Virtual Screening Methods Research Articles

Peptide-ligand conjugate based immunotherapeutic approach for targeted dismissal of non-structural protein 1 of dengue virus: A novel therapeutic solution for mild and severe dengue infections

Dengue virus infection has significantly increased, with reported cases soaring from 505,430 in 2000 to 2,809,818 in 2022, emphasizing the need for effective treatments. Among the eleven structural and non-structural proteins of DENV, Non-structural protein 1 (NS1) has emerged as a promising target due to its diverse role in modulating the immune response, inducing vascular leakage, and facilitating viral replication and assembly. Monoclonal antibodies are the sole therapeutics to target NS1, but concerns about their cross-reactivity persist. Given these concerns, our study focuses on designing a novel Peptide Ligand Conjugate (PLC) as a potential alternative immunotherapeutic agent against NS1. This PLC aims to mediate the immune elimination of soluble NS1 and NS1-presenting DENV-infected host cells by pre-existing vaccine-induced immunity. By employing the High Throughput Virtual Screening (HTVS) method, QikProp analysis, and Molecular Dynamics studies, we identified three hits from Asinex Biodesigned Ligands out of 220,177 compounds that show strong binding affinity towards the monoclonal binding site of NS1 protein. After a rigorous analysis of physicochemical characteristics, antigenicity, allergenicity, and toxicity using various servers, we selected two peptides: the minimum epitopic region of the Diphtheria and Tetanus toxins as the peptide components of the PLCs. A non-cleavable, non-reactive oxime linker connected the ligand with the peptide through oxime and amide bonds. DPT vaccine is widely used in dengue-endemic countries, and it has been reported that antibodies titer against MER of Diphtheria toxin and Tetanus toxins persist lifelong in DPT-vaccinated people. Therefore, once the rationally designed PLCs bind to NS1 through the ligands, the peptide will induce an immune response against NS1 by triggering pre-existing DPT antibodies and activating memory cells. This orchestrated immune response will destroy soluble NS1 and NS1-expressing DENV-infected cells, thereby reducing the illness of severe dengue hemorrhagic fever and the DENV infection, respectively. Given the increasing demand for new therapeutics for DENV treatment, further investigation into this novel immune-therapeutic strategy may offer a new avenue for treating mild and severe dengue infections.

Discovery of novel Akt1 inhibitors by an ensemble-based virtual screening method, molecular dynamics simulation, and in vitro biological activity testing.

Akt1, as an important member of the Akt family, plays a controlled role in cancer cell growth and survival. Inhibition of Akt1 activity can promote cancer cell apoptosis and inhibit tumor growth. Therefore, in this investigation, a multilayer virtual screening approach, including receptor-ligand interaction-based pharmacophore, 3D-QSAR, molecular docking, and deep learning methods, was utilized to construct a virtual screening platform for Akt1 inhibitors. 17 representative compounds with different scaffolds were identified as potential Akt1 inhibitors from three databases. Among these 17 compounds, the Hit9 exhibited the best inhibitory activity against Akt1 with inhibition rate of 33.08% at concentration of 1μM. The molecular dynamics simulations revealed that Hit9 and Akt1 could form a compact and stable complex. Moreover, Hit9 interacted with some key residues by hydrophobic, electrostatic, and hydrogen bonding interactions and induced substantial conformation changes in the hinge region of the Akt1 active site. The average binding free energies for the Akt1-CQU, Akt1-Ipatasertib, and Akt1-Hit9 systems were - 34.44, - 63.37, and - 39.14kJ mol-1, respectively. In summary, the results obtained in this investigation suggested that Hit9 with novel scaffold may be a promising lead compound for developing new Akt1 inhibitor for treatment of various cancers with Akt1 overexpressed.

Structure-based virtual screening and molecular dynamics studies to explore potential natural inhibitors against 3C protease of foot-and-mouth disease virus.

