Suitability ofMMGBSAfor the selection of correct ligand binding modes from docking results

Repurposing FDA-approved drugs for combating tigecycline resistance in Acinetobacter baumannii: in silico screening against BaeR protein

Acinetobacter baumannii is becoming a gravely threatening nosocomial infection with a higher mortality rate. The present study targets the BaeR protein that mediates resistance to tigecycline antibiotics. The BaeR protein, along with the aid of BaeS, senses the incoming antibiotics and stimulates the expression of resistance proteins. These resistance proteins efflux the antibiotics and protect the cells from its effect. The main goal of the current study is to determine potential inhibitors from already existing FDA-approved drugs that could mitigate the BaeR protein. A range of in silico approaches, including molecular dynamics, virtual screening, SIFT analysis, ADMET, DFT, MM/GBSA, MMPBSA and per residue interaction analysis, were performed to identify inhibitors against this protein. The screening of FDA-approved compounds against the BaeR protein yielded 620 compounds. These compounds were clustered by SIFT to distinguish related compounds, it resulted in 20 different clusters. The top five clusters that can accommodate the binding site with better interaction and score by fulfilling all criteria were selected. The DFT analysis showed a smaller energy gap among all the compounds, indicating the ability of the compound to form firm interactions. All the compounds showed less binding free energy in both MM/GBSA and MM/PBSA analyses. The compounds were observed to be stable throughout the simulation. The per-residue interaction analysis confirmed that interactions with binding site residues were stable throughout the simulation. As a result of the study, four compounds, namely ZINC000003801919, DB01203, DB11217 and ZINC0000000056652, were identified as efficient candidates to deal with antimicrobial resistance in A. baumannii.

In silico prediction of GRP78-CRIPTO binding sites to improve therapeutic targeting in glioblastoma

Glioblastoma multiforme (GBM) is one of the most malignant tumors in central nervous system (CNS) tumors. The glucose-regulated protein 78 (GRP78) and CRIPTO (Cripto-1), a protein that belongs to the EGF-CFC (epidermal growth factor cripto-1 FRL-1 cryptic) family, are overexpressed in GBM. A complex between GRP78 SBDβ (substrate binding domain beta) and CRIPTO CFC domain was reported in previous studies. This complex activates MAPK/AKT signaling, Src/PI3K/AKT, and Smad2/3 pathways which is a reason for tumor proliferation. In this work, we study how the two proteins form the complex figuring out binding sites between GRP78 and CRIPTO utilizing computational biophysics and bioinformatics tools, such as protein-protein docking, molecular dynamics simulation and MMGBSA calculations. Haddock web server results of 4 regions from the CFC domain (region1 (- 70.4), region2 (- 78.7), region3 (- 74.2), region4 (- 86.8)) with selected residues of the SBDβ are then simulated for 100 ns MDS then MMGBSA were calculated for the four complexes. The results reveal the stability of the complexes with binding free energy (complex1 (- 15.07 kcal/mol), complex2 (- 59.78 kcal/mol), complex3 (- 81.92 kcal/mol), complex4 (- 126.26 kcal/mol). All these findings ensure that GRP78 SBDβ associates with the CRIPTO CFC domain, and the binding sites suggested make stable interactions between the proteins.

Identification of phyto-compounds from Mangifera indica as inhibitors of 17β-hydroxysteroid dehydrogenase: a computational approach against prostate cancer

Prostate cancer (PrCa) is a serious health concern for the affected people and, there is an increasing demand for a viable therapy that can address the limitations of current treatments with minimal or no adverse effects. This study aims to evaluate phytocompounds extracts of Mangifera indica as a potential therapy development for prostate cancer. Herein, molecular docking, QSAR, molecular mechanics/generalized born surface area (MM/GBSA) estimation, ADME screening, and molecular dynamics (MD) simulation were performed using the Schrodinger suite to identify 17β-hydroxysteroid dehydrogenase antagonist from Mangifera indica. The results showed that fisetin (-11.669), riboflavin (-10.918), quercetin (-10.843), gallic acid 6-phenylhexyl ester (-10.817), cianidanol (-10.608), (-)-epicatechin (-10.603), ellagic acid (-10.522), Butin (-10.124) in kcal/mol were predicted to possess greater inhibitory activities against the protein target based on their high binding energies and remarkable stability compared to the standard drug, docetaxel (-7.374 kcal/mol). Fisetin (-718.37), and riboflavin (-722.37) also have better induce fit score than docetaxel (-714.02) in kcal/mol with better pharmacokinetics profile compared to the standard drug.MD simulation over 100 ns predicts that Fisetin forms stable interactions with vital residues at the catalytic site of the protein. The observations from this study predict fisetin as a putative antagonist of 17β-hydroxysteroid dehydrogenase and should be experimentally verified as a lead compound for prostate cancer therapy.

Discovery of potent CSK inhibitors through integrated virtual screening and molecular dynamic simulation

Oncogenic overexpression or activation of C-terminal Src kinase (CSK) has been shown to play an important role in triple-negative breast cancer (TNBC) progression, including tumor initiation, growth, metastasis, drug resistance. This revelation has pivoted the focus toward CSK as a potential target for novel treatments. However, until now, there are few inhibitors designed to target the CSK protein. Responding to this, our research has implemented a comprehensive virtual screening protocol. By integrating energy-based screening methods with AI-driven scoring functions, such as Attentive FP, and employing rigorous rescoring methods like Glide docking and molecular mechanics generalized Born surface area (MM/GBSA), we have systematically sought out inhibitors of CSK. This approach led to the discovery of a compound with a potent CSK inhibitory activity, reflected by an IC50 value of 1.6 nM under a homogeneous time-resolved fluorescence (HTRF) bioassay. Subsequently, molecule 2 exhibits strong growth inhibition of MD anderson - metastatic breast (MDA-MB) -231, Hs578T, and SUM159 cells, showing a level of growth inhibition comparable to that observed with dasatinib. Treatment with molecule 2 also induced significant G1 phase accumulation and cell apoptosis. Furthermore, we have explored the explicit binding interactions of the compound with CSK using molecular dynamics simulations, providing valuable insights into its mechanism of action.

