Integrating UPLC-MS/MS with in Silico and in Vitro Screening Accelerates the Discovery of Active Compounds in Stephania epigaea.

Traditional Chinese medicines (TCMs) are popular in clinic because of their safety and efficacy. They contain abundant natural active compounds, which are important sources of new drug discovery. However, how to efficiently identify active compounds from complex ingredients remains a challenge. In this study, a method combining UHPLC-MS/MS characterization and in silico screening was developed to discover compounds with dopamine D2 receptor (D2R) activity in Stephania epigaea (S. epigaea). By combining the compounds identified in S. epigaea by UHPLC-MS/MS with reported compounds, a virtual library of 80 compounds was constructed for in silico screening. Potentially active compounds were chosen based on screening scores and subsequently tested for in vitro activity on a transfected cell line CHO-K1-D2 model using label-free cellular phenotypic assay. Three D2R agonists and five D2R antagonists were identified. (-)-Asimilobine, N-nornuciferine and (-)-roemerine were reported for the first time as D2R agonists, with EC50 values of 0.35 ± 0.04 µM, 1.37 ± 0.10 µM and 0.82 ± 0.22 µM, respectively. Their target specificity was validated by desensitization and antagonism assay. (-)-Isocorypalmine, (-)-tetrahydropalmatine, (-)-discretine, (+)-corydaline and (-)-roemeroline showed strong antagonistic activity on D2R with IC50 values of 92 ± 9.9 nM, 1.73 ± 0.13 µM, 0.34 ± 0.02 µM, 2.09 ± 0.22 µM and 0.85 ± 0.08 µM, respectively. Their kinetic binding profiles were characterized using co-stimulation assay and they were both D2R competitive antagonists. We docked these ligands with human D2R crystal structure and analyzed the structure-activity relationship of aporphine-type D2R agonists and protoberberine-type D2R antagonists. These results would help to elucidate the mechanism of action of S. epigaea for its analgesic and sedative efficacy and benefit for D2R drug design. This study demonstrated the potential of integrating UHPLC-MS/MS with in silico and in vitro screening for accelerating the discovery of active compounds from TCMs.

Investigating the effects of low-salt processing on the umami peptides of dry-cured ham using peptidomics techniques.

This study investigated the effect of low-salt processing on the umami peptide profile of dry-cured hams. Peptidomics data showed 633 umami peptides in the low- and full-salt groups. Among them, 36.2% and 26.5% of shared umami peptides in the low-salt group were significantly down- and up-regulated in relative abundance. Multivariate statistical analysis showed 1011 significantly different umami peptides (SDUPs) in the low- and full-salt groups. Creatine kinase M-type (CKM) and fast skeletal muscle troponin T (TnTf) were the main precursor proteins of these SDUPs. At the end of processing, the relative expression of CKM was lower in the low-salt group than in the full-salt group (P < 0.05), but there was no significant difference in TnTf. More dipeptidyl peptidase cleavage sites were observed in CKM and TnTf proteins in the low-salt group.

Fast, accurate ranking of engineered proteins by target-binding propensity using structure modeling.

Deep-learning-based methods for protein structure prediction have achieved unprecedented accuracy, yet their utility in the engineering of protein-based binders remains constrained due to a gap between the ability to predict the structures of candidate proteins and the ability toprioritize proteins by their potential to bind to a target. To bridge this gap, we introduce Automated Pairwise Peptide-Receptor Analysis for Screening Engineered proteins (APPRAISE), a method for predicting the target-binding propensity of engineered proteins. After generating structural models of engineered proteins competing for binding to a target using an established structure prediction tool such as AlphaFold-Multimer or ESMFold, APPRAISE performs a rapid (under 1 CPU second per model) scoring analysis that takes into account biophysical and geometrical constraints. As proof-of-concept cases, we demonstrate that APPRAISE can accurately classify receptor-dependent vs. receptor-independent adeno-associated viral vectors and diverse classes of engineered proteins such as miniproteins targeting the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike, nanobodies targeting a G-protein-coupled receptor, and peptides that specifically bind to transferrin receptor or programmed death-ligand 1 (PD-L1). APPRAISE is accessible through a web-based notebook interface using Google Colaboratory (https://tiny.cc/APPRAISE). With its accuracy, interpretability, and generalizability, APPRAISE promises to expand the utility of protein structure prediction and accelerate protein engineering for biomedical applications.

