Exploring the chemical space of influenza neuraminidase inhibitors

Natural Product-Derived Phytochemicals for Influenza A Virus (H1N1) Prevention and Treatment

Influenza A (H1N1) viruses are prone to antigenic mutations and are more variable than other influenza viruses. Therefore, they have caused continuous harm to human public health since the pandemic in 2009 and in recent times. Influenza A (H1N1) can be prevented and treated in various ways, such as direct inhibition of the virus and regulation of human immunity. Among antiviral drugs, the use of natural products in treating influenza has a long history, and natural medicine has been widely considered the focus of development programs for new, safe anti-influenza drugs. In this paper, we focus on influenza A (H1N1) and summarize the natural product-derived phytochemicals for influenza A virus (H1N1) prevention and treatment, including marine natural products, flavonoids, alkaloids, terpenoids and their derivatives, phenols and their derivatives, polysaccharides, and derivatives of natural products for prevention and treatment of influenza A (H1N1) virus. We further discuss the toxicity and antiviral mechanism against influenza A (H1N1) as well as the druggability of natural products. We hope that this review will facilitate the study of the role of natural products against influenza A (H1N1) activity and provide a promising alternative for further anti-influenza A drug development.

Machine learning-guided design of potent darunavir analogs targeting HIV-1 proteases: A computational approach for antiretroviral drug discovery

In the pursuit of novel antiretroviral therapies for human immunodeficiency virus type-1 (HIV-1) proteases (PRs), recent improvements in drug discovery have embraced machine learning (ML) techniques to guide the design process. This study employs ensemble learning models to identify crucial substructures as significant features for drug development. Using molecular docking techniques, a collection of 160 darunavir (DRV) analogs was designed based on these key substructures and subsequently screened using molecular docking techniques. Chemical structures with high fitness scores were selected, combined, and one-dimensional (1D) screening based on beyond Lipinski's rule of five (bRo5) and ADME (absorption, distribution, metabolism, and excretion) prediction implemented in the Combined Analog generator Tool (CAT) program. A total of 473 screened analogs were subjected to docking analysis through convolutional neural networks scoring function against both the wild-type (WT) and 12 major mutated PRs. DRV analogs with negative changes in binding free energy (ΔΔ G bind ) compared to DRV could be categorized into four attractive groups based on their interactions with the majority of vital PRs. The analysis of interaction profiles revealed that potent designed analogs, targeting both WT and mutant PRs, exhibited interactions with common key amino acid residues. This observation further confirms that the ML model-guided approach effectively identified the substructures that play a crucial role in potent analogs. It is expected to function as a powerful computational tool, offering valuable guidance in the identification of chemical substructures for synthesis and subsequent experimental testing.

Navigating the Chemical Space of ENR Inhibitors: A Comprehensive Analysis

Antimicrobial resistance is a global health threat that requires innovative strategies against drug-resistant bacteria. Our study focuses on enoyl-acyl carrier protein reductases (ENRs), in particular FabI, FabK, FabV, and InhA, as potential antimicrobial agents. Despite their promising potential, the lack of clinical approvals for inhibitors such as triclosan and isoniazid underscores the challenges in achieving preclinical success. In our study, we curated and analyzed a dataset of 1412 small molecules recognized as ENR inhibitors, investigating different structural variants. Using advanced cheminformatic tools, we mapped the physicochemical landscape and identified specific structural features as key determinants of bioactivity. Furthermore, we investigated whether the compounds conform to Lipinski rules, PAINS, and Brenk filters, which are crucial for the advancement of compounds in development pipelines. Furthermore, we investigated structural diversity using four different representations: Chemotype diversity, molecular similarity, t-SNE visualization, molecular complexity, and cluster analysis. By using advanced bioinformatics tools such as matched molecular pairs (MMP) analysis, machine learning, and SHAP analysis, we were able to improve our understanding of the activity cliques and the precise effects of the functional groups. In summary, this chemoinformatic investigation has unraveled the FAB inhibitors and provided insights into rational antimicrobial design, seamlessly integrating computation into the discovery of new antimicrobial agents.