Tanshinone I limits inflammasome activation of macrophage via docking into Syk to alleviate DSS-induced colitis in mice

Tanshinone I (Tan I) has been proven to exert an anti-inflammatory effect, but the complete mechanism remains unclear. In this study, Tan I was described to have no effect on Syk expression in resting or LPS-stimulated macrophages ex vivo, but dramatically suppressed Syk phosphorylation and CD80, CD86, and IL-1β expression of macrophages. The inflammatory activity of macrophages in ApoC3-transgenic (ApoC3TG) mice is upregulated by Syk activation. Tan I was determined to downregulate Syk phosphorylation and inflammatory activity of macrophages in ApoC3TG mice, both ex vivo and in vivo. Intraperitoneal injection of Tan I (4 mg/kg) effectively alleviated DSS-induced colitis in mice, accompanying with suppressing the activation of intestinal macrophages. Mechanistically, Tan I-treated macrophages exhibited a decrease in cytoplasmic ROS, NLRP3, GSDMD, and IL-1β, which suggested that the alternative pathway of inflammasome activation in macrophages was suppressed. The SPR assay demonstrated that Tan I bound to Syk protein with a dissociation constant (KD) of 2.473 × 10-6 M. When Syk expression was knocked down by its shRNA, the inhibitory effects of Tan I on macrophages were blocked. Collectively, Tanshinone I effectively alleviated DSS-induced colitis in mice by inhibiting Syk-stimulated inflammasome activation, hence suppressing the inflammatory activity of macrophages.

Building shape-focused pharmacophore models for effective docking screening

The performance of molecular docking can be improved by comparing the shape similarity of the flexibly sampled poses against the target proteins' inverted binding cavities. The effectiveness of these pseudo-ligands or negative image-based models in docking rescoring is boosted further by performing enrichment-driven optimization. Here, we introduce a novel shape-focused pharmacophore modeling algorithm O-LAP that generates a new class of cavity-filling models by clumping together overlapping atomic content via pairwise distance graph clustering. Top-ranked poses of flexibly docked active ligands were used as the modeling input and multiple alternative clustering settings were benchmark-tested thoroughly with five demanding drug targets using random training/test divisions. In docking rescoring, the O-LAP modeling typically improved massively on the default docking enrichment; furthermore, the results indicate that the clustered models work well in rigid docking. The C+ +/Qt5-based algorithm O-LAP is released under the GNU General Public License v3.0 via GitHub ( https://github.com/jvlehtonen/overlap-toolkit ). SCIENTIFIC CONTRIBUTION: This study introduces O-LAP, a C++/Qt5-based graph clustering software for generating new type of shape-focused pharmacophore models. In the O-LAP modeling, the target protein cavity is filled with flexibly docked active ligands, the overlapping ligand atoms are clustered, and the shape/electrostatic potential of the resulting model is compared against the flexibly sampled molecular docking poses. The O-LAP modeling is shown to ensure high enrichment in both docking rescoring and rigid docking based on comprehensive benchmark-testing.

Structure-based pharmacophore modeling for precision inhibition of mutant ESR2 in breast cancer: A systematic computational approach

BACKGROUND

Breast cancer, a leading cause of female mortality, is closely linked to mutations in estrogen receptor beta (ESR2), particularly in the ligand-binding domain, which contributed to altered signaling pathways and uncontrolled cell growth.

OBJECTIVES/AIMS

This study investigates the molecular and structural aspects of ESR2 mutant proteins to identify shared pharmacophoric regions of ESR2 mutant proteins and potential therapeutic targets aligned within the pharmacophore model.

METHODS

This study was initiated by establishing a common pharmacophore model among three mutant ESR2 proteins (PDB ID: 2FSZ, 7XVZ, and 7XWR). The generated shared feature pharmacophore (SFP) includes four primary binding interactions: Hydrogen bond donors (HBD), hydrogen bond acceptors (HBA), hydrophobic interactions (HPho), and Aromatic interactions (Ar), along with halogen bond donors (XBD) and totalling 11 features (HBD: 2, HBA: 3, HPho: 3, Ar: 2, XBD: 1). By employing an in-house Python script, these 11 features distributed into 336 combinations, which were used as query to isolate a drug library of 41,248 compounds and subjected to virtual screening through the generated SFP.

RESULTS

The virtual screening demonstrated 33 hits showing potential pharmacophoric fit scores and low RMSD value. The top four compounds: ZINC94272748, ZINC79046938, ZINC05925939, and ZINC59928516 showed a fit score of more than 86% and satisfied the Lipinski rule of five. These four compounds and a control underwent molecular (XP Glide mode) docking analysis against wild-type ESR2 protein (PDB ID: 1QKM), resulting in binding affinity of -8.26, -5.73, -10.80, and -8.42 kcal/mol, respectively, along with the control -7.2 kcal/mol. Furthermore, the stability of the selected candidates was determined through molecular dynamics (MD) simulations of 200 ns and MM-GBSA analysis.

CONCLUSION

Based on MD simulations and MM-GBSA analysis, our study identified ZINC05925939 as a promising ESR2 inhibitor among the top four hits. However, it is essential to conduct further wet lab evaluation to assess its efficacy.

Isoliquiritigenin limits inflammasome activation of macrophage via docking into Syk to alleviate murine non-alcoholic fatty liver disease

Isoliquiritigenin (ISL) is a chalcone-type flavonoid derived from the root of licorice with antioxidant, anti-inflammatory, anti-tumour and neuroprotective properties. ISL has been proven to downregulate the productions of IL-1β, TNF-α and IL-6 by macrophages. However, detailed molecular mechanisms of this modulation remain elusive. Here, ISL suppressed Syk phosphorylation and CD80, CD86, IL-1β, TNF-α and IL-6 expressions in lipopolysaccharide-stimulated macrophages ex vivo. ApoC3-transgenic (ApoC3TG) mice had more activated macrophages. ISL was also able to downregulate the inflammatory activities of macrophages from ApoC3TG mice. Administration of ISL inhibited Syk activation and inflammatory activities of macrophages in ApoC3TG mice in vivo. The treatment of ISL further alleviated MCD-induced non-alcoholic fatty liver disease (NAFLD) in wild-type and ApoC3TG mice, accompanied by less recruitment and activation of liver macrophages. Due to the inhibition of Syk phosphorylation, ISL-treated macrophages displayed less production of cytoplasmic ROS, NLRP3, cleaved-GSDMD and cleaved-IL-1β, suggesting less inflammasome activation. Finally, the molecular docking study demonstrated that ISL bound to Syk directly with the Kd of 1.273 × 10-8 M. When the Syk expression was knocked down by its shRNA, the inhibitory effects of ISL on activated macrophages disappeared, indicating that Syk was at least one of key docking-molecules of ISL. Collectively, ISL could alleviate MCD-induced NAFLD in mice involved with the inhibition of macrophage inflammatory activity by the blockade of Syk-induced inflammasome activation.

