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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 solution to motion sickness in VR and demonstrate significant reduction of nausea in VR users with META Quest 2 headsets when using a whole-body controller to experience VR in a more comfortable and immersive way.

Finding an effective drug to prevent or treat COVID-19 is of utmost importance in tcurrentpandemic. Since developing a new treatment takes a significant amount of time, drug repurposingcan be an effective option for achieving a rapid response. This study used a combined in silico virtualscreening protocol for candidate SARS-CoV-2 PLpro inhibitors. The Drugbank database was searchedfirst, 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 inhibitionin vitro and was found to be active against PLpro. The current study’s findings are significantbecause it is critical to identify COVID-19 therapies that are efficient, affordable, and have a favorablesafety profile. This article belongs to the Special Issue Molecular Interactions and Mechanisms of COVID-19 Inhibition 2.0

Background Chronic graft-versus-host disease (cGvHD) is one of the most common life-threatening complications following allogeneic haematopoietic stem cell transplantation (alloHSCT). Understanding outcome after alloHSCT requires a full evaluation of the patient’s health status, including cGvHD and patient reported outcomes (PROs). In an effort to better understand practice patterns across European countries, a survey was initiated by the Integrated European Network on cGvHD (an EU-funded COST Action CA17138 EUROGRAFT, www.gvhd.eu) and the Transplant Complications Working Party of the European Society for Blood and Marrow Transplantation (EBMT). This report shares results of the survey, offering a snapshot view of current practice patterns in the context of long-term care of cGvHD patients. Methods Our self-designed 38-item online survey (Supplementary Material) was intended to collect data regarding transplant center characteristics, data registration practices, the use of NIH criteria in clinical routine, biopsies/biomarkers for clinical assessment, cGvHD cell-based therapies, and PROs. The survey used computer adapted testing methods and took ~10 min to complete. All centers participating in the COST Action EUROGRAFT and all EBMT centers performing alloHSCT were invited by email for participation in the survey. Data were collected between July 2019 and July 2020. Appropriate descriptive statistics were used. In case of multiple entries for a single center (n = 4), only the entry from the most senior staff member was included for the analysis. Missing data was reported as such. Findings Center characteristics Survey results are summarized in Table 1. A total of 72 centers out of 424 invited centers from 24 countries responded to the survey, representing ~17% of all alloHSCT centers and 19.6% of all transplanted patients within the EBMT network [1]. The majority of participating alloHSCT centers were from Europe with exception of three centers based in Asia and one in Latin America. Survey responses were mainly submitted by physicians and data managers. Of note, the size of the transplant programs differed between responding (mean ± SD, n = 47 ± 40 transplants/year) vs. non-responding (mean ± SD, n = 39 ± 31 transplants/year) centers (Supplementary Material).

In the current pandemic, finding an effective drug to prevent or treat the infection is the highest priority. A rapid and safe approach to counteract COVID‐19 is in silico drug repurposing. The SARS‐CoV‐2 PLpro promotes viral replication and modulates the host immune system, resulting in inhibition of the host antiviral innate immune response, and therefore is an attractive drug target. In this study, we used a combined in silico virtual screening for candidates for SARS‐CoV‐2 PLpro protease inhibitors. We used the Informational spectrum method applied for Small Molecules for searching the Drugbank database followed by molecular docking. After in silico screening of drug space, we identified 44 drugs as potential SARS‐CoV‐2 PLpro inhibitors that we propose for further experimental testing., Combined in silico machinery, consisting of long‐ and short‐range molecular interaction methods is capable of filtering large compound databases, yielding a good candidate for a receptor. The advantage of this approach is a fast assessment of a candidate, starting from basic information on both protein and small organic molecule – FASTA sequence and SMILES notation.

A novel human coronavirus, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has become the leading threat to global health. An effective antiviral could not only help those still vulnerable to the virus but could be a critical treatment if a virus emerges toward evading coronavirus disease 2019 (COVID-19) vaccines. Despite the significant efforts to test already-approved drugs for their potential to kill the virus, researchers found very few actually worked. Methods: The present report uses the electronic molecular descriptors, the quasi-valence number (AQVN), and the electron-ion interaction potential (EIIP), for the analysis of natural compounds with proven therapeutic activity against the COVID-19. Results: Based on the analysis of the electronic properties of natural compounds which are effective against SARS-CoV-2 virus the simple theoretical criterion for the selection of candidate compounds for the treatment of COVID-19 is proposed. Conclusions: The proposed theoretical criterion can be used for the identification and optimization of new lead compounds for the treatment of the COVID-19 disease and for the selection of the food and food supplements which could have a beneficial effect on COVID-19 patients.