Foot-and-mouth disease (FMD) is a highly infectious animal disease caused by foot-and-mouth disease virus (FMDV) and primarily infects cloven-hoofed animals such as cattle, sheep, goats, and pigs. It has become a significant health concern in global livestock industries because of diverse serotypes, high mutation rates, and contagious nature. There is no specific antiviral treatment available for FMD. Hence, based on the importance of 3C protease in FMDV viral replication and pathogenesis, we have employed a structure-based virtual screening method by targeting 3C protease with a natural compounds dataset (n = 69,040) from the InterBioScreen database. Virtual screening results identified five potential compounds, STOCK1N-62634, STOCK1N-96109, STOCK1N-94672, STOCK1N-89819, and STOCK1N-80570, with a binding affinity of -9.576 kcal/mol, -8.1 kcal/mol, -7.744 kcal/mol, -7.647 kcal/mol, and - 7.778 kcal/mol, respectively. The compounds were further validated through physiochemical properties and density functional theory (DFT). Subsequently, the comparative 300-ns MD simulation of all five complexes exhibited overall structural stability from various MD analyses such as root mean square deviation (RMSD), root mean square fluctuation (RMSF), radius of gyration (Rg), solvent accessible surface area (SASA), H-bonds, principal component analysis (PCA), and free energy landscape (FEL). Furthermore, MM-PBSA calculation suggests that all five compounds, particularly STOCK1N-62634, STOCK1N-96109, and STOCK1N-94672, can be considered as potential inhibitors because of their strong binding affinity toward 3C protease. Thus, we hope that these identified compounds can be studied extensively to develop natural therapeutics for the better management of FMD.

Optimization of virtual screening against phosphoinositide 3-kinase delta: Integration of common feature pharmacophore and multicomplex-based molecular docking

Extensive research has accumulated which suggests that phosphatidylinositol 3-kinase delta (PI3Kδ) is closely related to the occurrence and development of various human diseases, making PI3Kδ a highly promising drug target. However, PI3Kδ exhibits high homology with other members of the PI3K family, which poses significant challenges to the development of PI3Kδ inhibitors. Therefore, in the present study, a hybrid virtual screening (VS) approach based on a ligand-based pharmacophore model and multicomplex-based molecular docking was developed to find novel PI3Kδ inhibitors. 13 crystal structures of the human PI3Kδ-inhibitor complex were collected to establish models. The inhibitors were extracted from the crystal structures to generate the common feature pharmacophore. The crystallographic protein structures were used to construct a naïve Bayesian classification model that integrates molecular docking based on multiple PI3Kδ conformations. Subsequently, three VS protocols involving sequential or parallel molecular docking and pharmacophore approaches were employed. External predictions demonstrated that the protocol combining molecular docking and pharmacophore resulted in a significant improvement in the enrichment of active PI3Kδ inhibitors. Finally, the optimal VS method was utilized for virtual screening against a large chemical database, and some potential hit compounds were identified. We hope that the developed VS strategy will provide valuable guidance for the discovery of novel PI3Kδ inhibitors.

Improving the sublimation stability of ligustrazine with gallic acid by forming pharmaceutical cocrystal based on the Etter's rules

To improve the high sublimation characteristics of ligustrazine (TMP), an unreported ligustrazine-gallic acid pharmaceutical cocrystal (C7H6O5·2(C8H12N2)·H2O, TMP-GA) was synthesized by using a virtual cocrystal screening method based on the rules of hydrogen bonding formation (Etter's rules). The results of the molecular surface electrostatic potential (MEPS) of TMP-GA showed that the actual hydrogen bonding sites of TMP-GA were the same as the predicted ones, and the calculated ΔE was not much different from the predicted ones. The results of physicochemical properties showed that TMP-GA had better physical stability against TMP sublimation without reducing the solubility and permeability of TMP. Combined with the interaction force analysis of the crystals, the reason may be that compared with the TMP monomer pharmaceutical structure, the intermolecular hydrogen bonding of the TMP-GA pharmaceutical cocrystal is increased, the structure is more stable, and the increase in the energy required for TMP sublimation makes the tendency to sublimate of the TMP-GA significantly lower. This suggests that we can utilize the virtual cocrystal screening method based on Etter's rules to introduce suitable cocrystal formers to reduce the sublimation tendency of active pharmaceutical ingredients.

Considerations Around Structure-Based Drug Discovery for KRAS Using DOCK.

