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Protein recognition is chiefly mediated by the CDR2 region in TREM2 - an in silico characterization

Journal of Molecular Graphics and Modelling 2025年 138卷

作者： Dantas, Pedro H.S. Matos, Amanda O. Colmenares, Mike T.C. Costa, Vinícius A.F. Felice, Andrei G. Neto, José R.C. Soares, Siomar C. Silva-Sales, Marcelle Neves, Bruno J. Sales-Campos, Helioswilton Laboratory of Mucosal Immunology and Immunoinformatics Institute of Tropical Pathology and Public Health Federal University of Goias Goiás Goiânia Brazil Laboratory of Cheminformatics Faculty of Pharmacy Federal University of Goiás Goiânia Brazil Laboratory of Immunology and Bioinformatics and Program in Tropical Medicine and Infectious Diseases Institute of Biological and Natural Sciences Federal University of Triângulo Mineiro Minas Gerais Uberaba Brazil Laboratory of Virology and Cellular Culture Institute of Tropical Pathology and Public Health Federal University of Goiás Goias Goiânia Brazil

The Triggering Receptor Expressed on Myeloid cells 2 (TREM2) is an immune receptor with three complementarity-determining regions (CDR1-3) that primarily interact with the receptor's ligands. Aside from its role in reducing inflammation, enhancing phagocytosis, and contributing to cellular maturation and survival, TREM2 also contributes to the pathophysiology of neurodegenerative disorders, cancer, and metabolic diseases. Therefore, understanding how the receptor interacts with its ligands is essential to mitigate its adverse effects and/or to foster the development of new therapeutic approaches. Thus, our research focused on understanding the interactions between TREM2 and its protein ligands: APOA1, APOA2, APOE3, APOE4, APOJ, C1q, Galectin-3, cyclophilin A, Heat shock protein 60 (HSP60), IL-34, IL-4, the SARS-CoV-2 membrane protein and the cholera toxin subunit B, TDP-43 using in silico methods, such as molecular docking and molecular dynamics simulations. TREM2 showed a higher affinity and stability with HSP60, APOA2, Cyclophilin A, Galectin-3, TDP-43 and C1q when compared to the other protein ligands. Notably, our data suggest that TREM2 interacts with its ligands predominantly through the CDR2 region by the following residues: N68, L69, W70, L71, L72, F74 and R76. Our findings indicate that the CDR2 region can be a crucial target for the development of inhibitory or agonistic approaches targeting the receptor's activity. © 2025 Elsevier Inc.

关键词： Molecular docking

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Patients with PSOriasis and Suppurative Hidradenitis (PSO-SH) share genetic risk factors and are at risk of increased morbidity

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Journal of the American Academy of Dermatology 2025年第6期92卷 1303-1311页

作者： Wiala, Antonia Elhage, Kareem G. Leung, Andrea Young, Albert T. Gregory, Mark Adrianto, Indra Zhou, Li Mi, Qing-Sheng Kumar, Sugandh Orcales, Faye Yeroushalmi, Samuel Haran, Kathryn Liu, Jared Naik, Haley B. Liao, Wilson Posch, Christian Department of Dermatology Clinic Landstrasse Vienna Austria Department of Dermatology University of California San Francisco California United States Center for Cutaneous Biology and Immunology Research Department of Dermatology Henry Ford Health Detroit MI United States Center for Bioinformatics Department of Public Health Sciences Henry Ford Health Detroit MI United States Henry Ford Health + Michigan State University Health Sciences East Lansing MI United States Immunology Research Program Henry Ford Cancer Institute Henry Ford Health Detroit MI United States Department of Medicine College of Human Medicine Michigan State University East Lansing MI United States Faculty of Medicine Sigmund Freud University Vienna Austria Department of Dermatology Clinic Hietzing Vienna Austria Department of Dermatology and Allergy School of Medicine German Cancer Consortium (DKTK) Technical University of Munich Munich Germany

Background: Select patients are diagnosed with both psoriasis (PSO) and hidradenitis suppurativa (HS), leading to a unique disease pattern. Genetic risk factors remain unidentified. Methods: The study harnessed an international collection of patients with PSO and HS (PSO-SH). Clinical and genetic data were collected and analyzed. Results: Eighty-seven PSO-SH patients (70% female) were identified. They had a high number of comorbidities (89%) and worse general physical health compared to PSO-only (OR: 3.09;95% CI: 1.56-6.12) or HS-only (OR: 2.5;95% CI: 1.23-5.00) patients. PSO-SH patients were at significantly higher risk of having Crohn's disease (OR: 4.6-11.9;95% CI). Data revealed the highest overall genetic risk score for PSO-SH patients (PSO-polygenic risk score;108.22), followed by PSO (101.18), HS (99.84), and healthy controls (98.58). High non-human leukocyte antigen scores were associated with an increased risk for developing both PSO and HS, indicating a distinct biological profile compared to HS-only and PSO-only individuals. Limitations: Some clinical information was collected retrospectively. Conclusions: This study highlights a shared genetic susceptibility of HS and PSO at non-human leukocyte antigen loci. Recognizing PSO-SH patients as a distinct patient group with high morbidity and increased risk for developing Crohn's disease will help to improve patient management. © 2025 The Author(s)

