Challenges, applications, and recent advances of protein-ligand docking in structure-based drug design

Prediction of the binding mechanism of a selective DNA methyltransferase 3A inhibitor by molecular simulation

DNA methylation is an epigenetic mechanism that introduces a methyl group at the C5 position of cytosine. This reaction is catalyzed by DNA methyltransferases (DNMTs) and is essential for the regulation of gene transcription. The DNMT1 and DNMT3A or -3B family proteins are known targets for the inhibition of DNA hypermethylation in cancer cells. A selective non-nucleoside DNMT3A inhibitor was developed that mimics S-adenosyl-l-methionine and deoxycytidine; however, the mechanism of selectivity is unclear because the inhibitor-protein complex structure determination is absent. Therefore, we performed docking and molecular dynamics simulations to predict the structure of the complex formed by the association between DNMT3A and the selective inhibitor. Our simulations, binding free energy decomposition analysis, structural isoform comparison, and residue scanning showed that Arg688 of DNMT3A is involved in the interaction with this inhibitor, as evidenced by its significant contribution to the binding free energy. The presence of Asn1192 at the corresponding residues in DNMT1 results in a loss of affinity for the inhibitor, suggesting that the interactions mediated by Arg688 in DNMT3A are essential for selectivity. Our findings can be applied in the design of DNMT-selective inhibitors and methylation-specific drug optimization procedures.

Design, Synthesis, and Antitumor Activity of Isoliquiritigenin Amino Acid Ester Derivatives

Isoliquiritigenin (ISL) is a chalcone that has shown great potential in the treatment of cancer. However, its relatively weak activity and low water solubility limit its clinical application. In this study, we designed and synthesized 21 amino acid ester derivatives of ISL and characterized the compounds using 1H NMR and 13C NMR. Among them, compound 9 (IC50 = 14.36 μM) had a better inhibitory effect on human cervical cancer (Hela) than ISL (IC50 = 126.5 μM), and it was superior to the positive drug 5-FU (IC50 = 33.59 μM). The mechanism of the action experiment showed that compound 9 could induce Hela cell apoptosis and autophagy through the PI3K/Akt/mTOR pathway.

In Vitro and In Silico Anti-Glioblastoma Activity of Hydroalcoholic Extracts of Artemisia annua L. and Artemisia vulgaris L

Glioblastoma, the most aggressive and challenging brain tumor, is a key focus in neuro-oncology due to its rapid growth and poor prognosis. The C6 glioma cell line is often used as a glioblastoma model due to its close simulation of human glioma characteristics, including rapid expansion and invasiveness. Alongside, herbal medicine, particularly Artemisia spp., is gaining attention for its anticancer potential, offering mechanisms like apoptosis induction, cell cycle arrest, and the inhibition of angiogenesis. In this study, we optimized extraction conditions of polyphenols from Artemisia annua L. and Artemisia vulgaris L. herbs and investigated their anticancer effects in silico and in vitro. Molecular docking of the main phenolic compounds of A. annua and A. vulgaris and potential target proteins, including programmed cell death (apoptosis) pathway proteins proapoptotic Bax (PDB ID 6EB6), anti-apoptotic Bcl-2 (PDB ID G5M), and the necroptosis pathway protein (PDB ID 7MON), mixed lineage kinase domain-like protein (MLKL), in complex with receptor-interacting serine/threonine-protein kinase 3 (RIPK3), revealed the high probability of their interactions, highlighting the possible influence of chlorogenic acid in modulating necroptosis processes. The cell viability of rat C6 glioma cell line was assessed using a nuclear fluorescent double-staining assay with Hoechst 33342 and propidium iodide. The extracts from A. annua and A. vulgaris have demonstrated anticancer activity in the glioblastoma model, with the synergistic effects of their combined compounds surpassing the efficacy of any single compound. Our results suggest the potential of these extracts as a basis for developing more effective glioblastoma treatments, emphasizing the importance of further research into their mechanisms of action and therapeutic applications.

Identification of new potent NLRP3 inhibitors by multi-level in-silico approaches

Nod-like receptor protein 3 (NLRP-3), is an intracellular sensor that is involved in inflammasome activation, and the aberrant expression of NLRP3 is responsible for diabetes mellitus, its complications, and many other inflammatory diseases. NLRP3 is considered a promising drug target for novel drug design. Here, a pharmacophore model was generated from the most potent inhibitor, and its validation was performed by the Gunner-Henry scoring method. The validated pharmacophore was used to screen selected compounds databases. As a result, 646 compounds were mapped on the pharmacophore model. After applying Lipinski's rule of five, 391 hits were obtained. All the hits were docked into the binding pocket of target protein. Based on docking scores and interactions with binding site residues, six compounds were selected potential hits. To check the stability of these compounds, 100 ns molecular dynamic (MD) simulations were performed. The RMSD, RMSF, DCCM and hydrogen bond analysis showed that all the six compounds formed stable complex with NLRP3. The binding free energy with the MM-PBSA approach suggested that electrostatic force, and van der Waals interactions, played a significant role in the binding pattern of these compounds. Thus, the outcomes of the current study could provide insights into the identification of new potential NLRP3 inflammasome inhibitors against diabetes and its related disorders.

A new paradigm for applying deep learning to protein-ligand interaction prediction

Protein-ligand interaction prediction presents a significant challenge in drug design. Numerous machine learning and deep learning (DL) models have been developed to accurately identify docking poses of ligands and active compounds against specific targets. However, current models often suffer from inadequate accuracy or lack practical physical significance in their scoring systems. In this research paper, we introduce IGModel, a novel approach that utilizes the geometric information of protein-ligand complexes as input for predicting the root mean square deviation of docking poses and the binding strength (pKd, the negative value of the logarithm of binding affinity) within the same prediction framework. This ensures that the output scores carry intuitive meaning. We extensively evaluate the performance of IGModel on various docking power test sets, including the CASF-2016 benchmark, PDBbind-CrossDocked-Core and DISCO set, consistently achieving state-of-the-art accuracies. Furthermore, we assess IGModel's generalizability and robustness by evaluating it on unbiased test sets and sets containing target structures generated by AlphaFold2. The exceptional performance of IGModel on these sets demonstrates its efficacy. Additionally, we visualize the latent space of protein-ligand interactions encoded by IGModel and conduct interpretability analysis, providing valuable insights. This study presents a novel framework for DL-based prediction of protein-ligand interactions, contributing to the advancement of this field. The IGModel is available at GitHub repository https://github.com/zchwang/IGModel.

Exploring novel Kokumi peptides in Agaricus bisporus: selection, identification, and tasting mechanism investigation through sensory evaluation and computer simulation analysis

Agaricus bisporus contains amino acids associated with thickness and full-mouthfeel, making it a potential candidate for salt substitutes and flavor enhancers in various food applications. Kokumi peptides were isolated from the enzymatic digest of Agaricus bisporus using ultrafiltration nanofiltration, gel chromatographic separation, and RP-HPLC, coupled with sensory evaluation. Subsequently, the peptides, EWVPVTK and EYPPLGR, were selected for solid-phase synthesis based on molecular docking. Sensory analysis, including thresholds, time intensity, and dose-configuration relationships, indicated that EWVPVTK and EYPPLGR exhibited odor thresholds of 0.6021 mmol L-1 and 2.332 mmol L-1 in an aqueous solution. Molecular docking scores correlated with low sensory thresholds, signifying strong taste sensitivities. EWVPVTK, in particular, demonstrated a higher sense of richness at lower concentrations compared to EYPPLGR. Molecular docking and dynamics simulations elucidated that the interactions between Kokumi peptides and the CaSR receptor primarily involved hydrogen bonding, electrostatic interactions, and hydrophobic interactions. Both EWVPVTK and EYPPLGR exhibited stable binding to the CaSR receptor. Active binding sites were identified, with EWVPVTK interacting at Arg 66, Asp 216, Gln 245, and Asn 102, while EYPPLGR engaged with Ser 272, Gln 193, Glu 297, Ala-298, Tyr-2, and Agr-66 in hydrophilic interactions through hydrogen bonds. Notably, these two Kokumi peptides were found to be enriched in umami and sweet amino acids, underscoring their pivotal role in umami perception. This study not only identifies novel Kokumi peptides from Agaricus bisporus but also contributes theoretical foundations and insights for future studies in the realm of Kokumi peptides.