Molecular docking is a popular computational tool in drug discovery. Leveraging structural information, docking software predicts binding poses of small molecules to cavities on the surfaces of proteins. Virtual screening for ligand discovery is a useful application of docking software. In this chapter, using the enigmatic KRAS protein as an example system, we endeavor to teach the reader about best practices for performing molecular docking with UCSF DOCK. We discuss methods for virtual screening and docking molecules on KRAS. We present the following six points to optimize our docking setup for prosecuting a virtual screen: protein structure choice, pocket selection, optimization of the scoring function, modification of sampling spheres and sampling procedures, choosing an appropriate portion of chemical space to dock, and the choice of which top scoring molecules to pick for purchase.

Molecular mechanism of ChaiShi JieDu granule in treating dengue based on network pharmacology and molecular docking: A review

Dengue fever is a frequently occurring infectious disease caused by the Dengue virus, prevalent in tropical and subtropical regions. Chaishi Jiedu Granules (CSJD) is an empirical prescription of the Eighth Affiliated Hospital of Guangzhou Medical University in the treatment of dengue fever, which has been widely used in the treatment of dengue fever, and has shown good efficacy in improving the clinical symptoms of patients. This study aims to explore the molecular mechanism of CSJD in treating dengue fever using network pharmacology, molecular docking techniques, and virtual screening methods. The results showed that luteolin, quercetin and other compounds in CSJD could target important targets related to dengue virus, including STAT3, AKT1, TNF, IL-6, and other key genes, thus playing an antiviral role. Among them, luteolin and wogonin in CSJD also inhibited dengue virus replication and reduced inflammation, and showed good binding force with IL-6 and TNF. Therefore, this study provides an important reference for the development of CSJD as a potential drug for dengue fever treatment and a new perspective for research and development in this field.

Based on virtual screening methods and biological activity assessment, AK-968/13030056 has been identified as a potential mTOR inhibitor

The dysregulation of the PI3K/Akt/mTOR signaling pathway is one of the crucial pathways, leading to cancer development. mTOR (Rapamycin/mammalian target of rapamycin) participates in various intracellular signaling processes, regulating cellular metabolism, proliferation, and apoptosis. Numerous mTOR inhibitors have entered clinical research but are not yet on the market. There is an urgent need to develop a drug discovery strategy to accelerate the process. This study applied a virtual screening strategy to identify an mTOR inhibitor with potential inhibitory activity against mTOR. Firstly, we employed the Vina software to dock active and inactive datasets with two proteins (4L23 and 4JT6). The sensitive protein which is based on the docking results and ROC curve analysis is selected. Then, using the KNIME molecular screening program, virtual screening, cluster analysis, and binding model analysis, the 14 hit compounds are identified. Thirdly, through kinase assay and anti-proliferative activity evaluation, the hit compound AK-968/13030056 was ultimately confirmed. Finally, we conducted molecular docking studies and anti-tumor activity research on this compound. The inhibition rate of this compound on mTOR kinase was 70.62 %, exhibiting the strongest inhibitory activity against MCF-7 cells with an IC50 of 7.49 ± 0.87 μM. Subsequently, the biocompatibility of this compound was confirmed through hemolysis testing. Pharmacological experiments including AO staining, JC-1 assays, cell migration, and quantitative real-time PCR analysis also validated its anti-tumor activity. This study has developed a precise and rapid drug discovery strategy, successfully identifying a novel compound with inhibitory activity against the mTOR protein.

HIt Discovery using docking ENriched by GEnerative Modeling (HIDDEN GEM): A novel computational workflow for accelerated virtual screening of ultra-large chemical libraries.

Recent rapid expansion of make-on-demand, purchasable, chemical libraries comprising dozens of billions or even trillions of molecules has challenged the efficient application of traditional structure-based virtual screening methods that rely on molecular docking. We present a novel computational methodology termed HIDDEN GEM (HIt Discovery using Docking ENriched by GEnerative Modeling) that greatly accelerates virtual screening. This workflow uniquely integrates machine learning, generative chemistry, massive chemical similarity searching and molecular docking of small, selected libraries in the beginning and the end of the workflow. For each target, HIDDEN GEM nominates a small number of top-scoring virtual hits prioritized from ultra-large chemical libraries. We have benchmarked HIDDEN GEM by conducting virtual screening campaigns for 16 diverse protein targets using Enamine REAL Space library comprising 37 billion molecules. We show that HIDDEN GEM yields the highest enrichment factors as compared to state of the art accelerated virtual screening methods, while requiring the least computational resources. HIDDEN GEM can be executed with any docking software and employed by users with limited computational resources.

