Revolutionizing GPCR-ligand predictions: DeepGPCR with experimental validation for high-precision drug discovery.

G-protein coupled receptors (GPCRs), crucial in various diseases, are targeted of over 40% of approved drugs. However, the reliable acquisition of experimental GPCRs structures is hindered by their lipid-embedded conformations. Traditional protein-ligand interaction models falter in GPCR-drug interactions, caused by limited and low-quality structures. Generalized models, trained on soluble protein-ligand pairs, are also inadequate. To address these issues, we developed two models, DeepGPCR_BC for binary classification and DeepGPCR_RG for affinity prediction. These models use non-structural GPCR-ligand interaction data, leveraging graph convolutional networks and mol2vec techniques to represent binding pockets and ligands as graphs. This approach significantly speeds up predictions while preserving critical physical-chemical and spatial information. In independent tests, DeepGPCR_BC surpassed Autodock Vina and Schrödinger Dock with an area under the curve of 0.72, accuracy of 0.68 and true positive rate of 0.73, whereas DeepGPCR_RG demonstrated a Pearson correlation of 0.39 and root mean squared error of 1.34. We applied these models to screen drug candidates for GPR35 (Q9HC97), yielding promising results with three (F545-1970, K297-0698, S948-0241) out of eight candidates. Furthermore, we also successfully obtained six active inhibitors for GLP-1R. Our GPCR-specific models pave the way for efficient and accurate large-scale virtual screening, potentially revolutionizing drug discovery in the GPCR field.

A deep learning based multi-model approach for predicting drug-like chemical compound's toxicity.

Ensuring the safety and efficacy of chemical compounds is crucial in small-molecule drug development. In the later stages of drug development, toxic compounds pose a significant challenge, losing valuable resources and time. Early and accurate prediction of compound toxicity using deep learning models offers a promising solution to mitigate these risks during drug discovery. In this study, we present the development of several deep-learning models aimed at evaluating different types of compound toxicity, including acute toxicity, carcinogenicity, hERG_cardiotoxicity (the human ether-a-go-go related gene caused cardiotoxicity), hepatotoxicity, and mutagenicity. To address the inherent variations in data size, label type, and distribution across different types of toxicity, we employed diverse training strategies. Our first approach involved utilizing a graph convolutional network (GCN) regression model to predict acute toxicity, which achieved notable performance with Pearson R 0.76, 0.74, and 0.65 for intraperitoneal, intravenous, and oral administration routes, respectively. Furthermore, we trained multiple GCN binary classification models, each tailored to a specific type of toxicity. These models exhibited high area under the curve (AUC) scores, with an impressive AUC of 0.69, 0.77, 0.88, and 0.79 for predicting carcinogenicity, hERG_cardiotoxicity, mutagenicity, and hepatotoxicity, respectively. Additionally, we have used the approved drug dataset to determine the appropriate threshold value for the prediction score in model usage. We integrated these models into a virtual screening pipeline to assess their effectiveness in identifying potential low-toxicity drug candidates. Our findings indicate that this deep learning approach has the potential to significantly reduce the cost and risk associated with drug development by expediting the selection of compounds with low toxicity profiles. Therefore, the models developed in this study hold promise as critical tools for early drug candidate screening and selection.

Importance of Inter-residue Contacts for Understanding Protein Folding and Unfolding Rates, Remote Homology, and Drug Design.

Inter-residue interactions in protein structures provide valuable insights into protein folding and stability. Understanding these interactions can be helpful in many crucial applications, including rational design of therapeutic small molecules and biologics, locating functional protein sites, and predicting protein-protein and protein-ligand interactions. The process of developing machine learning models incorporating inter-residue interactions has been improved recently. This review highlights the theoretical models incorporating inter-residue interactions in predicting folding and unfolding rates of proteins. Utilizing contact maps to depict inter-residue interactions aids researchers in developing computer models for detecting remote homologs and interface residues within protein-protein complexes which, in turn, enhances our knowledge of the relationship between sequence and structure of proteins. Further, the application of contact maps derived from inter-residue interactions is highlighted in the field of drug discovery. Overall, this review presents an extensive assessment of the significant models that use inter-residue interactions to investigate folding rates, unfolding rates, remote homology, and drug development, providing potential future advancements in constructing efficient computational models in structural biology.

