VPatho: a deep learning-based two-stage approach for accurate prediction of gain-of-function and loss-of-function variants.

Determining the pathogenicity and functional impact (i.e. gain-of-function; GOF or loss-of-function; LOF) of a variant is vital for unraveling the genetic level mechanisms of human diseases. To provide a 'one-stop' framework for the accurate identification of pathogenicity and functional impact of variants, we developed a two-stage deep-learning-based computational solution, termed VPatho, which was trained using a total of 9619 pathogenic GOF/LOF and 138 026 neutral variants curated from various databases. A total number of 138 variant-level, 262 protein-level and 103 genome-level features were extracted for constructing the models of VPatho. The development of VPatho consists of two stages: (i) a random under-sampling multi-scale residual neural network (ResNet) with a newly defined weighted-loss function (RUS-Wg-MSResNet) was proposed to predict variants' pathogenicity on the gnomAD_NV + GOF/LOF dataset; and (ii) an XGBOD model was constructed to predict the functional impact of the given variants. Benchmarking experiments demonstrated that RUS-Wg-MSResNet achieved the highest prediction performance with the weights calculated based on the ratios of neutral versus pathogenic variants. Independent tests showed that both RUS-Wg-MSResNet and XGBOD achieved outstanding performance. Moreover, assessed using variants from the CAGI6 competition, RUS-Wg-MSResNet achieved superior performance compared to state-of-the-art predictors. The fine-trained XGBOD models were further used to blind test the whole LOF data downloaded from gnomAD and accordingly, we identified 31 nonLOF variants that were previously labeled as LOF/uncertain variants. As an implementation of the developed approach, a webserver of VPatho is made publicly available at http://csbio.njust.edu.cn/bioinf/vpatho/ to facilitate community-wide efforts for profiling and prioritizing the query variants with respect to their pathogenicity and functional impact.

MMPatho: Leveraging Multilevel Consensus and Evolutionary Information for Enhanced Missense Mutation Pathogenic Prediction.

Understanding the pathogenicity of missense mutation (MM) is essential for shed light on genetic diseases, gene functions, and individual variations. In this study, we propose a novel computational approach, called MMPatho, for enhancing missense mutation pathogenic prediction. First, we established a large-scale nonredundant MM benchmark data set based on the entire Ensembl database, complemented by a focused blind test set specifically for pathogenic GOF/LOF MM. Based on this data set, for each mutation, we utilized Ensembl VEP v104 and dbNSFP v4.1a to extract variant-level, amino acid-level, individuals' outputs, and genome-level features. Additionally, protein sequences were generated using ENSP identifiers with the Ensembl API, and then encoded. The mutant sites' ESM-1b and ProtTrans-T5 embeddings were subsequently extracted. Then, our model group (MMPatho) was developed by leveraging upon these efforts, which comprised ConsMM and EvoIndMM. To be specific, ConsMM employs individuals' outputs and XGBoost with SHAP explanation analysis, while EvoIndMM investigates the potential enhancement of predictive capability by incorporating evolutionary information from ESM-1b and ProtT5-XL-U50, large protein language embeddings. Through rigorous comparative experiments, both ConsMM and EvoIndMM were capable of achieving remarkable AUROC (0.9836 and 0.9854) and AUPR (0.9852 and 0.9902) values on the blind test set devoid of overlapping variations and proteins from the training data, thus highlighting the superiority of our computational approach in the prediction of MM pathogenicity. Our Web server, available at http://csbio.njust.edu.cn/bioinf/mmpatho/, allows researchers to predict the pathogenicity (alongside the reliability index score) of MMs using the ConsMM and EvoIndMM models and provides extensive annotations for user input. Additionally, the newly constructed benchmark data set and blind test set can be accessed via the data page of our web server.

Discovery of novel halogenated 8-hydroxyquinoline-based anti-MRSA agents: In vitro and QSAR studies.

