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Dysregulation of brain and choroid plexus cell types in severe COVID-19 (vol 595, pg 565, 2021)

NATURE 2021年第7882期598卷 E4-E4页

作者： Yang, Andrew C. Kern, Fabian Losada, Patricia M. Agam, Maayan R. Maat, Christina A. Schmartz, Georges P. Fehlmann, Tobias Stein, Julian A. Schaum, Nicholas Lee, Davis P. Calcuttawala, Kruti Vest, Ryan T. Berdnik, Daniela Lu, Nannan Hahn, Oliver Gate, David McNerney, M. Windy Channappa, Divya Cobos, Inma Ludwig, Nicole Schulz-Schaeffer, Walter J. Keller, Andreas Wyss-Coray, Tony Department of Bioengineering Stanford University School of Medicine Stanford CA USA ChEM-H Stanford University Stanford CA USA Department of Neurology and Neurological Sciences Stanford University School of Medicine Stanford CA USA Wu Tsai Neurosciences Institute Stanford University Stanford CA USA Paul F. Glenn Center for the Biology of Aging Stanford University School of Medicine Stanford CA USA Department of Pathology Stanford University School of Medicine Stanford CA USA Chair for Clinical Bioinformatics Saarland University Saarbrücken Germany Institute for Neuropathology Saarland University Hospital and Medical Faculty of Saarland University Homburg Germany Department of Psychiatry Stanford University School of Medicine Stanford CA USA Department of Human Genetics Saarland University Homburg Germany

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A universal language for finding mass spectrometry data patterns

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Nature Methods 2025年 1-8页

作者： Damiani, Tito Jarmusch, Alan K. Aron, Allegra T. Petras, Daniel Phelan, Vanessa V. Zhao, Haoqi Nina Bittremieux, Wout Acharya, Deepa D. Ahmed, Mohammed M. A. Bauermeister, Anelize Bertin, Matthew J. Boudreau, Paul D. Borges, Ricardo M. Bowen, Benjamin P. Brown, Christopher J. Chagas, Fernanda O. Clevenger, Kenneth D. Correia, Mario S. P. Crandall, William J. Crüsemann, Max Fahy, Eoin Fiehn, Oliver Garg, Neha Gerwick, William H. Gilbert, Jeffrey R. Globisch, Daniel Gomes, Paulo Wender P. Heuckeroth, Steffen James, C. Andrew Jarmusch, Scott A. Kakhkhorov, Sarvar A. Kang, Kyo Bin Kessler, Nikolas Kersten, Roland D. Kim, Hyunwoo Kirk, Riley D. Kohlbacher, Oliver Kontou, Eftychia E. Liu, Ken Lizama-Chamu, Itzel Luu, Gordon T. Luzzatto Knaan, Tal Mannochio-Russo, Helena Marty, Michael T. Matsuzawa, Yuki McAvoy, Andrew C. McCall, Laura-Isobel Mohamed, Osama G. Nahor, Omri Neuweger, Heiko Niedermeyer, Timo H. J. Nishida, Kozo Northen, Trent R. Overdahl, Kirsten E. Rainer, Johannes Reher, Raphael Rodriguez, Elys Sachsenberg, Timo T. Sanchez, Laura M. Schmid, Robin Stevens, Cole Subramaniam, Shankar Tian, Zhenyu Tripathi, Ashootosh Tsugawa, Hiroshi van der Hooft, Justin J. J. Vicini, Andrea Walter, Axel Weber, Tilmann Xiong, Quanbo Xu, Tao Pluskal, Tomáš Dorrestein, Pieter C. Wang, Mingxun Institute of Organic Chemistry and Biochemistry of the Czech Academy of Sciences Prague Czech Republic Metabolomics Core Facility Immunity Inflammation and Disease Laboratory Division of Intramural Research National Institute of Environmental Health Sciences National Institutes of Health Research Triangle Park NC United States Department of Chemistry and Biochemistry University of Denver Denver CO United States Functional Metabolomics Lab CMFI Cluster of Excellence University of Tuebingen Tuebingen Germany Department of Biochemistry University of California Riverside Riverside CA United States Department of Pharmaceutical Sciences Skaggs School of Pharmacy and Pharmaceutical Sciences University of Colorado Anschutz Medical Campus Aurora CO United States Collaborative Mass Spectrometry Innovation Center Skaggs School of Pharmacy and Pharmaceutical Sciences University of California San Diego La Jolla CA United States Biologicals and Natural Products Discovery Crop Protection R & D Corteva Agrisciences Indianapolis IN United States BioMolecular Sciences School of Pharmacy University of Mississippi Oxford MS United States Pharmacognosy Faculty of Pharmacy Al-Azhar University Nasr City Egypt Department of Fundamental Chemistry Institute of Chemistry University of São Paulo São Paulo Brazil Department of Chemistry Case Western Reserve University Cleveland OH United States Walter Mors Institute of Research on Natural Products Federal University of Rio de Janeiro Rio de Janeiro Brazil Environmental Genomics and Systems Biology Division Lawrence Berkeley National Lab Berkeley CA United States The Joint Genome Institute Lawrence Berkeley National Lab Berkeley CA United States Mass Spectrometry Center of Expertise Regulatory and Stewardship Corteva Agrisciences Indianapolis IN United States Biologicals and Natural Products Crop Protection R & D Corteva Agrisciences Indianapolis IN United States Department of Chemistry – BMC Science for Life Laborat