The need for an effective drug against COVID-19, is, after almost 18 months since the global pandemics outburst, still very high. A very quick and safe approach to counteract COVID-19 is in silico drug repurposing. The SARS-CoV-2 PLpro promotes vi-ral replication and modulates the host immune system, resulting in inhibition of the host antiviral innate immune response, and there-fore is an attractive drug target. In this study, we used a combined in silico virtual screening candidates for SARS-CoV-2 PLpro protease inhibitors. We used the Informational spectrum method applied for Small Molecules for searching the Drugbank database and further followed by molecular docking. After in silico screening of drug space, we identified 44 drugs as potential SARS-CoV-2 PLpro inhibitors that we propose for further experimental testing.

For the last couple of decades, there has been a significant growth in sequencing data, leading to an extraordinary increase in the number of gene variants. This places a challenge on the bioinformatics research community to develop and improve computational tools for functional annotation of new variants. Genes coding for epigenetic regulators have important roles in cancer pathogenesis and mutations in these genes show great potential as clinical biomarkers, especially in hematologic malignancies. Therefore, we developed a model that specifically focuses on these genes, with an assumption that it would outperform general models in predicting the functional effects of amino acid substitutions. EpiMut is a standalone software that implements a sequence based alignment-free method. We applied a two-step approach for generating sequence based features, relying on the biophysical and biochemical indices of amino acids and the Fourier Transform as a sequence transformation method. For each gene in the dataset, the machine learning algorithm–Naïve Bayes was used for building a model for prediction of the neutral or disease-related status of variants. EpiMut outperformed state-of-the-art tools used for comparison, PolyPhen-2, SIFT and SNAP2. Additionally, EpiMut showed the highest performance on the subset of variants positioned outside conserved functional domains of analysed proteins, which represents an important group of cancer-related variants. These results imply that EpiMut can be applied as a first choice tool in research of the impact of gene variants in epigenetic regulators, especially in the light of the biomarker role in hematologic malignancies. EpiMut is freely available at https://www.vin.bg.ac.rs/180/tools/epimut.php.

A safe and effective vaccine is urgently needed to bring the current SARS-CoV-2 pandemic under control. The spike protein (SP) of SARS-CoV-2 represents the principal target for most vaccines currently under development. Despite the presence of a CoV proof-reading function in viral replication, SP protein from SARS-CoV still extensively mutates, which might have an impact on current and future vaccine development. Here, we present analysis of more than 1600 SP unique variants suggesting that vaccine candidates based on the Wuhan-Hu-1 reference strain would be effective against most of currently circulated SARS-CoV-2 viruses, but that further monitoring of the evolution of this virus is important for identification of other mutations, which could affect the effectiveness of vaccines.

The complete understanding of the genomic contribution to complex traits, diseases, and response to treatments, as well as genomic medicine application to the well-being of all humans will be achieved through the global variome that encompasses fine-scale genetic diversity. Despite significant efforts in recent years, uneven representation still characterizes genomic resources and among the underrepresented European populations are the Western Balkans including the Serbian population. Our research addresses this gap and presents the first ever targeted sequencing dataset of variants in clinically relevant genes. By measuring population differentiation and applying the Principal Component and Admixture analysis we demonstrated that the Serbian population differs little from other European populations, yet we identified several novel and more frequent variants that appear as its unique genetic determinants. We explored thoroughly the functional impact of frequent variants and its correlation with the health burden of the population of Serbia based on a sample of 144 individuals. Our variants catalogue improves the understanding of genetics of modern Serbia, contributes to research on ancestry, and aids in improvements of well-being and health equity. In addition, this resource may also be applicable in neighboring regions and valuable in worldwide functional analyses of genetic variants in individuals of European descent.

We investigated the mechanisms and the role of autophagy in the differentiation of HL-60 human acute myeloid leukemia cells induced by protein kinase C (PKC) activator phorbol myristate acetate (PMA). PMA-triggered differentiation of HL-60 cells into macrophage-like cells was confirmed by cell-cycle arrest accompanied by elevated expression of macrophage markers CD11b, CD13, CD14, CD45, EGR1, CSF1R, and IL-8. The induction of autophagy was demonstrated by the increase in intracellular acidification, accumulation/punctuation of autophagosome marker LC3-II, and the increase in autophagic flux. PMA also increased nuclear translocation of autophagy transcription factors TFEB, FOXO1, and FOXO3, as well as the expression of several autophagy-related (ATG) genes in HL-60 cells. PMA failed to activate autophagy inducer AMP-activated protein kinase (AMPK) and inhibit autophagy suppressor mechanistic target of rapamycin complex 1 (mTORC1). On the other hand, it readily stimulated the phosphorylation of mitogen-activated protein (MAP) kinases extracellular signal-regulated kinase (ERK) and c-Jun N-terminal kinase (JNK) via a protein kinase C-dependent mechanism. Pharmacological or genetic inhibition of ERK or JNK suppressed PMA-triggered nuclear translocation of TFEB and FOXO1/3, ATG expression, dissociation of pro-autophagic beclin-1 from its inhibitor BCL2, autophagy induction, and differentiation of HL-60 cells into macrophage-like cells. Pharmacological or genetic inhibition of autophagy also blocked PMA-induced macrophage differentiation of HL-60 cells. Therefore, MAP kinases ERK and JNK control PMA-induced macrophage differentiation of HL-60 leukemia cells through AMPK/mTORC1-independent, TFEB/FOXO-mediated transcriptional and beclin-1-dependent post-translational activation of autophagy.

