Exploring the Antiviral Potential of Natural Compounds against Influenza: A Combined Computational and Experimental Approach.

The influenza A virus nonstructural protein 1 (NS1), which is crucial for viral replication and immune evasion, has been identified as a significant drug target with substantial potential to contribute to the fight against influenza. The emergence of drug-resistant influenza A virus strains highlights the urgent need for novel therapeutics. This study proposes a combined theoretical criterion for the virtual screening of molecular libraries to identify candidate NS1 inhibitors. By applying the criterion to the ZINC Natural Product database, followed by ligand-based virtual screening and molecular docking, we proposed the most promising candidate as a potential NS1 inhibitor. Subsequently, the selected natural compound was experimentally evaluated, revealing measurable virus replication inhibition activity in cell culture. This approach offers a promising avenue for developing novel anti-influenza agents targeting the NS1 protein.

In Silico and In Vitro Inhibition of SARS-CoV-2 PLpro with Gramicidin D.

Finding an effective drug to prevent or treat COVID-19 is of utmost importance in tcurrent pandemic. Since developing a new treatment takes a significant amount of time, drug repurposing can be an effective option for achieving a rapid response. This study used a combined in silico virtual screening protocol for candidate SARS-CoV-2 PLpro inhibitors. The Drugbank database was searched first, using the Informational Spectrum Method for Small Molecules, followed by molecular docking. Gramicidin D was selected as a peptide drug, showing the best in silico interaction profile with PLpro. After the expression and purification of PLpro, gramicidin D was screened for protease inhibition in vitro and was found to be active against PLpro. The current study's findings are significant because it is critical to identify COVID-19 therapies that are efficient, affordable, and have a favorable safety profile.

Novel Entropy-Based Phylogenetic Algorithm: A New Approach for Classifying SARS-CoV-2 Variants.

The SARS-CoV-2 virus, the causative agent of COVID-19, is known for its genetic diversity. Virus variants of concern (VOCs) as well as variants of interest (VOIs) are classified by the World Health Organization (WHO) according to their potential risk to global health. This study seeks to enhance the identification and classification of such variants by developing a novel bioinformatics criterion centered on the virus's spike protein (SP1), a key player in host cell entry, immune response, and a mutational hotspot. To achieve this, we pioneered a unique phylogenetic algorithm which calculates EIIP-entropy as a distance measure based on the distribution of the electron-ion interaction potential (EIIP) of amino acids in SP1. This method offers a comprehensive, scalable, and rapid approach to analyze large genomic data sets and predict the impact of specific mutations. This innovative approach provides a robust tool for classifying emergent SARS-CoV-2 variants into potential VOCs or VOIs. It could significantly augment surveillance efforts and understanding of variant characteristics, while also offering potential applicability to the analysis and classification of other emerging viral pathogens and enhancing global readiness against emerging and re-emerging viral pathogens.

In Silico Screening of Natural Compounds for Candidates 5HT6 Receptor Antagonists against Alzheimer's Disease.

Alzheimer's disease (AD), a devastating neurodegenerative disease, is the focus of pharmacological research. One of the targets that attract the most attention for the potential therapy of AD is the serotonin 5HT6 receptor, which is the receptor situated exclusively in CNS on glutamatergic and GABAergic neurons. The neurochemical impact of this receptor supports the hypothesis about its role in cognitive, learning, and memory systems, which are of critical importance for AD. Natural products are a promising source of novel bioactive compounds with potential therapeutic potential as a 5HT6 receptor antagonist in the treatment of AD dementia. The ZINC-natural product database was in silico screened in order to find the candidate antagonists of 5-HT6 receptor against AD. A virtual screening protocol that includes both short-and long-range interactions between interacting molecules was employed. First, the EIIP/AQVN filter was applied for in silico screening of the ZINC database followed by 3D QSAR and molecular docking. Ten best candidate compounds were selected from the ZINC Natural Product database as potential 5HT6 Receptor antagonists and were proposed for further evaluation. The best candidate was evaluated by molecular dynamics simulations and free energy calculations.