In silico investigations and molecular insights for designing tRNA-encoded peptides as potential therapeutics for targeting over-expressed receptors in breast cancer

tRNA- Encoded Peptides (tREPs) have recently been discovered as new functional peptides and hold promise as therapeutics for anti-parasitic applications. In this study, in silico investigations were conducted to design tRNA-encoded peptides with the potential to target over-expressed receptors in breast cancer cells. tRNA genes were translated into corresponding peptides (tREPs) using computational tools. The tREPs, which were predicted as anticancer peptides, were then screened for various ADMET properties. Molecular docking studies were conducted for three cancer target receptors, the Estrogen Receptor (ER), Peroxisome Proliferator-Activated Receptor (PPAR) and the Epidermal Growth Factor Receptor (EGFR). Based on the docking results, specific tREPs were screened and molecular dynamics simulations were performed, and the binding energies were further explored using MMPBSA calculations. The peptide Pep1 (DWIAWRHHNDIVSWLTCGPRFKSWS) and Pep2 (GFIAWWSRHLELAQTRFKSWWS) exhibited a good binding affinity against the Estrogen Receptor (ER) and the Peroxisome Proliferator-Activated Receptor Alpha (PPAR) cancer target. The Pep1-ER and Pep1-PPAR complex maintained an average of two hydrogen bonds throughout the simulation and demonstrated a higher negative binding free energy of -72.27 kcal/mol and -65.16 kcal/mol respectively, as calculated by MMPBSA. Therefore, the tREPs designed as anticancer peptides in this study provide novel approaches for potential anticancer therapeutic modalities.

Computational insights into potential marine natural products as selective inhibitors of Mycobacterium tuberculosis InhA: A structure-based virtual screening study

Several factors are associated with the emergence of drug resistance mechanisms, such as impermeable cell walls, gene mutations, and drug efflux systems. Consequently, bacteria acquire resistance, leading to a decrease in drug efficacy. A new and innovative strategy is required to combat drug resistance in tuberculosis (TB) effectively. Therefore, targeting the mycolic acid biosynthesis pathway, which is involved in synthesising mycolic acids (MAs), essential structural components responsible for mycobacterial pathogenicity, has garnered interest in TB research and the concept of drug resistance. In this context, InhA, which plays a crucial role in the fatty acid synthase-II (FAS-II) system of the MA biosynthetic pathway, was selected as a druggable target for screening investigation. To identify potential lead molecules against InhA, diverse marine natural products (MNPs) were collected from the comprehensive marine natural products database (CMNPD). Virtual screening studies aided in selecting potential lead molecules that best fit within the substrate-binding pocket (SBP) of InhA, forming crucial hydrogen bond interaction with the catalytic residue Tyr158. Three MNPs, CMNPD30814, CMNPD1702, and CMNPD27355, were chosen as prospective alternative molecules due to their favorable pharmacokinetic properties and lack of toxicity according to ProTox-II predictions. Additionally, improved reactivity of the MNPs was observed in the results of density functional theory (DFT) studies. Furthermore, comparative molecular dynamics simulation (MDS), principal component (PC)-based free energy landscape (FEL) analysis, and molecular mechanics Poisson-Boltzmann surface area (MM-PBSA) were employed to show enhanced structural stability, increased H-bond potential, and high binding affinity toward the target InhA. Moreover, the hot spot residues that contributed to the high binding energy profile and anchored the stability of the complexes were revealed with their individual interaction energy. The computational insights from this study provide potential avenues to combat TB through the multifaceted mode of action of these marine lead molecules, which can be further explored in future experimental investigations.

Prediction of protein-ligand binding affinity via deep learning models

Accurately predicting the binding affinity between proteins and ligands is crucial in drug screening and optimization, but it is still a challenge in computer-aided drug design. The recent success of AlphaFold2 in predicting protein structures has brought new hope for deep learning (DL) models to accurately predict protein-ligand binding affinity. However, the current DL models still face limitations due to the low-quality database, inaccurate input representation and inappropriate model architecture. In this work, we review the computational methods, specifically DL-based models, used to predict protein-ligand binding affinity. We start with a brief introduction to protein-ligand binding affinity and the traditional computational methods used to calculate them. We then introduce the basic principles of DL models for predicting protein-ligand binding affinity. Next, we review the commonly used databases, input representations and DL models in this field. Finally, we discuss the potential challenges and future work in accurately predicting protein-ligand binding affinity via DL models.

CSC01 shows promise as a potential inhibitor of the oncogenic G13D mutant of KRAS: an in silico approach

About 30% of malignant tumors include KRAS mutations, which are frequently required for the development and maintenance of malignancies. KRAS is now a top-priority cancer target as a result. After years of research, it is now understood that the oncogenic KRAS-G12C can be targeted. However, many other forms, such as the G13D mutant, are yet to be addressed. Here, we used a receptor-based pharmacophore modeling technique to generate potential inhibitors of the KRAS-G13D oncogenic mutant. Using a comprehensive virtual screening workflow model, top hits were selected, out of which CSC01 was identified as a promising inhibitor of the oncogenic KRAS mutant (G13D). The stability of CSC01 upon binding the switch II pocket was evaluated through an exhaustive molecular dynamics simulation study. The several post-simulation analyses conducted suggest that CSC01 formed a stable complex with KRAS-G13D. CSC01, through a dynamic protein-ligand interaction profiling analysis, was also shown to maintain strong interactions with the mutated aspartic acid residue throughout the simulation. Although binding free energy analysis through the umbrella sampling approach suggested that the affinity of CSC01 with the switch II pocket of KRAS-G13D is moderate, our DFT analysis showed that the stable interaction of the compound might be facilitated by the existence of favorable molecular electrostatic potentials. Furthermore, based on ADMET predictions, CSC01 demonstrated a satisfactory drug likeness and toxicity profile, making it an exemplary candidate for consideration as a potential KRAS-G13D inhibitor.

