Target Binding Pocket Research Articles

Generative adversarial network (GAN) model-based design of potent SARS-CoV-2 Mpro inhibitors using the electron density of ligands and 3D binding pockets: insights from molecular docking, dynamics simulation, and MM-GBSA analysis.

Deep learning-based generative adversarial network (GAN) frameworks have recently been developed to expedite the drug discovery process. These models generate novel molecules from scratch and validate them through molecular docking simulation to identify the most promising candidates for a given drug target. In this study, the SARS-CoV-2 main protease (Mpro) was selected as the drug target. Two distinct GAN algorithms were employed to generate novel small molecules. One approach utilized experimental electron density (ED-based) data of ligands for training to generate drug-like molecules, while the second approach leveraged the target binding pocket to capture spatial and bonding relationship between atoms within the binding pockets. The ED-based approach generated approximately 26,000 molecules, whereas the binding pocket-based method produced around 100 molecules. These generated molecules were subsequently ranked based on molecular docking results using the glide XP score (both flexible and rigid docking) and AutoDock Vina. To identify the most potent GAN-derived molecules, molecular docking was also performed on co-crystallized inhibitor molecules of Mpro. The six most promising molecules from these GAN approaches were further evaluated for stability, interactions, and MM-GBSA binding free energy through molecular dynamics simulations. This analysis led to the identification of four potent Mpro inhibitor molecules, all featuring a 2-benzyl-6-bromophenol scaffold. The binding free energies of these compounds were compared with those of other Mpro inhibitors, revealing that our compounds demonstrated better affinity for Mpro than some broad-spectrum protease inhibitors. The dynamic cross-correlation matrix plot indicated strongly correlated and anti-correlated regions, potentially linked to ligand binding.

Advance Screening of Bis- Azetidinone Derivatives: Synthesis, Spectroscopy, Antioxidant and Antimicrobial Analysis with Molecular Docking Assessment

Introduction: This study includes synthesis, characterizations, antimicrobial, antiox-idant, and docking molecular study of novel Bis-Azetidinone compounds that combined two units of β-lactam rings .In the present investigation, the aromatic aldehydes with primary amine were condensed to create Schiff's base, which was then reacted with chloroacetylchloride to pro-duce bis-Azetidinone compounds. Methods: Melting points, FTIR, and NMR spectrum analyses were used to examine the mor-phological and topological characteristics of the Bis-Azetidinone compounds. The results indi-cate that the prepared Compounds synthesis has excellent antimicrobial activity against both Gram-negative (Escherichia coli,), Gram-positive bacteria (Staphylococcus aureus) and fungal (Candida albicans) and also indicated that the Compounds synthesis (A2) gave a higher antimi-crobial effect than the B2, C2. The synergistic activity was examined against the pathogenic microbial strains. It was observed that employing compound synthesis combined with antibiot-ics enhanced the synergistic efficacy compared to using compound synthesis alone or antibiotic alone on Gram-positive bacteria and fungi. Results: The antioxidant efficiency was assessed by DPPH, the results show that the compound synthesis has antioxidant activity, and also indicated that the synthesized compound (A2) gave a higher antioxidant effect than the B2, C2. Docking study confirmed via redocking of crystal-ized substrate or inhibitor within target binding pocket. The docking results reveal that the syn-thesized compounds, with a total binding affinity of less than -48 kcal/mol, could be clinically used for future therapeutic purposes. Conclusion: The present research demonstrates the advantageous effectiveness of a simpler pro-duction procedure, novel Bis-Azetidinone compounds, for producing high-purity with low haz-ard that may be utilized as future possible medical therapies.

PocketDTA: an advanced multimodal architecture for enhanced prediction of drug-target affinity from 3D structural data of target binding pockets.