Tally-2.0: upgraded validator of tandem repeat detection in protein sequences.

MOTIVATION: Proteins containing tandem repeats (TRs) are abundant, frequently fold in elongated non-globular structures and perform vital functions. A number of computational tools have been developed to detect TRs in protein sequences. A blurred boundary between imperfect TR motifs and non-repetitive sequences gave rise to necessity to validate the detected TRs. RESULTS: Tally-2.0 is a scoring tool based on a machine learning (ML) approach, which allows to validate the results of TR detection. It was upgraded by using improved training datasets and additional ML features. Tally-2.0 performs at a level of 93% sensitivity, 83% specificity and an area under the receiver operating characteristic curve of 95%. AVAILABILITY AND IMPLEMENTATION: Tally-2.0 is available, as a web tool and as a standalone application published under Apache License 2.0, on the URL https://bioinfo.crbm.cnrs.fr/index.php? route=tools&tool=27. It is supported on Linux. Source code is available upon request. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Biological Rationale for the Repurposing of BCG Vaccine against SARS-CoV-2.

The Bacillus Calmette-Guerin vaccine is still widely used in the developing world. The vaccination prevents infant death not only from tuberculosis but also from unrelated infectious agents, especially respiratory tract infections and neonatal sepsis. It is proposed that these off-target protective effects of the BCG vaccine are mediated by the general long-term boosting of innate immune mechanisms, also termed "trained innate immunity". Recent studies indicate that both COVID-19 incidence and total deaths are strongly associated with the presence or absence of national mandatory BCG vaccination programs and encourage the initiation of several clinical studies with the expectation that revaccination with BCG could reduce the incidence and severity of COVID-19. Here, presented results from the bioinformatics analysis of the Mycobacterium bovis (strain BCG/Pasteur 1173P2) proteome suggests four immunodominant antigens that could induce an immune response against SARS-CoV-2.

DiNGO: standalone application for Gene Ontology and Human Phenotype Ontology term enrichment analysis.

SUMMARY: Although various tools for Gene Ontology (GO) term enrichment analysis are available, there is still room for improvement. Hence, we present DiNGO, a standalone application based on an open source code from BiNGO, a widely-used application to assess the overrepresentation of GO categories. Besides facilitating GO term enrichment analyses, DiNGO has been developed to allow for convenient Human Phenotype Ontology (HPO) term overrepresentation investigation. This is an important contribution considering the increasing interest in HPO in scientific research and its potential in clinical settings. DiNGO supports gene/protein identifier conversion and an automatic updating of GO and HPO annotation resources. Finally, DiNGO can rapidly process a large amount of data due to its multithread design. AVAILABILITY AND IMPLEMENTATION: DiNGO is implemented in the JAVA language, and its source code, example datasets and instructions are available on GitHub: https://github.com/radoslav180/DiNGO. A pre-compiled jar file is available at: https://www.vin.bg.ac.rs/180/tools/DiNGO.php. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Drug Repurposing for Candidate SARS-CoV-2 Main Protease Inhibitors by a Novel In Silico Method.

The SARS-CoV-2 outbreak caused an unprecedented global public health threat, having a high transmission rate with currently no drugs or vaccines approved. An alternative powerful additional approach to counteract COVID-19 is in silico drug repurposing. The SARS-CoV-2 main protease is essential for viral replication and an attractive drug target. In this study, we used the virtual screening protocol with both long-range and short-range interactions to select candidate SARS-CoV-2 main protease inhibitors. First, the Informational spectrum method applied for small molecules was used for searching the Drugbank database and further followed by molecular docking. After in silico screening of drug space, we identified 57 drugs as potential SARS-CoV-2 main protease inhibitors that we propose for further experimental testing.