Ferulic Acid: A Review of Pharmacology, Toxicology, and Therapeutic Effects on Pulmonary Diseases

Ferulic acid (FA), a prevalent dietary phytochemical, has many pharmacological effects, including anti-oxidation and anti-inflammation effects, and has been widely used in the pharmaceutical, food, and cosmetics industries. Many studies have shown that FA can significantly downregulate the expression of reactive oxygen species and activate nuclear factor erythroid-2-related factor-2/heme oxygenase-1 signaling, exerting anti-oxidative effects. The anti-inflammatory effect of FA is mainly related to the p38 mitogen-activated protein kinase and nuclear factor-kappaB signaling pathways. FA has demonstrated potential clinical applications in the treatment of pulmonary diseases. The transforming growth factor-β1/small mothers against decapentaplegic 3 signaling pathway can be blocked by FA, thereby alleviating pulmonary fibrosis. Moreover, in the context of asthma, the T helper cell 1/2 imbalance is restored by FA. Furthermore, FA ameliorates acute lung injury by inhibiting nuclear factor-kappaB and mitogen-activated protein kinase pathways via toll-like receptor 4, consequently decreasing the expression of downstream inflammatory mediators. Additionally, there is a moderate neuraminidase inhibitory activity showing a tendency to reduce the interleukin-8 level in response to influenza virus infections. Although the application of FA has broad prospects, more preclinical mechanism-based research should be carried out to test these applications in clinical settings. This review not only covers the literature on the pharmacological effects and mechanisms of FA, but also discusses the therapeutic role and toxicology of FA in several pulmonary diseases.

Prediction of KRASG12C inhibitors using conjoint fingerprint and machine learning-based QSAR models

Kirsten rat sarcoma virus G12C (KRAS^G12C) is the major protein mutation associated with non-small cell lung cancer (NSCLC) severity. Inhibiting KRAS^G12C is therefore one of the key therapeutic strategies for NSCLC patients. In this paper, a cost-effective data driven drug design employing machine learning-based quantitative structure-activity relationship (QSAR) analysis was built for predicting ligand affinities against KRAS^G12C protein. A curated and non-redundant dataset of 1033 compounds with KRAS^G12C inhibitory activity (pIC₅₀) was used to build and test the models. The PubChem fingerprint, Substructure fingerprint, Substructure fingerprint count, and the conjoint fingerprint-a combination of PubChem fingerprint and Substructure fingerprint count-were used to train the models. Using comprehensive validation methods and various machine learning algorithms, the results clearly showed that the XGBoost regression (XGBoost) achieved the highest performance in term of goodness of fit, predictivity, generalizability and model robustness (R² = 0.81, Q²_CV = 0.60, Q²_Ext = 0.62, R² - Q²_Ext = 0.19, R²_Y-Random = 0.31 ± 0.03, Q²_Y-Random = -0.09 ± 0.04). The top 13 molecular fingerprints that correlated with the predicted pIC₅₀ values were SubFPC274 (aromatic atoms), SubFPC307 (number of chiral-centers), PubChemFP37 (≥1 Chlorine), SubFPC18 (Number of alkylarylethers), SubFPC1 (number of primary carbons), SubFPC300 (number of 1,3-tautomerizables), PubChemFP621 (N-C:C:C:N structure), PubChemFP23 (≥1 Fluorine), SubFPC2 (number of secondary carbons), SubFPC295 (number of C-ONS bonds), PubChemFP199 (≥4 6-membered rings), PubChemFP180 (≥1 nitrogen-containing 6-membered ring), and SubFPC180 (number of tertiary amine). These molecular fingerprints were virtualized and validated using molecular docking experiments. In conclusion, this conjoint fingerprint and XGBoost-QSAR model demonstrated to be useful as a high-throughput screening tool for KRAS^G12C inhibitor identification and drug design.