Iterative In Silico Screening for Optimizing Stable Conformation of Anti-SARS-CoV-2 Nanobodies.

Nanobodies (Nbs or VHHs) are single-domain antibodies (sdAbs) derived from camelid heavy-chain antibodies. Nbs have special and unique characteristics, such as small size, good tissue penetration, and cost-effective production, making Nbs a good candidate for the diagnosis and treatment of viruses and other pathologies. Identifying effective Nbs against COVID-19 would help us control this dangerous virus or other unknown variants in the future. Herein, we introduce an in silico screening strategy for optimizing stable conformation of anti-SARS-CoV-2 Nbs. Firstly, various complexes containing nanobodies were downloaded from the RCSB database, which were identified from immunized llamas. The primary docking between Nbs and the SARS-CoV-2 spike protein receptor-binding domain was performed through the ClusPro program, with the manual screening leaving the reasonable conformation to the next step. Then, the binding distances of atoms between the antigen-antibody interfaces were measured through the NeighborSearch algorithm. Finally, filtered nanobodies were acquired according to HADDOCK scores through HADDOCK docking the COVID-19 spike protein with nanobodies under restrictions of calculated molecular distance between active residues and antigenic epitopes less than 4.5 Å. In this way, those nanobodies with more reasonable conformation and stronger neutralizing efficacy were acquired. To validate the efficacy ranking of the nanobodies we obtained, we calculated the binding affinities (∆G) and dissociation constants (Kd) of all screened nanobodies using the PRODIGY web tool and predicted the stability changes induced by all possible point mutations in nanobodies using the MAESTROWeb server. Furthermore, we examined the performance of the relationship between nanobodies' ranking and their number of mutation-sensitive sites (Spearman correlation > 0.68); the results revealed a robust correlation, indicating that the superior nanobodies identified through our screening process exhibited fewer mutation hotspots and higher stability. This correlation analysis demonstrates the validity of our screening criteria, underscoring the suitability of these nanobodies for future development and practical implementation. In conclusion, this three-step screening strategy iteratively in silico greatly improved the accuracy of screening desired nanobodies compared to using only ClusPro docking or default HADDOCK docking settings. It provides new ideas for the screening of novel antibodies and computer-aided screening methods.

Data on molecular docking and molecular dynamics targeting Mycobacterium tuberculosis shikimic acid kinase.

We have previously performed a hierarchical in silico screening of a Mycobacterium tuberculosis shikimic acid kinase [1]. Specifically, 11 compounds were screened from a library of 154,118 compounds provided by ChemBridge [2] using UCSF DOCK [3] and the GOLD [4] program in the first and second steps, respectively. Molecular dynamic simulations were further performed on compound 2 (2-[(5Z)-5-(1-benzyl-5bromo-2-oxoindol-3-(5Z)-5-(1-benzyl-5-bromo-2-oxoindol-3-(5Z)-4-oxo-2 ylidene)-4oxo-2-sulfanylidene-1,3-thiazolidin-3-yl] acetic acid), which showed antimicrobial efficacy. These processes yielded ligand docking scores and trajectories. In this data article, we have added solvent-accessible surface area and PCA analyses, which were calculated from the raw docking scores and trajectories. Data obtained from molecular docking and molecular dynamic simulations are useful in two ways: (1) Further support for previous work (2) Provides a stepping stone for experimental scientists to conduct in silico studies and research ideas for other drug discovery researchers and computational biologists. We believe that this article will provide an opportunity to develop new Mycobacterium tuberculosis therapeutics through searching for analogs and inhibitors against new targets.

Ligand-based virtual-screening identified a novel CFTR ligand which improves the defective cell surface expression of misfolded ABC transporters.