Avanafil mediated dual inhibition of IKKβ and TNFR1 in an experimental paradigm of Alzheimer's disease: in silico and in vivo approach

In Alzheimer's disease pathology, inhibitors of nuclear factor kappa-β kinase subunit β (IKKB) and Tumor necrosis factor receptor 1 (TNFR1) signaling are linked to neuroinflammation-mediated cognitive decline. We explored the role of a phosphodiesterase 5 inhibitor (PDE5I) with dual antagonistic action on IKKB and TNFR1 to inhibit nuclear factor kappa B (NF-kB) and curb neuroinflammation. In the in silico approach, the FDA-approved Zinc 15 library was docked with IKKB and TNFR1. The top compound with dual antagonistic action on IKKB and TNFR1 was selected based on bonding and non-bonding interactions. Further, induced fit docking (IFD), molecular mechanics-generalized Born and surface area (MMGBSA), and molecular dynamic studies were carried out and evaluated. Lipopolysaccharide (LPS) administration caused a neuroinflammation-mediated cognitive decline in mice. Two doses of avanafil were administered for 28 days while LPS was administered for 10 days. Morris water maze (MWM) along with the passive avoidance test (PAT) were carried out. Concurrently brain levels of inflammatory markers, oxidative parameters, amyloid beta (Aβ), IKKB and NF-kB levels were estimated. Avanafil produced good IKKB and TNFR1 binding ability. It interacted with crucial inhibitory amino acids of IKKB and TNFR1. MD analysis predicted good stability of avanafil with TNFR1 and IKKB. Avanafil 6 mg/kg could significantly improve performance in MWM, PAT and oxidative parameters and reduce Aβ levels and inflammatory markers. As compared to avanafil 3 mg/kg, 6 mg/kg dose was found to exert better efficacy against elevated Aβ , neuroinflammatory cytokines and oxidative markers while improving behavioural parameters.Communicated by Ramaswamy H. Sarma.

Multilayer precision-based screening of potential inhibitors targeting Mycobacterium tuberculosis acetate kinase using in silico approaches

Tuberculosis (TB), caused by Mycobacterium tuberculosis (MTB), remains a major threat to global health, with the emergence of multi-drug and extensively drug-resistant strains posing a serious challenge. Thereby, understanding the molecular basis of MTB virulence and disease pathogenesis is critical for developing effective therapeutic strategies. Targeting proteins involved in central metabolism has been recognized as a promising therapeutic approach to combat MTB. In this regard, the enzyme AckA of the acetate metabolic pathway which produces acetate from acetyl phosphate, is an important drug target for various pathogenic organisms. Therefore, this study aimed to identify potential AckA inhibitors through in silico methods, including molecular modeling, molecular dynamics simulation (MDS), and high-throughput virtual screening (HTVS) followed by ADMETox, MMGBSA, Density Functional Theory (DFT) calculations. HTVS of one million compounds from the ZINC database against AckA resulted in the top five hits (ZINC82048449, ZINC1219737510, ZINC1771921358, ZINC119699567, and ZINC1427100376) with better binding affinity and optimal binding free energy. MDS studies on complexes revealed that key residues, Asn195, Asp266, Phe267, Gly314, and Asn318 played a significant role in stable interactions of the top-ranked compounds to AckA. These outcomes provide insights into the optimal binding of the leads to inhibit the acetate pathway and aid in the rational design of novel therapeutic agents. Thus, the identified leads may act as promising compounds for targeting AckA and may serve as a potential therapeutic modality for treating TB. Our findings offer valuable insights into the inhibition of the acetate pathway, while also serving as a blueprint for rational drug design. The identified leads hold promise as compelling compounds for targeting AckA, thereby offering a potential therapeutic avenue for tackling TB. Thus, our study uncovers a pathway toward promising TB therapeutics by elucidating AckA inhibitors. By leveraging in silico methodologies, potent compounds that hold the potential to thwart AckA's role in MTB's acetate pathway have been unveiled. This breakthrough fosters optimism in the quest for novel and effective TB treatments, addressing a global health challenge with renewed vigor.

Structural snapshots of Mycobacterium tuberculosis enolase reveal dual mode of 2PG binding and its implication in enzyme catalysis

Enolase, a ubiquitous enzyme, catalyzes the reversible conversion of 2-phosphoglycerate (2PG) to phosphoenolpyruvate (PEP) in the glycolytic pathway of organisms of all three domains of life. The underlying mechanism of the 2PG to PEP conversion has been studied in great detail in previous work, however that of the reverse reaction remains to be explored. Here we present structural snapshots of Mycobacterium tuberculosis (Mtb) enolase in apo, PEP-bound and two 2PG-bound forms as it catalyzes the conversion of PEP to 2PG. The two 2PG-bound complex structures differed in the conformation of the bound product (2PG) viz the widely reported canonical conformation and a novel binding pose, which we refer to here as the alternate conformation. Notably, we observed two major differences compared with the forward reaction: the presence of MgB is non-obligatory for the reaction and 2PG assumes an alternate conformation that is likely to facilitate its dissociation from the active site. Molecular dynamics studies and binding free energy calculations further substantiate that the alternate conformation of 2PG causes distortions in both metal ion coordination and hydrogen-bonding interactions, resulting in an increased flexibility of the active-site loops and aiding product release. Taken together, this study presents a probable mechanism involved in PEP to 2PG catalysis that is likely to be mediated by the conformational change of 2PG at the active site.

Marine-Derived Compounds for CDK5 Inhibition in Cancer: Integrating Multi-Stage Virtual Screening, MM/GBSA Analysis and Molecular Dynamics Investigations

Cyclin-dependent kinase 5 (CDK5) plays a crucial role in various biological processes, including immune response, insulin secretion regulation, apoptosis, DNA (deoxyribonucleic acid) damage response, epithelial-mesenchymal transition (EMT), cell migration and invasion, angiogenesis, and myogenesis. Overactivation of CDK5 is associated with the initiation and progression of cancer. Inhibiting CDK5 has shown potential in suppressing cancer development. Despite advancements in CDK5-targeted inhibitor research, the range of compounds available for clinical and preclinical trials remains limited. The marine environment has emerged as a prolific source of diverse natural products with noteworthy biological activities, including anti-cancer properties. In this study, we screened a library of 47,450 marine natural compounds from the comprehensive marine natural product database (CMNPD) to assess their binding affinity with CDK5. Marine compounds demonstrating superior binding affinity compared to a reference compound were identified through high-throughput virtual screening, standard precision and extra-precision Glide docking modes. Refinement of the selected molecules involved evaluating molecular mechanics-generalized born surface area (MM/GBSA) free binding energy. The three most promising compounds, (excoecariphenol B, excoecariphenol A, and zyzzyanone B), along with the reference, exhibiting favorable binding characteristics were chosen for molecular dynamics (MD) simulations for 200 nanoseconds. These compounds demonstrated interaction stability with the target during MD simulations. The marine compounds identified in this study hold potential as effective CDK5 inhibitors and warrant subsequent experimental validation.