A virtual insight into mushroom secondary metabolites: 3D-QSAR, docking, pharmacophore-based analysis and molecular modeling to analyze their anti-breast cancer potential

Breast cancer is a major issue of investigation in drug discovery due to its rising frequency and global dominance. Plants are significant natural sources for the development of novel medications and therapies. Medicinal mushrooms have many biological response modifiers and are used for the treatment of many physical illnesses. In this research, a database of 89 macro-molecules with anti-breast cancer activity, which were previously isolated from the mushrooms in literature, has been selected for the three-dimensional quantitative structure-activity relationships (3D-QSAR) studies. The 3D-QSAR model was necessarily used in Pharmacopoeia virtual evaluation of the database to develop novel MCF-7 inhibitors. With the known potential targets of breast cancer, the docking studies were achieved. Using molecular dynamics simulations, the targets' stability with the best-chosen natural product molecule was found. Furthermore, the absorption, distribution, metabolism, excretion, and toxicity of three compounds, resulting after the docking study, were predicted. The compound C1 (Pseudonocardian A) showed the features of effective compounds because it has bioavailability from different coral species and is toxicity-free for the prevention of many dermatological illnesses. C1 is chemically active and possesses charge transfer inside the monomer, as seen by the band gaps of highest occupied molecular orbital (HOMO) and lowest unoccupied molecular orbital (LUMO) electrons. The reactivity descriptors ionization potential, electron affinity, chemical potential (μ), hardness (η), softness (S), electronegativity (χ), and electrophilicity index (ω) have been estimated using the energies of frontier molecular orbitals (HOMO-LUMO). Additionally, molecular electrostatic potential maps were created to show that the C1 is reactive.Communicated by Ramaswamy H. Sarma.

An optimized relational database for querying structural patterns in proteins

A database is an essential component in almost any software system, and its creation involves more than just data modeling and schema design. It also includes query optimization and tuning. This paper focuses on a web system called GSP4PDB, which is used for searching structural patterns in proteins. The system utilizes a normalized relational database, which has proven to be inefficient even for simple queries. This article discusses the optimization of the GSP4PDB database by implementing two techniques: denormalization and indexing. The empirical evaluation described in the article shows that combining these techniques enhances the efficiency of the database when querying both real and artificial graph-based structural patterns.

Template-guided method for protein-ligand complex structure prediction: Application to CASP15 protein-ligand studies

Critical Assessment of Structure Prediction 15 (CASP15) added a new category of ligand prediction to promote the development of protein/RNA-ligand modeling methods, which have become important tools in modern drug discovery. A total of 22 targets were released, including 18 protein-ligand targets and 4 RNA-ligand targets. We applied our recently developed template-guided method to the protein-ligand complex structure predictions. The method combined a physicochemical, molecular docking method, and a bioinformatics-based ligand similarity method. The Protein Data Bank was scanned for template structures containing the target protein, homologous proteins, or proteins sharing a similar fold with the target protein. The binding modes of the co-bound ligands in the template structures were used to guide the complex structure prediction for the target. The CASP assessment results show that the overall performance of our method was ranked second when the top predicted model was considered for each target. Here, we analyzed our predictions in detail, and discussed the challenges including protein conformational changes, large and flexible ligands, and multiple diverse ligands in a binding pocket.

Multi-task bioassay pre-training for protein-ligand binding affinity prediction

Protein-ligand binding affinity (PLBA) prediction is the fundamental task in drug discovery. Recently, various deep learning-based models predict binding affinity by incorporating the three-dimensional (3D) structure of protein-ligand complexes as input and achieving astounding progress. However, due to the scarcity of high-quality training data, the generalization ability of current models is still limited. Although there is a vast amount of affinity data available in large-scale databases such as ChEMBL, issues such as inconsistent affinity measurement labels (i.e. IC50, Ki, Kd), different experimental conditions, and the lack of available 3D binding structures complicate the development of high-precision affinity prediction models using these data. To address these issues, we (i) propose Multi-task Bioassay Pre-training (MBP), a pre-training framework for structure-based PLBA prediction; (ii) construct a pre-training dataset called ChEMBL-Dock with more than 300k experimentally measured affinity labels and about 2.8M docked 3D structures. By introducing multi-task pre-training to treat the prediction of different affinity labels as different tasks and classifying relative rankings between samples from the same bioassay, MBP learns robust and transferrable structural knowledge from our new ChEMBL-Dock dataset with varied and noisy labels. Experiments substantiate the capability of MBP on the structure-based PLBA prediction task. To the best of our knowledge, MBP is the first affinity pre-training model and shows great potential for future development. MBP web-server is now available for free at: https://huggingface.co/spaces/jiaxianustc/mbp.

Identification of ACHE as the hub gene targeting solasonine associated with non-small cell lung cancer (NSCLC) using integrated bioinformatics analysis

Background

Solasonine, as a major biological component of Solanum nigrum L., has demonstrated anticancer effects against several malignancies. However, little is understood regarding its biological target and mechanism in non-small cell lung cancer (NSCLC).

Methods

We conducted an analysis on transcriptomic data to identify differentially expressed genes (DEGs), and employed an artificial intelligence (AI) strategy to predict the target protein for solasonine. Subsequently, genetic dependency analysis and molecular docking were performed, with Acetylcholinesterase (ACHE) selected as a pivotal marker for solasonine. We then employed a range of bioinformatic approaches to explore the relationship between ACHE and solasonine. Furthermore, we investigated the impact of solasonine on A549 cells, a human lung cancer cell line. Cell inhibition of A549 cells following solasonine treatment was analyzed using the CCK8 assay. Additionally, we assessed the protein expression of ACHE, as well as markers associated with apoptosis and inflammation, using western blotting. To investigate their functions, we employed a plasmid-based ACHE overexpression system. Finally, we performed dynamics simulations to simulate the interaction mode between solasonine and ACHE.

Results

The results of the genetic dependency analysis revealed that ACHE could be identified as the pivotal target with the highest docking affinity. The cell experiments yielded significant findings, as evidenced by the negative regulatory effect of solasonine treatment on tumor cells, as demonstrated by the CCK8 assay. Western blotting analysis revealed that solasonine treatment resulted in the downregulation of the Bcl-2/Bax ratio and upregulation of cleaved caspase-3 protein expression levels. Moreover, we observed that ACHE overexpression promoted the expression of the Bcl-2/Bax ratio and decreased cleaved caspase-3 expression in the OE-ACHE group. Notably, solasonine treatment rescued the Bcl-2/Bax ratio and cleaved caspase-3 expression in OE-ACHE cells compared to OE-ACHE cells without solasonine treatment, suggesting that solasonine induces apoptosis. Besides, solasonine exhibited its anti-inflammatory effects by inhibiting P38 MAPK. This was supported by the decline in protein levels of IL-1β and TNF-α, as well as the phosphorylated forms of JNK and P38 MAPK. The results from the molecular docking and dynamics simulations further confirmed the potent binding affinity and effective inhibitory action between solasonine and ACHE.

Conclusions

The findings of the current investigation show that solasonine exerts its pro-apoptosis and anti-inflammatory effects by suppressing the expression of ACHE.

Identification of potential inhibitors of Zika virus targeting NS3 helicase using molecular dynamics simulations and DFT studies

Despite the various research efforts towards the drug discovery program for Zika virus treatment, no antiviral drugs or vaccines have yet been discovered. The spread of the mosquito vector and ZIKV infection exposure is expected to accelerate globally due to continuing global travel. The NS3-Hel is a non-structural protein part and involved in different functions such as polyprotein processing, genome replication, etc. It makes an NS3-Hel protein an attractive target for designing novel drugs for ZIKV treatment. This investigation identifies the novel, potent ZIKV inhibitors by virtual screening and elucidates the binding pattern using molecular docking and molecular dynamics simulation studies. The molecular dynamics simulation results indicate dynamic stability between protein and ligand complexes, and the structures keep significantly unchanged at the binding site during the simulation period. All inhibitors found within the acceptable range having drug-likeness properties. The synthetic feasibility score suggests that all screened inhibitors can be easily synthesizable. Therefore, possible inhibitors obtained from this study can be considered a potential inhibitor for NS3 Hel, and further, it could be provided as a lead for drug development.