Accurately predicting the drug-target binding affinity (DTA) is crucial to drug discovery and repurposing. Although deep learning has been widely used in this field, it still faces challenges with insufficient generalization performance, inadequate use of 3D information, and poor interpretability. To alleviate these problems, we developed the PocketDTA model. This model enhances the generalization performance by pre-trained models ESM-2 and GraphMVP. It ingeniously handles the first 3 (top-3) target binding pockets and drug 3D information through customized GVP-GNN Layers and GraphMVP-Decoder. In addition, it uses a bilinear attention network to enhance interpretability. Comparative analysis with state-of-the-art (SOTA) methods on the optimized Davis and KIBA datasets reveals that the PocketDTA model exhibits significant performance advantages. Further, ablation studies confirm the effectiveness of the model components, whereas cold-start experiments illustrate its robust generalization capabilities. In particular, the PocketDTA model has shown significant advantages in identifying key drug functional groups and amino acid residues via molecular docking and literature validation, highlighting its strong potential for interpretability. Code and data are available at: https://github.com/zhaolongNCU/PocketDTA.

3D molecular generative framework for interaction-guided drug design

Deep generative modeling has a strong potential to accelerate drug design. However, existing generative models often face challenges in generalization due to limited data, leading to less innovative designs with often unfavorable interactions for unseen target proteins. To address these issues, we propose an interaction-aware 3D molecular generative framework that enables interaction-guided drug design inside target binding pockets. By leveraging universal patterns of protein-ligand interactions as prior knowledge, our model can achieve high generalizability with limited experimental data. Its performance has been comprehensively assessed by analyzing generated ligands for unseen targets in terms of binding pose stability, affinity, geometric patterns, diversity, and novelty. Moreover, the effective design of potential mutant-selective inhibitors demonstrates the applicability of our approach to structure-based drug design.

Comparative binding effects of celecoxib/ poly(lactic-co-glycolic acid) microparticles with enhancing anti-inflammatory and anti-cancer activities: Density functional theory, molecular Docking, and ADMET calculations

This study is to evaluate the enhancement of anti-inflammatory and anti-cancer activities of celecoxib (CXB) in a dichloromethane (DCM) environment by poly(lactic-co-glycolic acid) (PLGA), using density functional dispersion correction (DFT-D), time-dependent density functional theory (TDDFT), molecular docking (MD), and ADMET calculations. The CXB, via its sulfonamide (-NH2SO2) group contact with the PLGA in state B through hydrogen bonds, is stronger than in states A and C, according to our computed analyses based on binding energy and chemical reactivity parameters. According to the theoretical analysis of IR spectra, the interaction of CXB with PLGA causes shifts in both the pure PLGA and the CXB. In the presence of stronger negative Gibbs free solvation energy (Gsolv) values, the CXB interaction with PLGA results in the increment of dipole moment, higher solubility, and the energy gap (Eg) reduction. Further, MD calculations on states A, B, and C were able to inhibit tumor necrosis factor-alpha (TNF-α), interleukin 1 beta (IL-1β), cyclooxygenase-2 (COX-2), and human epidermal growth factor receptor 2 (HER2). Based on the MD calculations, the obtained data illustrated a strong interaction with the target binding pocket (CXB via the sulfonamide group interacting with PLGA in the state B), leading to the improvement of the anti-inflammatory and anti-cancer activities in comparison with the pure CXB.

Crystal Structure of the SH3 Domain of ASAP1 in Complex with the Proline Rich Motif (PRM) of MICAL1 Reveals a Unique SH3/PRM Interaction Mode