In Silico Discovery of Small-Molecule Inhibitors Targeting SARS-CoV-2 Main Protease

The COVID-19 pandemic has caused severe health threat globally, and novel SARS-Cov-2 inhibitors are urgently needed for antiviral treatment. The main protease (M^pro) of the virus is one of the most effective and conserved targets for anti-SARS-CoV-2 drug development. In this study, we utilized a molecular docking-based virtual screening approach against the conserved catalytic site to identify small-molecule inhibitors of SARS-CoV-2 M^pro. Further biological evaluation helped us identify two compounds, AF-399/40713777 and AI-942/42301830, with moderate inhibitory activity. Besides that, the in silico data, including molecular dynamics (MD) simulation, binding free energy calculations, and AMDET profiles, suggested that these two hits could serve as the starting point for the future development of COVID-19 intervention treatments.

Discovery of dual-target natural antimalarial agents against DHODH and PMT of Plasmodium falciparum: pharmacophore modelling, molecular docking, quantum mechanics, and molecular dynamics simulations

Malaria is a lethal disease that claims thousands of lives worldwide annually. The objective of this study was to identify new natural compounds that can target two P. falciparum enzymes; P. falciparum Dihydroorotate dehydrogenase (PfDHODH) and P. falciparum phosphoethanolamine methyltransferase (PfPMT). To accomplish this, e-pharmacophore modelling and molecular docking were employed against PfDHODH. Following this, 1201 natural compounds with docking scores of ≤ -7 kcal/mol were docked into the active site of the second enzyme PMT. The top nine compounds were subjected to further investigation using MM-GBSA free binding energy calculations and ADME analysis. The results revealed favourable free binding energy values better than the references, as well as acceptable pharmacokinetic properties. Compounds ZINC000013377887, ZINC000015113777, and ZINC000085595753 were scrutinized to assess their interaction stability with the PfDHODH enzyme, and chemical stability reactivity using molecular dynamics (MD) simulation and density functional theory (DFT) calculations. These findings indicate that the three natural compounds are potential candidates for dual PfDHODH and PfPMT inhibitors for malaria treatment.

Selectivity and Ranking of Tight-Binding JAK-STAT Inhibitors Using Markovian Milestoning with Voronoi Tessellations

Janus kinases (JAK), a group of proteins in the nonreceptor tyrosine kinase (NRTKs) family, play a crucial role in growth, survival, and angiogenesis. They are activated by cytokines through the Janus kinase-signal transducer and activator of a transcription (JAK-STAT) signaling pathway. JAK-STAT signaling pathways have significant roles in the regulation of cell division, apoptosis, and immunity. Identification of the V617F mutation in the Janus homology 2 (JH2) domain of JAK2 leading to myeloproliferative disorders has stimulated great interest in the drug discovery community to develop JAK2-specific inhibitors. However, such inhibitors should be selective toward JAK2 over other JAKs and display an extended residence time. Recently, novel JAK2/STAT5 axis inhibitors (N-(1H-pyrazol-3-yl)pyrimidin-2-amino derivatives) have displayed extended residence times (hours or longer) on target and adequate selectivity excluding JAK3. To facilitate a deeper understanding of the kinase-inhibitor interactions and advance the development of such inhibitors, we utilize a multiscale Markovian milestoning with Voronoi tessellations (MMVT) approach within the Simulation-Enabled Estimation of Kinetic Rates v.2 (SEEKR2) program to rank order these inhibitors based on their kinetic properties and further explain the selectivity of JAK2 inhibitors over JAK3. Our approach investigates the kinetic and thermodynamic properties of JAK-inhibitor complexes in a user-friendly, fast, efficient, and accurate manner compared to other brute force and hybrid-enhanced sampling approaches.

Molecular dynamics, MMGBSA, and docking studies of natural products conjugated to tumor-targeted peptide for targeting BRAF V600E and MERTK receptors

Recent studies have revealed that MERTK and BRAF V600E receptors have been found to be over-expressed in several types of cancers including melanoma, making these receptors targets for drug design. In this study, we have designed novel peptide conjugates with the natural products vanillic acid, thiazole-2-carboxylic acid, cinnamic acid, theanine, and protocatechuic acid. Each of these compounds was conjugated with the tumor targeting peptide sequence TAASGVRSMH, known to bind to NG2 and target tumor neovasculature. We examined their binding affinities and stability with MERTK and BRAF V600E receptors using molecular docking and molecular dynamics studies. Compared to the neat compounds, the peptide conjugates displayed higher binding affinity toward both receptors. In the case of MERTK, the most stable complexes were formed with di-theaninate-peptide, vanillate-peptide, and thiazole-2-amido peptide conjugates and binding occurred in the hinge region. Additionally, it was discovered that the peptide alone also had high binding ability and stability with the MERTK receptor. In the case of BRAF V600E, the peptide conjugates of protocatechuate, vanillate and thiazole-2-amido peptide conjugates showed the formation of the most stable complexes and binding occurred in the ATP binding cleft. Further analysis revealed that the number of hydrogen bonds and hydrophobic interactions played a critical role in enhanced stability of the complexes. Docking studies also revealed that binding affinities for NG2 were similar to MERTK and higher for BRAF V600E. MMGBSA studies of the trajectories revealed that the protocatechuate-peptide conjugate showed the highest binding energy with BRAF V600E while the peptide-TAASGVRSMH showed the highest binding energy with MERTK. ADME studies revealed that each of the compounds showed medium to high permeability toward MDCK cells and were not hERG blockers. Furthermore, the conjugates were not CYP inhibitors or substrates, but they were found to be Pgp substrates. Our results indicated that the protocatechuate-TAASGVRSMH, thiazole-2-amido-TAASGVRSMH, and vanillate-TAASGVRSMH conjugates may be furthered developed for in vitro and in vivo studies as novel tumor targeting compounds for tumor cells over-expressing BRAF V600E, while di-theaninate-amido-TAASGVRSMH and thiazole-2-amido-TAASGVRSMH conjugates may be developed for targeting MERTK receptors. These studies provide insight into the molecular interactions of natural product-peptide conjugates and their potential for binding to and targeting MERTK and BRAF V600E receptors in developing new therapeutics for targeting cancer.