Methicillin-resistant Staphylococcus aureus (MRSA) infection has been considered to be one of global health problems due to limited classes of effective antimicrobial drugs. Herein, 8-hydroxyquinoline (8HQ) and its derivatives (1-7) were investigated for their anti-MRSA and antioxidant activities. Cloxyquin (2), a halogenated 8HQ, exerted the highest antimicrobial activity (MIC50 ≤ 5.57 µM) with high safety index, whereas an amino-derivative 7 showed the strongest antioxidant activity. Additionally, quantitative structure-activity relationship (QSAR) study demonstrated that mass, polarizability, topological charge, and van der Waals volume are essential properties governing the anti-MRSA activity. Taken together, cloxyquin was highlighted as a promising compound for further development as a novel anti-MRSA agent. QSAR findings would also benefit for further rational design of novel 8HQ-based compounds to combat the MRSA resistance.

Synthesis, molecular docking, and QSAR study of bis-sulfonamide derivatives as potential aromatase inhibitors.

A library of bis-sulfonamides (9-26) were synthesized and tested for their aromatase inhibitory activities. Interestingly, all bis-sulfonamide derivatives inhibited the aromatase with IC50 range of 0.05-11.6 µM except for compound 23. The analogs 15 and 16 bearing hydrophobic chloro and bromo groups exhibited the potent aromatase inhibitory activity in sub-micromolar IC50 values (i.e., 50 and 60 nM, respectively) with high safety index. Molecular docking revealed that the chloro and bromo benzenesulfonamides (15 and 16) may play role in the hydrophobic interaction with Leu477 of the aromatase to mimic steroidal backbone of the natural substrate, androstenedione. QSAR study also revealed that the most potent activity of compounds was governed by van der Waals volume (GATS6v) and mass (Mor03m) descriptors. Finally, the two compounds (15 and 16) were highlighted as promising compounds to be further developed as novel aromatase inhibitors.

Large-scale structure-activity relationship study of hepatitis C virus NS5B polymerase inhibition using SMILES-based descriptors.

Hepatitis C virus (HCV) is composed of structural and non-structural proteins involved in viral transcription and propagation. In particular, NS5B is an RNA-dependent RNA polymerase for viral transcription and genome replication and is a target for designing anti-viral agents. In this study, classification and quantitative structure-activity relationship (QSAR) models of HCV NS5B inhibitors were constructed using the Correlation and Logic software. Molecular descriptors for a set of 970 HCV NS5B inhibitors were encoded using the simplified molecular input line entry system notation, and predictive models were built via the Monte Carlo method. The QSAR models provided acceptable correlation coefficients of [Formula: see text] and [Formula: see text] in the ranges of 0.6038-0.7344 and 0.6171-0.7294, respectively, while the classification models displayed sensitivity, specificity, and accuracy in ranges of 88.24-98.84, 83.87-93.94, and 86.50-94.41 %, respectively. Furthermore, molecular fragments as substructures involved in increased and decreased inhibitory activities were explored. The results provide information on QSAR and classification models for high-throughput screening and mechanistic insights into the inhibitory activity of HCV NS5B polymerase.

Predicting Metabolic Syndrome Using the Random Forest Method.

AIMS: This study proposes a computational method for determining the prevalence of metabolic syndrome (MS) and to predict its occurrence using the National Cholesterol Education Program Adult Treatment Panel III (NCEP ATP III) criteria. The Random Forest (RF) method is also applied to identify significant health parameters. MATERIALS AND METHODS: We used data from 5,646 adults aged between 18-78 years residing in Bangkok who had received an annual health check-up in 2008. MS was identified using the NCEP ATP III criteria. The RF method was applied to predict the occurrence of MS and to identify important health parameters surrounding this disorder. RESULTS: The overall prevalence of MS was 23.70% (34.32% for males and 17.74% for females). RF accuracy for predicting MS in an adult Thai population was 98.11%. Further, based on RF, triglyceride levels were the most important health parameter associated with MS. CONCLUSION: RF was shown to predict MS in an adult Thai population with an accuracy >98% and triglyceride levels were identified as the most informative variable associated with MS. Therefore, using RF to predict MS may be potentially beneficial in identifying MS status for preventing the development of diabetes mellitus and cardiovascular diseases.

Review of Computational Methods and Database Sources for Predicting the Effects of Coding Frameshift Small Insertion and Deletion Variations.