Emerging antiviral therapies and drugs for the treatment of influenza

INTRODUCTION

Both vaccines and antiviral drugs represent the mainstay for preventing and treating influenza. However, approved M2 ion channel inhibitors, neuraminidase inhibitors, polymerase inhibitors, and various vaccines cannot meet therapeutic needs because of viral resistance. Thus, the discovery of new targets for the virus or host and the development of more effective inhibitors are essential to protect humans from the influenza virus.

AREAS COVERED

This review summarizes the latest progress in vaccines and antiviral drug research to prevent and treat influenza, providing the foothold for developing novel antiviral inhibitors.

EXPERT OPINION

Vaccines embody the most effective approach to preventing influenza virus infection, and recombinant protein vaccines show promising prospects in developing next-generation vaccines. Compounds targeting the viral components of RNA polymerase, hemagglutinin and nucleoprotein, and the modification of trusted neuraminidase inhibitors are future research directions for anti-influenza virus drugs. In addition, some host factors affect the replication of virus in vivo, which can be used to develop antiviral drugs.

Discovery of oseltamivir-based novel PROTACs as degraders targeting neuraminidase to combat H1N1 influenza virus

Annual and sporadic influenza outbreaks pose a great threat to human health and the economy worldwide. Moreover, the frequent mutation of influenza viruses caused by antigen drift complicates the application of antiviral therapeutics. As such, there is an urgent need for novel antiviral agents to tackle the problem of insufficient efficacy of licensed drugs. Inspired by the success of the newly emerged PROTACs (PROteolysis TArgeting Chimeras) strategy, we report herein the design and synthesis of novel PROTAC molecules based on an oseltamivir scaffold to combat severe annual influenza outbreaks. Among these, several compounds showed good anti-H1N1 activity and efficient influenza neuraminidase (NA) degradation activity. The best compound, 8e, effectively induced influenza NA degradation in a dose-dependent manner and relied on the ubiquitin-proteasome pathway. Moreover, Compound 8e exhibited potent antiviral activity toward both wild-type H1N1 virus and an oseltamivir-resistant strain (H1N1, H274Y). A molecular docking study demonstrated that Compound 8e had good hydrogen-bonding and hydrophobic interactions with both the active sites of NA and Von Hippel-Lindau (VHL) proteins, which could effectively drive the favorable interaction of these two proteins. Thus, as the first report of a successful anti-influenza PROTAC, this proof of concept will greatly widen the application range of the PROTAC technique to antiviral drug discovery.

Optimization of an Inclusion Body-Based Production of the Influenza Virus Neuraminidase in Escherichia coli

Neuraminidase (NA), as an important protein of influenza virus, represents a promising target for the development of new antiviral agents for the treatment and prevention of influenza A and B. Bacterial host strain Escherichia coli BL21 (DE3)pLysS containing the NA gene of the H1N1 influenza virus produced this overexpressed enzyme in the insoluble fraction of cells in the form of inclusion bodies. The aim of this work was to investigate the effect of independent variables (propagation time, isopropyl β-d-1-thiogalactopyranoside (IPTG) concentration and expression time) on NA accumulation in inclusion bodies and to optimize these conditions by response surface methodology (RSM). The maximum yield of NA (112.97 ± 2.82 U/g) was achieved under optimal conditions, namely, a propagation time of 7.72 h, IPTG concentration of 1.82 mM and gene expression time of 7.35 h. This study demonstrated that bacterially expressed NA was enzymatically active.