Probing the bioactive compounds of Kigelia africana as novel inhibitors of TNF-α converting enzyme using HPLC/GCMS analysis, FTIR and molecular modelling

Tumor Necrosis Factor Alpha Converting Enzyme (TACE) mediates inflammatory disorder and contributes to the pathophysiology of a variety of illnesses, such as chronic inflammation and cancer. This study identified metabolites in solvent extracts of Kigelia africana as putative TACE inhibitors due to the plant's known anti-inflammatory properties. HPLC-MS/GCMS analysis was used to characterize tentative phytochemicals from K. africana. The identified metabolites (n = 123) were docked with TACE to reveal the lead compounds. Binding free energy, ADMET prediction, molecular dynamics simulation at 100 ns, and DFT calculation were further conducted. The results revealed that K. africana contains sterol, phenols, alkaloids, terpenes and flavonoids. The FTIR shows that the extracts had peaks that correspond to the presence of different functional groups. The quantum polarized ligand docking (QPLD) analysis identified compound (n = 3) with binding affinity higher than standard compound IK-682. The hits also had modest ADMET profiles, interacted with essential residues within TACE binding pockets, and formed stable complexes with the protein. The 100 ns MD simulation shows that the compounds formed fairly stable interactions and complex with the protein as evidenced through RMSF, RMSD and MM-GBA results. The HOMO/LUMO, global descriptive molecular electrostatic potential Fukui function aid in the identification of the compounds' atomic sites prone to electrophilic/neutrophilic attacks, and non-covalent interactions. This study suggests that K. africana's bioactive compounds are capable of mitigating inflammation by inhibiting TACE.Communicated by Ramaswamy H. Sarma.

In Silico Design and Investigation of Novel Thiazetidine Derivatives as Potent Inhibitors of PrpR in Mycobacterium tuberculosis

Tuberculosis is one of the most life-threatening acute infectious diseases diagnosed in humans. In the present investigation, a series of 16 new disubstituted 1,3-thiazetidines derivatives is designed, and investigated via various in silico methods for their potential as anti-tubercular agent by evaluating their ability to block the active site of PrpR transcription factor protein of Mycobacterium tuberculosis. The efficacy of the molecules was initially assessed with the help of AutoDock Vina algorithm. Further Glide module is used to redock the previously docked complexes. The binding energies and other physiochemical properties of the designed molecules were evaluated using the Prime-MM/GBSA and the QikProp module, respectively. The results of docking revealed the nature, site of interaction and the binding affinity between the proposed candidates and the active site of PrpR. Further the inhibitory effect of the scaffolds was predicted and evaluated employing a machine learning-based algorithm and was used accordingly. Further, the molecular dynamics simulation studies ascertained the binding characteristics of the unique 13, when analysed across a time frame of 100 ns with GROMACS software. The results show that the proposed 1,3-thiazetidine derivatives such as 10, 11, 13 and 14 could be potent and selective anti-tubercular agents as compared to the standard drug Pyrazinamide. Finally, this study concludes that designed thiazetidines can be employed as anti-tubercular agents. Undeniably, the results may guide the experimental biologists to develop safe and non-toxic drugs against tuberculosis by demanding further in vivo and in vitro analyses.

Virtual screening of natural compounds as selective inhibitors of polo-like kinase-1 at C-terminal polo box and N-terminal catalytic domain

The over-expression of Polo-like kinase-1 (PLK1) is associated with cancer prognosis due to its pivotal role in cell proliferation. The N-terminal catalytic domain (NCD) and C-terminal polo box domain (PBD) of PLK1 are critical for the activity of the protein. Drugs that inhibit PLK1 by targeting these domains are on clinical trials, but so far, none has been approved by FDA. Thus, this study targets the two domains of PLK1 to identify compounds with inhibitory potential. Four validated e-pharmacophore models from NCD (PDB ID: 2OU7 and 4J52) and PBD (PDB ID: 5NEI and 5NN2) were used to screen over 26,000 natural compounds from NPASS database. Hits were identified after the well-fitted compounds were subjected to molecular docking study and ADME prediction. The pIC50 and electronic behaviour of the identified hits selectively targeting NCD and PBD of PLK1 were predicted via an externally validated QSAR model and quantum mechanics. The results showed that CAA180504, CAA197326, CAA74619, CAA328856 modulating PLK1 at NCD, and CBB130581, CBB230713, CBB206123, CBB12656 and CBB267117 modulating PLK1 at PBD had better molecular docking scores, pharmacokinetics and drug-like properties than NCD (volasertib) and PBD (purpurogallin) reference inhibitors. The compounds all had satisfactory inhibitory (pIC50) values which range from 6.187 to 7.157. The electronic behaviours of understudied compounds using HOMO/LUMO and global descriptive parameters revealed the atomic portion of the compounds prone to donating and accepting electrons. In conclusion, the hit compounds identified from the library of natural compounds are worthy of further experimental validation.Communicated by Ramaswamy H. Sarma.

Design and Structural Optimization of Orally Bioavailable SOS1 Inhibitors for the Treatment of KRAS-Driven Carcinoma

KRAS mutations (G12C, G12D, etc.) are implicated in the oncogenesis and progression of many refractory cancers. Son of sevenless homolog 1 (SOS1) is a key regulator of KRAS to modulate KRAS from inactive to active states. Herein, we disclosed efficacy-improving tetra-cyclic quinazoline derivatives as an enhanced scaffold for inhibiting the SOS1-KRAS interaction. Compound 37, which conjugated 1-carbonitrile-cyclopropane to tetra-cyclic quinazoline, showed a twofold higher oral drug exposure and 2.5-fold longer half-life than BI-3406 in CD-1 mouse plasma. In a Mia-paca-2 xenograft model, 37 administrated alone inhibited tumor growth by 71%. Preclinical investigations demonstrated that 37 had a limited inhibition of CYP and hERG. Overall, our studies showed that 37 was a promising drug candidate for treatment of KRAS-driven cancer.

