Virtual screening and directional preparation of xanthine oxidase inhibitory peptides derived from hemp seed protein

The traditional nutritional and medical hemp(Cannabis sativa L.)seed protein were explored for the discovery and directional preparation of new xanthine oxidase inhibitory(XOI)peptides by structure-based virtual screening,compound synthesis,in vitro bioassay and proteolysis.Six subtypes of hemp seed edestin and albumin were in silico hydrolyzed by 29 proteases,and 192 encrypted bioactive peptides were screened out.Six peptides showed to be XOI peptides,of which four(about 67%)were released by elastase hydrolysis.The peptide DDNPRRFY displayed the highest XOI activity(IC50=(2.10±0.06)mg/mL),acting as a mixed inhibitor.The pancreatic elastase directionally prepared XOI hemp seed protein hydrolysates,from which 6 high-abundance XOI peptides encrypted 3 virtually-screened ones including the DDNPRRFY.The novel outstanding hemp seed protein-derived XOI peptides and their virtual screening and directed preparation methods provide a promising and applicable approach to conveniently and efficiently explore food-derived bioactive peptides.

Machine Learning Based Virtual Screening for Biodegradable Polyesters

Current biodegradation timelines show that polyesters take over 200 years to break down. A crucial component of several industries, polyesters are relied upon for materials development and thus require sustainable alternatives. Recent works in generative modeling have made it possible to produce large sets of chemical structures, but current molecular screening methods are expensive, not scalable, and are oversimplified. This work evaluates whether a molecule’s biodegradability potential can be accurately predicted by training a model on recent experimental data. Additionally, three chemical descriptors were evaluated on the final molecules for their effects on biodegradability: molecular structure, bond types, and solubility. A Gradient Boosted Machine was trained on a dataset of 600 molecules and their binary labels on biodegradability. The classification model effectively captured the biodegradability property, yielding an Area Under the Receiver Operating Characteristics, AUROC, of 84% and an Area Under the Precision Recall Curve, or AUPRC, of 87%. Additionally, an existing amortized synthetic tree generation model, SynNet, validated each molecule by showing chemical synthesizability and producing simple and interpretable synthesis pathways. This approach of filtering by prediction and chemical rule interpretation is inexpensive, highly scalable and can capture the necessary complexity. Using this method, novel polyester candidates can be polymerized and produced into sustainable fabrics, reducing environmental stress from textile-reliant industries.

Ligand Based Virtual Screening of Molecular Compounds in Drug Discovery Using GCAN Fingerprint and Ensemble Machine Learning Algorithm

The drug development process takes a long time since it requires sorting through a large number of inactive compounds from a large collection of compounds chosen for study and choosing just the most pertinent compounds that can bind to a disease protein.The use of virtual screening in pharmaceutical research is growing in popularity.During the early phases of medication research and development,it is crucial.Chemical compound searches are nowmore narrowly targeted.Because the databases containmore andmore ligands,thismethod needs to be quick and exact.Neural network fingerprints were created more effectively than the well-known Extended Connectivity Fingerprint(ECFP).Only the largest sub-graph is taken into consideration to learn the representation,despite the fact that the conventional graph network generates a better-encoded fingerprint.When using the average or maximum pooling layer,it also contains unrelated data.This article suggested the Graph Convolutional Attention Network(GCAN),a graph neural network with an attention mechanism,to address these problems.Additionally,it makes the nodes or sub-graphs that are used to create the molecular fingerprint more significant.The generated fingerprint is used to classify drugs using ensemble learning.As base classifiers,ensemble stacking is applied to Support Vector Machines(SVM),Random Forest,Nave Bayes,Decision Trees,AdaBoost,and Gradient Boosting.When compared to existing models,the proposed GCAN fingerprint with an ensemble model achieves relatively high accuracy,sensitivity,specificity,and area under the curve.Additionally,it is revealed that our ensemble learning with generated molecular fingerprint yields 91%accuracy,outperforming earlier approaches.

New lead discovery for novel M_1 agonists:pharmacophore model based on DISCO computation and virtual screening

To discover new lead compounds for M1 agonists. Ten typical M1 agonists were superimposed to build a M1 agonists 3D-pharmacophore model using distance-comparisons （DISCO） method without the previous knowledge of the three-dimensional structure of M1 receptor. Virtual screening strategy was used to analyze the Available Chemicals Directory-Screening Compounds （ACD-SC） to identify possible new hits. Twenty-two compounds which fit the pharmacophore model well and are not similar with known M1 agonists were purchased in order to evaluate their M1 receptor agonist activity. One of them shows M1 receptor agonist activity with EC50 of 4.90 μmol/L and maximum response. Multiple of 10.0 which shows it worthy of further study as a new lead compound for M1 agonists.

