Novel Method for Pharmacophore Analysis by Examining the Joint Pharmacophore Space

Ligand-based cheminformatics and free energy-inspired molecular simulations for prioritizing and optimizing G-protein coupled receptor kinase-6 (GRK6) inhibitors in multiple myeloma treatment

Multiple myeloma (MM) is the second most frequently diagnosed hematological malignancy, presenting limited treatment options with no curative potential and significant drug resistance. Recent studies involving genetic knockdown established the crucial role of GRK6 in upholding the viability of MM cells, emphasizing the need to identify potential inhibitors. Computational exploration of GRK6 inhibitors has not been attempted previously. Herein, the present study reports a multilayered lead prioritization and optimization framework using chemometrics and molecular simulations. 2D QSAR studies revealed that hydrogen bonding and polar interactions enhanced GRK6 inhibitory activity, while increased electron accessibility posed a risk of off-target effects. The pharmacophore hypothesis (DDHRRR_1) featured two hydrogen bond donors, one hydrophobic region, and three aromatic rings, laying the foundation for the 3D QSAR models. Hydrophobic groups, such as pyridine and pyrazole, were shown to enhance inhibition, while smaller groups, like ethyl and hydroxyl, reduced activity. 12,557 DrugBank compounds were screened using the developed chemometric models and molecular docking in tandem, which led to the identification of 7 potential parent leads for subsequent QSAR-guided structural optimizations. 350 lead analogs were generated and the top 4 were further analyzed using molecular docking, ADMET, molecular dynamics, and metadynamics analysis based on Principal Component Analysis (PCA), Probability Density Function (PDF), and Free Energy Landscapes (FEL). Upon cumulative retrospection, we propose a novel analog of DB07168 (DB07168-A13) (docking score: -11.2 kcal/mol, MM-GBSA binding energy: -55.2 kcal/mol) as the most promising GRK6 inhibitor, warranting further in vitro validation, for addressing prospective therapeutic intervention in MM.

Computationally Designed Molecules Modulate ALS-Related Amyloidogenic TDP-43307-319 Aggregation

Abnormal cytosolic aggregation of TAR DNA-binding protein of 43 kDa (TDP-43) is observed in multiple diseases, including amyotrophic lateral sclerosis (ALS), frontotemporal lobar degeneration, and Alzheimer's disease. Previous studies have shown that TDP-43307-319 located at the C-terminal of TDP-43 can form higher-order oligomers and fibrils. Of particular interest are the hexamers that adopt a cylindrin structure that has been strongly correlated to neurotoxicity. In this study, we use the joint pharmacophore space (JPS) model to identify and generate potential TDP-43 inhibitors. Five JPS-designed molecules are evaluated using both experimental and computational methods: ion mobility mass spectrometry, thioflavin T fluorescence assay, circular dichroism spectroscopy, atomic force microscopy, and molecular dynamics simulations. We found that all five molecules can prevent the amyloid fibril formation of TDP-43307-319, but their efficacy varies significantly. Furthermore, among the five molecules, [AC0101] is the most efficient in preventing the formation of higher-order oligomers and dissociating preformed higher-order oligomers. Molecular dynamics simulations show that [AC0101] both is the most flexible and forms the most hydrogen bonds with the TDP-43307-319 monomer. The JPS-designed molecules can insert themselves between the β-strands in the hexameric cylindrin structure of TDP-43307-319 and can open its structure. Possible mechanisms for JPS-designed molecules to inhibit and dissociate TDP-43307-319 oligomers on an atomistic scale are proposed.