Targeting the Interactions of Small GTPase ARF with C9ORF72:SMCR8:WDR41 Complexes Implicated in Amyotrophic Lateral Sclerosis/Frontotemporal Dementia

AbstractBackgroundC9ORF72 plays important roles in many cellular processes, including autophagy, membrane trafficking and immune response. The C9ORF72 mutation is the most common cause of familial amyotrophic lateral sclerosis/frontotemporal dementia (ALS/FTD) which is attributed to both gain‐ and loss‐of‐function mechanisms. C9ORF72 forms a complex with SMCR8 and WDR41, which was reported to regulate signaling of small GTPases such as Rab and ARF proteins. Developing small molecules that modulate interfacial C9ORF72:SMCR8:WDR41 (CSW) and small GTPase interactions will aid in understanding their mode of actions and the potential roles of GTP‐dependent signaling in ALS/FTD. Since the C9ORF72 complex has been shown to elicit prominent GTPase activating protein (GAP) activity on ARF, a major regulator of membrane traffic, we aimed to develop novel small molecules that interfere with the binding between the C9ORF72 complex and ARF1.MethodTo identify novel ligands to interfere with the binding between CSW complex and ARF1, we applied in silico virtual screening methods. To this end, the crystal structure of the CSW in complex with ARF1 (PDB: 7MGE) was used for screening of 40,183,714 chemicals available in Mcule database to find hit compound (s).ResultWe defined the binding pocket in the interface of ARF1 and CSW complex including all essential amino acid residues. For filtering the compounds, stringent criteria such as the number of rotatable bonds and the number of heavy atoms were applied. The top 100 hits were identified using this structure‐based virtual screening which show various interactions with the defined binding site of target. Next, the prepared library (the 100 identified Hits) was used as input for virtual screening using Schrodinger software. Flexible docking was performed using Glide program running under LINUX operating system. Based on vina scoring function, 10% of most active compounds that showed the best docking scores were identified and kept for further analysis.ConclusionRational design, computer‐aided, and in silico screening help developing pharmacological modulators to regulate C9ORF72‐ARF signaling and investigate their potential roles in ALS/FTD.Acknowledgement: This study is supported in part by NIH GM146257 on ALS² Initiative and by The Wooten Foundation.

A high-throughput structural dynamics approach for identification of potential agonists of FFAR4 for type 2 diabetes mellitus therapy

Diabetes mellitus is a metabolic disorder that persists as a global threat to the world. A G-protein coupled receptor (GPCR), free fatty acid receptor 4 (FFAR4), has emerged as a potential target for type 2 diabetes mellitus (T2DM) and obesity-related disorders. The current study has investigated the FFAR4, deploying 3-dimensional structure modeling, molecular docking, machine learning, and high-throughput virtual screening methods to unravel the receptor’s crucial and non-crucial binding site residues. We screened four lakh compounds and shortlisted them based on binding energy, stereochemical considerations, non-bonded interactions, and pharmacokinetic profiling. Out of the screened compounds, four compounds were selected for ligand-bound simulations. The molecular dynamic simulations were carried out for 1µs for native FFAR4 and 500 ns each for complexes of FFAR4 with compound 1, compound 2, compound 3, and compound 4. Our findings showed that in addition to reported binding site residues ARG99, ARG183, and VAL98 in known agonists like TUG-891, the amino acids ARG22, ARG24, THR23, TRP305, and GLU43 were also critical binding site residues. These amino acids impart stability to the FFAR4 complexes and contribute to the stronger binding affinity of the compounds. The study also indicated that aromatic residues like PHE211 are crucial for recognizing the active site’s pi-pi and C-C double bonds. Since FFAR4 is a membrane protein, the simulation studies give an insight into the mechanisms of the crucial protein-lipid and lipid-water interactions. The analysis of the molecular dynamics trajectories showed all four compounds as potential hit molecules that can be developed further into potential agonists for T2DM therapy. Amongst the four compounds, compound 4 showed relatively better binding affinity, stronger non-bonded interactions, and a stable complex. Communicated by Ramaswamy H. Sarma

A review on in silico virtual screening methods in COVID-19 using anticancer drugs and other natural/chemical inhibitors.