Discovery of a Hidden Trypanosoma cruzi Spermidine Synthase Binding Site and Inhibitors through In Silico, In Vitro, and X-ray Crystallography

In drug discovery research, the selection of promising binding sites and understanding the binding mode of compounds are crucial fundamental studies. The current understanding of the proteins-ligand binding model extends beyond the simple lock and key model to include the induced-fit model, which alters the conformation to match the shape of the ligand, and the pre-existing equilibrium model, selectively binding structures with high binding affinity from a diverse ensemble of proteins. Although methods for detecting target protein binding sites and virtual screening techniques using docking simulation are well-established, with numerous studies reported, they only consider a very limited number of structures in the diverse ensemble of proteins, as these methods are applied to a single structure. Molecular dynamics (MD) simulation is a method for predicting protein dynamics and can detect potential ensembles of protein binding sites and hidden sites unobservable in a single-point structure. In this study, to demonstrate the utility of virtual screening with protein dynamics, MD simulations were performed on Trypanosoma cruzi spermidine synthase to obtain an ensemble of dominant binding sites with a high probability of existence. The structure of the binding site obtained through MD simulation revealed pockets in addition to the active site that was present in the initial structure. Using the obtained binding site structures, virtual screening of 4.8 million compounds by docking simulation, in vitro assays, and X-ray analysis was conducted, successfully identifying two hit compounds.

Analysis of Conocurvone, Ganoderic acid A and Oleuropein molecules against the main protease molecule of COVID-19 by in silico approaches: Molecular dynamics docking studies

Traditional medicines against COVID-19 have taken important outbreaks evidenced by multiple cases, controlled clinical research, and randomized clinical trials. Furthermore, the design and chemical synthesis of protease inhibitors, one of the latest therapeutic approaches for virus infection, is to search for enzyme inhibitors in herbal compounds to achieve a minimal amount of side-effect medications. Hence, the present study aimed to screen some naturally derived biomolecules with anti-microbial properties (anti-HIV, antimalarial, and anti-SARS) against COVID-19 by targeting coronavirus main protease via molecular docking and simulations. Docking was performed using SwissDock and Autodock4, while molecular dynamics simulations were performed by the GROMACS-2019 version. The results showed that Oleuropein, Ganoderic acid A, and conocurvone exhibit inhibitory actions against the new COVID-19 proteases. These molecules may disrupt the infection process since they were demonstrated to bind at the coronavirus major protease's active site, affording them potential leads for further research against COVID-19.

Docking and Molecular Dynamics Identify Leads against 5 Alpha Reductase 2 for Benign Prostate Hyperplasia Treatment

Steroid 5 alpha-reductase 2 (5αR-2) is a membrane-embedded protein that together with other isoforms plays a key role in the metabolism of steroids. This enzyme catalyzes the reduction of testosterone to the more potent ligand, dihydrotestosterone (DHT) in the prostate. Androgens, testosterone, and DHT play important roles in prostate growth, development, and function. At the same time, both testosterone and DHT have been implicated in the pathogenesis of benign prostate hyperplasia (BPH). Inhibition of the DHT formation, therefore, provides a therapeutic strategy that offers the possibility of preventing, delaying, or treating BPH. Currently, two steroidal drugs that inhibit 5αR-2, dutasteride and finasteride, have been approved for clinical use. These two come at a high cost and also portray undesirable sexual side effects which necessitate the need to find new chemotherapeutic alternatives for the disease. Based on the aforementioned, finasteride and dutasteride were subjected to scaffold hopping, fragment-based de novo design, molecular docking, and molecular dynamics simulations employing databases like ChEMBL, DrugBank, PubChem, ChemSpider, and Zinc15 in the identification of potential hits targeting 5αR-2. Altogether, ten novel compounds targeting 5αR-2 were identified with binding energies lower or comparable to finasteride and dutasteride, the main inhibitors for this target. Molecular docking and molecular dynamics simulations studies identify amino acid residues Glu57, Phe219, Phe223, and Leu224 to be critical for ligand binding and complex stability. The physicochemical and pharmacological profiling suggests the potential of the hit compounds to be drug-like and orally active. Similarly, the quality parameter assessments revealed the hits possess LELP greater than 3 implying their promise as lead-like molecules. The compounds A5, A9, and A10 were, respectively, predicted to treat prostate disorders with Pa (0.188, 0.361, and 0.270) and Pi (0.176, 0.050, and 0.093), while A8 and A9 were found to be associated with BPH treatment with Pa (0.09 and 0.127) and Pi (0.077 and 0.033), respectively. Structural similarity searches via DrugBank identified the drugs faropenem, acemetacin, estradiol valerate, and yohimbine to be useful for BPH treatment suggesting the de novo designed ligands as potential chemotherapeutic agents for treating this disease.

Identification of promising high-affinity inhibitors of SARS-CoV-2 main protease from African Natural Products Databases by Virtual Screening

With the rapid spread of the new severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the pathogen agent of COVID-19 pandemic created a serious threat to global public health, requiring the most urgent research for potential therapeutic agents. The availability of genomic data of SARS-CoV-2 and efforts to determine the protein structure of the virus facilitated the identification of potent inhibitors by using structure-based approach and bioinformatics tools. Many pharmaceuticals have been proposed for the treatment of COVID-19, although their effectiveness has not been assessed yet. However, it is important to find out new-targeted drugs to overcome the resistance concern. Several viral proteins such as proteases, polymerases or structural proteins have been considered as potential therapeutic targets. But the virus target must be essential for host invasion match some drugability criterion. In this Work, we selected the highly validated pharmacological target main protease Mpro and we performed high throughput virtual screening of African Natural Products Databases such as NANPDB, EANPDB, AfroDb, and SANCDB to identify the most potent inhibitors with the best pharmacological properties. In total, 8753 natural compounds were virtually screened by AutoDock vina against the main protease of SARS-CoV-2. Two hundred and five (205) compounds showed high-affinity scores (less than - 10.0 Kcal/mol), while fifty-eight (58) filtered through Lipinski's rules showed better affinity than known Mpro inhibitors (i.e., ABBV-744, Onalespib, Daunorubicin, Alpha-ketoamide, Perampanel, Carprefen, Celecoxib, Alprazolam, Trovafloxacin, Sarafloxacin, Ethyl biscoumacetate…). Those promising compounds could be considered for further investigations toward the developpement of SARS-CoV-2 drug development.

End-to-end sequence-structure-function meta-learning predicts genome-wide chemical-protein interactions for dark proteins

Systematically discovering protein-ligand interactions across the entire human and pathogen genomes is critical in chemical genomics, protein function prediction, drug discovery, and many other areas. However, more than 90% of gene families remain "dark"-i.e., their small-molecule ligands are undiscovered due to experimental limitations or human/historical biases. Existing computational approaches typically fail when the dark protein differs from those with known ligands. To address this challenge, we have developed a deep learning framework, called PortalCG, which consists of four novel components: (i) a 3-dimensional ligand binding site enhanced sequence pre-training strategy to encode the evolutionary links between ligand-binding sites across gene families; (ii) an end-to-end pretraining-fine-tuning strategy to reduce the impact of inaccuracy of predicted structures on function predictions by recognizing the sequence-structure-function paradigm; (iii) a new out-of-cluster meta-learning algorithm that extracts and accumulates information learned from predicting ligands of distinct gene families (meta-data) and applies the meta-data to a dark gene family; and (iv) a stress model selection step, using different gene families in the test data from those in the training and development data sets to facilitate model deployment in a real-world scenario. In extensive and rigorous benchmark experiments, PortalCG considerably outperformed state-of-the-art techniques of machine learning and protein-ligand docking when applied to dark gene families, and demonstrated its generalization power for target identifications and compound screenings under out-of-distribution (OOD) scenarios. Furthermore, in an external validation for the multi-target compound screening, the performance of PortalCG surpassed the rational design from medicinal chemists. Our results also suggest that a differentiable sequence-structure-function deep learning framework, where protein structural information serves as an intermediate layer, could be superior to conventional methodology where predicted protein structures were used for the compound screening. We applied PortalCG to two case studies to exemplify its potential in drug discovery: designing selective dual-antagonists of dopamine receptors for the treatment of opioid use disorder (OUD), and illuminating the understudied human genome for target diseases that do not yet have effective and safe therapeutics. Our results suggested that PortalCG is a viable solution to the OOD problem in exploring understudied regions of protein functional space.