Automated feature engineering improves prediction of protein-protein interactions.

Over the last decade, various machine learning (ML) and statistical approaches for protein-protein interaction (PPI) predictions have been developed to help annotating functional interactions among proteins, essential for our system-level understanding of life. Efficient ML approaches require informative and non-redundant features. In this paper, we introduce novel types of expert-crafted sequence, evolutionary and graph features and apply automatic feature engineering to further expand feature space to improve predictive modeling. The two-step automatic feature-engineering process encompasses the hybrid method for feature generation and unsupervised feature selection, followed by supervised feature selection through a genetic algorithm (GA). The optimization of both steps allows the feature-engineering procedure to operate on a large transformed feature space with no considerable computational cost and to efficiently provide newly engineered features. Based on GA and correlation filtering, we developed a stacking algorithm GA-STACK for automatic ensembling of different ML algorithms to improve prediction performance. We introduced a unified method, HP-GAS, for the prediction of human PPIs, which incorporates GA-STACK and rests on both expert-crafted and 40% of newly engineered features. The extensive cross validation and comparison with the state-of-the-art methods showed that HP-GAS represents currently the most efficient method for proteome-wide forecasting of protein interactions, with prediction efficacy of 0.93 AUC and 0.85 accuracy. We implemented the HP-GAS method as a free standalone application which is a time-efficient and easy-to-use tool. HP-GAS software with supplementary data can be downloaded from: http://www.vinca.rs/180/tools/HP-GAS.php .

TRI_tool: a web-tool for prediction of protein-protein interactions in human transcriptional regulation.

The TRI_tool, a sequence-based web tool for prediction of protein interactions in the human transcriptional regulation, is intended for biomedical investigators who work on understanding the regulation of gene expression. It has an improved predictive performance due to the training on updated, human specific, experimentally validated datasets. The TRI_tool is designed to test up to 100 potential interactions with no time delay and to report both probabilities and binarized predictions. AVAILABILITY AND IMPLEMENTATION: http://www.vin.bg.ac.rs/180/tools/tfpred.php CONTACT: vladaper@vinca.rs; nevenav@vinca.rsSupplementary information: Supplementary data are available at Bioinformatics online.

Common molecular mechanism of the hepatic lesion and the cardiac parasympathetic regulation in chronic hepatitis C infection: a critical role for the muscarinic receptor type 3.

BACKGROUND: The pathophysiological overlapping between Sjorgen's Syndrome (SS) and HCV, presence of anti- muscarinic receptor type 3 (M3R) antibodies in SS, the role that M3R plays in the regulation of the heart rate, has led to the assumption that cardiovagal dysfunction in HCV patients is caused by anti-M3R antibodies elicited by HCV proteins or by their direct interaction with M3R. RESULTS: To identify HCV protein which possibly is crossreactive with M3R or which binds to this receptor, we performed the Informational Spectrum Method (ISM) analysis of the HCV proteome. This analysis revealed that NS5A protein represents the most probable interactor of M3R or that this viral protein could elicit antibodies which modulate function of this receptor. Further detailed structure/function analysis of NS5A and M3R performed by the ISM method extended with other Digital Signal processing (DSP) approaches revealed domains of these proteins which participate in their crossreactivity or in their direct interaction, representing promising diagnostic and therapeutic targets. CONCLUSIONS: Application of the ISM with other compatible bioinformatics methods offers new perspectives for identifying diagnostic and therapeutic targets for complicated forms of HCV and other viral infections. We show how the electron-ion interaction potential (EIIP) amino-acid scale used in the ISM combined with a robust, high performance hydrophobicity scale can provide new insights for understanding protein structure/function and protein-protein interactions.

A combined ligand- and structure-based approach for the identification of rilmenidine-derived compounds which synergize the antitumor effects of doxorubicin.