Cystic fibrosis (CF) is a monogenetic disease caused by the mutation of CFTR, a cAMP-regulated Cl- channel expressing at the apical plasma membrane (PM) of epithelia. ∆F508-CFTR, the most common mutant in CF, fails to reach the PM due to its misfolding and premature degradation at the endoplasmic reticulum (ER). Recently, CFTR modulators have been developed to correct CFTR abnormalities, with some being used as therapeutic agents for CF treatment. One notable example is Trikafta, a triple combination of CFTR modulators (TEZ/ELX/IVA), which significantly enhances the functionality of ΔF508-CFTR on the PM. However, there's room for improvement in its therapeutic effectiveness since TEZ/ELX/IVA doesn't fully stabilize ΔF508-CFTR on the PM. To discover new CFTR modulators, we conducted a virtual screening of approximately 4.3 million compounds based on the chemical structures of existing CFTR modulators. This effort led us to identify a novel CFTR ligand named FR3. Unlike clinically available CFTR modulators, FR3 appears to operate through a distinct mechanism of action. FR3 enhances the functional expression of ΔF508-CFTR on the apical PM in airway epithelial cell lines by stabilizing NBD1. Notably, FR3 counteracted the degradation of mature ΔF508-CFTR, which still occurs despite the presence of TEZ/ELX/IVA. Furthermore, FR3 corrected the defective PM expression of a misfolded ABCB1 mutant. Therefore, FR3 may be a potential lead compound for addressing diseases resulting from the misfolding of ABC transporters.

In Silico Screening and Experimental Verification of Near-Infrared-Emissive Two-Boron-Doped Polycyclic Aromatic Hydrocarbons.

Embedding two boron atoms into a polycyclic aromatic hydrocarbon (PAH) leads to the formation of a neutral analogue that is isoelectronic to the corresponding dicationic PAH skeleton, which can significantly alter its electronic structure. Based on this concept, we explore herein the identification of near-infrared (NIR)-emissive PAHs with the aid of an in silico screening method. Using perylene as the PAH scaffold, we embedded two boron atoms and fused two thiophene rings to it. Based on this design concept, all possible structures (ca. 2500 entities) were generated using a comprehensive structure generator. Time-dependent DFT calculations were conducted on all these structures, and promising candidates were extracted based on the vertical excitation energy, transition dipole moment, and atomization energy per bond. One of the extracted dithieno-diboraperylene candidates was synthesized and indeed exhibited emission at 724 nm with a quantum yield of 0.40 in toluene, demonstrating the validity of this screening method. This modification was further applied to other PAHs, and a series of thienobora-modified PAHs was synthesized.

Identification of two novel chemical classes of Autotaxin (ATX) inhibitors using Enalos Asclepios KNIME nodes.

Autotaxin is a secreted lysophospholipase D which is a member of the ectonucleotide pyrophosphatase/phosphodiesterase family converting extracellular lysophosphatidylcholine and other non-choline lysophospholipids, such as lysophosphatidylethanolamine and lysophosphatidylserine, to the lipid mediator lysophosphatidic acid. Autotaxin is implicated in various fibroproliferative diseases including interstitial lung diseases, such as idiopathic pulmonary fibrosis and hepatic fibrosis, as well as in cancer. In this study, we present an effort of identifying ATX inhibitors that bind to allosteric ATX binding sites using the Enalos Asclepios KNIME Node. All the available PDB crystal structures of ATX were collected, prepared, and aligned. Visual examination of these structures led to the identification of four crystal structures of human ATX co-crystallized with four known inhibitors. These inhibitors bind to five binding sites with five different binding modes. These five binding sites were thereafter used to virtually screen a compound library of 14,000 compounds to identify molecules that bind to allosteric sites. Based on the binding mode and interactions, the docking score, and the frequency that a compound comes up as a top-ranked among the five binding sites, 24 compounds were selected for in vitro testing. Finally, two compounds emerged with inhibitory activity against ATX in the low micromolar range, while their mode of inhibition and binding pattern were also studied. The two derivatives identified herein can serve as "hits" towards developing novel classes of ATX allosteric inhibitors.

Absorption Distribution Metabolism Excretion and Toxicity Property Prediction Utilizing a Pre-Trained Natural Language Processing Model and Its Applications in Early-Stage Drug Development.