A rational in silico approach to identify inhibitors of Batroxrhagin from Bothrops atrox

Bothrops atrox venom comprises several types of bioactive molecules, enzymatic and non-enzymatic, among those, Batroxrhagin is the most predominant SVMP P-III enzyme, which are responsible for induction of local and systemic hemorrhage and muscle fibers damage, impairing regeneration. Due to great difficulties in establishing an antibothropic drug, new strategies must be addressed to achieve a more effective and efficient treatment. There are no studies of specific catalytic inhibitors of Batroxrhagin. However, there are in vitro studies that have described similar metalloprotease inhibitors. The inhibitor batimastat was used as a leading compound for the search and selection of similar candidates. This molecule is widely cited as a metalloprotease inhibitor and as an antimetastatic. In addition to batimastat-like molecules, four other reported metalloprotease inhibitors were included to compose the study's positive control group. Hence, 580 molecules were tested. The three-dimensional structure of B. atrox Batroxrhagin was predicted based on homologous structures using Modeller 9.20. Molecular docking calculation was performed using Autodock 4.2 and molecular surfaces and interactions were analyzed using Biovia/Discovery Studio 2017. Among 576 molecules, 42 similar to batismast resulted in a better energy of interaction than all positive controls, including batimastat itself. The batimastat-like molecules with lowest energy and positive controls were subjected to molecular dynamics for 30 ns in Gromacs 2019.4. This batimastat-like molecule produced better stability among all the Batroxrhagin-ligand complexes analyzed. Overall, the proposed compounds present justifiable evidence for future in vitro tests aiming to inhibit Batroxrhagin. Communicated by Ramaswamy H. Sarma.

CSAlign and CSAlign-Dock: Structure alignment of ligands considering full flexibility and application to protein-ligand docking

Structure prediction of protein-ligand complexes, called protein-ligand docking, is a critical computational technique that can be used to understand the underlying principle behind the protein functions at the atomic level and to design new molecules regulating the functions. Protein-ligand docking methods have been employed in structure-based drug discovery for hit discovery and lead optimization. One of the important technical challenges in protein-ligand docking is to account for protein conformational changes induced by ligand binding. A small change such as a single side-chain rotation upon ligand binding can hinder accurate docking. Here we report an increase in docking performance achieved by structure alignment to known complex structures. First, a fully flexible compound-to-compound alignment method CSAlign is developed by global optimization of a shape score. Next, the alignment method is combined with a docking algorithm to dock a new ligand to a target protein when a reference protein-ligand complex structure is available. This alignment-based docking method, called CSAlign-Dock, showed superior performance to ab initio docking methods in cross-docking benchmark tests. Both CSAlign and CSAlign-Dock are freely available as a web server at https://galaxy.seoklab.org/csalign.

An allosteric modulator activates BK channels by perturbing coupling between Ca2+ binding and pore opening

BK type Ca²⁺-activated K⁺ channels activate in response to both voltage and Ca²⁺. The membrane-spanning voltage sensor domain (VSD) activation and Ca²⁺ binding to the cytosolic tail domain (CTD) open the pore across the membrane, but the mechanisms that couple VSD activation and Ca²⁺ binding to pore opening  are not clear. Here we show that a compound, BC5, identified from in silico screening, interacts with the CTD-VSD interface and specifically modulates the Ca²⁺ dependent activation mechanism. BC5 activates the channel in the absence of Ca²⁺ binding but Ca²⁺ binding inhibits BC5 effects. Thus, BC5 perturbs a pathway that couples Ca²⁺ binding to pore opening to allosterically affect both, which is further supported by atomistic simulations and mutagenesis. The results suggest that the CTD-VSD interaction makes a major contribution to the mechanism of Ca²⁺ dependent activation and is an important site for allosteric agonists to modulate BK channel activation.

Molecular Docking and Green Synthesis of Bioinorganic TiO2 Nanoparticles against E.coli and S.aureus

This study used a simple solution evaporation approach to make a bioinorganic titanium dioxide (Bi-TiO2) photocatalyst for dye contaminant degradation. A variety of techniques, including X-ray diffraction (XRD), Fourier-transform infrared (FT-IR) spectroscopy, scanning electron microscopy (SEM) coupled with energy dispersive X-ray analysis (EDAX), and differential reflectance spectroscopy, had been employed to classify the structural and optical properties of the prepared bioinorganic photocatalyst (UV-DRS). Using simulated solar irradiation, the photocatalytic activity of the produced Bi-TiO2 nanoparticles was examined by detecting the degradation of a solution of methylene blue (MB) as a model dye molecule. The developed Bi-TiO2 photocatalyst demonstrates superior photocatalytic action than commercially available powder TiO2, according to photo-degradation experiments. E.coli and S.aureus bacterial strains were employed to assess the antibacterial activity of Bi-TiO2 nanoparticles. The most active molecules that gain antibacterial activity were examined in isolated or extracted components from the tulsi plant. The chosen compounds were docked with thymidylate kinase (TMPK), a potential therapeutic goal for the preparation of novel antibacterial drugs with the PDB ID of 4QGG. Five compounds, namely rosmarinic acid, vicenin-2, orientin, vitexin, and isoorientin, out of the 27 chosen compounds, showed a higher docking score and may aid in boosting antibacterial activity. The synthesized Bi-TiO2 nanoparticles produced antibacterial activity that was effective against Gram-positive bacteria. The nanomaterials that have been synthesized have a lot of potential in wastewater treatment and biomedical management technologies.

Agent-Based Models Help Interpret Patterns of Clinical Drug Resistance by Contextualizing Competition Between Distinct Drug Failure Modes

Introduction

Modern targeted cancer therapies are carefully crafted small molecules. These exquisite technologies exhibit an astonishing diversity of observed failure modes (drug resistance mechanisms) in the clinic. This diversity is surprising because back of the envelope calculations and classic modeling results in evolutionary dynamics suggest that the diversity in the modes of clinical drug resistance should be considerably smaller than what is observed. These same calculations suggest that the outgrowth of strong pre-existing genetic resistance mutations within a tumor should be ubiquitous. Yet, clinically relevant drug resistance occurs in the absence of obvious resistance conferring genetic alterations. Quantitatively, understanding the underlying biological mechanisms of failure mode diversity may improve the next generation of targeted anticancer therapies. It also provides insights into how intratumoral heterogeneity might shape interpatient diversity during clinical relapse.

Materials and Methods

We employed spatial agent-based models to explore regimes where spatial constraints enable wild type cells (that encounter beneficial microenvironments) to compete against genetically resistant subclones in the presence of therapy. In order to parameterize a model of microenvironmental resistance, BT20 cells were cultured in the presence and absence of fibroblasts from 16 different tissues. The degree of resistance conferred by cancer associated fibroblasts in the tumor microenvironment was quantified by treating mono- and co-cultures with letrozole and then measuring the death rates.

Results and Discussion

Our simulations indicate that, even when a mutation is more drug resistant, its outgrowth can be delayed by abundant, low magnitude microenvironmental resistance across large regions of a tumor that lack genetic resistance. These observations hold for different modes of microenvironmental resistance, including juxtacrine signaling, soluble secreted factors, and remodeled ECM. This result helps to explain the remarkable diversity of resistance mechanisms observed in solid tumors, which subverts the presumption that the failure mode that causes the quantitatively fastest growth in the presence of drug should occur most often in the clinic.

Conclusion

Our model results demonstrate that spatial effects can interact with low magnitude of resistance microenvironmental effects to successfully compete against genetic resistance that is orders of magnitude larger. Clinical outcomes of solid tumors are intrinsically connected to their spatial structure, and the tractability of spatial agent-based models like the ones presented here enable us to understand this relationship more completely.

