Hybrid receptor structure/ligand-based docking and activity prediction in ICM: development and evaluation in D3R Grand Challenge 3

Evaluation of the Therapeutic Potential of Sulfonyl Urea Derivatives as Soluble Epoxide Hydrolase (sEH) Inhibitors

The inhibition of soluble epoxide hydrolase (sEH) can reduce the level of dihydroxyeicosatrienoic acids (DHETs) effectively maintaining endogenous epoxyeicosatrienoic acids (EETs) levels, resulting in the amelioration of inflammation and pain. Consequently, the development of sEH inhibitors has been a prominent research area for over two decades. In the present study, we synthesized and evaluated sulfonyl urea derivatives for their potential to inhibit sEH. These compounds underwent extensive in vitro investigation, revealing their potency against human and mouse sEH, with 4f showing the most promising sEH inhibitory potential. When subjected to lipopolysaccharide (LPS)-induced acute lung injury (ALI) in studies in mice, compound 4f manifested promising anti-inflammatory efficacy. We investigated the analgesic efficacy of sEH inhibitor 4f in a murine pain model of tail-flick reflex. These results validate the role of sEH inhibition in inflammatory diseases and pave the way for the rational design and optimization of sEH inhibitors based on a sulfonyl urea template.

Unveiling G-protein coupled receptor kinase-5 inhibitors for chronic degenerative diseases: Multilayered prioritization employing explainable machine learning-driven multi-class QSAR, ligand-based pharmacophore and free energy-inspired molecular simulation

GRK5 holds a pivotal role in cellular signaling pathways, with its overexpression in cardiomyocytes, neuronal cells, and tumor cells strongly associated with various chronic degenerative diseases, which highlights the urgent need for potential inhibitors. In this study, multiclass classification-based QSAR models were developed using diverse machine learning algorithms. These models were built from curated compounds with experimentally derived GRK5 inhibitory activity. Additionally, a pharmacophore model was constructed using active compounds from the dataset. Among the models, the SVM-based approach proved most effective and was initially used to screen DrugBank compounds within the applicability domain. Compounds showing significant GRK5 inhibitory potential underwent evaluation for key pharmacophoric features. Prospective compounds were subjected to molecular docking to assess binding affinity towards GRK5's key active site amino acid residues. Stability at the binding site was analyzed through 200 ns molecular dynamics simulations. MM-GBSA analysis quantified individual free energy components contributing to the total binding energy with respect to binding site residues. Metadynamics analysis, including PCA, FEL, and PDF, provided crucial insights into conformational changes of both apo and holo forms of GRK5 at defined energy states. The study identifies DB02844 (S-Adenosyl-1,8-Diamino-3-Thiooctane) and DB13155 (Esculin) as promising GRK5 inhibitors, warranting further in vitro and in vivo validation studies.

Correlation of protein binding pocket properties with hits' chemistries used in generation of ultra-large virtual libraries

Although the size of virtual libraries of synthesizable compounds is growing rapidly, we are still enumerating only tiny fractions of the drug-like chemical universe. Our capability to mine these newly generated libraries also lags their growth. That is why fragment-based approaches that utilize on-demand virtual combinatorial libraries are gaining popularity in drug discovery. These à la carte libraries utilize synthetic blocks found to be effective binders in parts of target protein pockets and a variety of reliable chemistries to connect them. There is, however, no data on the potential impact of the chemistries used for making on-demand libraries on the hit rates during virtual screening. There are also no rules to guide in the selection of these synthetic methods for production of custom libraries. We have used the SAVI (Synthetically Accessible Virtual Inventory) library, constructed using 53 reliable reaction types (transforms), to evaluate the impact of these chemistries on docking hit rates for 40 well-characterized protein pockets. The data shows that the virtual hit rates differ significantly for different chemistries with cross coupling reactions such as Sonogashira, Suzuki-Miyaura, Hiyama and Liebeskind-Srogl coupling producing the highest hit rates. Virtual hit rates appear to depend not only on the property of the formed chemical bond but also on the diversity of available building blocks and the scope of the reaction. The data identifies reactions that deserve wider use through increasing the number of corresponding building blocks and suggests the reactions that are more effective for pockets with certain physical and hydrogen bond-forming properties.