Hexadecanoic acid analogs as potential CviR-mediated quorum sensing inhibitors in Chromobacterium violaceum: an in silico study.

Chromobacterium violaceum is a Gram-negative, rod-shaped and opportunistic human pathogen. C. violaceum is resistant to various antibiotics due to the production of quorum sensing (QS)-controlled virulence factor and biofilm formation. Hence, we need to find alternative strategies to overcome the antimicrobial resistance and biofilm formation in Gram-negative bacteria. QS is a mechanism in which bacteria's ability to regulate the virulence factors and biofilm formations leads to disease progression. Previously, hexadecanoic acid was identified as a CviR-mediated quorum-sensing inhibitor. In this study, we aimed to discover potential analogs of hexadecanoic acid as a CviR-mediated quorum-sensing inhibitor against C. violaceum by using ADME/T prediction, density functional theory, molecular docking, molecular dynamics and free energy binding calculations. ADME/T properties predicted for analogs were acceptable for human oral absorption and feasibility. The highest occupied molecular orbitals and lowest unoccupied molecular orbitals gap energies predicted and found oleic acid with -0.3748 energies. Docosatrienoic acid exhibited the highest binding affinity -8.15 Kcal/mol and strong and stable interactions with the amino acid residues on the active site of the CviR protein. These compounds on MD simulations for 100 ns show strong hydrogen-bonding interactions with the protein and remain stable inside the active site. Our results suggest hexadecanoic acid analogs could serve as anti-QS and anti-biofilm molecules for treating C. violaceum infections. However, further validation and investigation of these inhibitors against CviR are needed to claim their candidacy for clinical trials.Communicated by Ramaswamy H. Sarma.

Waking Up the Sleep Field: An Overview on the Implications of Genetics and Bioinformatics of Sleep.

Sleep genetics is an intriguing, as yet less understood, understudied, emerging area of biological and medical discipline. A generalist may not be aware of the current status of the field given the variety of journals that have published studies on the genetics of sleep and the circadian clock over the years. For researchers venturing into this fascinating area, this review thus includes fundamental features of circadian rhythm and genetic variables impacting sleep-wake cycles. Sleep/wake pathway medication exposure and susceptibility are influenced by genetic variations, and the responsiveness of sleep-related medicines is influenced by several functional polymorphisms. This review highlights the features of the circadian timing system and then a genetic perspective on wakefulness and sleep, as well as the relationship between sleep genetics and sleep disorders. Neurotransmission genes, as well as circadian and sleep/wake receptors, exhibit functional variability. Experiments on animals and humans have shown that these genetic variants impact clock systems, signaling pathways, nature, amount, duration, type, intensity, quality, and quantity of sleep. In this regard, the overview covers research on sleep genetics, the genomic properties of several popular model species used in sleep studies, homologs of mammalian genes, sleep disorders, and related genes. In addition, the study includes a brief discussion of sleep, narcolepsy, and restless legs syndrome from the viewpoint of a model organism. It is suggested that the understanding of genetic clues on sleep function and sleep disorders may, in future, result in an evidence-based, personalized treatment of sleep disorders.

Carbon nanoparticles fabricated microfilm: A potent filter for microplastics debased water.

Microplastics were found to be the major pollutant across the globe. Plastic microbeads, like 0.5 mm, are very small and mainly used for exfoliation. The marine species cannot distinguish between their usual food and these microbeads. Microbeads have the potential to transfer up the food chain, which may lead to consumption by humans in the end. Activated carbon from inexpensive sources has greatly interested separation systems, especially in water treatment. In that view, carbon nanoparticles were produced, combined with polyvinylidene fluoride (PVDF) polymer, and used as a membrane to trap the microplastic particles. UV-Vis, FTIR, TEM, and powder X-ray diffraction (XRD) analysis confirmed the produced carbon nanoparticles. FT-RAMAN Spectroscopy studies, microbial viable cell count, and turbidity analysis followed the membrane preparation and post-treatment. The carbon nanoparticle fabricated nanofilm effectively eliminates the microbial count and microplastics and reduces the turbidity (0.13 NTU). This study confirms that the membrane effectively filters microplastics and other contaminants. Nowadays, nanofiltration technologies have been considered beneficial for eliminating microplastics to an efficiency of 95%. Further research is needed to determine a feasible low-cost, ecologically suitable, and effective solution to remove the microplastics in water.