In Silico Interactions of the Components from the Schinus terebinthifolius Extract with Human Tyrosinase

The anti-tyrosinase activity of the leaf extract of Schinus terebinthifolius, also known as Brazilian peppertree, was evaluated using multiple in silico approaches, such as molecular homology, molecular docking, MM-GBSA, molecular dynamics, MM-PBSA, QSAR, and skin permeability predictions. With these computational tools, the compounds that downregulate tyrosinase enzyme activity could be evaluated, and more potent molecules could be identified. The results indicated that various compounds, especially luteolin, are accountable for the anti-tyrosinase activity of S. terebinthifolius. For cosmetic application, further studies with luteolin are especially recommended, for having presented a good performance both in theoretical inhibition (30.92 kJ mol^-1 ) and skin permeability (LogKp=-6.62 cm^-1 ).

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.

In Silico Studies of Tumor Targeted Peptide-Conjugated Natural Products for Targeting Over-Expressed Receptors in Breast Cancer Cells Using Molecular Docking, Molecular Dynamics and MMGBSA Calculations

In this work, in silico studies were carried out for the design of diterpene and polyphenol-peptide conjugates to potentially target over-expressed breast tumor cell receptors. Four point mutations were induced into the known tumor-targeting peptide sequence YHWYGYTPQN at positions 1, 2, 8 and 10, resulting in four mutated peptides. Each peptide was separately conjugated with either chlorogenate, carnosate, gallate, or rosmarinate given their known anti-tumor activities, creating dual targeting compounds. Molecular docking studies were conducted with the epidermal growth factor receptor (EGFR), to which the original peptide sequence is known to bind, as well as the estrogen receptor (ERα) and peroxisome proliferator-activated receptor (PPARα) using both Autodock Vina and FireDock. Based on docking results, peptide conjugates and peptides were selected and subjected to molecular dynamics simulations. MMGBSA calculations were used to further probe the binding energies. ADME studies revealed that the compounds were not CYP substrates, though most were Pgp substrates. Additionally, most of the peptides and conjugates showed MDCK permeability. Our results indicated that several of the peptide conjugates enhanced binding interactions with the receptors and resulted in stable receptor-ligand complexes; Furthermore, they may successfully target ERα and PPARα in addition to EGFR and may be further explored for synthesis and biological studies for therapeutic applications.

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.

Bufotenine and its derivatives: synthesis, analgesic effects identification and computational target prediction

Natural product bufotenine (5) which could be isolated from Venenum Bufonis, has been widely used as a tool in central nervous system (CNS) studies. We present here its quaternary ammonium salt (6) which was synthesized with high yields using 5-benzyloxyindole as raw materials, and we firstly discover its analgesic effects in vivo. The analgesic evaluation showed that compounds 5 and 6 had stronger effects on the behavior of formalin induced pain in mice. Moreover, the combination of compound 6 and morphine has a synergistic effect. We intended to explain the molecular mechanism of this effect. Therefore, 36 analgesic-related targets (including 15 G protein-coupled receptors, 6 enzymes, 13 ion channels, and 2 others) were systemically evaluated using reverse docking. The results indicate that bufotenine and its derivatives are closely related to acetyl cholinesterase (AChE) or α₄β₂ nicotinic acetylcholine receptor (nAChR). This study provides practitioners a new insight of analgesic effects.

Study on the binding mechanism of thiamethoxam with three model proteins:spectroscopic studies and theoretical simulations

As a top-selling neonicotinoid insecticide widely used in the field, thiamethoxam is an environmental pollutant because of the accumulation in ecosystem and has also been reported that it has potential risks to the health of mammals even humans. In order to understand the binding mechanism of thiamethoxam with biological receptors, spectroscopic techniques and theoretical simulations was used to explore the specific interactions between thiamethoxam and proteins. Interestingly, the results indicated that hydrophobic interaction as the main driving force, thiamethoxam formed a single binding site complex with proteins spontaneously, resulting in a decrease in the esterase-like activity of human serum albumin. The results of computer simulation showed that there were hydrophobic, electrostatic and hydrogen bonding interactions between thiamethoxam and receptors. The results of experiment and computer simulation were mutually confirmed, so a model was established for the interaction between the two which uncovered the structural characteristics of the binding site. This research provided new insights for the structure optimization of thiamethoxam, as well as gave an effective reference for evaluating the risk of thiamethoxam systemically in the future.

Thermodynamics and Kinetics of Drug-Target Binding by Molecular Simulation

Computational studies play an increasingly important role in chemistry and biophysics, mainly thanks to improvements in hardware and algorithms. In drug discovery and development, computational studies can reduce the costs and risks of bringing a new medicine to market. Computational simulations are mainly used to optimize promising new compounds by estimating their binding affinity to proteins. This is challenging due to the complexity of the simulated system. To assess the present and future value of simulation for drug discovery, we review key applications of advanced methods for sampling complex free-energy landscapes at near nonergodicity conditions and for estimating the rate coefficients of very slow processes of pharmacological interest. We outline the statistical mechanics and computational background behind this research, including methods such as steered molecular dynamics and metadynamics. We review recent applications to pharmacology and drug discovery and discuss possible guidelines for the practitioner. Recent trends in machine learning are also briefly discussed. Thanks to the rapid development of methods for characterizing and quantifying rare events, simulation's role in drug discovery is likely to expand, making it a valuable complement to experimental and clinical approaches.

KLF7: a new candidate biomarker and therapeutic target for high-grade serous ovarian cancer

BACKGROUND

In spite of great progress in the surgical and clinical management, until now no significant improvement in overall survival of High-Grade Serous Ovarian Cancer (HGSOC) patients has been achieved. Important aspects for disease control remain unresolved, including unclear pathogenesis, high heterogeneity and relapse resistance after chemotherapy. Therefore, further research on molecular mechanisms involved in cancer progression are needed to find new targets for disease management. The Krüppel-like factors (KLFs) are a family of transcriptional regulators controlling several basic cellular processes, including proliferation, differentiation and migration. They have been shown to play a role in various cancer-relevant processes, in a context-dependent way.