The present coronavirus disease 2019 (COVID-19) pandemic scenario has posed a difficulty for cancer treatment. Even under ideal conditions, malignancies like small cell lung cancer (SCLC) are challenging to treat because of their fast development and early metastases. The treatment of these patients must not be jeopardized, and they must be protected as much as possible from the continuous spread of the COVID-19 infection. Initially identified in December 2019 in Wuhan, China, the contagious coronavirus illness 2019 (COVID-19) is caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Finding inhibitors against the druggable targets of SARS-CoV-2 has been a significant focus of research efforts across the globe. The primary motivation for using molecular modeling tools against SARS-CoV-2 was to identify candidates for use as therapeutic targets from a pharmacological database. In the published study, scientists used a combination of medication repurposing and virtual drug screening methodologies to target many structures of SARS-CoV-2. This virus plays an essential part in the maturation and replication of other viruses. In addition, the total binding free energy and molecular dynamics (MD) modeling findings showed that the dynamics of various medications and substances were stable; some of them have been tested experimentally against SARS-CoV-2. Different virtual screening (VS) methods have been discussed as potential means by which the evaluated medications that show strong binding to the active site might be repurposed for use against SARS-CoV-2.

Ligand and structure based hierarchical virtual screening cascade for finding novel epidermal growth factor receptor inhibitors.

The epidermal growth factor receptor (EGFR) tyrosine kinase plays an important role in tumor formation and growth by mediating cell growth and other physiological processes. Therefore, EGFR is a promising target for the treatment of cancer. In this work, we combined ligand-based and structure-based virtual screening methods to identify novel EGFR inhibitors from a library of more than 103 thousand compounds. We first obtained hundreds of compounds with similar physiochemical properties through 3D molecular shape and electrostatic similarity screening with potent inhibitors AEE788 and Afatinib as queries. Next, we identified compounds with strong binding affinities to the EGFR pocket through molecular docking, which makes good use of the structure information of the receptor. After molecular scaffold analysis, our bioassay confirmed 13 compounds with EGFR inhibitory activity and three compounds had IC50 values below 1000 nM. In addition, we collected 5371 EGFR inhibitors from online databases, and clustered them into 7 groups by K-means method using their ECFP4 fingerprints as input. Each cluster had typical molecular fragments and corresponding activity characteristics, which could guide the design of EGFR inhibitors, and we concluded that the fragments from some of the hits are indicated in the highly active scaffolds.

Role of Docking in Anticancer Drug Discovery

Abstract: The computational method is widely used in the field of drug design as well as discovery. It aids the drug discovery and design process by making the procedure faster while also ensuring fewer human errors. Cancer is a condition with the development of abnormal cells expressing features like uncontrolled growth and cell division. This leads to abnormal tissue enlargement and interrupts the normal functioning of the tissue. Computational methods, mainly the molecular docking method, have been utilised extensively in the field of anticancer drug discovery. Docking is a virtual screening method that can be performed on a large database of compounds. Molecular docking helps in identifying the predominant binding modes of a ligand with a protein whose three-dimensional structure is known. The docking process can predict the method of inhibition of the target molecule by the ligand molecule. Utilities of molecular docking include structure-activity relationship studies, lead identification by virtual screening, optimization of the identified lead, combinatorial library design and more. This review discusses the process of docking, its role in anticancer drug discovery, and a comparison of different docking software. Docking programs are used to make the docking process much more quick, efficient, and with fewer human errors, as it mostly depends on computational algorithms. A description of some representative studies in anticancer drug discovery related to selected docking software, Autodock, SwissDock, ICM, GOLD and Glide, are also mentioned. This paper concludes by emphasizing the importance of docking programs in the field of drug discovery and how it influences the modern drug discovery processes.