DeepBindGCN: Integrating Molecular Vector Representation with Graph Convolutional Neural Networks for Protein-Ligand Interaction Prediction.

The core of large-scale drug virtual screening is to select the binders accurately and efficiently with high affinity from large libraries of small molecules in which non-binders are usually dominant. The binding affinity is significantly influenced by the protein pocket, ligand spatial information, and residue types/atom types. Here, we used the pocket residues or ligand atoms as the nodes and constructed edges with the neighboring information to comprehensively represent the protein pocket or ligand information. Moreover, the model with pre-trained molecular vectors performed better than the one-hot representation. The main advantage of DeepBindGCN is that it is independent of docking conformation, and concisely keeps the spatial information and physical-chemical features. Using TIPE3 and PD-L1 dimer as proof-of-concept examples, we proposed a screening pipeline integrating DeepBindGCN and other methods to identify strong-binding-affinity compounds. It is the first time a non-complex-dependent model has achieved a root mean square error (RMSE) value of 1.4190 and Pearson r value of 0.7584 in the PDBbind v.2016 core set, respectively, thereby showing a comparable prediction power with the state-of-the-art affinity prediction models that rely upon the 3D complex. DeepBindGCN provides a powerful tool to predict the protein-ligand interaction and can be used in many important large-scale virtual screening application scenarios.

Deep Learning-Based Bioactive Therapeutic Peptide Generation and Screening.

Many bioactive peptides demonstrated therapeutic effects over complicated diseases, such as antiviral, antibacterial, anticancer, etc. It is possible to generate a large number of potentially bioactive peptides using deep learning in a manner analogous to the generation of de novo chemical compounds using the acquired bioactive peptides as a training set. Such generative techniques would be significant for drug development since peptides are much easier and cheaper to synthesize than compounds. Despite the limited availability of deep learning-based peptide-generating models, we have built an LSTM model (called LSTM_Pep) to generate de novo peptides and fine-tuned the model to generate de novo peptides with specific prospective therapeutic benefits. Remarkably, the Antimicrobial Peptide Database has been effectively utilized to generate various kinds of potential active de novo peptides. We proposed a pipeline for screening those generated peptides for a given target and used the main protease of SARS-COV-2 as a proof-of-concept. Moreover, we have developed a deep learning-based protein-peptide prediction model (DeepPep) for rapid screening of the generated peptides for the given targets. Together with the generating model, we have demonstrated that iteratively fine-tuning training, generating, and screening peptides for higher-predicted binding affinity peptides can be achieved. Our work sheds light on developing deep learning-based methods and pipelines to effectively generate and obtain bioactive peptides with a specific therapeutic effect and showcases how artificial intelligence can help discover de novo bioactive peptides that can bind to a particular target.

Methanodibenzo[b,f][1,5]dioxocins as Novel Glutaminase Inhibitor with Anti-Glioblastoma Potential.

Glutamine metabolism is an important hallmark of several cancers with demonstrated antitumor activity in glioblastoma cancer cells (GBM). GBM cells regulate glutamine and use it as a major energy source for their proliferation through the glutaminolysis process. Enzymes, such as glutaminase in glutaminolysis, can be targeted by small-molecule inhibitors, thus exhibiting promising anticancer properties. The resistance to glutaminolysis demands the development of new therapeutic molecules to overcome drug resistance. Herein, we have reported a novel library of constrained methanodibenzo[b,f][1,5]dioxocin derivatives as glutaminase (GLS) inhibitors and their anti-GBM potential. The library consisting of seven molecules was obtained through self-condensation of 2'-hydroxyacetophenones, out of which three molecules, namely compounds 3, 5, and 6, were identified with higher binding energy values ranging between -10.2 and -9.8 kcal/mol with GLS (PDB ID; 4O7D). Pharmacological validation of these compounds also showed a higher growth inhibition effect in GBM cells than the standard drug temozolomide (TMZ). The most promising compound, 6, obeyed Lipinski's rule of five and was identified to interact with key residues Arg307, Asp326, Lys328, Lys399, and Glu403 of GLS. This compound exhibited the best cytotoxic effect with IC50 values of 63 µM and 83 µM in LN229 and SNB19 cells, respectively. The potential activation of GLS by the best-constrained dibenzo[b,f][1,5]dioxocin in the tested series increased apoptosis via reactive oxygen species production in both GBM cells, and exhibited anti-migratory and anti-proliferative properties over time in both cell lines. Our results highlight the activation mechanism of a dibenzo[b,f][1,5]dioxocin from the structural basis and demonstrate that inhibition of glutaminolysis may facilitate the pharmacological intervention for GBM treatment.