Genetic variations (including substitutions, insertions, and deletions) exert a profound influence on DNA sequences. These variations are systematically classified as synonymous, nonsynonymous, and nonsense, each manifesting distinct effects on proteins. The implementation of high-throughput sequencing has significantly augmented our comprehension of the intricate interplay between gene variations and protein structure and function, as well as their ramifications in the context of diseases. Frameshift variations, particularly small insertions and deletions (indels), disrupt protein coding and are instrumental in disease pathogenesis. This review presents a succinct review of computational methods, databases, current challenges, and future directions in predicting the consequences of coding frameshift small indels variations. We analyzed the predictive efficacy, reliability, and utilization of computational methods and variant account, reliability, and utilization of database. Besides, we also compared the prediction methodologies on GOF/LOF pathogenic variation data. Addressing the challenges pertaining to prediction accuracy and cross-species generalizability, nascent technologies such as AI and deep learning harbor immense potential to enhance predictive capabilities. The importance of interdisciplinary research and collaboration cannot be overstated for devising effective diagnosis, treatment, and prevention strategies concerning diseases associated with coding frameshift indels variations.

Insight into the binding mechanisms of fluorinated 2-aminothiazole sulfonamide and human serum albumin: Spectroscopic and in silico approaches.

4-Fluoro-N-(thiazol-2-yl)benzenesulfonamide (3) is a novel fluorinated compound, containing various biological activities. Therefore, absorption spectroscopy, fluorescence quenching, molecular docking, and molecular simulation were employed to investigate the interaction between 3 and HSA. Firstly, compound 3 meets all criteria for drug-likeness prediction. UV absorption spectra revealed the interaction of 3 with HSA altered the microenvironment of protein, as well as circular dichroism spectroscopic analysis indicated slightly conformational changes and a reduction in α-helical content. The binding parameters of the HSA-3 complex suggested that fluorescence quenching is driven by combined static and dynamic processes. Additionally, the stability of the complex is attributed to conventional hydrogen and hydrophobic bonding interactions. Furthermore, esterase-like activity indicated that the binding of 3 might disrupt HSA's bond networks, leading to structural alterations. Consequently, the strong binding constant (Ka ≈ 1.204 × 106 M-1) aligns with the predicted unbound fraction (0.28) in serum, indicating that thiazole 3 has good bioavailability in plasma and can be effectively transported to target sites, thereby exerting its pharmaceutical effects. However, careful dosage management is essential to prevent potential adverse effects. Overall, these findings highlight the potential of 3 as a therapeutic agent, emphasizing the need for further research to optimize its uses.

Exploring the chemical space of aromatase inhibitors.

Aromatase, a rate-limiting enzyme catalyzing the conversion of androgen to estrogen, is overexpressed in human breast cancer tissue. Aromatase inhibitors (AIs) have been used for the treatment of estrogen-dependent breast cancer in post-menopausal women by blocking the biosynthesis of estrogen. The undesirable side effects in current AIs have called for continued pursuit for novel candidates with aromatase inhibitory properties. This study explores the chemical space of all known AIs as a function of their physicochemical properties by means of univariate (i.e., statistical and histogram analysis) and multivariate (i.e., decision tree and principal component analysis) approaches in order to understand the origins of aromatase inhibitory activity. Such a non-redundant set of AIs spans a total of 973 compounds encompassing both steroidal and non-steroidal inhibitors. Substructure analysis of the molecular fragments provided pertinent information on the structural features important for ligands providing high and low aromatase inhibition. Analyses were performed on data sets stratified according to their structural scaffolds (i.e., steroids and non-steroids) and bioactivities (i.e., actives and inactives). These analyses have uncover a set of rules characteristic to active and inactive AIs as well as revealing the constituents giving rise to potent aromatase inhibition.

Promising 8-Aminoquinoline-Based Metal Complexes in the Modulation of SIRT1/3-FOXO3a Axis against Oxidative Damage-Induced Preclinical Neurons.