Structure-Based Virtual Screening of Helicobacter pylori SecA Inhibitors.

The human bacterial pathogen Helicobacter pylori causes a range of gastric diseases. The killing rate of Helicobacter pylori is declining year by year because of high antibiotics resistance. It is urgent to develop new target and novel anti- Helicobacter pylori drugs. As an "energy pump" for bacterial cells, SecA is essential for bacterial growth and drives bacterial protein transmembrane transport, moreover SecA is absent in mammals, all of which nominate SecA as an attractive antimicrobial target. Here, we provided a structure-based virtual screening method to screen the 3D-diversity natural-product-like screening library against SecA for novel anti- Helicobacter pylori inhibitors with novel scaffolds. In this study, homology modeling was used to construct the three-dimensional structure of Helicobacter pylori SecA. Two rounds of molecular docking were then used to find new small-molecule inhibitors of SecA, identifying six lead candidates that maintained key interactions with the binding pocket. After that, molecular dynamics simulations were used to explore more accurate ligand-receptor binding modes in states close to natural conditions. Encouragingly, all six compounds were relatively stable during the simulation. Apart from that the binding free energy calculation based on MM/PBSA demonstrated favorable results of < -13.642 kcal/mol. Finally, ADME-T analysis indicated that these compounds were also sufficiently druggable. All six compounds can be well combined with the crystal structure, which further facilitate the development of SecA inhibitors and lead compounds against Helicobacter pylori.

Molecular Docking, MM-GBSA, and Molecular Dynamics Approach: 5-MeO-DMT Analogues as Potential Antidepressants.

Since depression is a common mental illness affecting an estimated 5% of people worldwide, investigators are encouraged to develop effective antidepressants. According to the monoamine-deficiencyhypothesis, the underlying pathophysiology of depression is a deficiency of some neurotransmitters (serotonin, norepinephrine, or dopamine)in the central nervous system. The neurotransmitter serotonin has drawn the most attention concerning depression. As per research, 5-methoxy-N, N-dimethyltryptamine (5-MeO-DMT) elevates inter-synaptic serotonin levels when administered as a single inhalation of vapor from dried toad secretion and leads to higher life satisfaction, convergent thinking, higher ratings of mindfulness, lower ratings of depression, and anxiety. Furthermore, although 5-MeO-DMT lowers stress biomarkers such as cortisol, it is a psychedelic with hallucinogenic effects. In the present study, analogues of 5-MeO-DMT are designed with the hope that they might have better therapeutic activity and lower psychedelic side effects. The current study aimed to look at 5-MeO-DMT analogues as possible antidepressants. We used 70,000 5-MeO-DMT analogues that were sketched using Marvin to conduct a High Throughput Virtual Screening method in hopes of finding potential 5-MeO-DMT analogues against the 5-Hydroxytryptamine 1A receptor (5-HT1AR; 7E2Y.pdb) as an agonist. The prediction of the analogue-protein interaction and the evaluation of the binding affinity is accomplished by employing molecular docking. The Glide XP docking data indicated that a total of 21 compounds had Glide gscores ranging from -11.41 to -6.53 kcal/mol. When compared to the standard 5-MeO-DMT with the binding affinity of -7.75 kcal/mol, 14 compounds showed better binding affinity. Furthermore, Molecular Mechanics -Generalised Born and Surface Area solvation (MM-GBSA) indicated a binding free energy range of -63.55 to -35.37 kcal/mol, and 18 compounds showed better binding free energy than standard 5-MeO-DMT (-41.42 kcal/mol). Through ligand binding interactions with Asp116, Phe361, Phe362, Ser190, Ser199, Val117, Trp358, Ala365, Pro369, Ile189, Tyr195, Ala203, Ile167, Tyr390, Cys120, Trp358, Val364, Ala365, and Leu368, these complexes were stabilized, according to the molecular dynamic simulation of 20453/7E2Y in 100ns.