The clonidine-like central antihypertensive agent rilmenidine, which has high affinity for I1-type imidazoline receptors (I1-IR) was recently found to have cytotoxic effects on cultured cancer cell lines. However, due to its pharmacological effects resulting also from α2-adrenoceptor activation, rilmenidine cannot be considered a suitable anticancer drug candidate. Here, we report the identification of novel rilmenidine-derived compounds with anticancer potential and devoid of α2-adrenoceptor effects by means of ligand- and structure-based drug design approaches. Starting from a large virtual library, eleven compounds were selected, synthesized and submitted to biological evaluation. The most active compound 5 exhibited a cytotoxic profile similar to that of rilmenidine, but without appreciable affinity to α2-adrenoceptors. In addition, compound 5 significantly enhanced the apoptotic response to doxorubicin, and may thus represent an important tool for the development of better adjuvant chemotherapeutic strategies for doxorubicin-insensitive cancers.

The Polymorphism rs3024505 (C/T) Downstream of the IL10 Gene Is Associated with Crohn's Disease in Serbian Patients with Inflammatory Bowel Disease.

Inflammatory bowel disease (IBD), manifesting as Crohn's disease (CD) and ulcerative colitis (UC), is characterized by recurring episodes of inflammation in gastrointestinal tract, in which aberrant production of regulatory cytokine interleukin-10 (IL-10) presumably plays important role. Single nucleotide polymorphisms (SNPs) that affect IL-10 production, such as rs1800896 (G/A) at position -1082 and rs1800871 (C/T) at position -819 in the promoter region of the IL10 gene, have been associated with CD and/or UC, but the results were inconsistent. Another SNP that may alter IL-10 production, rs3024505 (C/T) located immediately downstream of the IL10 gene has been recently identified. T allele of rs3024505 was associated with both UC and CD in Western populations, but the studies from East European countries are lacking. Therefore, our aim was to assess the association of rs3024505, rs1800896 and rs1800871 with Serbian IBD patients. To this end, 107 CD and 99 UC patients and 255 healthy controls were genotyped. As a result, T allele of rs3024505 was associated with CD at allelic, genotypic (GT genotype) and haplotypic (GCCT haplotype) level, suggesting potential role of this variant in susceptibility to CD. In contrast, CD patients carrying C allele of rs3024505 had significantly increased risk of anemia and stricturing/penetrating behavior. No association was observed between rs3024505 and UC or SNPs in IL10 promoter region and any form of IBD. In conclusion, rs3024505 SNP flanking the IL10 gene is associated with susceptibility and severity of disease in Serbian CD patients, further validating its role as a potential biomarker in IBD.

Arginase Flavonoid Anti-Leishmanial in Silico Inhibitors Flagged against Anti-Targets.

Arginase, a drug target for the treatment of leishmaniasis, is involved in the biosynthesis of polyamines. Flavonoids are interesting natural compounds found in many foods and some of them may inhibit this enzyme. The MetIDB database containing 5667 compounds was screened using an EIIP/AQVN filter and 3D QSAR to find the most promising candidate compounds. In addition, these top hits were screened in silico versus human arginase and an anti-target battery consisting of cytochromes P450 2a6, 2c9, 3a4, sulfotransferase, and the pregnane-X-receptor in order to flag their possible interactions with these proteins involved in the metabolism of substances. The resulting compounds may have promise to be further developed for the treatment of leishmaniasis.

Idarubicin induces mTOR-dependent cytotoxic autophagy in leukemic cells.