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.

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 (VS) protocol with both long-range and short-range interactions to select candidate SARS-CoV-2 main protease inhibitors. First, the ISM 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.

Recent outbreaks of NDM-1-positive Entero-bacteriaceae in Great Britain and India and the highly pathogenic Escherichia coli strain EHEC O104:H4 in Germany and some other E.U. countries point out an urgent need for the ability to decide on appropriate antibiotics to treat multi-drug-resistant (MDR) bacteria. Here we propose a simple criterion for choosing antibiotics based on characteristics of electron donor and acceptor properties. Using molecular descriptors, such as electron-ion interaction potential (EIIP) and average quasi-valence number (AQVN), which can describe potential long-range interactions between therapeutic molecules and their targets, we have been able to suggest appropriate antibiotics for treatment of MDR bacterial infections. To demonstrate the prospective usefulness of these molecular descriptors we have used this informatics system to propose that pleuromutilins and nitrofurans could be effective against of NDM-1-positive Enterobacteriacea and that aminoglycosides, macrolides and pluromutilins (and possibly nitrofurans) could be suitable for treatment of the highly pathogenic Escherichia coli strain EHEC O104:H4. Similarly, because of their specific electronic properties, we can also suggest antibiotics that could be potentially effective against other MDR bacteria. The proposed antibiotics should be further evaluated for their treatment potentials.

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.

Background Glioblastoma multiforme (GMB) is the most malignant of all brain tumors with poor prognosis. Anticancer potential of xanthones, bioactive compounds found in Gentiana dinarica, is well-documented. Transformation of G. dinarica roots with Agrobacterium rhizogenes provides higher xanthones accumulation, which enables better exploitation of these anticancer compounds. Hypothesis/Purpose The aim of this study was to investigate antiglioma effect of three different G. dinarica extracts: E1—derived from untransformed roots, E2—derived from roots transformed using A. rhizogenes strain A4M70GUS, and E3—derived from roots transformed using A. rhizogenes strain 15834/PI. Further, mechanisms involved in anticancer potential of the most potent extract were examined in detail, and its active component was determined. Methods The cell viability was assessed using MTT and crystal violet test. Cell cycle analysis, the expression of differentiation markers, the levels of autophagy, and oxidative stress were analyzed by flow cytometry. Autophagy and related signaling pathways were assessed by immunoblotting. Results E3, in contrast to E1 and E2, strongly reduced growth of U251 human glioblastoma cells, triggered cell cycle arrest in G2/M phase, changed cellular morphology, and increased expression of markers of differentiated astrocytes (glial fibrillary acidic protein) and neurons (β-tubulin). E3 stimulated autophagy, as demonstrated by enhanced intracellular acidification, increased microtubule-associated light chain 3B (LC3-I) conversion to autophagosome associated LC3-II, and decreased level of selective autophagy target p62. Induction of autophagy was associated with Akt-dependent inhibition of main autophagy suppressor mammalian target of rapamycin (mTOR). Both genetic and pharmacological inhibition of autophagy suppressed the expression of differentiation markers, but had no effect on cell cycle arrest in E3-treated cells. E3 stimulated oxidative stress, and antioxidants vitamin E and N-acetyl cysteine inhibited autophagy and differentiation of E3-treated U251 cells. The most prevalent compound of E3, xanthone aglycone norswertianin, also arrested glioblastoma cell proliferation in G2/M phase and induced glioblastoma cell differentiation through induction of autophagy and oxidative stress. Conclusion These results indicate that E3 and its main active component norswertianin may serve as a potential candidate for differentiation therapy of glioblastoma.