DENVIS: Scalable and High-Throughput Virtual Screening Using Graph Neural Networks with Atomic and Surface Protein Pocket Features

Computational methods for virtual screening can dramatically accelerate early-stage drug discovery by identifying potential hits for a specified target. Docking algorithms traditionally use physics-based simulations to address this challenge by estimating the binding orientation of a query protein-ligand pair and a corresponding binding affinity score. Over the recent years, classical and modern machine learning architectures have shown potential for outperforming traditional docking algorithms. Nevertheless, most learning-based algorithms still rely on the availability of the protein-ligand complex binding pose, typically estimated via docking simulations, which leads to a severe slowdown of the overall virtual screening process. A family of algorithms processing target information at the amino acid sequence level avoid this requirement, however, at the cost of processing protein data at a higher representation level. We introduce deep neural virtual screening (DENVIS), an end-to-end pipeline for virtual screening using graph neural networks (GNNs). By performing experiments on two benchmark databases, we show that our method performs competitively to several docking-based, machine learning-based, and hybrid docking/machine learning-based algorithms. By avoiding the intermediate docking step, DENVIS exhibits several orders of magnitude faster screening times (i.e., higher throughput) than both docking-based and hybrid models. When compared to an amino acid sequence-based machine learning model with comparable screening times, DENVIS achieves dramatically better performance. Some key elements of our approach include protein pocket modeling using a combination of atomic and surface features, the use of model ensembles, and data augmentation via artificial negative sampling during model training. In summary, DENVIS achieves competitive to state-of-the-art virtual screening performance, while offering the potential to scale to billions of molecules using minimal computational resources.

A reinforcement learning approach for protein-ligand binding pose prediction

Protein ligand docking is an indispensable tool for computational prediction of protein functions and screening drug candidates. Despite significant progress over the past two decades, it is still a challenging problem, characterized by the still limited understanding of the energetics between proteins and ligands, and the vast conformational space that has to be searched to find a satisfactory solution. In this project, we developed a novel reinforcement learning (RL) approach, the asynchronous advantage actor-critic model (A3C), to address the protein ligand docking problem. The overall framework consists of two models. During the search process, the agent takes an action selected by the actor model based on the current location. The critic model then evaluates this action and predict the distance between the current location and true binding site. Experimental results showed that in both single- and multi-atom cases, our model improves binding site prediction substantially compared to a naïve model. For the single-atom ligand, copper ion (Cu²⁺), the model predicted binding sites have a median root-mean-square-deviation (RMSD) of 2.39 Å to the true binding sites when starting from random starting locations. For the multi-atom ligand, sulfate ion (SO₄^2-), the predicted binding sites have a median RMSD of 3.82 Å to the true binding sites. The ligand-specific models built in this study can be used in solvent mapping studies and the RL framework can be readily scaled up to larger and more diverse sets of ligands.

Characterization of the binding interaction between atrazine and human serum albumin: Fluorescence spectroscopy, molecular dynamics and quantum biochemistry

Atrazine (ATR), one of the most used herbicides worldwide, causes persistent contamination of water and soil due to its high resistance to degradation. ATR is associated with low fertility and increased risk of prostate cancer in humans, as well as birth defects, low birth weight and premature delivery. Describing ATR binding to human serum albumin (HSA) is clinically relevant to future studies about pharmacokinetics, pharmacodynamics and toxicity of ATR, as albumin is the most abundant carrier protein in plasma and binds important small biological molecules. In this work we characterize, for the first time, the binding of ATR to HSA by using fluorescence spectroscopy and performing simulations using molecular docking, classical molecular dynamics and quantum biochemistry based on density functional theory (DFT). We determine the most likely binding sites of ATR to HSA, highlighting the fatty acid binding site FA8 (located between subdomains IA-IB-IIA and IIB-IIIA-IIIB) as the most important one, and evaluate each nearby amino acid residue contribution to the binding interactions explaining the fluorescence quenching due to ATR complexation with HSA. The stabilization of the ATR/FA8 complex was also aided by the interaction between the atrazine ring and SER454 (hydrogen bond) and LEU481(alkyl interaction).

Reinforcement learning for systems pharmacology-oriented and personalized drug design

INTRODUCTION

Many multi-genic systemic diseases such as neurological disorders, inflammatory diseases, and the majority of cancers do not have effective treatments yet. Reinforcement learning powered systems pharmacology is a potentially effective approach to designing personalized therapies for untreatable complex diseases.

AREAS COVERED

In this survey, state-of-the-art reinforcement learning methods and their latest applications to drug design are reviewed. The challenges on harnessing reinforcement learning for systems pharmacology and personalized medicine are discussed. Potential solutions to overcome the challenges are proposed.

EXPERT OPINION

In spite of successful application of advanced reinforcement learning techniques to target-based drug discovery, new reinforcement learning strategies are needed to address systems pharmacology-oriented personalized de novo drug design.

Styrylpyrone Derivative (SPD) Extracted from Goniothalamus umbrosus Binds to Dengue Virus Serotype-2 Envelope Protein and Inhibits Early Stage of Virus Replication

A study was conducted to investigate the anti-viral effect of a styrylpyrone derivative (SPD) called goniothalamin and the effects on the dengue virus serotype 2 (DENV-2) replication cycle. The SPD was prepared from the root of Goniothalamus umbrosus after purification with petroleum ether. The isolated SPD was then subjected to gas chromatography–mass spectrometry (GC-MS) and nuclear magnetic resonance (NMR) analyses for structure validation. The cytotoxicity of the SPD was evaluated using a cell viability assay, while the anti-viral activity of the SPD towards DENV-2 was confirmed by conducting a foci reduction assay which involved virus yield reduction, time-of-addition, and time removal assays. Transcriptomic analysis via quantitative real-time polymerase chain reaction (qRT-PCR) using the DENV-2 E gene was conducted to investigate the level of gene transcript. Immunocytochemistry analysis was used to investigate the effects of SPD treatment on protein E expression. Finally, software molecular docking of the SPD and E protein was also performed. The cytotoxicity assay confirmed that the SPD was not toxic to Vero cells, even at the highest concentration tested. In the time-of-addition assay, more than 80% foci reduction was observed when SPDs were administered at 2 h post-infection (hpi), and the reduction percentage then dropped with the delay of the treatment time, suggesting the inhibition of the early replication cycle. However, the time removal assay showed that more than 80% reduction could only be observed after 96 h post-treatment with the SPD. Treatment with the SPD reduced the progeny infectivity when treated for 24 h and was dose-dependent. The result showed that transcript level of the E gene in infected cells treated with the SPD was reduced compared to infected cells without treatment. In immunocytochemistry analysis, the DENV-2 E protein exhibited similar expression trends, shown by the gene transcription level. Molecular docking showed that the SPD can interact with E protein through hydrogen bonds and other interactions. Overall, this study showed that SPDs have the potential to be anti-DENV-2 via a reduction in viral progeny infectivity and a reduction in the expression of the DENV-2 E gene and protein at different phases of viral replication. SPDs should be further researched to be developed into an effective anti-viral treatment, particularly for early-phase dengue viral infection.

Homology Modeling, de Novo Design of Ligands, and Molecular Docking Identify Potential Inhibitors of Leishmania donovani 24-Sterol Methyltransferase

The therapeutic challenges pertaining to leishmaniasis due to reported chemoresistance and toxicity necessitate the need to explore novel pathways to identify plausible inhibitory molecules. Leishmania donovani 24-sterol methyltransferase (LdSMT) is vital for the synthesis of ergosterols, the main constituents of Leishmania cellular membranes. So far, mammals have not been shown to possess SMT or ergosterols, making the pathway a prime candidate for drug discovery. The structural model of LdSMT was elucidated using homology modeling to identify potential novel 24-SMT inhibitors via virtual screening, scaffold hopping, and de-novo fragment-based design. Altogether, six potential novel inhibitors were identified with binding energies ranging from −7.0 to −8.4 kcal/mol with e-LEA3D using 22,26-azasterol and S1–S4 obtained from scaffold hopping via the ChEMBL, DrugBank, PubChem, ChemSpider, and ZINC15 databases. These ligands showed comparable binding energy to 22,26-azasterol (−7.6 kcal/mol), the main inhibitor of LdSMT. Moreover, all the compounds had plausible ligand efficiency-dependent lipophilicity (LELP) scores above 3. The binding mechanism identified Tyr92 to be critical for binding, and this was corroborated via molecular dynamics simulations and molecular mechanics Poisson–Boltzmann surface area (MM-PBSA) calculations. The ligand A1 was predicted to possess antileishmanial properties with a probability of activity (Pa) of 0.362 and a probability of inactivity (Pi) of 0.066, while A5 and A6 possessed dermatological properties with Pa values of 0.205 and 0.249 and Pi values of 0.162 and 0.120, respectively. Structural similarity search via DrugBank identified vabicaserin, daledalin, zanapezil, imipramine, and cefradine with antileishmanial properties suggesting that the de-novo compounds could be explored as potential antileishmanial agents.