Synergy between machine learning and natural products cheminformatics: Application to the lead discovery of anthraquinone derivatives

Cheminformatics utilizing machine learning (ML) techniques have opened up a new horizon in drug discovery. This is owing to vast chemical space expansion with rocketing numbers of expected hits and lead compounds that match druggable macromolecular targets, in particular from natural compounds. Due to the natural products' (NP) structural complexity, uniqueness, and diversity, they could occupy a bigger space in pharmaceuticals, allowing the industry to pursue more selective leads in the nanomolar range of binding affinity. ML is an essential part of each step of the drug design pipeline, such as target prediction, compound library preparation, and lead optimization. Notably, molecular mechanic and dynamic simulations, induced docking, and free energy perturbations are essential in predicting best binding poses, binding free energy values, and molecular mechanics force fields. Those applications have leveraged from artificial intelligence (AI), which decreases the computational costs required for such costly simulations. This review aimed to describe chemical space and compound libraries related to NPs. High-throughput screening utilized for fractionating NPs and high-throughput virtual screening and their strategies, and significance, are reviewed. Particular emphasis was given to AI approaches, ML tools, algorithms, and techniques, especially in drug discovery of macrocyclic compounds and approaches in computer-aided and ML-based drug discovery. Anthraquinone derivatives were discussed as a source of new lead compounds that can be developed using ML tools for diverse medicinal uses such as cancer, infectious diseases, and metabolic disorders. Furthermore, the power of principal component analysis in understanding relevant protein conformations, and molecular modeling of protein-ligand interaction were also presented. Apart from being a concise reference for cheminformatics, this review is a useful text to understand the application of ML-based algorithms to molecular dynamics simulation and in silico absorption, distribution, metabolism, excretion, and toxicity prediction.

Identification of 3-((1-(Benzyl(2-hydroxy-2-phenylethyl)amino)-1-oxo-3-phenylpropan-2-yl)carbamoyl)pyrazine-2-carboxylic Acid as a Potential Inhibitor of Non-Nucleosidase Reverse Transcriptase Inhibitors through InSilico Ligand- and Structure-Based Approaches

Non-nucleosidase reverse transcriptase inhibitors (NNRTIs) are highly promising agents for use in highly effective antiretroviral therapy. We implemented a rational approach for the identification of promising NNRTIs based on the validated ligand- and structure-based approaches. In view of our state-of-the-art techniques in drug design and discovery utilizing multiple modeling approaches, we report here, for the first time, quantitative pharmacophore modeling (HypoGen), docking, and in-house database screening approaches in the identification of potential NNRTIs. The validated pharmacophore model with three hydrophobic groups, one aromatic ring group, and a hydrogen-bond acceptor explains the interactions at the active site by the inhibitors. The model was implemented in pharmacophore-based virtual screening (in-house and commercially available databases) and molecular docking for prioritizing the potential compounds as NNRTI. The identified leads are in good corroboration with binding affinities and interactions as compared to standard ligands. The model can be utilized for designing and identifying the potential leads in the area of NNRTIs.

Modulating ALS-Related Amyloidogenic TDP-43307-319 Oligomeric Aggregates with Computationally Derived Therapeutic Molecules

TDP-43 aggregates are a salient feature of amyotrophic lateral sclerosis (ALS), frontotemporal dementia (FTD), and a variety of other neurodegenerative diseases, including Alzheimer's disease (AD). With an anticipated growth in the most susceptible demographic, projections predict neurodegenerative diseases will potentially affect 15 million people in the United States by 2050. Currently, there are no cures for ALS, FTD, or AD. Previous studies of the amyloidogenic core of TDP-43 have demonstrated that oligomers greater than a trimer are associated with toxicity. Utilizing a joint pharmacophore space (JPS) method, potential drugs have been designed specifically for amyloid-related diseases. These molecules were generated on the basis of key chemical features necessary for blood-brain barrier permeability, low adverse side effects, and target selectivity. Combining ion-mobility mass spectrometry and atomic force microscopy with the JPS computational method allows us to more efficiently evaluate a potential drug's efficacy in disrupting the development of putative toxic species. Our results demonstrate the dissociation of higher-order oligomers in the presence of these novel JPS-generated inhibitors into smaller oligomer species. Additionally, drugs approved by the Food and Drug Administration for the treatment of ALS were also evaluated and demonstrated to maintain higher-order oligomeric assemblies. Possible mechanisms for the observed action of the JPS molecules are discussed.