Machine Learning-Assisted High-Throughput Virtual Screening for On-Demand Customization of Advanced Energetic Materials

Finding energetic materials with tailored properties is always a significant challenge due to low research efficiency in trial and error.Herein,a methodology combining domain knowledge,a machine learning algorithm,and experiments is presented for accelerating the discovery of novel energetic materials.A high-throughput virtual screening(HTVS)system integrating on-demand molecular generation and machine learning models covering the prediction of molecular properties and crystal packing mode scoring is established.With the proposed HTVS system,candidate molecules with promising properties and a desirable crystal packing mode are rapidly targeted from the generated molecular space containing 25112 molecules.Furthermore,a study of the crystal structure and properties shows that the good comprehensive performances of the target molecule are in agreement with the predicted results,thus verifying the effectiveness of the proposed methodology.This work demonstrates a new research paradigm for discovering novel energetic materials and can be extended to other organic materials without manifest obstacles.

A Novel Angiotensin I Converting Enzyme Inhibitory Peptide from the Milk Casein:Virtual Screening and Docking Studies

Angiotensin I converting enzyme （ACE） plays an important physiological role in the regulation of hypertension. In this study, we applied virtual screening to discover a novel angiotensin I converting enzyme inhibitory peptides from milk casein. One potential hit was identified based on docking scores, subsequently confirmed by activity studies in vitro （IC50=20.85 μmol L-1）. The proposed peptide in this study contains a unique sequence, Lys-Val-Leu-Ile-Leu-Ala. Moreover, we performed the docking studies to understand the binding mode between the enzyme and peptide hit.

Virtual Screening of Human O-GIcNAc Transferase Inhibitors

O-GlcNAc transferase （OGT） is one of essential mammalian enzymes, which catalyze the transfer of N-acetylglucosamine from UDP-N-acetylglucosamine （UDP-GlcNAc） to hydroxyl groups of serines and threonines （Ser/Thr） in proteins. Dysregulations of cellular O-GlcNAc have been implicated in diabetes, neurodegenerative disease, and cancer, which brings great interest in developing potent and specific small-molecular OGT inhibitors. In this work, we performed virtual screening on OGT catalytic site to identify potential inhibitors. 7134792 drug-like compounds from ZINC （a free database of commercially available compounds for virtual screening） and 4287550 compounds generated by FOG （fragment optimized growth program） were screened and the top 116 compounds ranked by docking score were analyzed. By comparing the screening results, we found FOG program can generate more compounds with better docking scores than ZINC. The top ZINC compounds ranked by docking score were grouped into two classes, which held the binding positions of UDP and GlcNAc of UDP- GlcNAc. Combined with individual fragments in binding pocket, de novo compounds were designed and proved to have better docking score. The screened and designed compounds may become a starting point for developing new drugs.

Discovery of a Novel 5-HT_(2A) Inhibitor by Pharmacophore-based Virtual Screening

The serotonin 2A（5-HT2A） receptor has been implicated in several neurological conditions and potent 5-HT2A antagonists have therapeutic effects in the treatment of schizo phrenia and depression.In this study,a potent novel 5-HT2A inhibitor 05245768 with a Ki value of （593.89±34.10） nmol/L was discovered by integrating a set of computational approaches and experiments（protein structure prediction,pharmacophore-based virtual screening,automated molecular docking and pharmacological bioassay）.The 5-HT2A receptor showed a negatively charged bin-ding pocket.The binding mode of compound 05245768 with 5-HT2A was obtained by GOLD docking procedure,which revealed the conserved interaction between protonated nitrogen in compound 05245768 and carboxylate group of D3.32 at the active site of 5-HT2A.

Structural Dynamics of Amyloid β Peptide Binding to Acetylcholine Receptor and Virtual Screening for Effective Inhibitors

The interaction between Amyloid β(Aβ) peptide and acetylcholine receptor is the key for our understanding of how Aβ fragments block the ion channels within the synapses and thus induce Alzheimer’s disease.Here,molecular docking and molecular dynamics(MD)simulations were performed for the structural dynamics of the docking complex consisting of Aβ and α7-n ACh R(α7 nicotinic acetylcholine receptor),and the inter-molecular interactions between ligand and receptor were revealed.The results show that Aβ_(25-35) is bound toα7-n ACh R through hydrogen bonds and complementary shape,and the Aβ_(25-35) fragments would easily assemble in the ion channel of α7-n ACh R,then block the ion transfer process and induce neuronal apoptosis.The simulated amide-I band of Aβ_(25-35) in the complex is located at 1650.5 cm^(-1),indicating the backbone of Aβ_(25-35) tends to present random coil conformation,which is consistent with the result obtained from cluster analysis.Currently existing drugs were used as templates for virtual screening,eight new drugs were designed and semi-flexible docking was performed for their performance.The results show that,the interactions between new drugs and α7-n ACh R are strong enough to inhibit the aggregation of Aβ_(25-35) fragments in the ion channel,and also be of great potential in the treatment of Alzheimer’s disease.