Poisson-Boltzmann-based machine learning model for electrostatic analysis

Electrostatics is of paramount importance to chemistry, physics, biology, and medicine. The Poisson-Boltzmann (PB) theory is a primary model for electrostatic analysis. However, it is highly challenging to compute accurate PB electrostatic solvation free energies for macromolecules due to the nonlinearity, dielectric jumps, charge singularity, and geometric complexity associated with the PB equation. The present work introduces a PB-based machine learning (PBML) model for biomolecular electrostatic analysis. Trained with the second-order accurate MIBPB solver, the proposed PBML model is found to be more accurate and faster than several eminent PB solvers in electrostatic analysis. The proposed PBML model can provide highly accurate PB electrostatic solvation free energy of new biomolecules or new conformations generated by molecular dynamics with much reduced computational cost.

Virtual screening of ultra-large chemical libraries identifies cell-permeable small-molecule inhibitors of a "non-druggable" target, STAT3 N-terminal domain

STAT3 N-terminal domain is a promising molecular target for cancer treatment and modulation of immune responses. However, STAT3 is localized in the cytoplasm, mitochondria, and nuclei, and thus, is inaccessible to therapeutic antibodies. Its N-terminal domain lacks deep pockets on the surface and represents a typical "non-druggable" protein. In order to successfully identify potent and selective inhibitors of the domain, we have used virtual screening of billion structure-sized virtual libraries of make-on-demand screening samples. The results suggest that the expansion of accessible chemical space by cutting-edge ultra-large virtual compound databases can lead to successful development of small molecule drugs for hard-to-target intracellular proteins.

Affinity prediction using deep learning based on SMILES input for D3R grand challenge 4

Modern molecular docking comprises the prediction of pose and affinity. Prediction of docking poses is required for affinity prediction when three-dimensional coordinates of the ligand have not been provided. However, a large number of feature engineering is required for existing methods. In addition, there is a need for a robust model for the sequential combination of pose and affinity prediction due to the probabilistic deviation of the ligand position issue. We propose a pipeline using a bipartite graph neural network and transfer learning trained on a re-docking dataset. We evaluated our model on the released data from drug design data resource grand challenge 4 (D3R GC4). The two target protein data provided by the challenge have different patterns. The model outperformed the best participant by 9% on the BACE target protein from stage 2. Further, our model showed competitive performance on the CatS target protein.

Further exploration of the structure-activity relationship of dual soluble epoxide hydrolase/fatty acid amide hydrolase inhibitors

Fatty acid amide hydrolase (FAAH) is a membrane protein that hydrolyzes endocannabinoids, and its inhibition produces analgesic and anti-inflammatory effects. The soluble epoxide hydrolase (sEH) hydrolyzes epoxyeicosatrienoic acids (EETs) to dihydroxyeicosatetraenoic acids. EETs have anti-inflammatory and inflammation resolving properties, thus inhibition of sEH consequently reduces inflammation. Concurrent inhibition of both enzymes may represent a novel approach in the treatment of chronic pain. Drugs with multiple targets can provide a superior therapeutic effect and a decrease in side effects compared to ligands with single targets. Previously, microwave-assisted methodologies were employed to synthesize libraries of benzothiazole analogs from which high affinity dual inhibitors (e.g. 3, sEH IC₅₀ = 9.6 nM; FAAH IC₅₀ = 7 nM) were identified. Here, our structure-activity relationship studies revealed that the 4-phenylthiazole moiety is well tolerated by both enzymes, producing excellent inhibition potencies in the low nanomolar range (e.g. 6o, sEH IC₅₀ = 2.5 nM; FAAH IC₅₀ = 9.8 nM). Docking experiments show that the new class of dual inhibitors bind within the catalytic sites of both enzymes. Prediction of several pharmacokinetic/pharmacodynamic properties suggest that these new dual inhibitors are good candidates for further in vivo evaluation. Finally, dual inhibitor 3 was tested in the Formalin Test, a rat model of acute inflammatory pain. The data indicate that 3 produces antinociception against the inflammatory phase of the Formalin Test in vivo and is metabolically stable following intraperitoneal administration in male rats. Further, antinociception produced by 3 is comparable to that of ketoprofen, a traditional nonsteroidal anti-inflammatory drug. The results presented here will help toward the long-term goal of developing novel non-opioid therapeutics for pain management.