The discovery of novel bioactive molecules as potential multifunctional neuroprotective agents has clinically drawn continual interest due to devastating oxidative damage in the pathogenesis and progression of neurodegenerative diseases. Synthetic 8-aminoquinoline antimalarial drug is an attractive pharmacophore in drug development and chemical modification owing to its wide range of biological activities, yet the underlying molecular mechanisms are not fully elucidated in preclinical models for oxidative damage. Herein, the neuroprotective effects of two 8-aminoquinoline-uracil copper complexes were investigated on the hydrogen peroxide-induced human neuroblastoma SH-SY5Y cells. Both metal complexes markedly restored cell survival, alleviated apoptotic cascades, maintained antioxidant defense, and prevented mitochondrial function by upregulating the sirtuin 1 (SIRT1)/3-FOXO3a signaling pathway. Intriguingly, in silico molecular docking and pharmacokinetic prediction suggested that these synthetic compounds acted as SIRT1 activators with potential drug-like properties, wherein the uracil ligands (5-iodoracil and 5-nitrouracil) were essential for effective binding interactions with the target protein SIRT1. Taken together, the synthetic 8-aminoquinoline-based metal complexes are promising brain-targeting drugs for attenuating neurodegenerative diseases.

Synthesis of acetamidosulfonamide derivatives with antioxidative and QSAR studies.

A series of sixteen acetamidosulfonamide derivatives (1-16) have been synthesized and investigated for their antioxidant (radical scavenging and superoxide dismutase (SOD)) and antimicrobial activities. Most compounds exhibited antioxidant activities in which compound 15 displayed the most potent radical scavenging and SOD activities. Quantitative structure-activity relationship (QSAR) has been studied using multiple linear regression. The constructed QSAR models displayed high correlation coefficient (Q 2 LOO-CV = 0.9708 and 0.8753 for RSA and SOD activities, respectively), but low root mean square error (RMSE LOO-CV = 0.5105 and 1.3571 for RSA and SOD activities, respectively). The structure-activity relationship showed that an ethylene group connected to pyridine ring provided significant antioxidant activities. The QSAR models give insight into the rational designed of eighty new sulfonamides with various electron donating and withdrawing groups. The top five new designed sulfonamides with nitro group are potential antioxidants to be further developed for medicinal applications.

Identification of the ubiquitin-proteasome pathway domain by hyperparameter optimization based on a 2D convolutional neural network.

The major mechanism of proteolysis in the cytosol and nucleus is the ubiquitin-proteasome pathway (UPP). The highly controlled UPP has an effect on a wide range of cellular processes and substrates, and flaws in the system can lead to the pathogenesis of a number of serious human diseases. Knowledge about UPPs provide useful hints to understand the cellular process and drug discovery. The exponential growth in next-generation sequencing wet lab approaches have accelerated the accumulation of unannotated data in online databases, making the UPP characterization/analysis task more challenging. Thus, computational methods are used as an alternative for fast and accurate identification of UPPs. Aiming this, we develop a novel deep learning-based predictor named "2DCNN-UPP" for identifying UPPs with low error rate. In the proposed method, we used proposed algorithm with a two-dimensional convolutional neural network with dipeptide deviation features. To avoid the over fitting problem, genetic algorithm is employed to select the optimal features. Finally, the optimized attribute set are fed as input to the 2D-CNN learning engine for building the model. Empirical evidence or outcomes demonstrates that the proposed predictor achieved an overall accuracy and AUC (ROC) value using 10-fold cross validation test. Superior performance compared to other state-of-the art methods for discrimination the relations UPPs classification. Both on and independent test respectively was trained on 10-fold cross validation method and then evaluated through independent test. In the case where experimentally validated ubiquitination sites emerged, we must devise a proteomics-based predictor of ubiquitination. Meanwhile, we also evaluated the generalization power of our trained modal via independent test, and obtained remarkable performance in term of 0.862 accuracy, 0.921 sensitivity, 0.803 specificity 0.803, and 0.730 Matthews correlation coefficient (MCC) respectively. Four approaches were used in the sequences, and the physical properties were calculated combined. When used a 10-fold cross-validation, 2D-CNN-UPP obtained an AUC (ROC) value of 0.862 predicted score. We analyzed the relationship between UPP protein and non-UPP protein predicted score. Last but not least, this research could effectively analyze the large scale relationship between UPP proteins and non-UPP proteins in particular and other protein problems in general and our research work might improve computational biological research. Therefore, we could utilize the latest features in our model framework and Dipeptide Deviation from Expected Mean (DDE) -based protein structure features for the prediction of protein structure, functions, and different molecules, such as DNA and RNA.

Anticancer activity and QSAR study of sulfur-containing thiourea and sulfonamide derivatives.