Zerumbone, a humulane sesquiterpene from Syringa pinnatifolia, attenuates cardiac fibrosis by inhibiting of the TGF-β1/Smad signaling pathway after myocardial infarction in mice

BACKGROUND

Zerumbone (ZER) is a humulane sesquiterpene isolated from Syringa pinnatifolia Hemsl., a representative Mongolian herbal medicine that is used to treat cardiovascular diseases. Cardiac fibrosis is a common pathological process in cardiovascular disease that results from the excessive accumulation of extracellular matrix (ECM), and the transforming growth factor (TGF)-β/Smad pathway is a canonical signaling pathway that directly induces expressions of ECM-related genes. Currently, the cardioprotective effect and underlying mechanisms of ZER on the inhibition of cardiac fibrosis are not well known.

PURPOSE

To explore the cardioprotective properties and pharmacological mechanism of ZER against cardiac fibrosis via the TGF-β1/Smad signaling pathway.

METHODS

Myocardial infarction (MI) model was induced by ligation of the left anterior descending coronary artery in ICR mice. The mice were randomly divided into six groups: sham, model, low-dose ZER (ZER-L), medium-dose ZER (ZER-M), high-dose ZER (ZER-H) and fosinopril. Mice in each group were intragastrically administered treatments for 21 days, and cardiac function was evaluated by 2D echocardiography. The pathological structure of the heart was examined by hematoxylin and eosin (HE) and Masson staining. Content of collagen I and collagen III were assessed by immunofluorescence methods. The inhibitory effect of ZER on TGF-β1 protein expression was predicted by molecular docking technology. Reverse transcriptase polymerase chain reaction (RT-PCR) and western blotting were used to measure the levels of genes and proteins expressed in the TGF-β1/Smad signaling pathway and MMPs. TGF-β1-treated cardiac fibroblasts (CFs) of neonatal SD rats were adopted for in vitro studies.

RESULTS

Cardiac ejection fraction (EF) and fractional shortening (FS) in the model group were markedly decreased compared with those in the sham group, indicating that the MI model was successfully established. ZER and fosinopril elevated EF and FS values, suggesting cardioprotective effects. Pathological staining and immunofluorescence analysis showed that the content of collagen I and collagen III increased in the cardiac tissue of mice in model group, while ZER treatment obviously reduced collagen levels. The molecular docking simulations predicted the hydrophobic interactions between ZER and TGF-β1. In addition, the expression of TGF-β1, p-Smad2/3 and MMPs in the ZER treatment group was significantly decreased compared with the model group. In vitro studies further confirmed that α-smooth muscle actin (α-SMA) and p-Smad2/3 increased markedly in cardiac fibroblasts after incubation with TGF-β1, and treatment with ZER suppressed the expression of α-SMA and TGF-β1 downstream proteins in cardiac fibroblasts.

CONCLUSION

ZER rescues cardiac function by attenuating cardiac fibrosis, and the antifibrotic effect may be mediated by blocking the TGF-β1/Smad pathway.

Discovery of METTL3 Small Molecule Inhibitors by Virtual Screening of Natural Products

N⁶-Methyladenosine (m⁶A) is the most prevalent mRNA modification in mammalian cells that is mainly catalyzed by the methyltransferase complex of methyltransferase-like 3 and methyltransferase-like 14 (METTL3-METTL14). Many lines of evidence suggest that METTL3 plays important roles in several diseases such as cancers and viral infection. In the present study, 1,042 natural products from commercially available sources were chosen to establish a screening library, and docking-based high-throughput screening was performed to discover potential METTL3 inhibitors. The selected compounds were then further validated by an in vitro methyltransferase inhibition assay in which m⁶A content was determined by LC-MS/MS. A cellular assay of the inhibition of m⁶A methylation was performed to determine the METTL3 inhibitory activity of the selected compound. CCK-8 assay was applied to evaluate the effects of the selected compound on tumor cell viability. Additionally, binding mode analysis, molecular dynamics (MD) simulation, and binding free energy analysis were performed to study the process and characteristics of inhibitor binding. Finally, quercetin was identified as a METTL3 inhibitor with an IC₅₀ value of 2.73 μM. The cellular assay of m⁶A methylation inhibition showed that quercetin decreased m⁶A level in a dose-dependent manner in MIA PaCa-2 pancreatic cancer cells. CCK-8 assay showed quercetin efficiently inhibited the proliferation of MIA PaCa-2 and Huh7 tumor cells, with IC₅₀ values 73.51 ± 11.22 μM and 99.97 ± 7.03 μM, respectively. Molecular docking studies revealed that quercetin filled the pocket of the adenosine moiety of SAM but not the pocket of the SAM methionine in the METTL3 protein, and hydrogen bonds, hydrophobic interactions, and pi-stacking were formed. The values of the root mean square deviation (RMSD), the root mean square fluctuations (RMSF), and binding free energy suggested that quercetin can efficiently bind to the pocket of the METTL3 protein and form a stable protein-ligand complex. The present study is the first to identify METTL3 inhibitors from natural products, thus providing a basis for subsequent research and facilitating the development of METTL3-targeting drugs for diseases.

Identification of phytochemical inhibitors of SARS-CoV-2 protease 3CLpro from selected medicinal plants as per molecular docking, bond energies and amino acid binding energies

Recent worldwide outbreak of SARS-COV-2 pandemic has increased the thirst to discover and introduce antiviral drugs to combat it. The bioactive compounds from plant sources, especially terpenoid have protease inhibition activities so these may be much effective for the control of viral epidemics and may reduce the burden on health care system worldwide. Present study aims the use of terpenoid from selected plant source through bioinformatics tools for the inhibition of SARS-COV-2. This study is based on descriptive analysis. The Protein Data Bank and PubChem database were used for the analysis of SARS-COV-2 protease and plant source terpenoids. Molecular docking by using molegro virtual docker (MVD) software was carried out. The findings of study are based on the inhibitory actions of different plant sourced terpenoid against SARS-COV-2. As per the available resources and complementary analysis these phytochemicals have capacity to inhibit 3CL^pro protease. The study reports that (3,3-dimethylally) isoflavone (Glycine max), licoleafol (Glycyrrhiza uralensis), myricitrin (Myrica cerifera), thymoquinone (Nigella sativa), bilobalide, ginkgolide A (Ginkgo biloba), Salvinorin A (Salvia divinorum), citral (Backhousia citriodora) and prephenazine (drug) showed high activity against SARS-COV-2 protease 3CL^pro. The drug like and ADMET properties revealed that these compounds can safely be used as drugs. Cross structural analysis by using bioinformatics study concludes that these plant source terpenoid compounds can be effectively used as antiprotease drugs for SARS-COV-2 in future.

Advances in computer-aided drug design for type 2 diabetes

INTRODUCTION

The number of diabetic patients is increasing, posing a heavy social and economic burden worldwide. Traditional drug development technology is time-consuming and costly, and the emergence of computer-aided drug design (CADD) has changed this situation. This study reviews the applications of CADD in diabetic drug designing.

AREAS COVERED

In this article, the authors focus on the advance in CADD in diabetic drug design by elaborating the discovery, including peroxisome proliferator-activated receptor (PPAR), G protein-coupled receptor 40 (GPR40), dipeptidyl peptidase-IV (DDP-IV), protein tyrosine phosphatase 1B (PTP1B), sodium-dependent glucose transporter 2 (SGLT-2), and glucokinase (GK). Some drug discovery of these targets is related to CADD strategies.

EXPERT OPINION

There is no doubt that CADD has contributed to the discovery of novel anti-diabetic agents. However, there are still many limitations and challenges, such as lack of co-crystal complex, dynamic simulations, water, and metal ion treatment. In the near future, artificial intelligence (AI) may be a promising strategy to accelerate drug discovery and reduce costs by identifying candidates. Moreover, AlphaFold, a deep learning model that predicts the 3D structure of proteins, represents a considerable advancement in the structural prediction of proteins, especially in the absence of homologous templates for protein structures.

