MetaDOCK: A Combinatorial Molecular Docking Approach

Compromise in Docking Power of Liganded Crystal Structures of Mpro SARS-CoV-2 Surpasses 90% Success Rate

Herein, we present the capacity of three different molecular docking programs (AutoDock, AutoDock Vina, and PLANTS) to identify and reproduce the binding modes of ligands present in 247 covalent and 169 noncovalent complex crystal structures of the severe acute respiratory syndrome coronavirus 2 main protease (Mpro). The compromise in docking power is evaluated with respect to their ability to generate poses similar to the crystal structure binding mode (heavy atoms' root-mean-square deviation < 2 Å) and their ability to recognize the native binding mode with an included compensation for the scoring function error. Noncovalently bound inhibitors are best modeled by AutoDock Vina (90.6% success rate in the active site), while the most relevant results for covalently bound inhibitors are produced by PLANTS (93.0%). AutoDock shows acceptable performance for both types of ligands, 81.1 and 76.4% for noncovalent and covalent complexes, respectively. All three programs manifest worse performance when reproducing surface-bound ligands. Comparison with other works illustrates the importance of crystal structure processing (12% of noncovalent and 26% of covalent ligands had to be manually corrected), proper sampling protocol settings, and inclusion of root-mean-square deviation (RMSD)/scoring function error compensations in crystal structure pose identification. Results are analyzed with respect to a clustering scheme of the noncovalently bound ligands and the chemical reaction type of the covalent ligand bound to the Cys145 residue. A comparison of screening power based on the docking scores of noncovalent ligands from the crystal structures with a "Directory of Useful Decoys, Enhanced" set of known decoys (6562 compounds) and ZINC15 in vivo subset (60,394 compounds) is provided. Ligand and protein input files are provided for future benchmarking purposes.

Molecular Gas-Phase Conformational Ensembles

Accurately determining the global minima of a molecular structure is important in diverse scientific fields, including drug design, materials science, and chemical synthesis. Conformational search engines serve as valuable tools for exploring the extensive conformational space of molecules and for identifying energetically favorable conformations. In this study, we present a comparison of Auto3D, CREST, Balloon, and ETKDG (from RDKit), which are freely available conformational search engines, to evaluate their effectiveness in locating global minima. These engines employ distinct methodologies, including machine learning (ML) potential-based, semiempirical, and force field-based approaches. To validate these methods, we propose the use of collisional cross-section (CCS) values obtained from ion mobility-mass spectrometry studies. We hypothesize that experimental gas-phase CCS values can provide experimental evidence that we likely have the global minimum for a given molecule. To facilitate this effort, we used our gas-phase conformation library (GPCL) which currently consists of the full ensembles of 20 small molecules and can be used by the community to validate any conformational search engine. Further members of the GPCL can be readily created for any molecule of interest using our standard workflow used to compute CCS values, expanding the ability of the GPCL in validation exercises. These innovative validation techniques enhance our understanding of the conformational landscape and provide valuable insights into the performance of conformational generation engines. Our findings shed light on the strengths and limitations of each search engine, enabling informed decisions for their utilization in various scientific fields, where accurate molecular structure determination is crucial for understanding biological activity and designing targeted interventions. By facilitating the identification of reliable conformations, this study significantly contributes to enhancing the efficiency and accuracy of molecular structure determination, with particular focus on metabolite structure elucidation. The findings of this research also provide valuable insights for developing effective workflows for predicting the structures of unknown compounds with high precision.

Considerations Around Structure-Based Drug Discovery for KRAS Using DOCK

Molecular docking is a popular computational tool in drug discovery. Leveraging structural information, docking software predicts binding poses of small molecules to cavities on the surfaces of proteins. Virtual screening for ligand discovery is a useful application of docking software. In this chapter, using the enigmatic KRAS protein as an example system, we endeavor to teach the reader about best practices for performing molecular docking with UCSF DOCK. We discuss methods for virtual screening and docking molecules on KRAS. We present the following six points to optimize our docking setup for prosecuting a virtual screen: protein structure choice, pocket selection, optimization of the scoring function, modification of sampling spheres and sampling procedures, choosing an appropriate portion of chemical space to dock, and the choice of which top scoring molecules to pick for purchase.

Bone-Adhesive Hydrogel for Effective Inhibition of M. tuberculosis and Osteoblast Regeneration

Currently, bone tuberculosis (TB) treatment largely involves lifelong drug prescriptions and surgical intervention, resulting in poor quality of life for patients. Therefore, the fabrication of injectable scaffolds to form a solid framework around the defective bone region is gaining importance over the extensive use of antimicrobial inhibitors. Herein, we synthesized a novel bone-adhesive and thermoresponsive hydrogel via conjugation of poly(N-isopropylacrylamide-co-glycidyl methacrylate) (PNIPAM-co-GMA) and cysteine (CYS). Thiolation of the polymer enables chemical cross-linking with the bone glycoprotein, enhancing bone adhesion and permitting control of scaffold retention time. The PNIPAM-co-GMA-CYS hydrogel shows higher cross-linking behavior at 37 °C, forms a strong gel in 260 s, and has 151 kPa adhesion strength on cortical bone. The lead compounds 5-methyl-5H-[1,2,4]triazino[5,6-b]indole-3-thiol (MTIT) and N-tert-butyl-4-methyl-6-(5-methyl-5H-[1,2,4]triazino[5,6-b]indol-3-ylthio)pyrimidin-2-amine (TMTIPA) were identified by a high-throughput screening method. Effective MTIT and TMTIPA are encapsulated in bone-adhesive hydrogel separately, and both have a high release rate above >70% in 180 h. The MTIT- and TMTIPA-loaded PNIPAM-co-GMA-CYS showed an excellent bactericidal effect, reducing the relative intracellular bacterial survival in macrophages. Furthermore, the as-synthesized hydrogel has outstanding mechanical and biocompatibility properties to become a bone-replacing material and provide support to promote bone repair. This work presents a novel bone-adhesive PNIPAM-co-GMA-CYS for the sustained release of lead compounds toward promising alternative bone TB treatment.