Background and Aims Transplantation is the best treatment option for end stage kidney disease. The most common early complications in post-transplant period are acute rejection (AR) of the graft and delayed graft function (DGF). The underlying mechanisms in these events are heterogeneous and at least in part involve cytokine genes which regulate immune response to allograft. We have investigated whether functional single nucleotide polymorphisms (SNP) in the genes encoding IFN-γ (IFNG), TNF (TNFA), IL-10 (IL10) and p40 subunit of IL-12/IL-23 (IL12B) could predict risk of AR and DGF in kidney allograft recipients. Methods Our study involved 152 kidney transplant recipients on standard immunosuppressive regimen which included calcineurin inhibitors, mycophenolic acid derivatives and corticosteroids. Genotyping of IFNG, TNFA, IL10 and IL12B was performed using commercial TaqMan assays. Results We found association between the carriers of AA genotype of IL12B +1188A/C polymorphism (rs3212227) and a lower rate of DGF (p = 0.037, OR = 0.45, 95% CI = 0.21–0.96), implying protective role of A allele in the pathogenesis of DGF in kidney transplant recipients, whereas no such association was observed with AR. None of the analyzed SNPs in TNFA (−308G/A), IFNG (+874T/A), IL10 (−1082G/A, −819T/C, −592C/A) were associated with AR or DGF in our patients. Conclusions Our study shows a preliminary evidence that the AA genotype of rs3212227 SNP in the IL12B gene might be associated with a lower risk for DGF after kidney transplantation. In the future, additional well-designed large studies are required for the validation of our results.

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.

The Critical Assessment of Functional Annotation (CAFA) is an ongoing, global, community-driven effort to evaluate and improve the computational annotation of protein function. Here we report on the results of the third CAFA challenge, CAFA3, that featured an expanded analysis over the previous CAFA rounds, both in terms of volume of data analyzed and the types of analysis performed. In a novel and major new development, computational predictions and assessment goals drove some of the experimental assays, resulting in new functional annotations for more than 1000 genes. Specifically, we performed experimental whole-genome mutation screening in Candida albicans and Pseudomonas aureginosa genomes, which provided us with genome-wide experimental data for genes associated with biofilm formation and motility (P. aureginosa only). We further performed targeted assays on selected genes in Drosophila melanogaster, which we suspected of being involved in long-term memory. We conclude that, while predictions of the molecular function and biological process annotations have slightly improved over time, those of the cellular component have not. Term-centric prediction of experimental annotations remains equally challenging; although the performance of the top methods is significantly better than expectations set by baseline methods in C. albicans and D. melanogaster, it leaves considerable room and need for improvement. We finally report that the CAFA community now involves a broad range of participants with expertise in bioinformatics, biological experimentation, biocuration, and bioontologies, working together to improve functional annotation, computational function prediction, and our ability to manage big data in the era of large experimental screens.

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. © 2019, Springer-Verlag GmbH Austria, part of Springer Nature.