Despite being information rich, the vast majority of untargeted mass spectrometry data are underutilized;most analytes are not used for downstream interpretation or reanalysis after publication. The inability to dive into these rich raw mass spectrometry datasets is due to the limited flexibility and scalability of existing software tools. Here we introduce a new language, the Mass Spectrometry Query Language (MassQL), and an accompanying software ecosystem that addresses these issues by enabling the community to directly query mass spectrometry data with an expressive set of user-defined mass spectrometry patterns. Illustrated by real-world examples, MassQL provides a data-driven definition of chemical diversity by enabling the reanalysis of all public untargeted metabolomics data, empowering scientists across many disciplines to make new discoveries. MassQL has been widely implemented in multiple open-source and commercial mass spectrometry analysis tools, which enhances the ability, interoperability and reproducibility of mining of mass spectrometry data for the research community. © The Author(s), under exclusive licence to Springer Nature America, Inc. 2025.

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Identification of key molecular pathways and genes in BRCA1 and BRCA2-mutant ovarian cancer: evidence from bioinformatics analysis

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Genome Instability & Disease 2024年第4期5卷 164-182页

作者： Awaji, Aeshah A. Maigoro, Abdulkadir Yusif Aborode, Abdullahi Tunde Akintola, Ashraf Akintayo Fatoba, Dorcas Oladayo Idris, Einass Babikir Idris, Abeer Babiker Jafri, Saman Shoaib, Ekram Onifade, Isreal Ayobami Olapade, Zainab Oladayo, Modupe Ihemegbulem, Ifeyinwa Anne Ipede, Oluwaseun Idowu, Abidemi Ruth Alabi, Funke V. Aruorivwooghene, Ibude Jane Enaworu, Oghenetanure Ryan Jamiu, Abdullahi Bakre, Adetolase A. Kwon, Hyung Wook Hwang, Ui Wook Adesola, Ridwan Olamilekan Department of Biology Faculty of Science University College of Taymaa University of Tabuk Tabuk Saudi Arabia NOBLEKINMAT Ltd. Bioinformatics Research Group Ibadan Nigeria Department of Life Sciences College of Life Sciences and Bioengineering Incheon National University Seoul South Korea Convergence Research Center for Insect Vector Incheon National University Seoul South Korea Department of Chemistry Mississippi State University Starkville USA School of Industrial Technology Advances Kyungpook National University Daegu Republic of Korea The University of Tennessee Health Science Center (UTHSC) Memphis USA Health Research Incorporated New York USA Noblekinmat Research Group Ibadan Nigeria Department of Medical Microbiology Rashid Complex Medical Clinics Riyadh Saudi Arabia Institute of Natural and Applied Sciences Erciyes University Kayseri Turkey Interdisciplinary Biotechnology Unit Aligarh Muslim University Aligarh India Clinical Laboratory Sciences Department College of Applied Medical Sciences University of Hafr Al-Batin Hafr Al-Batin Saudi Arabia Department of Biology University at Albany Albany USA Department of Biology Lamar University Beaumont USA Department of Chemistry and Biochemistry Central Washington University Ellensburg USA Department of Pharmacy University of Jos Jos Nigeria School of Earth Environment and Sustainability Georgia Southern University Statesboro USA Department of Microbiology and Immunology School of Medicine University of Louisville Louisville USA Department of Biomedical Informatics Nova Southeastern University Fort Lauderdale USA Department of Dietetics and Nutrition Medical Center University of Kansas Kansas City USA Department of Advanced Studies in Clinical Research University of California San Diego La Jolla USA Department of Biomedical Sciences and Pathobiology Virginia-Maryland College of Veterinary Medicine Virginia Polytechnic Institute and State University Blacksburg USA Department of Veterinary Me