Sulfur-containing compounds are considered as attractive pharmacophores for discovery of new drugs regarding their versatile properties to interact with various biological targets. Quantitative structure-activity relationship (QSAR) modeling is one of well-recognized in silico tools for successful drug discovery. In this work, a set of 38 sulfur-containing derivatives (Types I-VI) were evaluated for their in vitro anticancer activities against 6 cancer cell lines. In vitro findings indicated that compound 13 was the most potent cytotoxic agent toward HuCCA-1 cell line (IC50 = 14.47 µM). Compound 14 exhibited the most potent activities against 3 investigated cell lines (i.e., HepG2, A549, and MDA-MB-231: IC50 range = 1.50-16.67 µM). Compound 10 showed the best activity for MOLT-3 (IC50 = 1.20 µM) whereas compound 22 was noted for T47D (IC50 = 7.10 µM). Subsequently, six QSAR models were built using multiple linear regression (MLR) algorithm. All constructed QSAR models provided reliable predictive performance (training sets: Rtr range = 0.8301-0.9636 and RMSEtr = 0.0666-0.2680; leave-one-out cross validation sets: RCV range = 0.7628-0.9290 and RMSECV = 0.0926-0.3188). From QSAR modeling, chemical properties such as mass, polarizability, electronegativity, van der Waals volume, octanol-water partition coefficient, as well as frequency/presence of C-N, F-F, and N-N bonds in the molecule are essential key predictors for anticancer activities of the compounds. In summary, a series of promising fluoro-thiourea derivatives (10, 13, 14, 22) were suggested as potential molecules for future development as anticancer agents. Key structure-activity knowledge obtained from the QSAR modeling was suggested to be advantageous for suggesting the effective rational design of the related sulfur-containing anticancer compounds with improved bioactivities and properties.

Interpretable SMILES-based QSAR Model of Inhibitory Activity of Sirtuins 1 and 2.

BACKGROUND: Sirtuin 1 (Sirt1) and sirtuin 2 (Sirt2) are NAD+-dependent histone deacetylases which play important functional roles in the removal of the acetyl group of acetyllysine substrates. Considering the dysregulation of Sirt1 and Sirt2 as etiological causes of diseases, Sirt1 and Sirt2 are lucrative target proteins for treatment, thus there has been great interest in the development of Sirt1 and Sirt2 inhibitors. OBJECTIVE: This study compiled the bioactivity data of Sirt1 and Sirt2 for the construction of quantitative structure-activity relationship (QSAR) models in accordance with the OECD principles. METHODS: Simplified molecular-input line-entry system (SMILES)-based molecular descriptors were used to characterize the molecular features of inhibitors while the Monte Carlo method of the CORAL software was employed for multivariate analysis. The dataset was subjected to 3 random splits in which each split separated the data into 4 subsets consisting of training, invisible training, calibration, and external sets. RESULTS: Statistical indices for the evaluation of QSAR models suggested the good statistical quality of models of Sirt1 and Sirt2 inhibitors. Furthermore, mechanistic interpretation of molecular substructures that are responsible for modulating the bioactivity (i.e., promoters of increase or decrease of bioactivity) was extracted via the analysis of correlation weights. It exhibited molecular features involved in Sirt1 and Sirt2 inhibitors. CONCLUSION: It is anticipated that QSAR models presented herein can be useful as guidelines in the rational design of potential Sirt1 and Sirt2 inhibitors for the treatment of Sirtuin-related diseases.

Biochemical and cellular investigation of Vitreoscilla hemoglobin (VHb) variants possessing efficient peroxidase activity.

Peroxidase-like activity of Vitreoscilla hemoglobin (VHb) has been recently disclosed. To maximize such activity, two catalytically conserved residues (histidine and arginine) found in distal pocket of peroxidases have successfully been introduced into that of the VHb. Fifteen-fold increase in catalytic constant (k(cat)) was obtained in P54R variant, which was presumably attributable to the lower rigidity and higher hydrophilicity of distal cavity arising from substitution of proline to arginine. None of the modifications altered the affinity towards either H(2)O(2) or ABTS substrate. Spectroscopic studies revealed that VHb variants harboring T29H mutation apparently demonstrated the spectral shift in both ferric and ferrous forms (406-408 to 411 nm and 432 to 424-425 nm, respectively). All VHb proteins in ferrous state had lambda(soret) peak at approximately 419 nm following the carbon monoxide (CO) binding. Expression of P54R mutant mediated the down-regulation of iron superoxide dismutase (FeSOD) as identified by 2-dimensional gel electrophoresis (2-DE) and peptide mass fingerprinting (PMF). According to the high peroxidase activity of P54R, it could effectively eliminate autoxidation-derived H2O2, which is a cause of heme degradation and iron release. This decreased the iron availability and consequently reduced the formation of Fe(2+)-ferric uptake regulator protein (Fe(2+)-Fur), an inducer of FeSOD expression.