SH3 domains are common protein binding modules. The target sequence of SH3 domains is usually a proline-rich motif (PRM) containing a minimal "PxxP" sequence. The mechanism of how different SH3 domains specifically choose their targets from vast PxxP-containing sequences is still not very clear, as many reported SH3/PRM interactions are weak and promiscuous. Here, we identified the binding of the SH3 domain of ASAP1 to the PRM of MICAL1 with a sub-μM binding affinity, and determined the crystal structure of ASAP1-SH3 and MICAL1-PRM complex. Our structural and biochemical analyses revealed that the target-binding pocket of ASAP1-SH3 contains two negatively charged patches to recognize the "xPx + Px+" sequence in MICAL1-PRM and consequently strengthen the interaction, differing from the typical SH3/PRM interaction. This unique PRM-binding pocket is also found in the SH3 domains of GTPase Regulator associated with focal adhesion kinase (GRAF) and Src kinase associated phosphoprotein 1 (SKAP1), which we named SH3AGS. In addition, we searched the Swiss-Prot database and found ~130 proteins with the SH3AGS-binding PRM in silico. Finally, gene ontology analysis suggests that the strong interaction between the SH3AGS-containing proteins and their targets may play roles in actin cytoskeleton regulation and vesicle trafficking.

Intertwined Wdr47-NTD dimer recognizes a basic-helical motif in Camsap proteins for proper central-pair microtubule formation

Calmodulin-regulated spectrin-associated proteins (Camsaps) bind to the N-terminal domain of WD40-repeat 47 (Wdr47-NTD; featured with a LisH-CTLH motif) to properly generate axonemal central-pair microtubules (CP-MTs) for the planar beat pattern of mammalian motile multicilia. The underlying molecular mechanism, however, remains unclear. Here, we determine the structures of apo-Wdr47-NTD and Wdr47-NTD in complex with a characteristic Wdr47-binding region (WBR) from Camsap3. Wdr47-NTD forms an intertwined dimer with a special cross-over region (COR) in addition to the canonical LisH and globular α-helical core (GAC). The basic WBR peptide adopts an α-helical conformation and anchors to a tailored acidic pocket embedded in the COR. Mutations in this target-binding pocket disrupt the interaction between Wdr47-NTD and Camsap3. Impairing Wdr47-Camsap interactions markedly reduces rescue effects of Wdr47 on CP-MTs and ciliary beat of Wdr47-deficient ependymal cells. Thus, Wdr47-NTD functions by recognizing a specific basic helical motif in Camsap proteins via its non-canonical COR, a target-binding site in LisH-CTLH-containing domains.

PacDOCK: A Web Server for Positional Distance-Based and Interaction-Based Analysis of Docking Results.

Molecular docking is a key method for structure-based drug design used to predict the conformations assumed by small drug-like ligands when bound to their target. However, the evaluation of molecular docking studies can be hampered by the lack of a free and easy to use platform for the complete analysis of results obtained by the principal docking programs. To this aim, we developed PacDOCK, a freely available and user-friendly web server that comprises a collection of tools for positional distance-based and interaction-based analysis of docking results, which can be provided in several file formats. PacDOCK allows a complete analysis of molecular docking results through root mean square deviation (RMSD) calculation, molecular visualization, and cluster analysis of docked poses. The RMSD calculation compares docked structures with a reference structure, also when atoms are randomly labelled, and their conformational and positional differences can be visualised. In addition, it is possible to visualise a ligand into the target binding pocket and investigate the key receptor–ligand interactions. Moreover, PacDOCK enables the clustering of docking results by identifying a restrained number of clusters from many docked poses. We believe that PacDOCK will contribute to facilitating the analysis of docking results to improve the efficiency of computer-aided drug design.

Targeting Streptomyces-Derived Streptenol Derivatives against Gynecological Cancer Target PIK3CA: An In Silico Approach.