Protein Flexibility and Multiple Docking in Ligand Docking and Virtual Screening to the BRAF(TypeⅠ1/2)Inhibitors

BRAF has been recognized as a promising target for cancer therapy. A number of crystal structures have been published. Molecular docking is one of the most effective techniques in the field of computer-aided drug design（CADD）. Appropriate protein conformation and docking method are essential for the successful virtual screening experiments. One approach considering protein flexibility and multiple docking methods was proposed in this study. Six DFG-in/αC-helix-out crystal structures of BRAF, three docking programs（Glide, GOLD and Ligand Fit） and 12 scoring functions were applied for the best combination by judging from the results of pose prediction and retrospective virtual screening（VS）. The most accurate results（mean RMSD of about 0.6 A） of pose prediction were obtained with two complex structures（PDB： 3 C4 C and 3 SKC） using Glide SP. From the retrospective VS, the most active compounds were identified by using the complex structure of 3 SKC, indicated by a ROC/AUC score of 0.998 and an EF of 20.6 at 5% of the database screen with Glide-SP. On the whole, PDB 3 SKC could achieve a higher rate of correct reproduction, a better enrichment and more diverse compounds. A comparison of 3 SKC and the other X-ray crystal structures led to a rationale for the docking results. PDB 3 SKC could achieve a broad range of sulfonamide substitutions through an expanded hydrophobic pocket formed by a further shift of the αC-helix. Our study emphasized the necessity and significance of protein flexibility and scoring functions in both ligand docking and virtual screening.

Pharmacophore Based Virtual Screening for Identification of Novel CDK₂Inhibitors

CDK2 is one of the most important members of Cyclin-dependent kinases. It is a critical modulator of various oncogenic signaling pathways, and its activity is vital for loss of proliferative control during oncogenesis. This work has focused on developing a pharmacophore model for CDK2 inhibitors by using a dataset of known inhibitors as a pre-filter throughout the virtual screening and docking process. Consequently, the best pharmacophore model was made of one hydrogen bond acceptor, and two aromatic ring features with a high correlation value of 0.906. The validation findings proved out that the selected model can be used as a filter to screen new molecules like Enamine kinase hinge region directed library against CDK2. As a result, 69 hits were subjected to molecular docking studies. Eventually, three compounds (5909, 701 and 8397) scored good interaction energy values and strong molecular interactions. Hence, they were identified as leads for novel CDK2 inhibitors as anticancer drugs.

Virtual Screening of Inhibitors for Chitosanases EAG1

Chitosanases EAG1 is a classical glycoside hydrolase from Bacillus ehimensis. The previous researches showed that this Chitosanases can not only hydrolyze the b1,4-glycosidic bonds of chitosan to COS in different sizes but also keep a high catalytic activity in organic, which was useful for producing chitooligosaccharides and GlcN for use in the food and pharmacological industries. While it is instable in the liquid state. This shortcoming seriously restricts its industrial application. Here we used the modeled structure of EAG1 and the molecular modeling software package to screen the free chemical database ZINC. Moreover, the strategies including “initial filter” and consensus scoring were applied to accelerate the process and improve the success rate of virtual screening. Finally, five compounds were screened and they were purchased or synthetized to test their binding affinity against EAG1. The test results showed that one of them could inhibit the enzyme with an apparent Ki of 1.5 μM. The result may take the foundation for further inhibitor screening and design against EAG1 and the screened compound may also help to improve the liquid stability of EAG1 and expand its industrial application.