Discovery of hydrazide-containing oseltamivir analogues as potent inhibitors of influenza A neuraminidase

Neuraminidase (NA) inhibitors play a prime role in treating influenza. However, a variety of viruses containing mutant NAs have developed severe drug resistance towards NA inhibitors, so it is of crucial significance to solve this problem. Encouraged by urea-containing compound 12 disclosed by our lab, we designed a series of oseltamivir derivatives bearing hydrazide fragment for targeting the 150 cavity. Among the synthesized compounds, compound 17a showed 8.77-fold, 4.12-fold, 203-fold and 6.23-fold more potent activity than oseltamivir carboxylate against NAs from H5N1, H1N1, H5N1-H274Y, H1N1-H274Y, respectively. Meanwhile, the best compound 17a exhibited satisfactory metabolic stability in vitro. This study offers an important reference for the structural optimization of oseltamivir aiming at potent inhibition against H274Y mutant of NAs.

Recent progress in chemical approaches for the development of novel neuraminidase inhibitors

Influenza virus is the main cause of an infectious disease called influenza affecting the respiratory system including the throat, nose and lungs. Neuraminidase inhibitors are reagents used to block the enzyme called neuraminidase to prevent the influenza infection from spreading. Neuraminidase inhibitors are widely used in the treatment of influenza infection, but still there is a need to develop more potent agents for the more effective treatment of influenza. Complications of the influenza disease lead to death, and one of these complications is drug resistance; hence, there is an urgent need to develop more effective agents. This review focuses on the recent advances in chemical synthesis pathways used for the development of new neuraminidase agents along with the medicinal aspects of chemically modified molecules, including the structure-activity relationship, which provides further rational designs of more active small molecules.

Five Novel Non-Sialic Acid-Like Scaffolds Inhibit In Vitro H1N1 and H5N2 Neuraminidase Activity of Influenza a Virus

Neuraminidase (NA) of influenza viruses enables the virus to access the cell membrane. It degrades the sialic acid contained in extracellular mucin. Later, it is responsible for releasing newly formed virions from the membrane of infected cells. Both processes become key functions within the viral cycle. Therefore, it is a therapeutic target for research of the new antiviral agents. Structure–activity relationships studies have revealed which are the important functional groups for the receptor–ligand interaction. Influenza virus type A NA activity was inhibited by five scaffolds without structural resemblance to sialic acid. Intending small organic compound repositioning along with drug repurposing, this study combined in silico simulations of ligand docking into the known binding site of NA, along with in vitro bioassays. The five proposed scaffolds are N-acetylphenylalanylmethionine, propanoic 3-[(2,5-dimethylphenyl) carbamoyl]-2-(piperazin-1-yl) acid, 3-(propylaminosulfonyl)-4-chlorobenzoic acid, ascorbic acid (vitamin C), and 4-(dipropylsulfamoyl) benzoic acid (probenecid). Their half maximal inhibitory concentration (IC₅₀) was determined through fluorometry. An acidic reagent 2′-O-(4-methylumbelliferyl)-α-dN-acetylneuraminic acid (MUNANA) was used as substrate for viruses of human influenza H1N1 or avian influenza H5N2. Inhibition was observed in millimolar ranges in a concentration-dependent manner. The IC₅₀ values of the five proposed scaffolds ranged from 6.4 to 73 mM. The values reflect a significant affinity difference with respect to the reference drug zanamivir (p < 0.001). Two compounds (N-acetyl dipeptide and 4-substituted benzoic acid) clearly showed competitive mechanisms, whereas ascorbic acid reflected non-competitive kinetics. The five small organic molecules constitute five different scaffolds with moderate NA affinities. They are proposed as lead compounds for developing new NA inhibitors which are not analogous to sialic acid.

