining that the primary target site of our benchmark diaryl ethers was acetolactate synthase (ALS), we further leveraged this copper-catalyzed methodology to conduct a scaffold hopping campaign in the hope of uncovering an additional mode of action with fewer documented cases of resistance. Our comprehensive and systematic investigation revealed that while the herbicidal activity of this area seems to be exclusively linked to ALS inhibition, our molecules represent a structurally distinct class of Group 2 herbicides. The structure-activity relationships that led us to this conclusion are described herein.

Brazilin from Caesalpinia sappan L. as a Proprotein Convertase Subtilisin/Kexin Type 9 (PCSK9) Inhibitor: Pharmacophore-Based Virtual Screening, In Silico Molecular Docking, and In Vitro Studies

Background

Proprotein convertase subtilisin/kexin type 9 (PCSK9) is a crucial regulator of low-density lipoprotein cholesterol (LDL-c) levels, as it binds to and degrades the LDL receptor (LDLR) in the lysosome of hepatocytes. Elevated levels of PCSK9 have been linked to an increased LDL-c plasma levels, thereby increasing the risk of cardiovascular disease (CVD), making it an attractive target for therapeutic interventions. As a way to inhibit PCSK9 action, we searched for naturally derived small molecules which can block the binding of PCSK9 to the LDLR.

Methods

In this study, we carried out in silico studies which consist of virtual screening using an optimized pharmacophore model and molecular docking studies using Pyrx 0.98. Effects of the candidate compounds were evaluated using in vitro PCSK9-LDLR binding assays kit.

Results

Eleven natural compounds that bind to PCSK9 were virtually screened form HerbalDB database, including brazilin. Next, molecular docking studies using Pyrx 0.98 showed that brazilin had the highest binding affinity with PCSK9 at -9.0 (Kcal/mol), which was higher than that of the other ten compounds. Subsequent in vitro PCSK9-LDLR binding assays established that brazilin decreased the binding of PCSK9 to the EGF-A fragment of the LDLR in a dose-dependent manner, with an IC50 value of 2.19 μM.

Conclusion

We have identified brazilin, which is derived from the Caesalpinia sappan herb, which can act as a small molecule inhibitor of PCSK9. Our findings suggest that screening for small molecules that can block the interaction between PCSK9 and the LDLR in silico and in vitro may be a promising approach for developing novel lipid-lowering therapy.

Concepts and applications of chemical fingerprint for hit and lead screening

Molecular fingerprints are used to represent chemical (structural, physicochemical, etc.) properties of large-scale chemical sets in a low computational cost way. They have a prominent role in transforming chemical data sets into consistent input formats (bit strings or numeric values) suitable for in silico approaches. In this review, we summarize and classify common and state-of-the-art fingerprints into eight different types (dictionary based, circular, topological, pharmacophore, protein-ligand interaction, shape based, reinforced, and multi). We also highlight applications of fingerprints in early drug research and development (R&D). Thus, this review provides a guide for the selection of appropriate fingerprints of compounds (or ligand-protein complexes) for use in drug R&D.

Advances in the Development of Shape Similarity Methods and Their Application in Drug Discovery

Molecular similarity is a key concept in drug discovery. It is based on the assumption that structurally similar molecules frequently have similar properties. Assessment of similarity between small molecules has been highly effective in the discovery and development of various drugs. Especially, two-dimensional (2D) similarity approaches have been quite popular due to their simplicity, accuracy and efficiency. Recently, the focus has been shifted toward the development of methods involving the representation and comparison of three-dimensional (3D) conformation of small molecules. Among the 3D similarity methods, evaluation of shape similarity is now gaining attention for its application not only in virtual screening but also in molecular target prediction, drug repurposing and scaffold hopping. A wide range of methods have been developed to describe molecular shape and to determine the shape similarity between small molecules. The most widely used methods include atom distance-based methods, surface-based approaches such as spherical harmonics and 3D Zernike descriptors, atom-centered Gaussian overlay based representations. Several of these methods demonstrated excellent virtual screening performance not only retrospectively but also prospectively. In addition to methods assessing the similarity between small molecules, shape similarity approaches have been developed to compare shapes of protein structures and binding pockets. Additionally, shape comparisons between atomic models and 3D density maps allowed the fitting of atomic models into cryo-electron microscopy maps. This review aims to summarize the methodological advances in shape similarity assessment highlighting advantages, disadvantages and their application in drug discovery.