Machine learning techniques are extensively employed in drug discovery, with a significant focus on developing QSAR models that interpret the structural information of potential drugs. In this study, the pre-trained natural language processing (NLP) model, ChemBERTa, was utilized in the drug discovery process. We proposed and evaluated four core model architectures as follows: deep neural network (DNN), encoder, concatenation (concat), and pipe. The DNN model processes physicochemical properties as input, while the encoder model leverages the simplified molecular input line entry system (SMILES) along with NLP techniques. The latter two models, concat and pipe, incorporate both SMILES and physicochemical properties, operating in parallel and with sequential manners, respectively. We collected 5238 entries from DrugBank, including their physicochemical properties and absorption, distribution, metabolism, excretion, and toxicity (ADMET) features. The models' performance was assessed by the area under the receiver operating characteristic curve (AUROC), with the DNN, encoder, concat, and pipe models achieved 62.4%, 76.0%, 74.9%, and 68.2%, respectively. In a separate test with 84 experimental microsomal stability datasets, the AUROC scores for external data were 78% for DNN, 44% for the encoder, and 50% for concat, indicating that the DNN model had superior predictive capabilities for new data. This suggests that models based on structural information may require further optimization or alternative tokenization strategies. The application of natural language processing techniques to pharmaceutical challenges has demonstrated promising results, highlighting the need for more extensive data to enhance model generalization.

Effects of fermentation with Lactiplantibacillus plantarum NCU116 on the antihypertensive activity and protein structure of black sesame seed.

Effects of fermentation by Lactobacillus Plantarum NCU116 on the antihypertensive potential of black sesame seed (BSS) and structure characteristics of fermented black sesame seed protein (FBSSP) were investigated. Angiotensin-I-converting enzyme (ACE) inhibition and zinc chelating ability of fermented black sesame seed hydrolysate (FBSSH) reached the highest of 60.78 ± 3.67 % and 2.93 ± 0.04 mg/mL at 48 h and 60 h of fermentation, respectively. Additionally, the antioxidant activities of FBSSH and surface hydrophobicity of FBSSP were increased noticeably by fermentation. The α-helix and ß-rotation of FBSSP tended to decrease and increase, respectively, during fermentation. Correlation analysis indicated strong positive relationships between ß-turn and ACE inhibition activity as well as zinc chelating ability with correlation coefficients r of 0.8976 and 0.8932. Importantly, novel ACE inhibitory peptides LLLPYY (IC50 = 12.20 µM) and ALIPSF (IC50 = 558.99 µM) were screened from FBSSH at 48 h using in silico method. Both peptides showed high antioxidant activities in vitro. Molecular docking analysis demonstrated that the hydrogen bond connected with zinc ions of ACE mainly attributed to the potent ACE inhibitory activity of LLLPYY. The findings indicated that fermentation by Lactobacillus Plantarum NCU116 is an effective method to enhance the antihypertensive potential of BSS.

In silico and structural investigation of sulfonamides targeting VraSR two component system in methicillin-resistant Staphylococcus aureus.

Drug-resistant Staphylococcus aureus strains are global health concerns. Several studies have shown that these strains can develop defences against cell wall antibiotics such as ß-lactams, glycopeptides and daptomycin which target cell wall biosynthesis. The coordination of these responses have been associated with two component system (TCS) regulated by histidine kinase protein (VraS) and its cognate regulator VraR which influences the target DNA upon signal recognition. Computer-based screening methods, predictions and simulations have emerged as more efficient and quick ways to identify promising new compound leads from large databases against emerging drug targets thus allowing prediction of small select set of molecules for further validations. These combined approaches conserve valuable time and resources. Due to methicillin resistance, sulfonamide-derivative medications have been found to be effective treatment strategy to treat S. aureus infections. The current study used ligand-based virtual screening (LBVS) to identify powerful sulfonamide derivative inhibitors from an antibacterial compound library against VraSR signaling components, VraS and VraR. We identified promising sulfonamide derivative [compound 5: (4-[(1-{[(3,5-Dimethoxyphenyl)Carbamoyl]Methyl}-2,4-Dioxo-1,2,3,4-Tetrahydroquinazolin-3-Yl)Methyl]-N-[(Furan-2-Yl)Methyl]Benzamide)] with reasonable binding parameters of -31.38 kJ/mol and ΔGbind score of -294.32 kJ/mol against ATP binding domain of sensor kinase VraS. We further identified four compounds N1 (PCID83276726), N3 (PCID83276757), N9 (PCID3672584), and N10 (PCID20900589) against VraR DNA binding domain (VraRC) with ΔGbind energies of -190.27, -237.54, -165.21, and -190.88 kJ/mol, respectively. Structural and simulation analyses further suggest their stable interactions with DNA interacting residues and potential to disrupt DNA binding domain dimerization; therefore, it is prudent to further investigate and characterize them as VraR dimer disruptors and inhibit other promoter binding site. Interestingly, the discovery of drugs that target VraS and VraR may open new therapeutic avenues for drug-resistant S. aureus. These predictions based on screening, simulations and binding affinities against VraSR components hold promise for opening novel therapeutic avenues against drug-resistant S. aureus and present opportunities for repositioning efforts. These efforts aim to create analogs with enhanced potency and selectivity against two-component signaling systems that significantly contribute to virulence in MRSA or VRSA. These analyses contribute valuable insights into potential avenues for combating antibiotic-resistant S. aureus through computationally driven drug discovery strategies.Communicated by Ramaswamy H. Sarma.