Harbingers of Hope: Scientists and the Pursuit of World Peace.

The ongoing wars in many regions-such as the conflict between Israel and Hamas-as well as the effects of war on communities, social services, and mental health are covered in this special editorial. This article emphasizes the need for international efforts to promote peace, offer humanitarian aid, and address the mental health challenges faced by individuals and communities affected by war and violence.

DeepBindBC: A practical deep learning method for identifying native-like protein-ligand complexes in virtual screening.

Identifying native-like protein-ligand complexes (PLCs) from an abundance of docking decoys is critical for large-scale virtual drug screening in early-stage drug discovery lead searching efforts. Providing reliable prediction is still a challenge for most current affinity predicting models because of a lack of non-binding data during model training, lost critical physical-chemical features, and difficulties in learning abstract information with limited neural layers. In this work, we proposed a deep learning model, DeepBindBC, for classifying putative ligands as binding or non-binding. Our model incorporates information on non-binding interactions, making it more suitable for real applications. ResNet model architecture and more detailed atom type representation guarantee implicit features can be learned more accurately. Here, we show that DeepBindBC outperforms Autodock Vina, Pafnucy, and DLSCORE for three DUD.E testing sets. Moreover, DeepBindBC identified a novel human pancreatic α-amylase binder validated by a fluorescence spectral experiment (Ka = 1.0 × 105 M). Furthermore, DeepBindBC can be used as a core component of a hybrid virtual screening pipeline that incorporating many other complementary methods, such as DFCNN, Autodock Vina docking, and pocket molecular dynamics simulation. Additionally, an online web server based on the model is available at http://cbblab.siat.ac.cn/DeepBindBC/index.php for the user's convenience. Our model and the web server provide alternative tools in the early steps of drug discovery by providing accurate identification of native-like PLCs.

An Efficient Modern Strategy to Screen Drug Candidates Targeting RdRp of SARS-CoV-2 With Potentially High Selectivity and Specificity.

Desired drug candidates should have both a high potential binding chance and high specificity. Recently, many drug screening strategies have been developed to screen compounds with high possible binding chances or high binding affinity. However, there is still no good solution to detect whether those selected compounds possess high specificity. Here, we developed a reverse DFCNN (Dense Fully Connected Neural Network) and a reverse docking protocol to check a given compound's ability to bind diversified targets and estimate its specificity with homemade formulas. We used the RNA-dependent RNA polymerase (RdRp) target as a proof-of-concept example to identify drug candidates with high selectivity and high specificity. We first used a previously developed hybrid screening method to find drug candidates from an 8888-size compound database. The hybrid screening method takes advantage of the deep learning-based method, traditional molecular docking, molecular dynamics simulation, and binding free energy calculated by metadynamics, which should be powerful in selecting high binding affinity candidates. Also, we integrated the reverse DFCNN and reversed docking against a diversified 102 proteins to the pipeline for assessing the specificity of those selected candidates, and finally got compounds that have both predicted selectivity and specificity. Among the eight selected candidates, Platycodin D and Tubeimoside III were confirmed to effectively inhibit SARS-CoV-2 replication in vitro with EC50 values of 619.5 and 265.5 nM, respectively. Our study discovered that Tubeimoside III could inhibit SARS-CoV-2 replication potently for the first time. Furthermore, the underlying mechanisms of Platycodin D and Tubeimoside III inhibiting SARS-CoV-2 are highly possible by blocking the RdRp cavity according to our screening procedure. In addition, the careful analysis predicted common critical residues involved in the binding with active inhibitors Platycodin D and Tubeimoside III, Azithromycin, and Pralatrexate, which hopefully promote the development of non-covalent binding inhibitors against RdRp.