Design of novel lead molecules against RhoG protein as cancer target - a computational study

Cancer is a class of diseases characterized by uncontrolled cell growth. Every year more than 2 million people are affected by the disease. Rho family proteins are actively involved in cytoskeleton regulation. Over-expression of Rho family proteins show oncogenic activity and promote cancer progression. In the present work RhoG protein is considered as novel target of cancer. It is a member of Rho family and Rac subfamily protein, which plays pivotal role in regulation of microtubule formation, cell migration and contributes in cancer progression. In order to understand the binding interaction between RhoG protein and the DH domain of Ephexin-4 protein, the 3D structure of RhoG was evaluated and Molecular Dynamic Simulations was performed to stabilize the structure. The 3D structure of RhoG protein was validated and active site identified using standard computational protocols. Protein-protein docking of RhoG with Ephexin-4 was done to understand binding interactions and the active site structure. Virtual screening was carried out with ligand databases against the active site of RhoG protein. The efficiency of virtual screening is analysed with enrichment factor and area under curve values. The binding free energy of docked complexes was calculated using prime MM-GBSA module. The SASA, FOSA, FISA, PISA and PSA values of ligands were carried out. New ligands with high docking score, glide energy and acceptable ADME properties were prioritized as potential inhibitors of RhoG protein.

Application of the 4D fingerprint method with a robust scoring function for scaffold-hopping and drug repurposing strategies

Two factors contribute to the inefficiency associated with screening pharmaceutical library collections as a means of identifying new drugs: [1] the limited success of virtual screening (VS) methods in identifying new scaffolds; [2] the limited accuracy of computational methods in predicting off-target effects. We recently introduced a 3D shape-based similarity algorithm of the SABRE program, which encodes a consensus molecular shape pattern of a set of active ligands into a 4D fingerprint descriptor. Here, we report a mathematical model for shape similarity comparisons and ligand database filtering using this 4D fingerprint method and benchmarked the scoring function HWK (Hamza–Wei–Korotkov), using the 81 targets of the DEKOIS database. Subsequently, we applied our combined 4D fingerprint and HWK scoring function VS approach in scaffold-hopping and drug repurposing using the National Cancer Institute (NCI) and Food and Drug Administration (FDA) databases, and we identified new inhibitors with different scaffolds of MycP₁ protease from the mycobacterial ESX-1 secretion system. Experimental evaluation of nine compounds from the NCI database and three from the FDA database displayed IC₅₀ values ranging from 70 to 100 μM against MycP₁ and possessed high structural diversity, which provides departure points for further structure–activity relationship (SAR) optimization. In addition, this study demonstrates that the combination of our 4D fingerprint algorithm and the HWK scoring function may provide a means for identifying repurposed drugs for the treatment of infectious diseases and may be used in the drug-target profile strategy.

Novel mycosin protease MycP₁ inhibitors identified by virtual screening and 4D fingerprints

The rise of drug-resistant Mycobacterium tuberculosis lends urgency to the need for new drugs for the treatment of tuberculosis (TB). The identification of a serine protease, mycosin protease-1 (MycP₁), as the crucial agent in hydrolyzing the virulence factor, ESX-secretion-associated protein B (EspB), potentially opens the door to new tuberculosis treatment options. Using the crystal structure of mycobacterial MycP₁ in the apo form, we performed an iterative ligand- and structure-based virtual screening (VS) strategy to identify novel, nonpeptide, small-molecule inhibitors against MycP₁ protease. Screening of ∼485,000 ligands from databases at the Genomics Research Institute (GRI) at the University of Cincinnati and the National Cancer Institute (NCI) using our VS approach, which integrated a pharmacophore model and consensus molecular shape patterns of active ligands (4D fingerprints), identified 81 putative inhibitors, and in vitro testing subsequently confirmed two of them as active inhibitors. Thereafter, the lead structures of each VS round were used to generate a new 4D fingerprint that enabled virtual rescreening of the chemical libraries. Finally, the iterative process identified a number of diverse scaffolds as lead compounds that were tested and found to have micromolar IC₅₀ values against the MycP₁ target. This study validated the efficiency of the SABRE 4D fingerprints as a means of identifying novel lead compounds in each screening round of the databases. Together, these results underscored the value of using a combination of in silico iterative ligand- and structure-based virtual screening of chemical libraries with experimental validation for the identification of promising structural scaffolds, such as the MycP₁ inhibitors.