RETRACTED ARTICLE: IspH inhibitors kill Gram-negative bacteria and mobilize immune clearance

Isoprenoids are vital to all organisms in supporting core functions of life, like respiration and membrane stability.¹ IspH, an enzyme in the methyl erythritol phosphate pathway of isoprenoid synthesis, is essential to gram-negative bacteria, mycobacteria and apicomplexans.^2,3 The IspH substrate, HMBPP, is not produced in humans and other metazoans and activates cytotoxic Vγ9Vδ2 T-cells in humans and primates at extremely low concentrations.^4-6 We describe novel IspH inhibitors and through structure-guided analog design, refine their potency to nanomolar levels. We have modified these into prodrugs for delivery into bacteria and report that they kill clinical isolates of several multidrug resistant bacterial species such as Acinetobacter, Pseudomonas, Klebsiella, Enterobacter, Vibrio, Shigella, Salmonella, Yersinia, Mycobacterium and Bacillus, while being relatively non-toxic to mammalian cells. Proteomic analysis reveals that bacteria treated with prodrugs resemble those with conditional IspH knockdown. Notably, these prodrugs also cause expansion and activation of human Vγ9Vδ2 T-cells in a humanized mouse model of bacterial infection. These IspH prodrugs synergize direct antibiotic killing with a simultaneous rapid immune response by cytotoxic γδ T-cells, which may limit the rise of antibiotic resistant bacterial populations.

D3R grand challenge 4: blind prediction of protein-ligand poses, affinity rankings, and relative binding free energies

The Drug Design Data Resource (D3R) aims to identify best practice methods for computer aided drug design through blinded ligand pose prediction and affinity challenges. Herein, we report on the results of Grand Challenge 4 (GC4). GC4 focused on proteins beta secretase 1 and Cathepsin S, and was run in an analogous manner to prior challenges. In Stage 1, participant ability to predict the pose and affinity of BACE1 ligands were assessed. Following the completion of Stage 1, all BACE1 co-crystal structures were released, and Stage 2 tested affinity rankings with co-crystal structures. We provide an analysis of the results and discuss insights into determined best practice methods.

Docking rigid macrocycles using Convex-PL, AutoDock Vina, and RDKit in the D3R Grand Challenge 4

The D3R Grand Challenge 4 provided a brilliant opportunity to test macrocyclic docking protocols on a diverse high-quality experimental data. We participated in both pose and affinity prediction exercises. Overall, we aimed to use an automated structure-based docking pipeline built around a set of tools developed in our team. This exercise again demonstrated a crucial importance of the correct local ligand geometry for the overall success of docking. Starting from the second part of the pose prediction stage, we developed a stable pipeline for sampling macrocycle conformers. This resulted in the subangstrom average precision of our pose predictions. In the affinity prediction exercise we obtained average results. However, we could improve these when using docking poses submitted by the best predictors. Our docking tools including the Convex-PL scoring function are available at https://team.inria.fr/nano-d/software/.

Exploring fragment-based target-specific ranking protocol with machine learning on cathepsin S

Cathepsin S (CatS), a member of cysteine cathepsin proteases, has been well studied due to its significant role in many pathological processes, including arthritis, cancer and cardiovascular diseases. CatS inhibitors have been included in D3R-GC3 for both docking pose prediction and affinity ranking, and in D3R-GC4 for binding affinity ranking. The difficulties posed by CatS inhibitors in D3R mainly come from three aspects: large size, high flexibility and similar chemical structures. We have participated in GC4; our best submitted model, which employs a similarity-based alignment docking and Vina scoring protocol, yielded Kendall's τ of 0.23 for 459 binders in GC4. In our further explorations with machine learning, by curating a CatS specific training set, adopting a similarity-based constrained docking method as well as an arm-based fragmentation strategy which can describe large inhibitors in a locality-sensitive fashion, our best structure-based ranking protocol can achieve Kendall's τ of 0.52 for all binders in GC4. In this exploration process, we have demonstrated the importance of training data, docking approaches and fragmentation strategies in inhibitor-ranking protocol development with machine learning.