DEPACT and PACMatch: A Workflow of Designing De Novo Protein Pockets to Bind Small Molecules

Engineering of new functional proteins such as enzymes and biosensors involves the design of new protein pockets for the specific binding of small molecules. Here, we report a workflow composed of two new computational methods to execute this task. The DEPACT (Design Pocket as a Cluster based on Templates) method is a data-driven approach to design and evaluate small-molecule-binding pockets as isolated clusters, while the PACMatch method is a computational approach to match pocket residues in a cluster model to positions on given protein scaffolds. Using DEPACT and its scoring function, pocket clusters of natural-pocket-like chemical compositions and protein-ligand interaction strength can be designed. DEPACT can design pocket clusters containing water- or metal-ion-mediated protein-ligand interactions. While being able to efficiently treat relatively large pocket cluster models (e.g., of around 10 pocket residues), PACMatch outperforms previous methods in test cases of recovering the native positions of pocket residues in natural enzyme-substrate complexes.

Superfast Synthesis of Stabilized Silver Nanoparticles Using Aqueous Allium sativum (Garlic) Extract and Isoniazid Hydrazide Conjugates: Molecular Docking and In-Vitro Characterizations

Green synthesis of silver nanoparticles (AgNPs) was synthesized from fresh garlic extract coupled with isoniazid hydrazide (INH), a commonly used antibiotic to treat tuberculosis. A molecular docking study conducted with the selected compounds compared with anthranilate phosphoribosyltransferase (trpD) from Mycobacterium tuberculosis. The aqueous extract of garlic was prepared and mixed with silver nitrate (AgNO3) solution for the superfast synthesis of stable AgNPs. INH was then conjugated with AgNPs at different ratios (v/v) to obtain stable INH-AgNPs conjugates (AgNCs). The resulting AgNCs characterized by FTIR spectra revealed the ultrafast formation of AgNPs (<5 s) and perfectly conjugated with INH. The shifting of λmax to longer wavelength, as found from UV spectral analysis, confirmed the formation of AgNCs, among which ideal formulations (F7, F10, and F13) have been pre-selected. The zeta particle size (PS) and the zeta potential (ZP) of AgNPs were found to be 145.3 ± 2.1 nm and −33.1 mV, respectively. These data were significantly different compared to that of AgNCs (160 ± 2.7 nm and −14.4 mV for F7; 208.9 ± 2.9 nm and −19.8 mV for F10; and 281.3 ± 3.6 nm and −19.5 mV for F13), most probably due to INH conjugation. The results of XRD, SEM and EDX confirmed the formation of AgNCs. From UV spectral analysis, EE of INH as 51.6 ± 5.21, 53.6 ± 6.88, and 70.01 ± 7.11 %, for F7, F10, and F13, respectively. The stability of the three formulations was confirmed in various physiological conditions. Drug was released in a sustainable fashion. Besides, from the preferred 23 compounds, five compounds namely Sativoside R2, Degalactotigonin, Proto-desgalactotigonin, Eruboside B and Sativoside R1 showed a better docking score than trpD, and therefore may help in promoting anti-tubercular activity.

Homology modelling of Trypanosoma brucei major surface proteases and molecular docking of variant surface glycoproteins and inhibitor ligands for drug design

Trypanosomes, which cause animal African trypanosomiasis, escape host immune responses by renewing their variable surface glycoprotein (VSG) coat. Chemotherapy is currently the only form of external intervention available. However, the efficacy of current trypanocides is poor due to overuse leading to an increase in drug resistance. Major surface proteases (MSPs) of trypanosomes, which are zinc-dependent metalloproteases, are possible drug targets. A Trypanosoma brucei MSP-B (TbMSP-B) mediates parasite antigenic variation via cleavage of 60% of VSG molecules. Whilst TbMSP-A has no apparent role in VSG cleavage; it is not known if TbMSP-C is involved in VSG cleavage. In this study, three-dimensional structures of TbMSP-A, TbMSP-B and TbMSP-C were modelled. By comparing the docking poses of the C-terminal domains of VSG substrates into the models, TbMSP-C showed an affinity for similar VSG substrate sites as TbMSP-B, but these sites differed from those recognised by TbMSP-A. This observation suggests that TbMSP-C may be involved in VSG cleavage during antigenic variation. Furthermore, by docking small inhibitor ligands into the TbMSP-B and TbMSP-C homology models, followed by molecular dynamics simulations, ligands with potential anti-trypanosomal activity were identified. Docking studies also revealed the depth of the S₁' pockets of TbMSP-B and TbMSP-C, which is influential in ligand and substrate binding, thereby identifying the protease subsite pocket that should be targeted in drug design.

Exploration of Dark Chemical Genomics Space via Portal Learning: Applied to Targeting the Undruggable Genome and COVID-19 Anti-Infective Polypharmacology

Advances in biomedicine are largely fueled by exploring uncharted territories of human biology. Machine learning can both enable and accelerate discovery, but faces a fundamental hurdle when applied to unseen data with distributions that differ from previously observed ones-a common dilemma in scientific inquiry. We have developed a new deep learning framework, called Portal Learning, to explore dark chemical and biological space. Three key, novel components of our approach include: (i) end-to-end, step-wise transfer learning, in recognition of biology's sequence-structure-function paradigm, (ii) out-of-cluster meta-learning, and (iii) stress model selection. Portal Learning provides a practical solution to the out-of-distribution (OOD) problem in statistical machine learning. Here, we have implemented Portal Learning to predict chemical-protein interactions on a genome-wide scale. Systematic studies demonstrate that Portal Learning can effectively assign ligands to unexplored gene families (unknown functions), versus existing state-of-the-art methods. Compared with AlphaFold2-based protein-ligand docking, Portal Learning significantly improved the performance by 79% in PR-AUC and 27% in ROC-AUC, respectively. The superior performance of Portal Learning allowed us to target previously "undruggable" proteins and design novel polypharmacological agents for disrupting interactions between SARS-CoV-2 and human proteins. Portal Learning is general-purpose and can be further applied to other areas of scientific inquiry.

Molecular Docking Analysis of Ficus religiosa Active Compound with Anti-Inflammatory Activity by Targeting Tumour Necrosis Factor Alpha and Vascular Endothelial Growth Factor Receptor in Diabetic Wound Healing

BACKGROUND: Diabetes mellitus contributes to the delayed healing of wounds causes disturbance of inflammatory cytokine. Tumour necrosis factor alpha (TNF-alpha) and Vascular Endothelial Growth Factor Receptor (VEGFR) both have a role in the persistent inflammation associated with diabetic wounds. Ficus religiosa has developed a reputation as a traditional wound healer among some java people in Indonesia. AIM: Our study aims to discover the molecular interaction between the active constituents of F. religiosa with TNF-alpha and VEGFR. MATERIALS AND METHODS: This research was conducted in computerized molecular docking using Protein database, Pymol, Discovery studio, and Pyrex software. A thorough literature search was conducted to identify the potential compound and molecular target for diabetic wounds. Analysis of its anti-inflammatory properties was also carried out using a passonline webserver. Pharmacokinetic analysis was performed using the Lipinski Rule of Five websites and the PreADMET website. RESULTS: Each of the study’s active compounds has a good pharmacokinetic profile. The predictions of the compound’s structure indicate that it has a strong anti-inflammatory impact. Lupenyl acetate and Lanosterol bind more strongly to the TNF-alpha than the natural ligand, but Piperine binds more strongly to VEGFR. CONCLUSIONS: Lupenyl acetate, Lanosterol, and Piperine compounds have anti-inflammatory effects through inhibition of TNF-alpha and VEGFR. In addition, this compound has potential to become a drug because it has good pharmacokinetics. Future studies are required to determine the effectiveness and toxicity of Lupenyl acetate, Lanosterol, and Piperine as potential treatment in diabetic wounds.

OnionNet-2: A Convolutional Neural Network Model for Predicting Protein-Ligand Binding Affinity Based on Residue-Atom Contacting Shells

One key task in virtual screening is to accurately predict the binding affinity (△G) of protein-ligand complexes. Recently, deep learning (DL) has significantly increased the predicting accuracy of scoring functions due to the extraordinary ability of DL to extract useful features from raw data. Nevertheless, more efforts still need to be paid in many aspects, for the aim of increasing prediction accuracy and decreasing computational cost. In this study, we proposed a simple scoring function (called OnionNet-2) based on convolutional neural network to predict △G. The protein-ligand interactions are characterized by the number of contacts between protein residues and ligand atoms in multiple distance shells. Compared to published models, the efficacy of OnionNet-2 is demonstrated to be the best for two widely used datasets CASF-2016 and CASF-2013 benchmarks. The OnionNet-2 model was further verified by non-experimental decoy structures from docking program and the CSAR NRC-HiQ data set (a high-quality data set provided by CSAR), which showed great success. Thus, our study provides a simple but efficient scoring function for predicting protein-ligand binding free energy.