We investigated if the antileukemic drug idarubicin induces autophagy, a process of programmed cellular self-digestion, in leukemic cell lines and primary leukemic cells. Transmission electron microscopy and acridine orange staining demonstrated the presence of autophagic vesicles and intracellular acidification, respectively, in idarubicin-treated REH leukemic cell line. Idarubicin increased punctuation/aggregation of microtubule-associated light chain 3B (LC3B), enhanced the conversion of LC3B-I to autophagosome-associated LC3B-II in the presence of proteolysis inhibitors, and promoted the degradation of the selective autophagic target p62, thus indicating the increase in autophagic flux. Idarubicin inhibited the phosphorylation of the main autophagy repressor mammalian target of rapamycin (mTOR) and its downstream target p70S6 kinase. The treatment with the mTOR activator leucine prevented idarubicin-mediated autophagy induction. Idarubicin-induced mTOR repression was associated with the activation of the mTOR inhibitor AMP-activated protein kinase and down-regulation of the mTOR activator Akt. The suppression of autophagy by pharmacological inhibitors or LC3B and beclin-1 genetic knockdown rescued REH cells from idarubicin-mediated oxidative stress, mitochondrial depolarization, caspase activation and apoptotic DNA fragmentation. Idarubicin also caused mTOR inhibition and cytotoxic autophagy in K562 leukemic cell line and leukocytes from chronic myeloid leukemia patients, but not healthy controls. By demonstrating mTOR-dependent cytotoxic autophagy in idarubicin-treated leukemic cells, our results warrant caution when considering combining idarubicin with autophagy inhibitors in leukemia therapy.

In Silico Exploration of CD200 as a Therapeutic Target for COVID-19.

SARS-CoV-2, the pathogen causing COVID-19, continues to pose a significant threat to public health and has had major economic implications. Developing safe and effective vaccines and therapies offers a path forward for overcoming the COVID-19 pandemic. The presented study, performed by using the informational spectrum method (ISM), representing an electronic biology-based tool for analysis of protein-protein interactions, identified the highly conserved region of spike protein (SP) from SARS-CoV-2 virus, which is essential for recognition and targeting between the virus and its protein interactors on the target cells. This domain is suggested as a promising target for the drug therapy and vaccines, which could be effective against all currently circulating variants of SARS-CoV-2 viruses. The analysis of the virus/host interaction, performed by the ISM, also revealed OX-2 membrane glycoprotein (CD200) as a possible interactor of SP, which could serve as a novel therapeutic target for COVID-19 disease.

Novel neurodigital interface reduces motion sickness in virtual reality.

Virtual reality (VR) is a computer-created 3D environment with a focus on realistic scenes and pictures created for entertainment, medical and/or educational and training purposes. One of the major side effects of VR immersion reported in the scientific literature, media and social media is Visually Induced Motion Sickness (VIMS), with clinical symptoms such as disorientation, nausea, and oculomotor discomfort. VIMS is mostly caused by the discrepancy between the visual and vestibular systems and can lead to dizziness, nausea, and disorientation. In this study, we present one potential novel solution to combat motion sickness in VR, showcasing a significant reduction of nausea in VR users employing the META Quest 2 headsets in conjunction with a whole-body controller. Using a neurodigital approach, we facilitate a more immersive and comfortable VR experience. Our findings indicate a marked reduction in VR-induced nausea, paving the way to promote VR technology for broader applications across various fields.

Feature-based classification of amino acid substitutions outside conserved functional protein domains.

There are more than 500 amino acid substitutions in each human genome, and bioinformatics tools irreplaceably contribute to determination of their functional effects. We have developed feature-based algorithm for the detection of mutations outside conserved functional domains (CFDs) and compared its classification efficacy with the most commonly used phylogeny-based tools, PolyPhen-2 and SIFT. The new algorithm is based on the informational spectrum method (ISM), a feature-based technique, and statistical analysis. Our dataset contained neutral polymorphisms and mutations associated with myeloid malignancies from epigenetic regulators ASXL1, DNMT3A, EZH2, and TET2. PolyPhen-2 and SIFT had significantly lower accuracies in predicting the effects of amino acid substitutions outside CFDs than expected, with especially low sensitivity. On the other hand, only ISM algorithm showed statistically significant classification of these sequences. It outperformed PolyPhen-2 and SIFT by 15% and 13%, respectively. These results suggest that feature-based methods, like ISM, are more suitable for the classification of amino acid substitutions outside CFDs than phylogeny-based tools.