Streptomyces is amongst the most amenable genera for biotechnological applications, and it is extensively used as a scaffold for drug development. One of the most effective therapeutic applications in the treatment of cancer is targeted therapy. Small molecule therapy is one of them, and it has gotten a lot of attention recently. Streptomyces derived compounds namely streptenols A, C, and F–I and streptazolin were subjected for ADMET property assessment. Our computational studies based on molecular docking effectively displayed the synergistic effect of streptomyces-derived compounds on the gynecological cancer target PIK3CA. These compounds were observed with the highest docking scores as well as promising intermolecular interaction stability throughout the molecular dynamic simulation. Molecular docking and molecular dynamic modeling techniques were utilized to investigate the binding mode stability of drugs using a pharmacophore scaffold, as well as physicochemical and pharmacokinetic aspects linked to alpelisib. With a root mean square fluctuation of the protein backbone of less than 0.7 nm, they demonstrated a steady binding mode in the target binding pocket. They have also prompted hydrogen bonding throughout the simulations, implying that the chemicals have firmly occupied the active site. A comprehensive study showed that streptenol D, streptenol E, streptenol C, streptenol G, streptenol F, and streptenol B can be considered as lead compounds for PIK3CA-based inhibitor design. To warrant the treatment efficacy against cancer, comprehensive computational research based on proposed chemicals must be assessed through in vitro studies.

RELATION: A Deep Generative Model for Structure-Based De Novo Drug Design.

Deep learning (DL)-based de novo molecular design has recently gained considerable traction. Many DL-based generative models have been successfully developed to design novel molecules, but most of them are ligand-centric and the role of the 3D geometries of target binding pockets in molecular generation has not been well-exploited. Here, we proposed a new 3D-based generative model called RELATION. In the RELATION model, the BiTL algorithm was specifically designed to extract and transfer the desired geometric features of the protein-ligand complexes to a latent space for generation. The pharmacophore conditioning and docking-based Bayesian sampling were applied to efficiently navigate the vast chemical space for the design of molecules with desired geometric properties and pharmacophore features. As a proof of concept, the RELATION model was used to design inhibitors for two targets, AKT1 and CDK2. The calculation results demonstrated that the RELATION model could efficiently generate novel molecules with favorable binding affinity and pharmacophore features.

Site-Specific Labeling of Endogenous Proteins Using CoLDR Chemistry

Chemical modifications of native proteins can affect their stability, activity, interactions, localization, and more. However, there are few nongenetic methods for the installation of chemical modifications at a specific protein site in cells. Here we report a covalent ligand directed release (CoLDR) site-specific labeling strategy, which enables the installation of a variety of functional tags on a target protein while releasing the directing ligand. Using this approach, we were able to label various proteins such as BTK, K-RasG12C, and SARS-CoV-2 PLpro with different tags. For BTK we have shown selective labeling in cells of both alkyne and fluorophores tags. Protein labeling by traditional affinity methods often inhibits protein activity since the directing ligand permanently occupies the target binding pocket. We have shown that using CoLDR chemistry, modification of BTK by these probes in cells preserves its activity. We demonstrated several applications for this approach including determining the half-life of BTK in its native environment with minimal perturbation, as well as quantification of BTK degradation by a noncovalent proteolysis targeting chimera (PROTAC) by in-gel fluorescence. Using an environment-sensitive “turn-on” fluorescent probe, we were able to monitor ligand binding to the active site of BTK. Finally, we have demonstrated efficient CoLDR-based BTK PROTACs (DC50 < 100 nM), which installed a CRBN binder onto BTK. This approach joins very few available labeling strategies that maintain the target protein activity and thus makes an important addition to the toolbox of chemical biology.

Computational design of novel AKT inhibitors

The study entails the computational design of novel anticancer compounds which specifically targets the AKT signaling pathway by inhibit the AKT protein. The work was done by carrying out a molecular docking study on nine (9) identified approved FDA drugs collected from the bindingDB.org website. The results of the docking study revealed that A-674563 had the best-free energy of binding value of − 32.63 kcal/mol, while AT-7867 and AZD563 molecules were reported to have − 30.978 and − 27.668 kcal/mol binding energy value, respectively. Molecule A674563 was selected as lead compound further subjected to a structure-based drug design, where a fragment was incorporated or attached to the side chain of the lead and re-docked in the same binding pocket of the AKT receptor target. The results of our findings led to the identification of two novel compounds (molecules 5a and 7a), which were found to better inhibit AKT protein with a binding affinity values of − 36.015 kcal/mol and − 33.324 kcal/mol, respectively. Since their values were significantly better than that of the lead molecule especially when considering their stability in terms of the number of hydrogen bond they formed the amino acids in the AKT-binding pocket.