Graph attention neural networks for mapping materials and molecules beyond short-range interatomic correlations.

Bringing advances in machine learning to chemical science is leading to a revolutionary change in the way of accelerating materials discovery and atomic-scale simulations. Currently, most successful machine learning schemes can be largely traced to the use of localized atomic environments in the structural representation of materials and molecules. However, this may undermine the reliability of machine learning models for mapping complex systems and describing long-range physical effects because of the lack of non-local correlations between atoms. To overcome such limitations, here we report a graph attention neural network as a unified framework to map materials and molecules into a generalizable and interpretable representation that combines local and non-local information of atomic environments from multiple scales. As an exemplary study, our model is applied to predict the electronic structure properties of metal-organic frameworks (MOFs) which have notable diversity in compositions and structures. The results show that our model achieves the state-of-the-art performance. The clustering analysis further demonstrates that our model enables high-level identification of MOFs with spatial and chemical resolution, which would facilitate the rational design of promising reticular materials. Furthermore, the application of our model in predicting the heat capacity of complex nanoporous materials, a critical property in a carbon capture process, showcases its versatility and accuracy in handling diverse physical properties beyond electronic structures.

Machine Learning Application for Medicinal Chemistry: Colchicine Case, New Structures, and Anticancer Activity Prediction.

In the contemporary era, the exploration of machine learning (ML) has gained widespread attention and is being leveraged to augment traditional methodologies in quantitative structure-activity relationship (QSAR) investigations. The principal objective of this research was to assess the anticancer potential of colchicine-based compounds across five distinct cell lines. This research endeavor ultimately sought to construct ML models proficient in forecasting anticancer activity as quantified by the IC50 value, while concurrently generating innovative colchicine-derived compounds. The resistance index (RI) is computed to evaluate the drug resistance exhibited by LoVo/DX cells relative to LoVo cancer cell lines. Meanwhile, the selectivity index (SI) is computed to determine the potential of a compound to demonstrate superior efficacy against tumor cells compared to its toxicity against normal cells, such as BALB/3T3. We introduce a novel ML system adept at recommending novel chemical structures predicated on known anticancer activity. Our investigation entailed the assessment of inhibitory capabilities across five cell lines, employing predictive models utilizing various algorithms, including random forest, decision tree, support vector machines, k-nearest neighbors, and multiple linear regression. The most proficient model, as determined by quality metrics, was employed to predict the anticancer activity of novel colchicine-based compounds. This methodological approach yielded the establishment of a library encompassing new colchicine-based compounds, each assigned an IC50 value. Additionally, this study resulted in the development of a validated predictive model, capable of reasonably estimating IC50 values based on molecular structure input.

Repurposed drugs in combinations exert additive anti-chikungunya virus activity: an in-vitro study.

Chikungunya virus (CHIKV) infection causes chikungunya, a viral disease that currently has no specific antiviral treatment. Several repurposed drug candidates have been investigated for the treatment of the disease. In order to improve the efficacy of the known drugs, combining drugs for treatment is a promising approach. The current study was undertaken to explore the antiviral activity of a combination of repurposed drugs that were reported to have anti-CHIKV activity. We explored the effect of different combinations of six effective drugs (2-fluoroadenine, emetine, lomibuvir, enalaprilat, metyrapone and resveratrol) at their non-toxic concentrations against CHIKV under post infection treatment conditions in Vero cells. Focus-forming unit assay, real time RT-PCR, immunofluorescence assay, and western blot were used to determine the virus titre. The results revealed that the combination of 2-fluoroadenine with either metyrapone or emetine or enalaprilat exerted inhibitory activity against CHIKV under post-infection treatment conditions. The effect of these drug combinations was additive in nature compared to the effect of the individual drugs. The results suggest an additive anti-viral effect of these drug combinations against CHIKV. The findings could serve as an outline for the development of an innovative therapeutic approach in the future to treat CHIKV-infected patients.