BRCA1 and BRCA2 mutations significantly increase the risk of breast and ovarian cancers (OC) by affecting crucial cellular processes such as cell cycle regulation, DNA repair, and apoptosis. Despite understanding their broad impacts, the detailed effects on gene expression and signaling pathways in OC remain unclear. This study aims to elucidate these dynamics by analyzing RNA-Seq data from 24 OC patients with BRCA1/2 mutations and controls using advanced molecular techniques. Differential expression analysis identified 6136 DEGs in BRCA1-mutant and 3615 DEGs in BRCA2-mutant OCs, with 2041 overlapping DEGs. These shared genes underwent Gene Ontology (GO), KEGG, and Protein–protein interaction (PPI) network analyses, revealing pathways like MyD88-independent TLR signaling and mRNA vaccine activation of dendritic cells. Key genes (TRAF3, TICAM1, IRF7, CD40, IRF3, SARM1, RAB11FIP, and PRKCE) involved in TRIF-dependent TLR signaling showed distinct expression patterns in BRCA1/2-deficient OCs. The top 10 hub genes identified were evaluated for their prognostic significance using the Kaplan–Meier Plotter database. The findings highlight the genetic landscape and pathways altered by BRCA1/2 mutations in OC, offering insights for improved diagnostics and personalized treatments. Additionally, the impact on immunosurveillance through the TRIF-dependent TLR system suggests new immunotherapeutic strategies for OC patients with BRCA1/2 mutations, addressing the challenge of cancer recurrence.

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A multiple peptides vaccine against COVID-19 designed from the nucleocapsid phosphoprotein (N) and Spike Glycoprotein (S) via the immunoinformatics approach

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Informatics in Medicine Unlocked 2020年 21卷 100476页

作者： Abd Albagi, Sahar Obi Al-Nour, Mosab Yahya Elhag, Mustafa Tageldein Idris Abdelihalim, Asaad Musa Haroun, Esraa Adam Essa, Mohammed Elmujtba Abubaker, Mustafa Deka, Hemchandra Ghosh, Arabinda Hassan, Mohammed A. Department of Microbiology and Immunology AL Neelain University Khartoum Sudan Department of Pharmaceutical Chemistry Faculty of Pharmacy Omdurman Islamic University Khartoum Sudan Faculty of Medicine University of Seychelles-American Institute of Medicine Seychelles Clinical Immunology Council Sudan Medical Specialization Board Sudan Faculty of Medical Pharmacology Ahfad University for Women Sudan Department of Clinical Medical Medical and Cancer Research Institute Nyala Sudan Faculty of Medical Laboratory Sciences Sudan University of Science and Technology Sudan Department of Bioengineering and Technology GUIST Gauhati University Guwahati Assam India Microbiology Division Department of Botany Gauhati University Guwahati Assam India Department of Bioinformatics DETAGEN Genetics Diagnostic Center Kayseri Turkey