Analysis of non-structural proteins, NSPs of SARS-CoV-2 as targets for computational drug designing

BackgroundThe ORF1ab of Severe Acute Respiratory Syndrome, SARS Corona Virus, SARS-CoV-2 genome is processed into 15 non-structural proteins, NSPs by proteases and each NSP has a specific role in the life cycle and pathogenicity of the virus. This research analyzes possible drugs for these proteins as targets in computational drug designing using already available experimental drugs from the drug bank database. MethodsOut of 471 proteins and 8820 drugs download from Protein Data Bank, PDB and Drug Bank database respectively, 16 proteins similar to NSP 1–15 and 31 drugs as per the “Rule of three” were selected for docking. Out of 88 docking results using PyRx, 18 proteins/chains with three promising drugs, DB01977, DB07132 and DB07535 were analyzed using PyMOL for final results. ResultsNSPs 3, 5, 11, 14 and 15 were identified as targets for the drugs, DB01977, BD07132 and DB07535. Drugs, DB01977 and DB07535 bind in the same binding pockets of NSP 5 and NSP 15. Drug, DB07132 binds with more number of residues when compared with the other two drugs and this indicates that the strength of protein-drug association is more by this drug with the NSPs than other drugs. Binding pockets of NSPs for these three drugs are very close with many sharing residues in common suggesting of similarity of pharmacophore of these drugs with the target binding pockets. ConclusionThe binding pockets of NSPs are well matched with the pharmacophore of drugs and with polar surface of drugs less than or equal to 100 A2, drugs, DB01977, DB07132 and DB07535 bind individually and effectively with NSPs 3, 5, 11, 14 and 15 of ORF1ab of SARS-CoV-2 genome to bring changes in the activity of SARS-CoV-2 which may be useful for biological and clinical considerations.

Optimization of a Noncanonical Anti-infective: Interrogation of the Target Binding Pocket for a Small-Molecule Inhibitor of Escherichia coli Polysaccharide Capsule Expression.

We previously identified a small-molecule inhibitor of capsule biogenesis (designated DU011) and identified its target as MprA, a MarR family transcriptional repressor of multidrug efflux pumps. Unlike other proposed MprA ligands, such as salicylate and 2,4-dinitrophenol (DNP), DU011 does not alter Escherichia coli antibiotic resistance and has significantly enhanced inhibition of capsule expression. We hypothesized that the potency and the unique action of DU011 are due to novel interactions with the MprA binding pocket and the conformation assumed by MprA upon binding DU011 relative to other ligands. To understand the dynamics of MprA-DU011 interaction, we performed hydrogen-deuterium exchange mass spectrometry (HDX-MS); this suggested that four peptide regions undergo conformational changes upon binding DU011. We conducted isothermal calorimetric titration (ITC) to quantitatively characterize MprA binding to DU011 and canonical ligands and observed a distinct two-site binding isotherm associated with the binding reaction of MprA to DU011; however, salicylate and DNP showed a one-site binding isotherm with lower affinity. To elucidate the binding pocket(s) of MprA, we selected single point mutants of MprA that included mutated residues predicted to be within the putative binding pocket (Q51A, F58A, and E65D) as well as on or near the DNA-binding domain (L81A, S83T, and T86A). Our ITC studies suggest that two of the tested MprA mutants had lower affinity for DU011: Q51A and F58A. In addition to elucidating the MprA binding pocket for DU011, we studied the binding of these mutants to salicylate and DNP to reveal the binding pockets of these canonical ligands.

Proposing novel MDM2 inhibitors: Combined physics-driven high-throughput virtual screening and in vitro studies.