Integrated virtual screening and MD simulation approaches toward discovering potential inhibitors for targeting BRPF1 bromodomain in hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is one of the most aggressive and life-threatening cancers. Although multiple treatment options are available, the prognosis of HCC patients is poor due to metastasis and drug resistance. Hence, discovering novel targets is essential for better therapeutic development for HCC. In this study, we used the cancer genome atlas (TCGA) dataset to analyze the expression of bromodomain-containing proteins in HCC, as bromodomains are emerging attractive therapeutic targets. Our analysis identified BRPF1 as the most highly upregulated gene in HCC among the 43 bromodomain-containing genes. Upregulation of BRPF1 was significantly associated with poorer patient survival. Therefore, targeting BRPF1 may be an approach for HCC treatment. Previously, several potential inhibitors of BRPF1 bromodomain have been discovered. However, due to the limited clinical success of the current inhibitors, we aim to search for new inhibitors with high affinity and specificity for the BRPF1 bromodomain. In this study, we utilized high-throughput virtual screening methods to screen synthetic and natural compound databases against the BRPF1 bromodomain. In addition, we used machine learning-based QSAR modeling to predict the IC50 values of the selected BRPF1 bromodomain inhibitors. Extensive MD simulations were used to calculate the binding free energies of BRPF1 bromodomain and inhibitor complexes. Using this approach, we identified four lead scaffolds with a similar or better binding affinity towards the BRPF1 bromodomain than the previously reported inhibitors. Overall, this study discovered some promising compounds that have the potential to act as potent BRPF1 bromodomain inhibitors.

Drug-target binding affinity prediction using message passing neural network and self supervised learning

BackgroundDrug-target binding affinity (DTA) prediction is important for the rapid development of drug discovery. Compared to traditional methods, deep learning methods provide a new way for DTA prediction to achieve good performance without much knowledge of the biochemical background. However, there are still room for improvement in DTA prediction: (1) only focusing on the information of the atom leads to an incomplete representation of the molecular graph; (2) the self-supervised learning method could be introduced for protein representation.ResultsIn this paper, a DTA prediction model using the deep learning method is proposed, which uses an undirected-CMPNN for molecular embedding and combines CPCProt and MLM models for protein embedding. An attention mechanism is introduced to discover the important part of the protein sequence. The proposed method is evaluated on the datasets Ki and Davis, and the model outperformed other deep learning methods.ConclusionsThe proposed model improves the performance of the DTA prediction, which provides a novel strategy for deep learning-based virtual screening methods.

Repurposing existing drugs for monkeypox: applications of virtual screening methods

BackgroundMonkeypox is endemic to African region and has become of Global concern recently due to its outbreaks in non-endemic countries. Although, the disease was first recorded in 1970, no monkeypox specific drug or vaccine exists as of now.MethodsWe applied drug repositioning method, testing effectiveness of currently approved drugs against emerging disease, as one of the most affordable approaches for discovering novel treatment measures. Techniques such as virtual ligand-based and structure-based screening were applied to identify potential drug candidates against monkeypox.ResultsWe narrowed down our results to 6 antiviral and 20 anti-tumor drugs that exhibit theoretically higher potency than tecovirimat, the currently approved drug for monkeypox disease.ConclusionsOur results indicated that selected drug compounds displayed strong binding affinity for p37 receptor of monkeypox virus and therefore can potentially be used in future studies to confirm their effectiveness against the disease.

Shape-Based Virtual Screening of a Billion-Compound Library Identifies Mycobacterial Lipoamide Dehydrogenase Inhibitors.

Lpd (lipoamide dehydrogenase) in Mycobacterium tuberculosis (Mtb) is required for virulence and is a genetically validated tuberculosis (TB) target. Numerous screens have been performed over the last decade, yet only two inhibitor series have been identified. Recent advances in large-scale virtual screening methods combined with make-on-demand compound libraries have shown the potential for finding novel hits. In this study, the Enamine REAL library consisting of ∼1.12 billion compounds was efficiently screened using the GPU Shape screen method against Mtb Lpd to find additional chemical matter that would expand on the known sulfonamide inhibitor series. We identified six new inhibitors with IC50 in the range of 5-100 μM. While these compounds remained chemically close to the already known sulfonamide series inhibitors, some diversity was found in the cores of the hits. The two most potent hits were further validated by one-step potency optimization to submicromolar levels. The co-crystal structure of optimized analogue TDI-13537 provided new insights into the potency determinants of the series.