Artificial Intelligence and the Future of Diagnostic and Therapeutic Radiopharmaceutical Development:: In Silico Smart Molecular Design

Novel diagnostic and therapeutic radiopharmaceuticals are increasingly becoming a central part of personalized medicine. Continued innovation in the development of new radiopharmaceuticals is key to sustained growth and advancement of precision medicine. Artificial intelligence has been used in multiple fields of medicine to develop and validate better tools for patient diagnosis and therapy, including in radiopharmaceutical design. In this review, we first discuss common in silico approaches and focus on their usefulness and challenges in radiopharmaceutical development. Next, we discuss the practical applications of in silico modeling in design of radiopharmaceuticals in various diseases.

Quantum Mechanics Helps Uncover Atypical Recognition Features in the Flavin Mononucleotide Riboswitch

Estimating the binding energies of small molecules to RNA could help uncover their molecular recognition characteristics and be used to rationally design RNA-targeting chemical probes. Here, we leveraged the ability of the fragment molecular orbital (FMO) method to provide detailed pairwise energetic information to examine the interactions between the aptamer domain of the flavin mononucleotide (FMN)-responsive riboswitch and small-molecule ligands. After developing an efficient protocol for executing high-level FMO calculations on RNA-ligand complexes, we applied our protocol to nine FMN-aptamer-ligand complexes. We then used the results to identify "hot-spots" within the aptamer and decomposed pairwise interactions between the hot-spot residues and the ligands. Interestingly, we found that several of these hot-spot residues interact with the ligands via atypical CH···O hydrogen bonds and anion-π contacts, as well as (face-to-edge) T-shaped π-π interactions. We envision that our results should pave the way for the wider and more prominent use of FMO calculations to study structure-energy relationships in diverse RNA-ligand systems, which in turn may provide a basis for dissecting the molecular recognition characteristics of RNAs.

Systematic review on role of structure based drug design (SBDD) in the identification of anti-viral leads against SARS-Cov-2

The outbreak of existing public health distress is threatening the entire world with emergence and rapid spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The novel coronavirus disease 2019 (COVID-19) is mild in most people. However, in some elderly people with co-morbid conditions, it may progress to pneumonia, acute respiratory distress syndrome (ARDS) and multi organ dysfunction leading to death. COVID-19 has caused global panic in the healthcare sector and has become one of the biggest threats to the global economy. Drug discovery researchers are expected to contribute rapidly than ever before. The complete genome sequence of coronavirus had been reported barely a month after the identification of first patient. Potential drug targets to combat and treat the coronavirus infection have also been explored. The iterative structure-based drug design (SBDD) approach could significantly contribute towards the discovery of new drug like molecules for the treatment of COVID-19. The existing antivirals and experiences gained from SARS and MERS outbreaks may pave way for identification of potential drug molecules using the approach. SBDD has gained momentum as the essential tool for faster and costeffective lead discovery of antivirals in the past. The discovery of FDA approved human immunodeficiency virus type 1 (HIV-1) inhibitors represent the foremost success of SBDD. This systematic review provides an overview of the novel coronavirus, its pathology of replication, role of structure based drug design, available drug targets and recent advances in in-silico drug discovery for the prevention of COVID-19. SARSCoV- 2 main protease, RNA dependent RNA polymerase (RdRp) and spike (S) protein are the potential targets, which are currently explored for the drug development.

Medicinal plant compounds as promising inhibitors of coronavirus (COVID-19) main protease: an in silico study

The novel Coronavirus (COVID-19) has spread rapidly across the globe and has involved more than 215 countries and territories. Due to a lack of effective therapy or vaccine, urgent and concerted efforts are needed to identify therapeutic targets and medications. COVID-19 main protease represents a major target for drug treatment to inhibit viral function. The present study sought to evaluate medicinal plant compounds as potential inhibitors of the COVID-19 main protease using molecular docking and molecular dynamic analysis. The PDB files of COVID-19 main protease and some medicinal plant compounds were retrieved from the Protein Data Bank (http://www.rcsb.org) and Pubchem server, respectively. The Gromacs software was used for simulation studies, and molecular docking analysis was done using Autodock 4.2. The COVID-19 main protease simulation, compared with some phytochemicals docked to the COVID-19 main protease, were analyzed. Glabridin, catechin, and fisetin had the greatest tendency to interact with the COVID-19 main protease by hydrogen and hydrophobic interactions. Docking of these phytochemicals to COVID-19 main protease led to an increase in the radius of gyration (Rg), decrease in the Root mean square fluctuation (RMSF), and induced variation in COVID-19 main protease secondary structure. The high tendency interaction of glabridin, catechin, and fisetin to COVID-19 main protease induced conformational changes on this enzyme. These interactions can lead to enzyme inhibition. This simulated study indicates that these phytochemicals may be considered as potent inhibitors of the viral protease; however, more investigations are required to explore their potential medicinal use.Communicated by Ramaswamy H. Sarma.

Lean-Docking: Exploiting Ligands' Predicted Docking Scores to Accelerate Molecular Docking

In structure-based virtual screening (SBVS), a binding site on a protein structure is used to search for ligands with favorable nonbonded interactions. Because it is computationally difficult, docking is time-consuming and any docking user will eventually encounter a chemical library that is too big to dock. This problem might arise because there is not enough computing power or because preparing and storing so many three-dimensional (3D) ligands requires too much space. In this study, however, we show that quality regressors can be trained to predict docking scores from molecular fingerprints. Although typical docking has a screening rate of less than one ligand per second on one CPU core, our regressors can predict about 5800 docking scores per second. This approach allows us to focus docking on the portion of a database that is predicted to have docking scores below a user-chosen threshold. Herein, usage examples are shown, where only 25% of a ligand database is docked, without any significant virtual screening performance loss. We call this method "lean-docking". To validate lean-docking, a massive docking campaign using several state-of-the-art docking software packages was undertaken on an unbiased data set, with only wet-lab tested active and inactive molecules. Although regressors allow the screening of a larger chemical space, even at a constant docking power, it is also clear that significant progress in the virtual screening power of docking scores is desirable.

Perspectives on High-Throughput Ligand/Protein Docking With Martini MD Simulations

Molecular docking is central to rational drug design. Current docking techniques suffer, however, from limitations in protein flexibility and solvation models and by the use of simplified scoring functions. All-atom molecular dynamics simulations, on the other hand, feature a realistic representation of protein flexibility and solvent, but require knowledge of the binding site. Recently we showed that coarse-grained molecular dynamics simulations, based on the most recent version of the Martini force field, can be used to predict protein/ligand binding sites and pathways, without requiring any a priori information, and offer a level of accuracy approaching all-atom simulations. Given the excellent computational efficiency of Martini, this opens the way to high-throughput drug screening based on dynamic docking pipelines. In this opinion article, we sketch the roadmap to achieve this goal.

Rapid Identification of Inhibitors and Prediction of Ligand Selectivity for Multiple Proteins: Application to Protein Kinases

Rapid identification of inhibitors for a family of proteins and prediction of ligand specificity are highly desirable for structure-based drug design. However, sequentially docking ligands into each protein target with conventional single-target docking methods is too computationally expensive to achieve these two goals, especially when the number of the targets is large. In this work, we use an efficient ensemble docking algorithm for simultaneous docking of ligands against multiple protein targets. We use protein kinases, a family of proteins that are highly important for many cellular processes and for rational drug design, as an example to demonstrate the feasibility of investigating ligand selectivity with this algorithm. Specifically, 14 human protein kinases were selected. First, native docking calculations were performed to test the ability of our energy scoring function to reproduce the experimentally determined structures of the ligand-protein kinase complexes. Next, cross-docking calculations were conducted using our ensemble docking algorithm to study ligand selectivity, based on the assumption that the native target of an inhibitor should have a more negative (i.e., favorable) energy score than the non-native targets. Staurosporine and Gleevec were studied as examples of nonselective and selective binding, respectively. Virtual ligand screening was also performed against five protein kinases that have at least seven known inhibitors. Our quantitative analysis of the results showed that the ensemble algorithm can be effective on screening for inhibitors and investigating their selectivities for multiple target proteins.