The mouse double minute 2 (MDM2) protein acts as a negative regulator of the p53 tumor suppressor. It directly binds to the N terminus of p53 and promotes p53 ubiquitination and degradation. Since the most common p53-suppressing mechanisms involve the MDM2, proposing novel inhibitors has been the focus of many in silico and also experimental studies. Thus, here we screened around 500,000 small organic molecules from Enamine database at the binding pocket of this oncogenic target. The screening was achieved systematically with starting from the high-throughput virtual screening method followed by more sophisticated docking approaches. The initial high number of screened molecules was reduced to 100 hits which then were studied extensively for their therapeutic activity and pharmacokinetic properties using binary QSAR models. The structural and dynamical profiles of the selected molecules at the binding pocket of the target were studied thoroughly by all-atom molecular dynamics simulations. The free energy of the binding of the hit molecules was estimated by the MM/GBSA method. Based on docking simulations, binary QSAR model results, and free energy calculations, 11 compounds (E1-E11) were selected for in vitro studies. HUVEC vascular endothelium, colon cancer, and breast cancer cell lines were used for testing the binding affinities of the identified hits and for further cellular effects on human cancer cell. Based on in vitro studies, six compounds (E1, E2, E5, E6, E9, and E11) in breast cancer cell lines and six compounds (E1, E2, E5, E6, E8, and E10) in colon cancer cell lines were found as active. Our results showed that these compounds inhibit proliferation and lead to apoptosis.

Identifying the Novel Pyrimidine-Based CDK2 Inhibitors as Anticancer Agents Using Text-Mining and Combined Molecular Modeling Approaches

The cycline-dependent kinase (CDK) protein is an important target used in anti-cancer drug designing studies. Pyrimidine is a crucial fragment which is used in many FDA-approved drugs including anti-cancer drugs. In the current study, a small molecule database (Specs SC) that includes more than 210.000 compounds were used in text mining studies and 6668 molecules that carry “pyrimidine” fragments were filtered. These compounds were then screened at the binding pocket of CDK-2 target using molecular docking and molecular dynamics (MD) simulations approaches. Binding free energies of screened compounds were also compared with pyrimidine-based FDA approved anti-cancer drug Abemaciclib which targets CDK. Based on comparison of docking scores of screened compounds, top-7 hits were used in 100 ns MD simulations. Same MD simulations protocol (100 ns) were also applied to Abemaciclib-bound CDK-2 complex structure. Average Molecular Mechanics/Generalized Born Surface Area (MM/GBSA) energies were then calculated. Our MM/GBSA results showed that 6 out of 7 compounds have better MM/GBSA scores than FDA approved compound Abemaciclib. Thus, together with combination of text mining and integrated molecular modeling approaches, we identified novel pyrimidine-based hits against CDK.

Pharmacore maping based on docking, ADME/toxicity, virtual screening on 3,5-dimethyl-1,3,4-hexanetriol and dodecanoic acid derivates for anticancer inhibitors

Plants produced natural generating products play a significant role in drug discovery of new bioactive compounds and these are used for advancement of innovative curative drugs for specific target health diseases. In this study Docking and ADME/T virtual screening method are apply for in drug discovery and can be divided into ligand- and target structure-based. The aim of this study was to analyze the Decalepis hamiltonii isolated compounds by using the evaluation of molecular docking and virtual screening of anticancer drugs. MOE docking ADME/Toxicity and virtual screening approaches. A docking energy −12.97 kcal/mol; −9.93- kcal/mol on cancer responsible protein was targeted. Further, the compounds were filtered through the rule of five, ADME/Toxicity risk and synthetic accessibility. The active compound were then docked to recognize the possible target binding pocket to obtain a set of a ligand poses and to prioritize the predicted active compounds. The scrutinize compounds, as well as their metabolites were evaluated for different pharmacokinetics parameter such as ADME/Toxicity. Therefore, the result shows that a large number of compounds were found to be ADME/toxicity positive to be a positive drug molecule against cancer, selected compounds under study satisfies parameters for ADME and Toxicity properties. The present study demonstrate to identifying the novel structures which are having similar structural feature with like activity with respect to the compounds 3,5-Dimethyl-1,3,4-Hexanetriol and Dodecanoic acid that are shown best binding energy with the receptors 4igk and 4b3z respectively. This study may provide significant clues for discovery novel drug inhibitors for cancer properties.