Identification of Novel CDK9 Inhibitors with Better Inhibitory Activity and Higher Selectivity for Cancer Treatment by an Effective Two-Stage Virtual Screening Strategy

The aberrant overexpression of cyclin-dependent kinase 9 (CDK9) in cancer cells results in the loss of proliferative control, making it an attractive therapeutic target for various cancers. However, the highly structural similarity between CDK9 and CDK2 makes the development of novel selective CDK9 inhibitors a challenging task and thus limits their clinical applications. Here, an effective two-stage virtual screening strategy was developed to identify novel CDK9 inhibitors with better inhibitory activity and higher selectivity. The first screening stage aims to select potential compounds with better inhibitory activity than Roniciclib, one of the most effective CDK9 inhibitors, through reliable structure-based pharmacophoric virtual screening and accurate molecular docking analyses. The second stage employs a very detailed visual inspection process, in which several structural criteria describing the major difference between the binding pockets of CDK9 and CDK2 are taken into consideration, to identify compounds with higher selectivity than CAN508, one of the CDK9 inhibitors with distinguished selectivity. Finally, three compounds (NCI207113 from NCI database and TCM0004 and TCM3282 from TCM database) with better inhibitory activity and higher selectivity were successfully identified as novel CDK9 inhibitors. These three compounds also display excellent binding stabilities, great pharmacokinetic properties and low toxicity in MD simulations and ADMET predictions. Besides, the results of binding free energy calculations suggest that enhancing van der Waals interaction and nonpolar solvation energy and/or reducing polar solvation energy can significantly improve the binding affinity of these CDK9 inhibitors. Their clinical potentials to serve as anticancer drug candidates can be further evaluated through a series of in vitro/in vivo bioassays in the future. To the best of our knowledge, this is the first attempt to identify novel CDK9 inhibitors with both better inhibitory activity and higher selectivity through an effective two-stage virtual screening strategy.

Virtual Screening and Molecular Docking for Arylalkylamine-N-Acetyltransferase(aaNAT)Inhibitors,a Key Enzyme of Aedes(Stegomyia)aegypti(L.)Metabolism

Background: Dengue is a Neglected tropical disease (NTDs) with high incidence in Brazil. This disease is caused by Dengue virus and is transmitted by Aedes aegypti mosquito. The search for new approaches for controlling of this disease is the subject of numerous studies. The aaNAT is a key enzyme in the metabolism of A. aegypti and is crucial in the sclerotization process, as well as regulation of circadian rhythm and inactivation of neurotransmitters. Computational techniques applied to studies of biological systems become an effective weapon in the mapping and management of 3D data structures, giving direction and guidance of potential ligands that can form stable complexes with targets of interest, using a Molecular Docking approach. The present study was conducted by a virtual screening, followed by docking calculations, in order to find molecules that could inhibit aaNAT. In this study, we used available compounds in SAM database (Bioinformatics and Medicinal Chemistry Laboratory—Southwest Bahia State University, Jequié-Bahia, Brazil), PubChem and ZINC. Results: The result of dockings with selected ligands showed good energy affinities, presenting potential inhibitory interactions with the enzyme active site. Conclusions: The Coa-S-acetyl-tryptamine and 3-indoleacriloil-coenzyme-A showed the same binding energies -8.9 Kcal/Mol and were described as possible inhibitors of aaNAT.

A review of deep learningg methods for ligand based drug virtual screening

Drug discovery is costly and time consuming,and modern drug discovery endeavors are progressively reliant on computational methodologies,aiming to mitigate temporal and financial expenditures associated with the process.In particular,the time required for vaccine and drug discovery is prolonged during emergency situations such as the coronavirus 2019 pandemic.Recently,the performance of deep learning methods in drug virtual screening has been particularly prominent.It has become a concern for researchers how to summarize the existing deep learning in drug virtual screening,select different models for different drug screening problems,exploit the advantages of deep learning models,and further improve the capability of deep learning in drug virtual screening.This review first introduces the basic concepts of drug virtual screening,common datasets,and data representation methods.Then,large numbers of common deep learning methods for drug virtual screening are compared and analyzed.In addition,a dataset of different sizes is constructed independently to evaluate the performance of each deep learning model for the difficult problem of large-scale ligand virtual screening.Finally,the existing challenges and future directions in the field of virtual screening are presented.

Comparison and discovery of potential non-covalent CD38 inhibitors by virtual screening strategy based on natural substrates and known inhibitors

As a type II or III transmembrane glycoprotein, human CD38 is ubiquitously expressed in all mammalian tissues. CD38 is a multi-functional enzyme and a member of the ADP-ribosyl cyclase family, and it catalyzes nicotinamide adenine dinucleotide （NAD^＋） and nicotinamide adenine dinucleotide phosphate （NADP＋） to two distinct Ca^2＋ messengers as follows： cyclic ADP-ribose （cADPR） and nicotinic acid adenine dinucleotide phosphate （NAADP）, respectively. Moreover, both cADPR and NAADP mediate mobilization of intracellular Ca^2＋ targeting endoplasmic stores and the lysosomes, respectively. In this study, we combined ligand-based and structure-based virtual screening strategies to compare the inhibitor discovery efficacy based on natural substrates and the known inhibitors. The similarity queries towards SPECS database were carried out using ROCS and EON modules of OpenEye software. The hits were further docked to CD38 using AutoDock 4.05 program. In addition, ADME studies were also processed considering solubility in water and membrane permeability. Finally, we identified 17 compotmds-based on natural substrates and 10 compounds based on known inhibitor models. The results showed that the known inhibitor H2-based model was more efficient in virtual screening of CD38 non-covalent inhibitors.