Tosatto, Silvio/0000-0003-4525-7793; Zhang, Feng/0000-0003-3447-897X; Gonzalez, Jose Maria Fernandez/0000-0002-4806-5140; Devignes, Marie-Dominique/0000-0002-0399-8713; Wass, Mark/0000-0001-5428-6479; Falda, Marco/0000-0003-2642-519X; Thurlby, Natalie/0000-0002-1007-0286; Zosa, Elaine/0000-0003-2482-0663; Dessimoz, Christophe/0000-0002-2170-853X; Yunes, Jeffrey/0000-0003-1869-3231; Hamid, Md Nafiz/0000-0001-8681-6526; Hoehndorf, Robert/0000-0001-8149-5890; Dogan, Tunca/0000-0002-1298-9763; NOTARO, MARCO/0000-0003-4309-2200; Cozzetto, Domenico/0000-0001-6752-5432; Lewis, Kimberley/0000-0003-3010-8453; Roche, Daniel/0000-0002-9204-1840; Martin, Maria-Jesus/0000-0001-5454-2815; Tress, Michael/0000-0001-9046-6370; Tolvanen, Martti/0000-0003-3434-7646; Cheng, Jianlin/0000-0003-0305-2853; Rose, Peter/0000-0001-9981-9750; Renaux, Alexandre/0000-0002-4339-2791; Kacsoh, Balint/0000-0001-9171-0611; O'Donovan, Claire/0000-0001-8051-7429; Kulmanov, Maxat/0000-0003-1710-1820; Friedberg, Iddo/0000-0002-1789-8000; Zhou, Naihui/0000-0001-6268-6149, WOS: 000498615000001, PubMed ID: 31744546, Background The Critical Assessment of Functional Annotation (CAFA) is an ongoing, global, community-driven effort to evaluate and improve the computational annotation of protein function. Results Here, we report on the results of the third CAFA challenge, CAFA3, that featured an expanded analysis over the previous CAFA rounds, both in terms of volume of data analyzed and the types of analysis performed. In a novel and major new development, computational predictions and assessment goals drove some of the experimental assays, resulting in new functional annotations for more than 1000 genes. Specifically, we performed experimental whole-genome mutation screening in Candida albicans and Pseudomonas aureginosa genomes, which provided us with genome-wide experimental data for genes associated with biofilm formation and motility. We further performed targeted assays on selected genes in Drosophila melanogaster, which we suspected of being involved in long-term memory. Conclusion We conclude that while predictions of the molecular function and biological process annotations have slightly improved over time, those of the cellular component have not. Term-centric prediction of experimental annotations remains equally challenging; although the performance of the top methods is significantly better than the expectations set by baseline methods in C. albicans and D. melanogaster, it leaves considerable room and need for improvement. Finally, we report that the CAFA community now involves a broad range of participants with expertise in bioinformatics, biological experimentation, biocuration, and bio-ontologies, working together to improve functional annotation, computational function prediction, and our ability to manage big data in the era of large experimental screens., National Science FoundationNational Science Foundation (NSF) [DBI1564756, DBI-1458359, DBI-1458390, DMS1614777, CMMI1825941, NSF 1458390]; Gordon and Betty Moore FoundationGordon and Betty Moore Foundation [GBMF 4552]; National Institutes of Health NIGMSUnited States Department of Health & Human ServicesNational Institutes of Health (NIH) - USANIH National Institute of General Medical Sciences (NIGMS) [P20 GM113132]; Cystic Fibrosis Foundation [CFRDP STANTO19R0]; BBSRCBiotechnology and Biological Sciences Research Council (BBSRC) [BB/K004131/1, BB/F00964X/1, BB/M025047/1, BB/M015009/1]; Consejo Nacional de Ciencia y Tecnologia Paraguay (CONACyT)Consejo Nacional de Ciencia y Tecnologia (CONACyT) [14-INV-088, PINV15-315]; NSFNational Science Foundation (NSF) [1660648, DBI 1759934, IIS1763246, DBI-1458477, 0965768, DMR-1420073, DBI-1458443]; NIHUnited States Department of Health & Human ServicesNational Institutes of Health (NIH) - USA [R01GM093123, DP1MH110234, UL1 TR002319, U24 TR002306]; Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany's Excellence Strategy-EXC 2155 "RESIST"German Research Foundation (DFG) [39087428]; National Institutes of HealthUnited States Department of Health & Human ServicesNational Institutes of Health (NIH) - USA [R01GM123055, R01GM60595, R15GM120650, GM083107, GM116960, AI134678, NIH R35-GM128637, R00-GM097033]; ERCEuropean Research Council (ERC) [StG 757700]; Spanish Ministry of Science, Innovation and Universities [BFU2017-89833-P]; Severo Ochoa award; Centre of Excellence project "BioProspecting of Adriatic Sea"; Croatian Government; European Regional Development FundEuropean Union (EU) [KK.01.1.1.01.0002]; ATT Tieto kayttoon grant; Academy of FinlandAcademy of Finland; University of Turku; CSC-IT Center for Science Ltd.; University of Miami; National Cancer Institute of the National Institutes of HealthUnited States Department of Health & Human ServicesNational Institutes of Health (NIH) - USANIH National Cancer Institute (NCI) [U01CA198942]; Helsinki Institute for Life Sciences; Academy of FinlandAcademy of Finland [292589]; National Natural Science Foundation of ChinaNational Natural Science Foundation of China [31671367, 31471245, 91631301, 61872094, 61572139]; National Key Research and Development Program of China [2016YFC1000505, 2017YFC0908402]; Italian Ministry of Education, University and Research (MIUR) PRIN 2017 projectMinistry of Education, Universities and Research (MIUR) [2017483NH8]; Shanghai Municipal Science and Technology Major Project [2017SHZDZX01, 2018SHZDZX01]; UK Biotechnology and Biological Sciences Research CouncilBiotechnology and Biological Sciences Research Council (BBSRC) [BB/N019431/1, BB/L020505/1, BB/L002817/1]; Elsevier; Extreme Science and Engineering Discovery Environment (XSEDE) award [MCB160101, MCB160124]; Ministry of Education, Science and Technological Development of the Republic of Serbia [173001]; Taiwan Ministry of Science and Technology [106-2221-E-004-011-MY2]; Montana State University; Bavarian Ministry for Education; Simons Foundation; NIH NINDSUnited States Department of Health & Human ServicesNational Institutes of Health (NIH) - USANIH National Institute of Neurological Disorders & Stroke (NINDS) [1R21NS103831-01]; University of Illinois at Chicago (UIC) Cancer Center award; UIC College of Liberal Arts and Sciences Faculty Award; UIC International Development Award; Yad Hanadiv [9660/2019]; National Institute of General Medical Science of the National Institute of Health [GM066099, GM079656]; Research Supporting Plan (PSR) of University of Milan [PSR2018-DIP-010-MFRAS]; Swiss National Science FoundationSwiss National Science Foundation (SNSF) [150654]; EMBL-European Bioinformatics Institute core funds; CAFA BBSRC [BB/N004876/1]; European Union's Horizon 2020 research and innovation program under the Marie Sklodowska-Curie grantEuropean Union (EU) [778247]; COST ActionEuropean Cooperation in Science and Technology (COST) [BM1405]; NIH/NIGMSUnited States Department of Health & Human ServicesNational Institutes of Health (NIH) - USANIH National Institute of General Medical Sciences (NIGMS) [R01 GM071749]; National Human Genome Research Institute of the National of Health [U41 HG007234]; INB Grant (ISCIII-SGEFI/ERDF) [PT17/0009/0001]; TUBITAKTurkiye Bilimsel ve Teknolojik Arastirma Kurumu (TUBITAK) [EEEAG-116E930]; KanSil [2016K121540]; Universita degli Studi di Milano; 111 ProjectMinistry of Education, China - 111 Project [B18015]; key project of Shanghai Science Technology [16JC1420402]; ZJLab; project Ribes Network POR-FESR 3S4H [TOPP-ALFREVE18-01]; PRID/SID of University of Padova [TOPP-SID19-01]; NIGMSUnited States Department of Health & Human ServicesNational Institutes of Health (NIH) - USANIH National Institute of General Medical Sciences (NIGMS) [R15GM120650]; King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research (OSR) [URF/1/3454-01-01, URF/1/3790-01-01]; "the Human Project from Mind, Brain and Learning" of the NCCU Higher Education Sprout Project by the Taiwan Ministry of Education; National Center for High-performance ComputingIstanbul Technical University, The work of IF was funded, in part, by the National Science Foundation award DBI-1458359. The work of CSG and AJL was funded, in part, by the National Science Foundation award DBI-1458390 and GBMF 4552 from the Gordon and Betty Moore Foundation. The work of DAH and KAL was funded, in part, by the National Science Foundation award DBI-1458390, National Institutes of Health NIGMS P20 GM113132, and the Cystic Fibrosis Foundation CFRDP STANTO19R0. The work of AP, HY, AR, and MT was funded by BBSRC grants BB/K004131/1, BB/F00964X/1 and BB/M025047/1, Consejo Nacional de Ciencia y Tecnologia Paraguay (CONACyT) grants 14-INV-088 and PINV15-315, and NSF Advances in BioInformatics grant 1660648. The work of JC was partially supported by an NIH grant (R01GM093123) and two NSF grants (DBI 1759934 and IIS1763246). ACM acknowledges the support by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany's Excellence Strategy -EXC 2155 "RESIST" - Project ID 39087428. DK acknowledges the support from the National Institutes of Health (R01GM123055) and the National Science Foundation (DMS1614777, CMMI1825941). PB acknowledges the support from the National Institutes of Health (R01GM60595). GB and BZK acknowledge the support from the National Science Foundation (NSF 1458390) and NIH DP1MH110234. FS was funded by the ERC StG 757700 "HYPER-INSIGHT" and by the Spanish Ministry of Science, Innovation and Universities grant BFU2017-89833-P. FS further acknowledges the funding from the Severo Ochoa award to the IRB Barcelona. TS was funded by the Centre of Excellence project "BioProspecting of Adriatic Sea", co-financed by the Croatian Government and the European Regional Development Fund (KK.01.1.1.01.0002). The work of SK was funded by ATT Tieto kayttoon grant and Academy of Finland. JB and HM acknowledge the support of the University of Turku, the Academy of Finland and CSC -IT Center for Science Ltd. TB and SM were funded by the NIH awards UL1 TR002319 and U24 TR002306. The work of CZ and ZW was funded by the National Institutes of Health R15GM120650 to ZW and start-up funding from the University of Miami to ZW. The work of PWR was supported by the National Cancer Institute of the National Institutes of Health under Award Number U01CA198942. PR acknowledges NSF grant DBI-1458477. PT acknowledges the support from Helsinki Institute for Life Sciences. The work of AJM was funded by the Academy of Finland (No. 292589). The work of FZ and WT was funded by the National Natural Science Foundation of China (31671367, 31471245, 91631301) and the National Key Research and Development Program of China (2016YFC1000505, 2017YFC0908402]. CS acknowledges the support by the Italian Ministry of Education, University and Research (MIUR) PRIN 2017 project 2017483NH8. SZ is supported by the National Natural Science Foundation of China (No. 61872094 and No. 61572139) and Shanghai Municipal Science and Technology Major Project (No. 2017SHZDZX01). PLF and RLH were supported by the National Institutes of Health NIH R35-GM128637 and R00-GM097033. JG, DTJ, CW, DC, and RF were supported by the UK Biotechnology and Biological Sciences Research Council (BB/N019431/1, BB/L020505/1, and BB/L002817/1) and Elsevier. The work of YZ and CZ was funded in part by the National Institutes of Health award GM083107, GM116960, and AI134678; the National Science Foundation award DBI1564756; and the Extreme Science and Engineering Discovery Environment (XSEDE) award MCB160101 and MCB160124.; The work of BG, VP, RD, NS, and NV was funded by the Ministry of Education, Science and Technological Development of the Republic of Serbia, Project No. 173001. The work of YWL, WHL, and JMC was funded by the Taiwan Ministry of Science and Technology (106-2221-E-004-011-MY2). YWL, WHL, and JMC further acknowledge the support from "the Human Project from Mind, Brain and Learning" of the NCCU Higher Education Sprout Project by the Taiwan Ministry of Education and the National Center for High-performance Computing for computer time and facilities. The work of IK and AB was funded by Montana State University and NSF Advances in Biological Informatics program through grant number 0965768. BR, TG, and JR are supported by the Bavarian Ministry for Education through funding to the TUM. The work of RB, VG, MB, and DCEK was supported by the Simons Foundation, NIH NINDS grant number 1R21NS103831-01 and NSF award number DMR-1420073. CJJ acknowledges the funding from a University of Illinois at Chicago (UIC) Cancer Center award, a UIC College of Liberal Arts and Sciences Faculty Award, and a UIC International Development Award. The work of ML was funded by Yad Hanadiv (grant number 9660/2019). The work of OL and IN was funded by the National Institute of General Medical Science of the National Institute of Health through GM066099 and GM079656. Research Supporting Plan (PSR) of University of Milan number PSR2018-DIP-010-MFRAS. AWV acknowledges the funding from the BBSRC (CASE studentship BB/M015009/1). CD acknowledges the support from the Swiss National Science Foundation (150654). CO and MJM are supported by the EMBL-European Bioinformatics Institute core funds and the CAFA BBSRC BB/N004876/1. GG is supported by CAFA BBSRC BB/N004876/1. SCET acknowledges funding from the European Union's Horizon 2020 research and innovation program under the Marie Sklodowska-Curie grant agreement No 778247 (IDPfun) and from COST Action BM1405 (NGP-net). SEB was supported by NIH/NIGMS grant R01 GM071749. The work of MLT, JMR, and JMF was supported by the National Human Genome Research Institute of the National of Health, grant numbers U41 HG007234. The work of JMF and JMR was also supported by INB Grant (PT17/0009/0001 - ISCIII-SGEFI/ERDF). VA acknowledges the funding from TUBITAK EEEAG-116E930. RCA acknowledges the funding from KanSil 2016K121540. GV acknowledges the funding from Universita degli Studi di Milano - Project "Discovering Patterns in Multi-Dimensional Data" and Project "Machine Learning and Big Data Analysis for Bioinformatics". SZ is supported by the National Natural Science Foundation of China (No. 61872094 and No. 61572139) and Shanghai Municipal Science and Technology Major Project (No. 2017SHZDZX01). RY and SY are supported by the 111 Project (NO. B18015), the key project of Shanghai Science & Technology (No. 16JC1420402), Shanghai Municipal Science and Technology Major Project (No. 2018SHZDZX01), and ZJLab. ST was supported by project Ribes Network POR-FESR 3S4H (No. TOPP-ALFREVE18-01) and PRID/SID of University of Padova (No. TOPP-SID19-01). CZ and ZW were supported by the NIGMS grant R15GM120650 to ZW and start-up funding from the University of Miami to ZW. The work of MK and RH was supported by the funding from King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research (OSR) under Award No. URF/1/3454-01-01 and URF/1/3790-01-01. The work of SDM is funded, in part, by NSF award DBI-1458443.