Structural modification of ellipticine derivatives with alkyl groups of varying length is influential on their effects on human DNA topoisomerase II: a combined experimental and computational study

The compounds reducing tumor cell viability and disrupting DNA topoisomerase reactions have been widely used in anticancer drug development. Ellipticine (5,11-dimethyl-6H-pyrido[4,3-b]carbazole) is a potent intercalating agent that interferes with nucleic acid processing through interaction with DNA topoisomerase II. Although ellipticine is a well-characterized compound, it is not a widely-accepted drug due to the adverse effects detected upon administration. We have previously reported two novel ellipticine derivatives, N-methyl-5-demethyl ellipticine (ET-1) and 2-methyl-N-methyl-5-demethyl ellipticinium iodide (ET-2) as potent compounds targeting DNA topoisomerase II. This study covers an extended synthesis, characterization, and activity data for five new salts of N-methyl 5-demetyl ellipticine (Z-1, Z-2, Z-4, Z-5 and Z-6) having several organic halides and their effects on human topoisomerase II enzymes. Moreover, combined in silico studies were conducted for better understanding of modes of action of studied molecules at the binding pocket of target. Our results showed that three of the derivatives (Z-1, Z-2, and Z-6) have considerable effect on the catalytic activity of human topoisomerase II implying the influence of alkyl groups added to the parental structure of ellipticine.

Solution structures of the SH3 domains from Shank scaffold proteins and their interactions with Cav1.3 calcium channels.

Shank proteins are abundant scaffold proteins in the postsynaptic density (PSD) region of brain synapses. Mutations in Shank proteins are associated with autism, schizophrenia, and Alzheimer's disease. To gain insights into Shank protein interactions at the PSD, we determined the solution structures of the src homology 3 (SH3) domains of all three mammalian Shank proteins. Our findings indicate that they have identical and typical SH3 folding motifs, but unusual target-binding pockets. An investigation into the interaction between the Shank SH3 domains and the proline-rich region of the Cav1.3 calcium channel revealed an atypical interaction in which the highly acidic specificity binding pocket of the SH3 domains binds to a Cav1.3 region containing a cluster of three Arg residues. Our study provides insights into Shank SH3-mediated interactions.

Virtual screening, Docking, ADMET and System Pharmacology studies on Garcinia caged Xanthone derivatives for Anticancer activity

Caged xanthones are bioactive compounds mainly derived from the Garcinia genus. In this study, a structure-activity relationship (SAR) of caged xanthones and their derivatives for anticancer activity against different cancer cell lines such as A549, HepG2 and U251 were developed through quantitative (Q)-SAR modeling approach. The regression coefficient (r2), internal cross-validation regression coefficient (q2) and external cross-validation regression coefficient (pred_r2) of derived QSAR models were 0.87, 0.81 and 0.82, for A549, whereas, 0.87, 0.84 and 0.90, for HepG2, and 0.86, 0.83 and 0.83, for U251 respectively. These models were used to design and screened the potential caged xanthone derivatives. Further, the compounds were filtered through the rule of five, ADMET-risk and synthetic accessibility. Filtered compounds were then docked to identify the possible target binding pocket, to obtain a set of aligned ligand poses and to prioritize the predicted active compounds. The scrutinized compounds, as well as their metabolites, were evaluated for different pharmacokinetics parameters such as absorption, distribution, metabolism, excretion, and toxicity. Finally, the top hit compound 1G was analyzed by system pharmacology approaches such as gene ontology, metabolic networks, process networks, drug target network, signaling pathway maps as well as identification of off-target proteins that may cause adverse reactions.