Elucidating the structure-activity relationships of the vasorelaxation and antioxidation properties of thionicotinic acid derivatives.

Nicotinic acid, known as vitamin B(3), is an effective lipid lowering drug and intense cutaneous vasodilator. This study reports the effect of 2-(1-adamantylthio)nicotinic acid (6) and its amide 7 and nitrile analog 8 on phenylephrine-induced contraction of rat thoracic aorta as well as antioxidative activity. It was found that the tested thionicotinic acid analogs 6-8 exerted maximal vasorelaxation in a dose-dependent manner, but their effects were less than acetylcholine (ACh)-induced nitric oxide (NO) vasorelaxation. The vasorelaxations were reduced, apparently, in both N(G)-nitro-L-arginine methyl ester (L-NAME) and indomethacin (INDO). Synergistic effects were observed in the presence of L-NAME plus INDO, leading to loss of vasorelaxation of both the ACh and the tested nicotinic acids. Complete loss of the vasorelaxation was noted under removal of endothelial cells. This infers that the vasorelaxations are mediated partially by endothelium-induced NO and prostacyclin. The thionicotinic acid analogs all exhibited antioxidant properties in both 2,2-diphenyl-1-picrylhydrazyl (DPPH) and superoxide dismutase (SOD) assays. Significantly, the thionicotinic acid 6 is the most potent vasorelaxant with ED(50) of 21.3 nM and is the most potent antioxidant (as discerned from DPPH assay). Molecular modeling was also used to provide mechanistic insights into the vasorelaxant and antioxidative activities. The findings reveal that the thionicotinic acid analogs are a novel class of vasorelaxant and antioxidant compounds which have potential to be further developed as promising therapeutics.

Rational design of novel coumarins: A potential trend for antioxidants in cosmetics.

Coumarins are well-known for their antioxidant effect and aromatic property, thus, they are one of ingredients commonly added in cosmetics and personal care products. Quantitative structure-activity relationships (QSAR) modeling is an in silico method widely used to facilitate rational design and structural optimization of novel drugs. Herein, QSAR modeling was used to elucidate key properties governing antioxidant activity of a series of the reported coumarin-based antioxidant agents (1-28). Several types of descriptors (calculated from 4 softwares i.e., Gaussian 09, Dragon, PaDEL and Mold2 softwares) were used to generate three multiple linear regression (MLR) models with preferable predictive performance (Q 2 LOO-CV = 0.813-0.908; RMSE LOO-CV = 0.150-0.210; Q 2 Ext = 0.875-0.952; RMSE Ext = 0.104-0.166). QSAR analysis indicated that number of secondary amines (nArNHR), polarizability (G2p), electronegativity (D467, D580, SpMin2_Bhe, and MATS8e), van der Waals volume (D491 and D461), and H-bond potential (SHBint4) are important properties governing antioxidant activity. The constructed models were also applied to guide in silico rational design of an additional set of 69 structurally modified coumarins with improved antioxidant activity. Finally, a set of 9 promising newly design compounds were highlighted for further development. Structure-activity analysis also revealed key features required for potent activity which would be useful for guiding the future rational design. In overview, our findings demonstrated that QSAR modeling could possibly be a facilitating tool to enhance successful development of bioactive compounds for health and cosmetic applications.

Synthesis of N-substituted 5-iodouracils as antimicrobial and anticancer agents.