Identification, characterization, and insights into the mechanism of novel dipeptidyl peptidase-IV inhibitory peptides from yak hemoglobin by in silico exploration, molecular docking, and in vitro assessment.

Dipeptidyl peptidase IV (DPP-IV) inhibitory peptides were screened and identified from yak hemoglobin for the first time by in silico analysis, molecular docking, and in vitro evaluation. Results showed that yak hemoglobin had a high potential to produce DPP-IV inhibitory peptides based on the sequence alignment and bioactive potential evaluation. Furthermore, "pancreatic elastase + stem bromelain" was the optimal combined-enzymatic strategy by simulated proteolysis. Additionally, 25 novel peptides were found from its simulated hydrolysate, among which 10 peptides had high binding affinities with DPP-IV by molecular docking. Most of these peptides were also in silico characterized with favorable physicochemical properties and biological potentials, including relatively low molecular weight, high hydrophobicity, several net charges, good water solubility, nontoxicity, acceptable sensory quality, and good human intestinal absorption. Finally, six novel DPP-IV inhibitory peptides were identified via in vitro assessment, among which EEKA (IC50 = 235.26 µM), DEV (IC50 = 339.45 µM), and HCDKL (IC50 = 632.93 µM) showed the strongest capacities. The hydrogen bonds and electrostatic attractions formed with core residues within the S2 pocket of DPP-IV could be mainly responsible for their inhibition performances. This work provided a time-saving method and broadened application for yak by-products development as sources of functional foods.

Discovery of furopyridine-based compounds as novel inhibitors of Janus kinase 2: In silico and in vitro studies.

Janus kinase 2 (JAK2), one of the JAK isoforms participating in a JAK/STAT signaling cascade, has been considered a potential clinical target owing to its critical role in physiological processes involved in cell growth, survival, development, and differentiation of various cell types, especially immune and hematopoietic cells. Substantial studies have proven that the inhibition of this target could disrupt the JAK/STAT pathway and provide therapeutic outcomes for cancer, immune disorders, inflammation, and COVID-19. Herein, we performed docking-based virtual screening of 63 in-house furopyridine-based compounds and verified the first-round screened compounds by in vitro enzyme- and cell-based assays. By shedding light on the integration of both in silico and in vitro methods, we could elucidate two promising compounds. PD19 showed cytotoxic effects on human erythroblast cell lines (TF-1 and HEL) with IC50 values of 57.27 and 27.28 µM, respectively, while PD12 exhibited a cytotoxic effect on TF-1 with an IC50 value of 83.47 µM by suppressing JAK2/STAT5 autophosphorylation. In addition, all screened compounds were predicted to meet drug-like criteria based on Lipinski's rule of five, and none of the extreme toxicity features were found. Molecular dynamic simulations revealed that PD12 and PD19 could form stable complexes with JAK2 in an aqueous environment, and the van der Waals interactions were the main force driving the complex formation. Besides, all compounds sufficiently interacted with surrounding amino acids in all crucial regions, including glycine, catalytic, and activation loops. Altogether, PD12 and PD19 identified here could potentially be developed as novel therapeutic inhibitors disrupting the JAK/STAT pathway.

Identification of a Histone Deacetylase 8 Inhibitor through Drug Screenings Based on Machine Learning.

Histone deacetylase 8 (HDAC8) is a zinc-dependent HDAC that catalyzes the deacetylation of nonhistone proteins. It is involved in cancer development and HDAC8 inhibitors are promising candidates as anticancer agents. However, most reported HDAC8 inhibitors contain a hydroxamic acid moiety, which often causes mutagenicity. Therefore, we used machine learning for drug screening and attempted to identify non-hydroxamic acids as HDAC8 inhibitors. In this study, we established a prediction model based on the random forest (RF) algorithm for screening HDAC8 inhibitors because it exhibited the best predictive accuracy in the training dataset, including data generated by the synthetic minority over-sampling technique (SMOTE). Using the trained RF-SMOTE model, we screened the Osaka University library for compounds and selected 50 virtual hits. However, the 50 hits in the first screening did not show HDAC8-inhibitory activity. In the second screening, using the RF-SMOTE model, which was established by retraining the dataset including 50 inactive compounds, we identified non-hydroxamic acid 12 as an HDAC8 inhibitor with an IC50 of 842 nM. Interestingly, its IC50 values for HDAC1 and HDAC3-inhibitory activity were 38 and 12 µM, respectively, showing that compound 12 has high HDAC8 selectivity. Using machine learning, we expanded the chemical space for HDAC8 inhibitors and identified non-hydroxamic acid 12 as a novel HDAC8 selective inhibitor.