Background: The significant number of protein-protein interactions (PPIs) discovered by harnessing concomitant advances in the fields of sequencing, crystallography, spectrometry and two-hybrid screening suggests astonishing prospects for remodelling drug discovery. The PPI space which includes up to 650 000 entities is a remarkable reservoir of potential therapeutic targets for every human disease. In order to allow modern drug discovery programs to leverage this, we should be able to discern complete PPI maps associated with a specific disorder and corresponding normal physiology. Objective: Here, we will review community available computational programs for predicting PPIs and web-based resources for storing experimentally annotated interactions. Methods: We compared the capacities of prediction tools: iLoops, Struck2Net, HOMCOS, COTH, PrePPI, InterPreTS and PRISM to predict recently discovered protein interactions. Results: We described sequence-based and structure-based PPI prediction tools and addressed their peculiarities. Additionally, since the usefulness of prediction algorithms critically depends on the quality and quantity of the experimental data they are built on; we extensively discussed community resources for protein interactions. We focused on the active and recently updated primary and secondary PPI databases, repositories specialized to the subject or species, as well as databases that include both experimental and predicted PPIs. Conclusion: PPI complexes are the basis of important physiological processes and therefore, possible targets for cell-penetrating ligands. Reliable computational PPI predictions can speed up new target discoveries through prioritization of therapeutically relevant protein–protein complexes for experimental studies.