关键词： co-occurrence genetics hidradenitis suppurativa HLA non-HLA PSO-SH psoriasis

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Using Folding Ensemble and Stem Probability Maximization Methods to Predict RNA H-Type Pseudoknots

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Tsinghua Science and Technology 2012年第6期17卷 691-700页

作者： Junilda Spirollari Shawn Xiong Wang Jason T.L. Wang Bioinformatics Program Department of Computer Science New Jersey Institute of Technology University Heights Department of Computer Science California State University

We present in this paper an ab initio method, named KnotFold, for RNA H-type pseudoknot prediction. Our method employs an ensemble of RNA folding tools and a filtering heuristic to generate a set of pseudoknot-free stems, and then predicts pseudoknots by utilizing a search technique with a pseudo-probability scoring scheme. Experimental results show that KnotFold achieves higher sensitivity than existing methods. The KnotFold package with documentation is freely available at http：//***/KnotFold.

关键词： RNA structure pseudoknots tool ensemble

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Users' interest assessment on job portal

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International Journal of Web Portals 2014年第1期6卷 64-75页

作者： Sudiana Pardamean, Bens Computer Science Graduate Program Bina Nusantara University Jakarta Indonesia Bioinformatics Research Group Bina Nusantara University Jakarta Indonesia

Job portal is considerably useful for both job seekers and employers. It enables job seekers to look for an employment, to advance their careers, or to market themselves. As for the employers, they can post vacant positions to be filled with suitable employees. A certain job portal received numerous complaints regarding its features that were unable to fulfill the users' needs. These complaints led to the decrease of the number of users. A study was conducted to determine factors that influenced users' interest by referring to the 7C framework. Questionnaires were distributed to numerous job seekers and employers. The data were analyzed using multiple linear regression technique. Chi-square analysis was also performed to further analyze which elements of the 7C framework were essential for the users. The results showed that only four out of seven elements from the framework affected the users' choice towards job portal, namely the context, the content, the community, and the commerce. Copyright © 2014, IGI Global.

关键词： Commerce

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Comprehensive and critical evaluation of individualized pathway activity measurement tools on pan-cancer data (vol 21, bby097, 2018)

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BRIEFINGS IN bioinformatics 2020年第3期21卷 1118-1118页

作者： Lim, Sangsoo Lee, Sangseon Jung, Inuk Rhee, Sungmin Kim, Sun Interdisciplinary Program in Bioinformatics Seoul National University Seoul Korea Department of Computer Science and Engineering Seoul National University Seoul Korea Bioinformatics Institute Seoul National University Seoul Korea Department of Computer Science and Engineering Seoul National University Seoul Korea Interdisciplinary Program in Bioinformatics Seoul National University Seoul Korea Department of Computer Science and Engineering Seoul National University Seoul Korea Bioinformatics Institute Seoul National University Seoul Korea Corresponding author: Sun Kim Interdisciplinary Program in Bioinformatics Department of Computer Science and Engineering and Bioinformatics Institute Seoul National University Seoul 08826 Korea. Tel.: +82-2-880-7280 Fax: +82-2-888-9623 Email: sunkim.bioinfo@snu.ac.kr

Motivation Biological pathways are extensively used for the analysis of transcriptome data to characterize biological mechanisms underlying various phenotypes. There are a number of computational tools that summarize transcriptome data at the pathway level. However, there is no comparative study on how well these tools produce useful information at the cohort level, enabling comparison of many samples or *** In this study, we systematically compared and evaluated 13 different pathway activity inference tools based on 5 comparison criteria using pan-cancer data set. This study has two major contributions. First, our study provides a comprehensive survey on computational techniques used by existing pathway activity inference tools. The tools use different strategies and assume different requirements on data: input transformation, use of labels, necessity of cohort-level input data, use of gene relations and scoring metric. Second, we performed extensive evaluations on the performance of these tools. Because different tools use different methods to map samples to the pathway dimension, the tools are evaluated at the pathway level using five comparison criteria. Starting from measuring how well a tool maintains the characteristics of original gene expression values, robustness was also investigated by adding noise into gene expression data. Classification tasks on three clinical variables (tumor versus normal, survival and cancer subtypes) were performed to evaluate the utility of tools for their clinical applications. In addition, the inferred activity values were compared between the tools to see how similar they are along with the scoring schemes they use.

关键词： pathway analysis individualized pathway activity pathway activity inference pan-cancer analysis

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A scalable approach for alignments in fuzzy biological networks

A scalable approach for alignments in fuzzy biological netwo...

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2010 International Conference on bioinformatics, Computational Biology, Genomics and Chemoinformatics, BCBGC 2010

作者： Liu, Yunkai Verone, Alissa Bioinformatics Program Computer and Information Science Department Gannon University United States

ISBN: (纸本)9781617820694

Interactions between biological entities, whether cells, proteins or genes are under meticulous study. As high throughput calculations have made biological network a popular target for study, methods for comparison have not been mastered. Multiple classes of bionetwork models exist, such as metabolic, protein-gene, or protein-protein interactions;the latter being of most importance to our research. Several different computational approaches have been taken by introducing bionetworks as tools to evaluate genomic or biological data. The primary focus was to develop a new method for comparison of bionetworks with great amount of noises. In this paper, an algorithm was designed for the pairwise comparison of bionetworks, considering the most conserved and most variable areas. Copyright© (2010) by the International Society for Research in Science and Technology.