Generating and screening de novo compounds against given targets using ultrafast deep learning models as core components.

Deep learning is an artificial intelligence technique in which models express geometric transformations over multiple levels. This method has shown great promise in various fields, including drug development. The availability of public structure databases prompted the researchers to use generative artificial intelligence models to narrow down their search of the chemical space, a novel approach to chemogenomics and de novo drug development. In this study, we developed a strategy that combined an accelerated LSTM_Chem (long short-term memory for de novo compounds generation), dense fully convolutional neural network (DFCNN), and docking to generate a large number of de novo small molecular chemical compounds for given targets. To demonstrate its efficacy and applicability, six important targets that account for various human disorders were used as test examples. Moreover, using the M protease as a proof-of-concept example, we find that iteratively training with previously selected candidates can significantly increase the chance of obtaining novel compounds with higher and higher predicted binding affinities. In addition, we also check the potential benefit of obtaining reliable final de novo compounds with the help of MD simulation and metadynamics simulation. The generation of de novo compounds and the discovery of binders against various targets proposed here would be a practical and effective approach. Assessing the efficacy of these top de novo compounds with biochemical studies is promising to promote related drug development.

Identification of oxazolo[4,5-g]quinazolin-2(1H)-one Derivatives as EGFR Inhibitors for Cancer Prevention.

OBJECTIVE: Abnormal expression of EGFR (epidermal growth factor receptor) results in different types of human tumors. Quinazoline-containing derivative signify an attractive platform for EGFR inhibitors. The present study aims to discover the potential binders of a group of compounds belonging to oxazolo[4,5-g]quinazoline-2(1H)-one derivative as EGFR inhibitors. METHODS: We apply multiple computational procedures, including pharmacophore-based virtual screening methods, to perform a preliminary screening against EGFR over compounds belonging to oxazolo[4,5-g]quinazoline-2(1H)-one derivative. Then, we carried a fine screening by molecular dynamics simulations, followed by free energy calculations. RESULTS: The best pharmacophore model created has five characteristics. Three hydrogen bonds acceptors (A) and two aromatic rings (R) make up AAARR (a sequential representation of chemical features of ligands). We have performed pharmacophore-based screening with different databases like Asinex, Chembridge, Lifechemicals, Maybridge, Specs, and Zinc. Top-scoring 30 molecules were considered for performing induced fit docking. Molecular Dynamics Simulations executed for the top five ligands confirmed that throughout the simulation, the protein-ligand complexes remained stable. CONCLUSION: Thus, the results obtained from this research provide insights for the development of oxazolo[4,5-g]quinazoline-2(1H)-one derivative as potent EGFR inhibitors.

E3 ligases: a potential multi-drug target for different types of cancers and neurological disorders.

Ubiquitylation is a posttranslational modification of proteins that is necessary for a variety of cellular processes. E1 ubiquitin activating enzyme, E2 ubiquitin conjugating enzyme, and E3 ubiquitin ligase are all involved in transferring ubiquitin to the target substrate to regulate cellular function. The objective of this review is to provide an overview of different aspects of E3 ubiquitin ligases that can lead to major biological system failure in several deadly diseases. The first part of this review covers the important characteristics of E3 ubiquitin ligases and their classification based on structural domains. Further, the authors provide some online resources that help researchers explore the data relevant to the enzyme. The following section delves into the involvement of E3 ubiquitin ligases in various diseases and biological processes, including different types of cancer and neurological disorders.

Evaluation of anti-cancer effect of zerumbone and cisplatin on N-nitrosodiethylamine induced hepatic cancer in freshwater fish (Danio rerio).