This study reports the synthesis of some substituted 5-iodouracils and their bioactivities. Alkylation of 5-iodouracils gave predominately N1-substituted-(R)-5-iodouracil compounds 7a-d (R = n-C(4)H(9), s-C(4)H(9), CH(2)C(6)H(11), CH(2)C(6)H(5)) together with N1,N3-disubstituted (R) analogs 8a-b (R = n-C(4)H(9), CH(2)C(6)H(11)). Their antimicrobial activity was tested against 27 strains of microorganisms using the agar dilution method. The analogs 7a, 7c and 7d displayed 25-50% inhibition against Branhamella catarrhalis, Neisseria mucosa and Streptococcus pyogenes at 0.128 mg/mL. No antimalarial activity was detected for any of the analogs when tested against Plasmodium falciparum (T9.94). Their anticancer activity was also examined. Cyclohexylmethyl analogs 7c and 8b inhibited the growth of HepG2 cells. Significantly, N1,N3-dicyclohexylmethyl analog 8b displayed the most potent anticancer activity, with an IC(50) of 16.5 microg/mL. These 5-iodouracil analogs represent a new group of anticancer and antibacterial agents with potential for development for medicinal applications.

Bioactive metabolites from Spilanthes acmella Murr.

Spilanthes acmella Murr. (Compositae) has been used as a traditional medicine for toothache, rheumatism and fever. Its extracts had been shown to exhibit vasorelaxant and antioxidant activities. Herein, its antimicrobial, antioxidant and cytotoxic activities were evaluated. Agar dilution method assays against 27 strains of microorganisms were performed. Results showed that fractions from the chloroform and methanol extracts inhibited the growth of many tested organisms, e.g. Corynebacterium diphtheriae NCTC 10356 with minimum inhibitory concentration (MIC) of 64-256 mg/mL and Bacillus subtilis ATCC 6633 with MIC of 128-256 mg/mL. The tested fractions all exhibited antioxidant properties in both DPPH and SOD assays. Potent radical scavenging activity was observed in the DPPH assay. No cytotoxic effects of the extracts against KB and HuCCA-1 cell lines were evident. Bioassay-guided isolation resulted in a diverse group of bioactive compounds such as phenolics [vanillic acid (2), trans-ferulic acid (5) and trans-isoferulic acid (6)], coumarin (scopoletin, 4) and triterpenoids like 3-acetylaleuritolic acid (1), b-sitostenone (3), stigmasterol and stigmasteryl-3-O-b-D-glucopyranosides, in addition to a mixture of stigmasteryl-and b-sitosteryl-3-O-b-D-glucopyranosides. The compounds 1-6 represent bioactive metabolites of S. acmella Murr. that were never previously reported. Our findings demonstrate for the first time the potential benefits of this medicinal plant as a rich source of high therapeutic value compounds for medicines, cosmetics, supplements and as a health food.

Rational Design of Colchicine Derivatives as anti-HIV Agents via QSAR and Molecular Docking.

BACKGROUND: Human immunodeficiency virus (HIV) is an infective agent that causes an acquired immunodeficiency syndrome (AIDS). Therefore, the rational design of inhibitors for preventing the progression of the disease is required. OBJECTIVE: This study aims to construct quantitative structure-activity relationship (QSAR) models, molecular docking and newly rational design of colchicine and derivatives with anti-HIV activity. METHODS: A data set of 24 colchicine and derivatives with anti-HIV activity were employed to develop the QSAR models using machine learning methods (e.g. multiple linear regression (MLR), artificial neural network (ANN) and support vector machine (SVM)), and to study a molecular docking. RESULTS: The significant descriptors relating to the anti-HIV activity included JGI2, Mor24u, Gm and R8p+ descriptors. The predictive performance of the models gave acceptable statistical qualities as observed by correlation coefficient (Q2) and root mean square error (RMSE) of leave-one out cross-validation (LOO-CV) and external sets. Particularly, the ANN method outperformed MLR and SVM methods that displayed LOO-CV 2 Q and RMSELOO-CV of 0.7548 and 0.5735 for LOOCV set, and Ext 2 Q of 0.8553 and RMSEExt of 0.6999 for external validation. In addition, the molecular docking of virus-entry molecule (gp120 envelope glycoprotein) revealed the key interacting residues of the protein (cellular receptor, CD4) and the site-moiety preferences of colchicine derivatives as HIV entry inhibitors for binding to HIV structure. Furthermore, newly rational design of colchicine derivatives using informative QSAR and molecular docking was proposed. CONCLUSION: These findings serve as a guideline for the rational drug design as well as potential development of novel anti-HIV agents.