Development of Small-Molecule Anti-HIV-1 Agents Targeting HIV-1 Capsid Proteins.

The capsid of human immunodeficiency virus type 1 (HIV-1) forms a conical structure by assembling oligomers of capsid (CA) proteins and is a virion shell that encapsulates viral RNA. The inhibition of the CA function could be an appropriate target for suppression of HIV-1 replication because the CA proteins are highly conserved among many strains of HIV-1, and the drug targeting CA, lenacapavir, has been clinically developed by Gilead Sciences, Inc. Interface hydrophobic interactions between two CA molecules via the Trp184 and Met185 residues in the CA sequence are indispensable for conformational stabilization of the CA multimer. Our continuous studies found two types of small molecules with different scaffolds, MKN-1 and MKN-3, designed by in silico screening as a dipeptide mimic of Trp184 and Met185 have significant anti-HIV-1 activity. In the present study, MKN-1 derivatives have been designed and synthesized. Their structure-activity relationship studies found some compounds having potent anti-HIV activity. The present results should be useful in the design of novel CA-targeting molecules with anti-HIV activity.

An efficient and universal In silico screening strategy for acquisition of high-affinity Aptamer and its application in analytical utility.

Numerous aptamers against various targets have been identified through the technology of systematic evolution of ligands by exponential enrichment (SELEX), but the affinity of these aptamers are often insufficient due to the limitations of SELEX. Therefore, a more rational in silico screening strategy (ISS) was developed for efficient screening of high affinity aptamers, which took shape complementarity and thermodynamic stability into consideration. Neuron specific enolase (NSE), a tumor marker, was selected as the target molecule. In the screening process, three aptamer candidates with good shape complementarity, lower ΔG values, and higher ZDOCK scores were produced. The dissociation constant (Kd) of these candidates to NSE was determined to be 10.13 nM, 14.82 nM, and 2.76 nM, respectively. Each of them exhibited higher affinity to NSE than the parent aptamer (Kd = 23.83 nM). Finally, an antibody-free fluorescence aptasensor assay, based on the aptamer with the highest affinity, P-5C8G, was conducted, resulting in a limit of detection (LOD) value of 1.8 nM, which was much lower than the parental aptamer (P, LOD = 12.6 nM). The proposed ISS approach provided an efficient and universal strategy to improve the aptamer to have a high affinity and good analytical utility.

In silico and in vitro analyses of a novel FoxO1 agonist reducing Aß levels via downregulation of BACE1.

AIMS: FoxO1 is an important target in the treatment of Alzheimer's disease (AD). However, FoxO1-specific agonists and their effects on AD have not yet been reported. This study aimed to identify small molecules that upregulate the activity of FoxO1 to attenuate the symptoms of AD. METHODS: FoxO1 agonists were identified by in silico screening and molecular dynamics simulation. Western blotting and reverse transcription-quantitative polymerase chain reaction assays were used to assess protein and gene expression levels of P21, BIM, and PPARγ downstream of FoxO1 in SH-SY5Y cells, respectively. Western blotting and enzyme-linked immunoassays were performed to explore the effect of FoxO1 agonists on APP metabolism. RESULTS: N-(3-methylisothiazol-5-yl)-2-(2-oxobenzo[d]oxazol-3(2H)-yl) acetamide (compound D) had the highest affinity for FoxO1. Compound D activated FoxO1 and regulated the expression of its downstream target genes, P21, BIM, and PPARγ. In SH-SY5Y cells treated with compound D, BACE1 expression levels were downregulated, and the levels of Aß1-40 and Aß1-42 were also reduced. CONCLUSIONS: We present a novel small-molecule FoxO1 agonist with good anti-AD effects. This study highlights a promising strategy for new drug discovery for AD.