[The model of prediction of the inhibition of neuraminidases of influenza A and B based on a reduced set of energy terms]

The overall model for prediction of IC50 values for inhibitors of neuraminidase influenza virus A and B has been created. It combines data about IC50 values of complexes of 40 variants of neuraminidases of influenza A (7 serotypes) and B and three known inhibitors (oseltamivir, zanamivir, peramivir). The model also uses only data of enthalpy contributions to the potential energy of inhibitor/protein and substrate (MUNANA)/protein complexes. The calculation procedures are ported to use software with support of GPU accelerators, that significant decrease the computation time. The corresponding correlation coefficient (R²) for pIC50 prediction was within 0.45-0.58, the SEM values of around 0.7 (the range of used pIC50 data set is from 4.55 to 10.22).

iQSP: A Sequence-Based Tool for the Prediction and Analysis of Quorum Sensing Peptides via Chou's 5-Steps Rule and Informative Physicochemical Properties

Understanding of quorum-sensing peptides (QSPs) in their functional mechanism plays an essential role in finding new opportunities to combat bacterial infections by designing drugs. With the avalanche of the newly available peptide sequences in the post-genomic age, it is highly desirable to develop a computational model for efficient, rapid and high-throughput QSP identification purely based on the peptide sequence information alone. Although, few methods have been developed for predicting QSPs, their prediction accuracy and interpretability still requires further improvements. Thus, in this work, we proposed an accurate sequence-based predictor (called iQSP) and a set of interpretable rules (called IR-QSP) for predicting and analyzing QSPs. In iQSP, we utilized a powerful support vector machine (SVM) cooperating with 18 informative features from physicochemical properties (PCPs). Rigorous independent validation test showed that iQSP achieved maximum accuracy and MCC of 93.00% and 0.86, respectively. Furthermore, a set of interpretable rules IR-QSP was extracted by using random forest model and the 18 informative PCPs. Finally, for the convenience of experimental scientists, the iQSP web server was established and made freely available online. It is anticipated that iQSP will become a useful tool or at least as a complementary existing method for predicting and analyzing QSPs.

Meta-iAVP: A Sequence-Based Meta-Predictor for Improving the Prediction of Antiviral Peptides Using Effective Feature Representation

In spite of the large-scale production and widespread distribution of vaccines and antiviral drugs, viruses remain a prominent human disease. Recently, the discovery of antiviral peptides (AVPs) has become an influential antiviral agent due to their extraordinary advantages. With the avalanche of newly-found peptide sequences in the post-genomic era, there is a great demand to develop a sequence-based predictor for timely identifying AVPs as this information is very useful for both basic research and drug development. In this study, we propose a novel sequence-based meta-predictor with an effective feature representation, called Meta-iAVP, for the accurate prediction of AVPs from given peptide sequences. Herein, the effective feature representation was extracted from a set of prediction scores derived from various machine learning algorithms and types of features. To the best of our knowledge, the model proposed herein represents the first meta-based approach for the prediction of AVPs. An overall accuracy and Matthews correlation coefficient of 95.20% and 0.90, respectively, was achieved from the independent test set on an objective benchmark dataset. Comparative analysis suggested that Meta-iAVP was superior to that of existing methods and therefore represents a useful tool for AVP prediction. Finally, in an effort to facilitate high-throughput prediction of AVPs, the model was deployed as the Meta-iAVP web server and is made freely available online at http://codes.bio/meta-iavp/ where users can submit query peptide sequences for determining the likelihood of whether or not these peptides are AVPs.

Design, synthesis and biological evaluation of "Multi-Site"-binding influenza virus neuraminidase inhibitors

Encouraged by our earlier discovery of neuraminidase inhibitors targeting 150-cavity or 430-cavity, herein, to yield more potent inhibitors, we designed, synthesized, and biologically evaluated a series of novel oseltamivir derivatives via modification of C-1 and C5-NH2 of oseltamivir by exploiting 150-cavity and/or 430-cavity. Among the synthesized compounds, compound 15e, the most potent N1-selective inhibitor targeting 150-cavity, showed 1.5 and 1.8 times greater activity than oseltamivir carboxylate (OSC) against N1 (H5N1) and N1 (H5N1-H274Y). In cellular assays, 15e also exhibited greater potency than OSC against H5N1 with EC50 of 0.66 μM. In addition, 15e demonstrated low cytotoxicity in vitro and low acute toxicity in mice. Molecular docking studies provided insights into the high potency of 15e against N1 and N1-H274Y mutant NA. Overall, we envisioned that the significant breakthrough in the discovery of potent group-1-specific neuraminidase inhibitors may lead to further investigation of more potent anti-influenza agents.