Due to the current Coronavirus (COVID-19) pandemic, the rapid discovery of a safe and effective vaccine is an essential issue. Consequently, this study aims to predict a potential COVID-19 peptide-based vaccine utilizing the Nucleocapsid phosphoprotein (N) and Spike Glycoprotein (S) via the Immunoinformatics approach. To achieve this goal, several Immune Epitope Database (IEDB) tools, molecular docking, and safety prediction servers were used. According to the results, The Spike peptide SQCVNLTTRTQLPPAYTNSFTRGVY is predicted to have the highest binding affinity to the B-Cells. The Spike peptide FTISVTTEI has the highest binding affinity to the Major Histocompatibility Complex class 1 (MHC I) Human Leukocyte Allele HLA-B*1503 (according to the MDockPeP and HPEPDOCK servers, docking scores were −153.9 and −229.356, respectively). The Nucleocapsid peptides KTFPPTEPK and RWYFYYLGTGPEAGL have the highest binding affinity to the MHC I HLA-A0202 allele and the three the Major Histocompatibility Complex class 2 (MHC II) Human Leukocyte Allele HLA-DPA1*01:03/DPB1*02:01, HLA-DQA1*01:02/DQB1-*06:02, HLA-DRB1, respectively. Docking scores of peptide KTFPPTEPK were −153.9 and −220.876. In contrast, docking scores of peptide RWYFYYLGTGPEAGL were ranged from 218 to 318. Furthermore, those peptides were predicted as non-toxic and non-allergen. Therefore, the combination of those peptides is predicted to stimulate better immunological responses with respectable safety. © 2020

关键词： And immunoinformatics COVID-19 Multiple peptides vaccine Nucleocapsid Spike

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P.139 Chemical models of dysfunctions of the brain 2-oxo acid dehydrogenases in vivo

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European Neuropsychopharmacology 2020年 40卷 S84-S85页

作者： Artiukhov, A. Graf, A. Kazantsev, A. Lukashev, N. Bunik, V. Lomonosov Moscow State University Faculty of Bioengineering and Bioinformatics Moscow Russia Lomonosov Moscow State University Faculty of Biology Moscow Russia Lomonosov Moscow State University Faculty of Chemistry Moscow Russia Lomonosov Moscow State University A.N.Belozersky Institute of Physico-Chemical Biology Moscow Russia

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Simulating the long-timescale structural behavior of bacterial and influenza neuraminidases with different HPC resources

Supercomputing Frontiers and Innovations

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Supercomputing Frontiers and Innovations 2018年第3期5卷 30-33页

作者： Sharapova, Yana A. Suplatov, Dmitry A. Švedas, Vytas K. Faculty of Bioengineering and Bioinformatics Lomonosov Moscow State University Moscow Russia Belozersky Institute of Physicochemical Biology Lomonosov Moscow State University Moscow Russia

Understanding the conformational dynamics which affects ligand binding by Neuraminidases is needed to improve the in silico selection of novel drug candidates targeting these pathogenicity factors and to adequately estimate the efficacy of potential drugs. Conventional molecular dynamics (MD) is a powerful tool to study conformational sampling, drug-target recognition and binding, but requires significant computational effort to reach timescales relevant for biology. In this work the advances in a computer power and specialized architectures were evaluated at simulating long MD trajectories of the structural behavior of Neuraminidases. We conclude that modern GPU accelerators enable calculations at the timescales that would previously have been intractable, providing routine access to microsecond-long trajectories in a daily laboratory practice. This opens an opportunity to move away from the "static" affinity-driven strategies in drug design towards a deeper understanding of ligand-specific conformational adaptation of target sites in protein structures, leading to a better selection of efficient drug candidates in silico. However, the performance of modern GPUs is yet far behind the deeply-specialized supercomputers co-designed for MD. Further development of affordable specialized architectures is needed to move towards the much-desired millisecond timescale to simulate large proteins at a daily routine. © The Authors 2018.