RISC: Rapid Inverted-Index Based Search of Chemical Fingerprints

The ability to search for a query molecule on massive molecular repositories is a fundamental task in chemoinformatics and drug-discovery. Chemical fingerprints are commonly used to characterize the structure and properties of molecules. Some fingerprints, particularly unfolded fingerprints, are often of extreme high dimension and sparse where only few features have a positive value. In this work, we propose a new searching algorithm, RISC, which exploits sparsity in high-dimensional fingerprints to derive effective pruning mechanisms and dramatically speed-up searching efficiency. RISC is robust enough to work on both binary and nonbinary chemical fingerprints. Extensive experiments on Range Queries and Top-k Queries across several molecular repositories demonstrate that at fingerprints of dimension 2048 and above, which is often the case with unfolded fingerprints, RISC is consistently faster than the state-of-the-art techniques. The source code of our implementation is available at http://www.cse.iitd.ac.in/~sayan/software.html .

Conservation and Relevance of Pharmacophore Point Types

Pharmacophore models in general use a variety of features for distinct chemical characteristics, such as hydrogen-bond properties, lipohilicity, and ionizability. Usually, features have to match onto their identical type. To clarify if this stringent one-to-one assignment is justified, we investigated a set of 581 unique ligands from the BindingDB with known orientation inside the respective binding pockets and conducted a statistical analysis of the likelihood of observed exchanges in between the pharmacophore features, respectively their degree of conservation. To find out if certain features are obsolete, we derived a ranking to determine the most relevant ones. We found that the most conserved one-to-one feature is the negative ionizable (acids), followed by hydrogen-bond donor, positive ionizable (basic nitrogens), hydrogen-bond acceptor, aromatic, nonaromatic π-systems, and other lipophilic characteristics. The most likely exchanges were found between carboxylate groups and hydrogen-bond acceptors and likewise between basic nitrogens and hydrogen-bond donors, which reflects the characteristics of Lewis acids and bases. Exchanges between hydrogen-bond donors and hydrogen-bond acceptors are hardly more likely than by chance. The kind of target (e.g., kinase, phosphatase, protease, phosphodiesterase, nuclear receptor, metal-containing, or transmembrane protein) did not show substantial influence on the degree of conservation. The relevance of the actual pharmacophore features was found to be strongly dependent on the applied ranking scheme. Mutual information ranks all hydrophobic features as least important, whereas the aromatic feature is put into second place by using a geometric series. Both ranking schemes see the negative ionizable feature of higher significance than the positively ionizable feature.

Inhibiting and Remodeling Toxic Amyloid-Beta Oligomer Formation Using a Computationally Designed Drug Molecule That Targets Alzheimer's Disease

Alzheimer's disease (AD) is rapidly reaching epidemic status among a burgeoning aging population. Much evidence suggests the toxicity of this amyloid disease is most influenced by the formation of soluble oligomeric forms of amyloid β-protein, particularly the 42-residue alloform (Aβ42). Developing potential therapeutics in a directed, streamlined approach to treating this disease is necessary. Here we utilize the joint pharmacophore space (JPS) model to design a new molecule [AC0107] incorporating structural characteristics of known Aβ inhibitors, blood-brain barrier permeability, and limited toxicity. To test the molecule's efficacy experimentally, we employed ion mobility mass spectrometry (IM-MS) to discover [AC0107] inhibits the formation of the toxic Aβ42 dodecamer at both high (1:10) and equimolar concentrations of inhibitor. Atomic force microscopy (AFM) experiments reveal that [AC0107] prevents further aggregation of Aβ42, destabilizes preformed fibrils, and reverses Aβ42 aggregation. This trend continues for long-term interaction times of 2 days until only small aggregates remain with virtually no fibrils or higher order oligomers surviving. Pairing JPS with IM-MS and AFM presents a powerful and effective first step for AD drug development. Graphical Abstract.