Background: Numerous in vitro and in vivo studies, in addition to clinical data, demonstrate that pomegranate juice can prevent or slow-down the progression of some types of cancers. Despite the well-documented effect of pomegranate ingredients on neoplastic changes, the molecular mechanism(s) underlying this phenomenon remains elusive. Methods: For the study of pomegranate ingredients the electron-ion interaction potential (EIIP) and the average quasi valence number (AQVN) were used. These molecular descriptors can be used to describe the long-range intermolecular interactions in biological systems and can identify substances with strong electron-acceptor properties. In this study, candidate human proteins interacting with pomegranate flavonoids have been analyzed by the informational spectrum method (ISM). This represents a virtual spectroscopy method for studying protein molecular interactions. Results: Our analysis indicates that the anti-cancer properties of pomegranate juice can be ascribed to the strong electron-acceptor properties of its chemical ingredients. This analysis also suggests that pomegranate flavonoids inhibit the “NF-kappaB” (NF-B) pathway, which plays a critical role in the pathogenesis of cancer. Conclusion: The results offer a possible explanation for an important molecular mechanism underlying the anticancer activity of pomegranate ingredients, which could also serve as a basis for the development of new therapeutic compositions of food supplements with pomegranate-like anticancer properties.

Polymorphisms of the multi drug resistance (MDR1) gene cause variability in P-glycoprotein mediated metabolism of tacrolimus. The aim of this study was to examine the relationship between MDR1 gene single nucleotide polymorphisms (SNPs) and their haplotypes with dosage of tacrolimus in kidney transplant recipients who were cytochrome (CYP) 3A5*3 homozygotes. This study included 91 kidney transplant recipients followed two years after transplantation. Detection and analysis of MDR1 gene polymorphisms in positions C1236T, G2677T/A and C3435T were performed using PCR method. Patients with variant alleles for SNPs G2677T/A and C3435T required higher doses of tacrolimus and had a lower level/dose (L/D) ratio than patients with wild alleles or heterozygotes. That difference was the most obvious for SNP G2677T/A where TT homozygotes required significantly higher doses of tacrolimus during whole follow-up. Their L/D was significantly lower in the first month after transplantation. Recipients with CTT/TTT haplotype also had lower L/D than those with CGC/TTT and CGC/CGC, significantly in the 10th and 20th days after transplantation respectively (p

The clonidine-like central antihypertensive agent rilmenidine, which has high affinity for I-1-type imidazoline receptors (I-1-IR) was recently found to have cytotoxic effects on cultured cancer cell lines. However, due to its pharmacological effects resulting also from alpha(2)-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 alpha(2)-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 alpha(2)-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. (C) 2016 Elsevier Ltd. All rights reserved.

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.

The genetic and environmental factors and their interaction contribute to sports performance. So far, it has been identified a large number of genetic markers associated with sports performance and risk of sports injuries. Sports genomics is a relatively young scientific discipline and the necessary additional complex research on a large number of participants is required before scientific results in this field could be applicable in practice. At present, the application of tests based on genetic information for sport talent identification or recommendations for personalized training, in order to achieve optimal sport performance, is not scientifically justified. It is also necessary to carefully consider all the ethical issues related to such testing in children.

A safe and effective vaccine is urgently needed to bring the current SARS-CoV-2 pandemic under control. The spike protein (SP) of SARS-CoV-2 represents the principal target for most vaccines currently under development. This protein is highly conserved indicating that vaccine based on this antigen will be efficient against all currently circulating SARS-CoV-2 strains. The present analysis of SP suggests that mutation D614G could significantly decrease the effectiveness of the COVID-19 vaccine through modulation of the interaction between SARS-CoV-2 and its principal receptor ACE2.