关键词： Molecular dynamics

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PDBe-KB: collaboratively defining the biological context of structural data

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NUCLEIC ACIDS RESEARCH 2022年第D1期50卷 D534-D542页

作者： Varadi, Mihaly Anyango, Stephen Armstrong, David Berrisford, John Choudhary, Preeti Deshpande, Mandar Nadzirin, Nurul Nair, Sreenath S. Pravda, Lukas Tanweer, Ahsan Al-Lazikani, Bissan Andreini, Claudia Barton, Geoffrey J. Bednar, David Berka, Karel Blundell, Tom Brock, Kelly P. Carazo, Jose Maria Damborsky, Jiri David, Alessia Dey, Sucharita Dunbrack, Roland Recio, Juan Fernandez Fraternali, Franca Gibson, Toby Helmer-Citterich, Manuela Hoksza, David Hopf, Thomas Jakubec, David Kannan, Natarajan Krivak, Radoslav Kumar, Manjeet Levy, Emmanuel D. London, Nir Macias, Jose Ramon Srivatsan, Madhusudhan M. Marks, Debora S. Martens, Lennart McGowan, Stuart A. McGreig, Jake E. Modi, Vivek Parra, R. Gonzalo Pepe, Gerardo Piovesan, Damiano Prilusky, Jaime Putignano, Valeria Radusky, Leandro G. Ramasamy, Pathmanaban Rausch, Atilio O. Reuter, Nathalie Rodriguez, Luis A. Rollins, Nathan J. Rosato, Antonio Rubach, Pawel Serrano, Luis Singh, Gulzar Skoda, Petr Sorzano, Carlos Oscar S. Stourac, Jan Sulkowska, Joanna, I Svobodova, Radka Tichshenko, Natalia Tosatto, Silvio C. E. Vranken, Wim Wass, Mark N. Xue, Dandan Zaidman, Daniel Thornton, Janet Sternberg, Michael Orengo, Christine Velankar, Sameer European Molecular Biology Laboratory European Bioinformatics Institute (EMBL-EBI) Wellcome Genome Campus Hinxton United Kingdom Cancer Research UK Cancer Therapeutics Unit Division of Cancer Therapeutics The Institute of Cancer Research London United Kingdom University of Florence C.I.R.M.M.P. Magnetic Resonance Center Sesto Fiorentino Italy School of Life Sciences University of Dundee Dundee United Kingdom Masaryk University International Centre for Clinical Research St. Anne’s University Hospital Brno Department of Experimental Biology and RECETOX Brno Czech Republic Palacky University Olomouc Department of Physical Chemistry Olomouc Czech Republic University of Cambridge Department of Biochemistry Cambridge United Kingdom Computational and Systems Biology Massachusetts Institute of Technology Cambridge MA United States CNB-CSIC Biocomputing Unit - Instruct Image Processing Center Madrid Spain Imperial College London London United Kingdom Weizmann Institute of Science Rehovot Israel Fox Chase Cancer Center Institute for Cancer Research Philadelphia PA United States Instituto de Ciencias de la Vid y del Vino (CSIC - Universidad de La Rioja - Gobierno de La Rioja) Oenology Barcelona Spain Randall Centre for Cell & Molecular Biophysics King’s College London London United Kingdom European Molecular Biology Laboratory Heidelberg Germany Centre for Molecular Bioinformatics Department of Biology University of Rome Tor Vergata Rome Italy Charles University Prague Czech Republic University of Georgia Department of Biochemistry and Molecular Biology Institute of Bioinformatics Athens United States Indian Institute of Science Education and Research Pune India Ghent University Department of Biomolecular Medicine Ghent Belgium VIB-UGent Center for Medical Biotechnology Ghent Belgium University of Kent Canterbury Kent United Kingdom Barcelona Supercomputing Center Life Sciences Department Barcelona Spain University of Padova Deptartment of Biomedical

The Protein Data Bank in Europe - Knowledge Base (PDBe-KB, https://***) is an open collaboration between world-leading specialist data resources contributing functional and biophysical annotations derived from or relevant to the Protein Data Bank (PDB). The goal of PDBe-KB is to place macromolecular structure data in their biological context by developing standardised data exchange formats and integrating functional annotations from the contributing partner resources into a knowledge graph that can provide valuable biological insights. Since we described PDBe-KB in 2019, there have been significant improvements in the variety of available annotation data sets and user functionality. Here, we provide an overview of the consortium, highlighting the addition of annotations such as predicted covalent binders, phosphorylation sites, effects of mutations on the protein structure and energetic local frustration. In addition, we describe a library of reusable web-based visualisation components and introduce new features such as a bulk download data service and a novel superposition service that generates clusters of superposed protein chains weekly for the whole PDB archive.