Discovery of Novel Androgen Receptor Ligands by Structure-based Virtual Screening and Bioassays

Androgen receptor(AR) is a ligand-activated transcription factor that plays a pivotal role in the development and progression of many severe diseases such as prostate cancer, muscle atrophy, and osteoporosis. Binding of ligands to AR triggers the conformational changes in AR that may affect the recruitment of coactivators and downstream response of AR signaling pathway.Therefore, AR ligands have great potential to treat these diseases. In this study, we searched for novel AR ligands by performing a docking-based virtual screening(VS) on the basis of the crystal structure of the AR ligand binding domain(LBD) in complex with its agonist. A total of 58 structurally diverse compounds were selected and subjected to LBD affinity assay, with five of them(HBP1-3, HBP1-17, HBP1-38, HBP1-51, and HBP1-58) exhibiting strong binding to AR-LBD. The IC50 values of HBP1-51 and HBP1-58 are 3.96 m M and 4.92 m M, respectively, which are even lower than that of enzalutamide(Enz, IC50= 13.87 m M), a marketed second-generation AR antagonist. Further bioactivity assays suggest that HBP1-51 is an AR agonist, whereas HBP1-58 is an AR antagonist. In addition, molecular dynamics(MD) simulations and principal components analysis(PCA) were carried out to reveal the binding principle of the newlyidentified AR ligands toward AR. Our modeling results indicate that the conformational changes of helix 12 induced by the bindings of antagonist and agonist are visibly different. In summary,the current study provides a highly efficient way to discover novel AR ligands, which could serve as the starting point for development of new therapeutics for AR-related diseases.

Pharmacophore modeling and virtual screening for the discovery of new fatty acid amide hydrolase inhibitors

A predictive pharmacophore model has been generated from a series of diverse fatty acid amide hydrolase(FAAH)inhibitors and the optimal pharmacophore model applied in virtual screening.The pharmacophore model was based on a training set of 21 compounds carefully selected from the published literatures.The optimal model Hypo-1 included four features(two hydrogen-bond acceptor units,one aromatic hydrophobic unit and one aromatic ring unit)and two excluded volumes.Cross-validation of the model confirmed that Hypo-1 was not generated by chance correlation.A large test set of 55 compounds showed that Hypo-1 performed well in classifying highly active and less active FAAH inhibitors.Superimposition analysis of the FAAH X-ray crystal structure and the pharmacophore Hypo-1 further validated the adequacy of the model.Virtual screening generated a total of 976 hits from the Zinc Natural Products database,a hit rate of 1.04%and enrichment of 83.89.The acceptable hit rate further supports the use of Hypo-1 as a 3D query tool for virtual screening.

Design, synthesis and biological evaluation of novel histone deacetylase inhibitors based on virtual screening

Ligand-and structure-based virtual screening methods were employed to identify novel non-hydroxamate histone deacetylase(HDAC)inhibitors.Based on the newly identified hit compound 17a,three series of compounds were synthesized and evaluated for both HDAC1 inhibitory activity and cytotoxicity.Binding modes of representative structures were analyzed using the docking method to explain the observed disparity in HDAC1 inhibitory activities.

Inhibitors of HIV-1 Integrase-Human LEDGF/p75 Interaction Identified from Natural Products via Virtual Screening

HIV-1 integrase （IN）-mediated integration of viral DNA into the host chromosome is an essential step in the virus life cycle. Human lens epithelium-derived growth factor （LEDGF/p75） has been found to function as a cellu- lar cofactor in this process. The LEDGF/p75-1N interaction hence represents an attractive target for anti-HIV ther- apy. In this study, natural products were virtually screened against the LEDGF/p75 binding pocket of HIV-1 IN. 24 compounds were selected and obtained from the National Compound Resource Center of China. AlphaScreen as- says characterized 8 of these 24 natural products as potent LEDGF/p75-IN interaction inhibitors. The active com- pounds whose ICs0 values ranged from 0.56 to 14.55 ~mol/L could be used as lead compounds for further investi- gation. This work confirmed that natural products are valuable resources for antiviral drug discovery.