关键词： Genes

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A Markov random field model of microarray gridding

A Markov random field model of microarray gridding

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Proceedings of the 2003 ACM Symposium on Applied Computing

作者： Katzer, Mathias Kummert, Franz Sagerer, Gerhard Graduate Program Bioinformatics Faculty of Technology Bielefeld University 33501 Bielefeld Germany

DNA microarray hybridisation is a popular high throughput technique in academic as well as industrial functional genomics research. In this paper we present a new approach to automatic grid segmentation of the raw fluorescence microarray images by Markov Random Field (MRF) techniques. The main objectives are applicability to various types of array designs and robustness to the typical problems encountered in microarray images, which are contaminations and weak signal. We briefly introduce microarray technology and give some background on MRFs. Our MRF model of microarray gridding is designed to integrate different application specific constraints and heuristic criteria into a robust and flexible segmentation algorithm. We show how to compute the model components efficiently and state our deterministic MRF energy minimization algorithm that was derived from the 'Highest Confidence First' algorithm by Chou et al. Since MRF segmentation may fail due to the properties of the data and the minimization algorithm, we use supplied or estimated print layouts to validate results. Finally we present results of tests on several series of microarray images from different sources, some of them test sets published with other microarray gridding software. Our MRF grid segmentation requires weaker assumptions about the array printing process than previously published methods and produces excellent results on many real datasets. An implementation of the described methods is available upon request from the authors.

关键词： Computer science

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BPS2025 - Characterizing oligomeric amyloid-β42 and POPC interactions through molecular dynamics: A computational approach for understanding Alzheimer’s disease

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Biophysical Journal 2025年第3期124卷 70a-70a页

作者： Emma M. Cleveland Kelsie M. King Anne M. Brown Systems Biology Virginia Tech Blacksburg VA USA Genetics Bioinformatics and Computational Biology program Virginia Tech Christiansburg VA USA Department of Biochemistry Virginia Tech University Blacksburg VA USA

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Bit-parallel alignment with substitution scoring 8

Bit-parallel alignment with substitution scoring

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8th International Conference on bioinformatics and Computational Biology, BICOB 2016

作者： Loving, Joshua Becker, Elizabeth Benson, Gary LBI Bioinformatics Program Boston University BostonMA United States Bioinformatics Program Boston University BostonMA United States Computer Science Boston University BostonMA United States

ISBN: (纸本)9781943436033

Large sequence data sets generated by high-throughput biology experiments have motivated development of more efficient sequence comparison algorithms. Bit-parallel alignment offers one approach to dealing with these large data sets. Bit-parallel alignment algorithms use bits in computer words to represent alignment scores within a single row of the alignment scoring matrix and use logic operations and addition to compute the scores in the next row. Recently, we developed BitPAl, a very fast, general method for bit-parallel global alignment when there is a single match and mismatch value. In this paper we address global alignment using substitution scoring, such as BLOSUM substitution scoring commonly used for protein sequences or transition-transversion scoring used for DNA sequences. Substitution scoring assigns a potentially different substitution weight to every pair of alphabet characters. We present a new approach based on partial sums of adjacent score differences. It runs in time O(mn log W/W ) where W is the number of values that are stored in a computer word (in our implementation, w/8 where w is the length of a computer word). Except for a pre-processing step, the running time does not depend on the substitution weights. In experiments reported here, our algorithm is over eight times faster than standard iterative dynamic programming and 30% faster than an accelerated SIMD version of dynamic programming. Copyright ISCA.

关键词： Iterative methods

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Decoding the non-coding genome: Opportunities and challenges of genomic and epigenomic consortium data

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Current Opinion in Systems Biology 2018年 11卷 82-90页

作者： Pratt, Henry Weng, Zhiping Program in Bioinformatics and Integrative Biology University of Massachusetts Medical School Worcester 01605 MA United States

Publicly-available next-generation sequencing data has greatly expanded in the past decade, particularly through the work of several international consortia. These collaborative efforts have applied numerous assays profiling diverse features of gene regulation, such as genome-wide chromatin structure, transcriptional activity, and transcription factor binding, to thousands of biosamples from several organisms. Newly-developed computational analyses and statistical methods link findings to gene expression changes and phenotypic changes. Integrative analysis of these datasets holds the potential to revolutionize our understanding of organismal development, cell type differentiation, cellular response to stimuli, and disease mechanisms. However, standardized methods for data access, uniform data processing, and integrative analysis largely do not exist, hindering the impacts of these efforts. Here we review advancements made by consortia and directions of ongoing efforts, as well as challenges in accessing and analyzing publicly-available consortium data and emerging tools for addressing these challenges. © 2018 Elsevier Ltd

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