METHODS

To investigate a possible role of KLF family members as prognostic biomarkers, we carried out a bioinformatic meta-analysis of ovarian transcriptome datasets in different cohorts of late-stage HGSOC patients. In vitro cellular models of HGSOC were used for functional studies exploring the role of KLF7 in disease development and progression. Finally, molecular modelling and virtual screening were performed to identify putative KLF7 inhibitors.

RESULTS

Bioinformatic analysis highlighted KLF7 as the most significant prognostic gene, among the 17 family members. Univariate and multivariate analyses identified KLF7 as an unfavourable prognostic marker for overall survival in late-stage TCGA-OV and GSE26712 HGSOC cohorts. Functional in vitro studies demonstrated that KLF7 can play a role as oncogene, driving tumour growth and dissemination. Mechanistic targets of KLF7 included genes involved in epithelial to mesenchymal transition, and in maintaining pluripotency and self-renewal characteristics of cancer stem cells. Finally, in silico analysis provided reliable information for drug-target interaction prediction.

CONCLUSIONS

Results from the present study provide the first evidence for an oncogenic role of KLF7 in HGSOC, suggesting it as a promising prognostic marker and therapeutic target.

In silico study for prediction of novel bioactivities of the endophytic fungal alkaloid, mycoleptodiscin B for human targets

Mycoleptodiscin B is a natural product extracted from the endophytic fungus Mycoleptodiscus sp. found in Sri Lanka and Panama with experimentally unexplored activities for human targets. In this study, a computational methodology was applied to determine druggable targets of mycoleptodiscin B. According to the computational toxicity and pharmacokinetics assessment, mycoleptodiscin B was proven to be a suitable drug candidate. Druggable targets for this compound, aromatase, acidic plasma glycoprotein and androgen receptor, were predicted using reverse docking. A two-step validation of those targets was performed using conventional molecular docking and molecular dynamic (MD) simulations, resulting in aromatase being determined as the potential therapeutic target. Based on molecular mechanics/Generalized Born Surface Area (GBSA) free energies and ligand stability inside the active site cavity during its 120 ns MD run, it can be concluded that mycoleptodiscin B is a potent aromatase inhibitor and could be subjected to further in vitro and in vivo experiments in the drug development pipeline. Consequently, natural product chemists can quickly identify the hidden medicinal properties of their miracle compounds using the computational approach applied in this research.

Interdimeric Curvature in Tubulin-Tubulin Complexes Delineates the Microtubule-Destabilizing Properties of Plocabulin

Plocabulin is a novel microtubule (MT) destabilizer agent with potent antineoplastic activity. This compound binds to the maytansine site at the longitudinal interface between tubulin dimers and exerts a hinge-like effect that disrupts normal microtubule assembly. Plocabulin has emerged as a valuable model for the rational design of novel MT destabilizers because of its unique structural and mechanistic features. To make progress on this matter, detailed molecular-level understanding of the ligand-protein interactions responsible for plocabulin association and the conformation and energetic effects arising from plocabulin binding on the longitudinal interaction between tubulin dimers must be provided. In this work, fully atomistic MD simulations and MM/GBSA binding free-energy calculations were used to examine the association of plocabulin to one or two tubulin dimers in longitudinal arrangement. Our results revealed that plocabulin binding is favored by the addition of a second tubulin dimer and that this ligand promotes the assembly of curved tetrameric arrangements with strengthened longitudinal interdimeric interactions compared to ligand-free systems. The applicability of these findings to the rational discovery of novel MT destabilizers was tested using MD and MM/GBSA calculations as filtering tools to narrow the results of virtual screening among an FDA-approved drug database. Our results confirmed that tight-binding ligands do not necessarily exert the expected conformational and energetic effects on longitudinal tubulin-tubulin interactions, which is a matter to consider in future design strategies.

Inhibition of human CYP1 enzymes by a classical inhibitor α-naphthoflavone and a novel inhibitor N-(3, 5-dichlorophenyl)cyclopropanecarboxamide: An in vitro and in silico study

Enzymes in the cytochrome P450 family 1 (CYP1) catalyze metabolic activation of procarcinogens and deactivation of certain anticancer drugs. Inhibition of these enzymes is a potential approach for cancer chemoprevention and treatment of CYP1-mediated drug resistance. We characterized inhibition of human CYP1A1, CYP1A2, and CYP1B1 enzymes by the novel inhibitor N-(3,5-dichlorophenyl)cyclopropanecarboxamide (DCPCC) and α-naphthoflavone (ANF). Depending on substrate, IC₅₀ values of DCPCC for CYP1A1 or CYP1B1 were 10-95 times higher than for CYP1A2. IC₅₀ of DCPCC for CYP1A2 was 100-fold lower than for enzymes in CYP2 and CYP3 families. DCPCC IC₅₀ values were 10-680 times higher than the ones of ANF. DCPCC was a mixed-type inhibitor of CYP1A2. ANF was a competitive tight-binding inhibitor of CYP1A1, CYP1A2, and CYP1B1. CYP1A1 oxidized DCPCC more rapidly than CYP1A2 or CYP1B1 to the same metabolite. Molecular dynamics simulations and binding free energy calculations explained the differences of binding of DCPCC and ANF to the active sites of all three CYP1 enzymes. We conclude that DCPCC is a more selective inhibitor for CYP1A2 than ANF. DCPCC is a candidate structure to modulate CYP1A2-mediated metabolism of procarcinogens and anticancer drugs.

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.

Fragment- and negative image-based screening of phosphodiesterase 10A inhibitors

A novel virtual screening methodology called fragment- and negative image-based (F-NiB) screening is introduced and tested experimentally using phosphodiesterase 10A (PDE10A) as a case study. Potent PDE10A-specific small-molecule inhibitors are actively sought after for their antipsychotic and neuroprotective effects. The F-NiB combines features from both fragment-based drug discovery and negative image-based (NIB) screening methodologies to facilitate rational drug discovery. The selected structural parts of protein-bound ligand(s) are seamlessly combined with the negative image of the target's ligand-binding cavity. This cavity- and fragment-based hybrid model, namely its shape and electrostatics, is used directly in the rigid docking of ab initio generated ligand 3D conformers. In total, 14 compounds were acquired using the F-NiB methodology, 3D quantitative structure-activity relationship modeling, and pharmacophore modeling. Three of the small molecules inhibited PDE10A at ~27 to ~67 μM range in a radiometric assay. In a larger context, the study shows that the F-NiB provides a flexible way to incorporate small-molecule fragments into the drug discovery.