关键词： mutation biophysics frustration internet libraries molecular structure phosphorylation graphical displays protein structure protein data bank molecule knowledge bases datasets

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P0017ESTIMATION OF KIDNEY MITOCHONDRIA TOLERANCE VIA FLUORESCENCE MICROSCOPY

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Nephrology Dialysis Transplantation 2020年第SUPPLEMENT_3期35卷

作者： Plotnikov, Egor Popkov, Vasily Tvorogova, Anna Andrianova, Nadezda Buyan, Marina Zorov, Dmitry Lomonosov Moscow State University A.N. Belozersky Institute of Physico-Chemical Biology Moscow Russia Sechenov First Moscow State Medical University Institute of Molecular Medicine Moscow Russia Lomonosov Moscow State University Faculty of Biology Moscow Russia Lomonosov Moscow State University Faculty of Bioengineering and Bioinformatics Moscow Russia

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Ten percent of conserved miRNA-binding sites in vertebrates are misaligned

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Biophysics (Russian Federation) 2017年第1期62卷 38-42页

作者： Prosvirov, K.A. Mironov, A.A. Soldatov, R.A. Faculty of Bioengineering and Bioinformatics Moscow State University Moscow 119234 Russian Federation Institute for Information Transmission Problems Russian Academy of Sciences Moscow 127051 Russian Federation

MiRNAs are small endogenous noncoding RNAs that inhibit protein expression at the translation level via complementary binding to a specific site in the mRNA. Most of the functional binding sites (miRNA nucleotides 2–7) located within the 3' untranslated region are conserved. These sites are predicted using the methods of comparative genomics, primarily, multiple sequence alignment. However, multiple sequence alignments are prone to accumulating errors due to strong divergence of species. Moreover, in the course of evolution, binding sites can migrate along the sequence. The aim of this work was to estimate the portion of conserved miRNA-binding sites that cannot be predicted using the existing tools because of these phenomena. The concept of L-conserved sites is introduced: a site is termed L-conserved if it is present in each sequence in the alignment within a frame of length L. We observed a significant increase in the number of additionally detected conserved sites without loss of sensitivity. The effect of species divergence on this increase was also evaluated. © 2017, Pleiades Publishing, Inc.

关键词： comparative genomics miRNA multiple sequence alignment

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Semantic Knowledge Graph Network Features for Drug Repurposing 10

Semantic Knowledge Graph Network Features for Drug Repurposi...

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10th International Conference on Semantic Web Applications and Tools for Health Care and Life Sciences, SWAT4LS 2017

作者： Malas, Tareq B. Kudrin, Roman Starikov, Sergei Thoen, Peter A.C. Peters, Dorien J.M. Roos, Marco Hettne, Kristina M. Department of Human Genetics Leiden University Medical Center Leiden2300 RC Netherlands Faculty of Bioengineering and Bioinformatics Moscow State University Moscow119234 Russia

Given the significant time and financial costs of developing a commercial drug, it remains important to constantly reform the drug discovery pipeline with novel technologies that can narrow the candidates down to the most promising lead compounds for clinical testing. Computational approaches are used to expedite the drug discovery processes. Semantic knowledge graphs can assist these computational approaches, because they connect different biological databases and reflect the relationships between genes, pathways and diseases. Here, we took advantage of the Euretos Knowledge Platform (EKP), a commercial database that integrates more than 170 different biological resources including DrugBank, and evaluated the usefulness of the underlying semantic knowledge graphs to predict novel drug-disease associations. We extracted network-based features from the semantic knowledge graph and tested their ability to separate between the positive and negative data sets of drug-disease pairs. Our results showed that the extracted features such as the total number of intermediate concepts (count), the number of different semantic categories (diversity), and the predicates connecting a drug-disease pair were successful in separating the positive from the negative sets. These features provide a proof of concept for using semantic knowledge graphs for drug repurposing efforts. Our work reveals the added value of integrating different biological databases for solving complex biological questions.

关键词： Database systems

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