Intestinal Colonization of Preterm Neonates with Carbapenem Resistant Enterobacteria at Hospital Discharge.

Our aim was to investigate gut colonization with carbapenem-resistant Enterobacterales (CRE) in the population of preterm neonates at discharge from a tertiary care center in Serbia. The study included 350 randomly selected neonates/infants discharged in the period April 2018-May 2019. CRE colonization was present in 88/350 (25.1%) of patients. Klebsiella pneumoniae producing KPC and OXA-48 carbapenemase were detected in 45 and 42 subjects, respectively, while NDM producing Escherichia coli was identified in one patient only. All OXA-48 strains harbored blaCTX-M-15, while both blaTEM and blaSHV were present in all but one KPC-producing strain. CRE isolates exhibited a multidrug resistance pattern with uniform fluoroquinolone resistance, universal susceptibility to colistin, and variable susceptibility to aminoglycosides. Administration of carbapenems was common (~50%) and it was strongly associated with colonization, as well as the combinational therapeutic regimens that included meropenem, contrary to ampicillin-sulbactam/colistin therapy and prolonged course of the initial therapy (ampicillin/amikacin ≥ 7 days). Other risk factors for CRE carriage were level of immaturity, admission to neonatal intensive care unit, prolonged hospitalization and invasive procedures. Although the rate of clinically and/or laboratory proven systemic infections was significantly higher among colonized patients, CRE infection was confirmed in one patient only (1.1%) that was colonized with NDM E. coli. Clonal relatedness of CRE isolates was high, with seven and eight clusters detected among KPC (N = 30) and OXA-48 (N = 37) producing strains, respectively. The follow up of the 31 KPC-colonized patients after discharge from hospital revealed common decolonization within one month (~68%). In conclusion, our results demonstrated a high rate of CRE colonization that is most likely related to carbapenem consumption and lack of screening as important infection prevention practice.

Cytokine Gene Polymorphisms in Patients with Chronic Inflammatory Demyelinating Polyneuropathy.

Innate and adaptive immune responses exert their role in CIDP pathogenesis through cytokine production. Single-nucleotide polymorphisms (SNPs) may alter cytokine gene expression, with a potential influence on the pathogenesis of autoimmune diseases. However, cytokine gene SNPs have not been assessed in CIDP patients yet. We assessed functional SNPs in the genes encoding IL-10 (rs1800896, rs1800871, rs1800872 and rs3024505), IL-6 (rs1800795), TNF (rs1800629 and rs361525), IL-12B (rs3212227), IFN-γ (rs2430561), GM-CSF (rs25882) and IL-17F (rs11465553) in a cohort of 88 CIDP patients and 486 healthy controls (HCs) via qPCR. We found an association of SNP in the IL10 promotor and CIDP occurrence. Major homozygotes (AA) were more frequent in the HCs compared to CIDP patients (p = 0.049), but the GA genotype prevailed among the patients (p = 0.032). A lower frequency of the C allele was observed for rs1800871 and rs1800872 in CIDP patients compared to the HCs (p = 0.048). A higher proportion of A carriers at position -1082 (rs1800896) (presumed to be a low IL-10 producer) was noted in patients with milder disability (low INCAT). All mild-INCAT patients were C carriers for rs1800871 and rs1800872 in IL10 (p = 0.038). Furthermore, the IL6 rs1800795 GG genotype was more frequent in patients (p = 0.049) and the CG heterozygote in the HCs (p = 0.013). Among the CIDP patients, being a G carrier for this SNP was associated with a higher frequency of type 2 diabetes (T2D) compared to being a non-carrier (p = 0.032). Our data indicate a possible association of the IL10 and IL6 SNPs with CIDP, but also with disease severity and T2D occurrence. Given the paucity of CIDP patients, multicentric studies are necessary to draw definite conclusions on these associations.