Novel N-Substituted oseltamivir derivatives as potent influenza neuraminidase inhibitors: Design, synthesis, biological evaluation, ADME prediction and molecular docking studies

The discovery of novel potent neuraminidase (NA) inhibitors remains an attractive approach for treating infectious diseases caused by influenza. In this study, we describe the design and synthesis of novel N-substituted oseltamivir derivatives for probing the 150-cavity which is nascent to the activity site of NA. NA inhibitory studies showed that new derivatives demonstrated the inhibitory activity with IC₅₀ values at nM level against NA of a clinical influenza virus strain. Moreover, the in silico ADME predictions showed that the selected compounds had comparable properties with oseltamivir carboxylate, which demonstrated the druggablity of these derivatives. Furthermore, molecular docking studies showed that the most potent compound 6f and 10i could adopt different modes of binding interaction with NA, which may provide novel solutions for treating oseltamivir-resistant influenza. Based on the research results, we consider that compounds 6f and 10i have the potential for further studies as novel antiviral agents.

ACPred: A Computational Tool for the Prediction and Analysis of Anticancer Peptides

Anticancer peptides (ACPs) have emerged as a new class of therapeutic agent for cancer treatment due to their lower toxicity as well as greater efficacy, selectivity and specificity when compared to conventional small molecule drugs. However, the experimental identification of ACPs still remains a time-consuming and expensive endeavor. Therefore, it is desirable to develop and improve upon existing computational models for predicting and characterizing ACPs. In this study, we present a bioinformatics tool called the ACPred, which is an interpretable tool for the prediction and characterization of the anticancer activities of peptides. ACPred was developed by utilizing powerful machine learning models (support vector machine and random forest) and various classes of peptide features. It was observed by a jackknife cross-validation test that ACPred can achieve an overall accuracy of 95.61% in identifying ACPs. In addition, analysis revealed the following distinguishing characteristics that ACPs possess: (i) hydrophobic residue enhances the cationic properties of α-helical ACPs resulting in better cell penetration; (ii) the amphipathic nature of the α-helical structure plays a crucial role in its mechanism of cytotoxicity; and (iii) the formation of disulfide bridges on β-sheets is vital for structural maintenance which correlates with its ability to kill cancer cells. Finally, for the convenience of experimental scientists, the ACPred web server was established and made freely available online.

HIVCoR: A sequence-based tool for predicting HIV-1 CRF01_AE coreceptor usage

Determination of HIV-1 coreceptor usage is strongly recommended before starting the coreceptor-specific inhibitors for HIV treatment. Currently, the genotypic assays are the most interesting tools due to they are more feasible than phenotypic assays. However, most of prediction models were developed and validated by data set of HIV-1 subtype B and C. The present study aims to develop a powerful and reliable model to accurately predict HIV-1 coreceptor usage for CRF01_AE subtype called HIVCoR. HIVCoR utilized random forest and support vector machine as the prediction model, together with amino acid compositions, pseudo amino acid compositions and relative synonymous codon usage frequencies as the input feature. The overall success rate of 93.79% was achieved from the external validation test on the objective benchmark dataset. Comparison results indicated that HIVCoR was superior to other bioinformatics tools and genotypic predictors. For the convenience of experimental scientists, a user-friendly webserver has been established at http://codes.bio/hivcor/.

PAAP: a web server for predicting antihypertensive activity of peptides

AIM

Hypertension is associated with development of cardiovascular disease and has become a significant health problem worldwide. Naturally-derived antihypertensive peptides have emerged as promising alternatives to synthetic drugs.