Cancer is the uncontrolled proliferation of abnormal cells in the body. There is a foreseeable need for an effective anti-carcinogenic drug. In this regard, zerumbone (ZER) is identified as one such therapeutic herbal compound that has been shown to enhance the anticancer activity of cisplatin (CIS), with negligible side effects. Yet, the fundamental mechanisms of co-treatment of ZER and CIS on Hepatocellular carcinoma remain indefinable. The current study is endeavored to evaluate the anti-cancer effect of the individual and co-treatment of ZER, CIS and its combination on Diethyl nitrosamine induced hepatic cancer in wild-type zebra fish (Danio Rerio) models. Our careful analysis on treated and untreated fishes shows that CIS + ZER combination group restricted further progression of hepatocellular carcinoma cells significantly, which concludes that co-treatment of ZER with CIS was therapeutically effective for treating human HCC cancer cells which were induced into zebra fish.

Molecular modeling and dynamics studies of the synthetic small molecule agonists with GPR17 and P2Y1 receptor.

The human Guanine Protein coupled membrane Receptor 17 (hGPR17), an orphan receptor that activates uracil nucleotides and cysteinyl leukotrienes is considered as a crucial target for the neurodegenerative diseases. Yet, the detailed molecular interaction of potential synthetic ligands of GPR17 needs to be characterized. Here, we have studied a comparative analysis on the interaction specificity of GPR17-ligands with hGPR17 and human purinergic G protein-coupled receptor (hP2Y1) receptors. Previously, we have simulated the interaction stability of synthetic ligands such as T0510.3657, AC1MLNKK, and MDL29951 with hGPR17 and hP2Y1 receptor in the lipid environment. In the present work, we have comparatively studied the protein-ligand interaction of hGPR17-T0510.3657 and P2Y1-MRS2500. Sequence analysis and structural superimposition of hGPR17 and hP2Y1 receptor revealed the similarities in the structural arrangement with the local backbone root mean square deviation (RMSD) value of 1.16 Å and global backbone RMSD value of 5.30 Å. The comparative receptor-ligand interaction analysis between hGPR17 and hP2Y1 receptor exposed the distinct binding sites in terms of geometrical properties. Further, the molecular docking of T0510.3657 with the hP2Y1 receptor have shown non-specific interaction. The experimental validation also revealed that Gi-coupled activation of GPR17 by specific ligands leads to the adenylyl cyclase inhibition, while there is no inhibition upon hP2Y1 activation. Overall, the above findings suggest that T0510.3657-GPR17 binding specificity could be further explored for the treatment of numerous neuronal diseases. Communicated by Ramaswamy H. Sarma.

CRISPR/Cas-Based Modifications for Therapeutic Applications: A Review.

The CRISPR-Cas genome editing system is an intrinsic property of a bacteria-based immune system. This employs a guide RNA to detect and cleave the PAM-associated target DNA or RNA in subsequent infections, by the invasion of a similar bacteriophage. The discovery of Cas systems has paved the way to overcome the limitations of existing genome editing tools. In this review, we focus on Cas proteins that are available for gene modifications among which Cas9, Cas12a, and Cas13 have been widely used in the areas of medicine, research, and diagnostics. Since CRISPR has been already proven for its potential research applications, the next milestone for CRISPR will be proving its efficacy and safety. In this connection, we systematically review recent advances in exploring multiple variants of Cas proteins and their modifications for therapeutic applications.

Structural basis for the inhibition of SARS-CoV2 main protease by Indian medicinal plant-derived antiviral compounds.

A novel coronavirus (SARS-CoV2) has caused a major outbreak in humans around the globe, and it became a severe threat to human healthcare than all other infectious diseases. Researchers were urged to discover and test various approaches to control and prevent such a deadly disease. Considering the emergency and necessity, we screened reported antiviral compounds present in the traditional Indian medicinal plants for the inhibition of SARS-CoV2 main protease. In this study, we used molecular docking to screen 41 reported antiviral compounds that exist in Indian medicinal plants and shown amentoflavone from the plant Torreyanucifera with a higher docking score. Furthermore, we performed a 40 ns atomic molecular dynamics simulation and free binding energy calculations to explore the stability of the top five protein-ligand complexes. Through the article, we insist that the amentoflavone, hypericin and Torvoside H from the traditional Indian medicinal plants may be used as a potential inhibitor of SARS-CoV2 main protease and further biochemical experiments could shed light on understanding the mechanism of inhibition by these plant-derived antiviral compounds.Communicated by Ramaswamy H. Sarma.