Synthesis and Screening of Chemical Agents Targeting Viral Protein Genome-Linked Protein of Telosma Mosaic Virus

The viral protein genome-linked protein (VPg) of telosma mosaic virus (TeMV) plays an important role in viral reproduction. In this study, the expression conditions of TeMV VPg were explored. A series of novel benzenesulfonamide derivatives were synthesized. The binding sites of the target compounds and TeMV VPg were studied by molecular docking, and the interaction was verified by microscale thermophoresis. The study revealed that the optimal expression conditions for TeMV VPg were in Escherichia coli Rosetta with IPTG concentration of 0.8 mM and induction temperature of 25 °C. Compounds A4, A6, A9, A16, and A17 exhibited excellent binding affinity to TeMV VPg, with Kd values of 0.23, 0.034, 0.19, 0.086, and 0.22 μM, respectively. LYS 121 is the key amino acid site. Compounds A9 inhibited the expression of TeMV VPg in Nicotiana benthamiana. The results suggested that TeMV VPg is a potential antiviral target to screen anti-TeMV compounds.

In silico molecular docking, ADME study and synthesis of new 1,3-diazetidin-2-one derivatives with high anti-proliferative activity

Background: Cancer and inflammation are strongly connected; tumor growth and spread are also greatly influenced by inflammation. Nitrogen-based heterocycle analogs are excellent suppliers of pharmaceuticals. Quaternary rings play a bigger role in drug development as bioactive scaffolds. For improved tolerance and synergistic benefits, heterocyclic nitrogen rings are present in many anticancer medications. Understanding how to bind to the EGFR and its prospective impacts on cancer cells, expect to construct new heterocyclic compounds that may help produce potent anticancer medicines with a high safety profile. Methods: Novel 1,3-diazetidin-2-one derivatives were designed, synthesized from mefenamic acid, and their cytotoxic activity against a lung cancer cell line (A549) was initially tested in vitro. These compounds were anchored to the crystal structure of the epidermal growth factor receptor (PDB code 1M17) in a molecular docking study to determine their binding affinity at the active site. The newly synthesized derivatives were verified and confirmed by elemental analysis and spectroscopic data (FT-IR, 1H-NMR, and 13C-NMR). In addition, physicochemical, drug-like, and toxicological predictions were performed for these derivatives. Results: Based on a molecular docking study, all compounds (M4a-e) demonstrated superior PLPfitness (84.70, 85.89, 91.90, 88.61, and 92.77, respectively) to erlotinib (76.20). The anti-proliferation evaluation of the A549 cell line revealed that compounds M4c and M4e had exceptional and promising anti-proliferative activity on this cell line to treat lung cancer, with IC50 values of 1.75 µm and 2.05 µm at 72 hours, respectively, making them significantly more active than the reference erlotinib, which had an IC50 value of 11.5 µm at 72 hours. Conclusions: The cytotoxicity investigation and the molecular docking study showed a robust association with the novel compounds (M4a-e). Suggest a comprehensive pharmacological survey to understand how these newly created chemicals combat cancer fully.

Mechanism of Taxanes in the Treatment of Lung Cancer Based on Network Pharmacology and Molecular Docking

Taxanes are natural compounds for the treatment of lung cancer, but the molecular mechanism behind the effects is unclear. In the present study, through network pharmacology and molecular docking, the mechanism of the target and pathway of taxanes in the treatment of lung cancer was studied. The taxanes targets were determined by PubChem database, and an effective compounds-targets network was constructed. The GeneCards database was used to determine the disease targets of lung cancer, and the intersection of compound targets and disease targets was obtained. The Protein-Protein Interaction (PPI) network of the intersection targets was analyzed, and the PPI network was constructed by Cytoscape 3.6.0 software. The hub targets were screened according to the degree value, and the binding activity between taxanes and hub targets was verified by molecular docking. The results showed that eight taxane-active compounds and 444 corresponding targets were screened out, and 131 intersection targets were obtained after mapping with lung cancer disease targets. The hub targets obtained by PPI analysis were TP53, EGFR, and AKT1. Gene Ontology (GO) biological function enrichment analysis obtained 1795 biological process (BP) terms, 101 cellular component (CC) terms, and 164 molecular function (MF) terms. There were 179 signaling pathways obtained by Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. Twenty signaling pathways were screened out, mainly pathways in cancer, proteoglycans in cancer pathway, microRNAs in cancer pathway, and so on. Molecular docking shows that the binding energies of eight taxanes with TP53, EGFR, and AKT1 targets were less than -8.8 kcal/mol, taxanes acts on TP53, EGFR, and AKT1 targets through pathways in cancer, proteoglycans in cancer pathway and microRNAs in cancer pathway, and plays a role in treating lung cancer in biological functions such as protein binding, enzyme binding, and identical protein binding.