MATERIALS & METHODS

This study introduces predictor of antihypertensive activity of peptides constructed using random forest classifier as a function of various combinations of amino acid, dipeptide and pseudoamino acid composition descriptors.

RESULTS

Classification models were assessed via independent test set that demonstrated accuracy of 84.73%. Feature importance analysis revealed the preference of proline and hydrophobic amino acids at the C-terminal as well as the preference of short peptides for robust activity.

CONCLUSION

Model presented herein serves as a useful tool for predicting and analysis of antihypertensive activity of peptides.

Optimization of N-Substituted Oseltamivir Derivatives as Potent Inhibitors of Group-1 and -2 Influenza A Neuraminidases, Including a Drug-Resistant Variant

On the basis of our earlier discovery of N1-selective inhibitors, the 150-cavity of influenza virus neuraminidases (NAs) could be further exploited to yield more potent oseltamivir derivatives. Among the synthesized compounds, 15b and 15c were exceptionally active against both group-1 and -2 NAs. Especially for 09N1, N2, N6, and N9 subtypes, they showed 6.80-12.47 and 1.20-3.94 times greater activity than oseltamivir carboxylate (OSC). They also showed greater inhibitory activity than OSC toward H274Y and E119V variant. In cellular assays, they exhibited greater potency than OSC toward H5N1, H5N2, H5N6, and H5N8 viruses. 15b demonstrated high metabolic stability, low cytotoxicity in vitro, and low acute toxicity in mice. Computational modeling and molecular dynamics studies provided insights into the role of R group of 15b in improving potency toward group-1 and -2 NAs. We believe the successful exploitation of the 150-cavity of NAs represents an important breakthrough in the development of more potent anti-influenza agents.

CryoProtect: A Web Server for Classifying Antifreeze Proteins from Nonantifreeze Proteins

Antifreeze protein (AFP) is an ice-binding protein that protects organisms from freezing in extremely cold environments. AFPs are found across a diverse range of species and, therefore, significantly differ in their structures. As there are no consensus sequences available for determining the ice-binding domain of AFPs, thus the prediction and characterization of AFPs from their sequence is a challenging task. This study addresses this issue by predicting AFPs directly from sequence on a large set of 478 AFPs and 9,139 non-AFPs using machine learning (e.g., random forest) as a function of interpretable features (e.g., amino acid composition, dipeptide composition, and physicochemical properties). Furthermore, AFPs were characterized using propensity scores and important physicochemical properties via statistical and principal component analysis. The predictive model afforded high performance with an accuracy of 88.28% and results revealed that AFPs are likely to be composed of hydrophobic amino acids as well as amino acids with hydroxyl and sulfhydryl side chains. The predictive model is provided as a free publicly available web server called CryoProtect for classifying query protein sequence as being either AFP or non-AFP. The data set and source code are for reproducing the results which are provided on GitHub.

Potential New H1N1 Neuraminidase Inhibitors from Ferulic Acid and Vanillin: Molecular Modelling, Synthesis and in Vitro Assay

We report the computational and experimental efforts in the design and synthesis of novel neuraminidase (NA) inhibitors from ferulic acid and vanillin. Two proposed ferulic acid analogues, MY7 and MY8 were predicted to inhibit H1N1 NA using molecular docking. From these two analogues, we designed, synthesised and evaluated the biological activities of a series of ferulic acid and vanillin derivatives. The enzymatic H1N1 NA inhibition assay showed MY21 (a vanillin derivative) has the lowest IC50 of 50 μM. In contrast, the virus inhibition assay showed MY15, a ferulic acid derivative has the best activity with the EC50 of ~0.95 μM. Modelling studies further suggest that these predicted activities might be due to the interactions with conserved and essential residues of NA with ΔGbind values comparable to those of oseltamivir and zanamivir, the two commercial NA inhibitors.