Investigations of genotoxic activity of antimicrobial/antiviral agent FS-1 in human lymphocytes and tumor cell

A very promising antiviral and antimicrobial agent FS-1 was studied for its ability to induce DNA damage and micronuclei in human tumor cell lines HeLa and Caco-2 at concentrations of 200, 500 and 1000 mg/ml without exogenous metabolic activation. The compound was additionally tested for DNA damaging ability in human lymphocytes at concentrations of 200, 400 and 800 mg/ml. Neither DNA damage nor micronucleus formation was observed after treatment of all types of cells with FS-1. Based on these results, FS-1 can be further studied for its safety to humans for potential application in clinical medicine as an antimicrobial/antiviral drug.Исследовали способность перспективного антивирусного и антибактериального соединения ФС-1 вызывать повреждения ДНК и микроядра в клеточных линиях опухоли человека HeLa и Caco-2 при концентрациях 200, 500 и 1000 мкг/мл без экзогенной метаболической активации. Соединение было дополнительно проверено на ДНК-повреждающую способность в лимфоцитах человека при концентрациях 200, 400 и 800 мкг/мл. Ни повреждения ДНК, ни формирования микроядер не наблюдалось после обработки всех типов клеток ФС-1. На основании этих результатов, ФС-1 может быть далее изучен на предмет безопасности для возможного применения в клинической медицине как антибактериального/противовирусного препарата.Досліджували здатність перспективного противірусного та антибактеріального препарату ФС-1 викликати пошкодження ДНК та мікроядра в клітинних лініях пухлини людини HeLa і Сасо-2 при концентраціях 200, 500 та 1000 мкг/мл без екзогенної метаболічної активації. Препарат був додатково перевірений на ДНК-пошкоджуючу здатність в лімфоцитах людини при концентраціях 200, 400, 800 мкг/мл. Ні пошкодження ДНК, ні формування мікроядер не спостерігалось після обробки всіх типів клітин ФС-1. Базуючись на цих результатах, ФС-1 може далі вивчатись на предмет безпеки для потенційного застосування в клінічній медицині як антибактеріального/противірусного препарату

Unsupervised protein embeddings outperform hand-crafted sequence and structure features at predicting molecular function

This work was supported by Keygene N.V., a crop innovation company in the Netherlands and by the Spanish MINECO/FEDER Project TEC201680141-P with the associated FPI grant BES-2017-079792.The authors thank Dr. Elvin Isufi and Chirag Raman for their valuable comments and feedback.Motivation: Protein function prediction is a difficult bioinformatics problem. Many recent methods use deep neural networks to learn complex sequence representations and predict function from these. Deep supervised models require a lot of labeled training data which are not available for this task. However, a very large amount of protein sequences without functional labels is available. Results: We applied an existing deep sequence model that had been pretrained in an unsupervised setting on the supervised task of protein molecular function prediction. We found that this complex feature representation is effective for this task, outperforming hand-crafted features such as one-hot encoding of amino acids, k-mer counts, secondary structure and backbone angles. Also, it partly negates the need for complex prediction models, as a two-layer perceptron was enough to achieve competitive performance in the third Critical Assessment of Functional Annotation benchmark. We also show that combining this sequence representation with protein 3D structure information does not lead to performance improvement, hinting that 3D structure is also potentially learned during the unsupervised pretraining.Keygene N.V., a crop innovation company in the NetherlandsSpanish MINECO/FEDER TEC201680141-PFPI grant BES-2017-07979

Semantic prioritization of novel causative genomic variants

Discriminating the causative disease variant(s) for individuals with inherited or de novo mutations presents one of the main challenges faced by the clinical genetics community today. Computational approaches for variant prioritization include machine learning methods utilizing a large number of features, including molecular information, interaction networks, or phenotypes. Here, we demonstrate the PhenomeNET Variant Predictor (PVP) system that exploits semantic technologies and automated reasoning over genotype-phenotype relations to filter and prioritize variants in whole exome and whole genome sequencing datasets. We demonstrate the performance of PVP in identifying causative variants on a large number of synthetic whole exome and whole genome sequences, covering a wide range of diseases and syndromes. In a retrospective study, we further illustrate the application of PVP for the interpretation of whole exome sequencing data in patients suffering from congenital hypothyroidism. We find that PVP accurately identifies causative variants in whole exome and whole genome sequencing datasets and provides a powerful resource for the discovery of causal variants.NS was funded by Wellcome Trust (Grant 100585/Z/12/Z) and the National Institute for Health Research Cambridge Biomedical Research Centre. IB, RBMR, MK, YH, VBB, RH were funded by the King Abdullah University of Science and Technology. GVG acknowledges funding from the National Science Foundation (NSF grant number: IOS-1340112) and the European Commision H2020 (Grant Agreement No. 731075)

The CAFA challenge reports improved protein function prediction and new functional annotations for hundreds of genes through experimental screens

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 aeruginosa 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

The CAFA challenge reports improved protein function prediction and new functional annotations for hundreds of genes through experimental screens

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.Peer reviewe

The CAFA challenge reports improved protein function prediction and new functional annotations for hundreds of genes through experimental screens

BackgroundThe Critical Assessment of Functional Annotation (CAFA) is an ongoing, global, community-driven effort to evaluate and improve the computational annotation of protein function.ResultsHere, 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.ConclusionWe 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.</p

EFFICIENCY OF THE NEW MEDICATION OF FS-1 IN THE TREATMENT OF EXPERIMENTAL TUBERCULOSIS

In vitro studies have proved the anti-tuberculosis eect of FS-1 medication both against susceptible strain of M. tuberculosis H37Rv, and the strain of M. tuberculosis MS-115 with multiple drug resistance.Conducted in vivo studies on guinea pigs infected with the pathogenic culture of human tuberculosis (strain of H37Rv), have shown that FS-1 provides expressed anti-inflammatory action on the course of experimental tuberculosis