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Cholesterol Allosteric Modulation of the Oxytocin Receptor

Biophysical journal 2025年

作者： Brennica Marlow Alexander Vogel Georg Kuenze Maik Pankonin Franziska Reinhardt Peter F Stadler Peter W Hildebrand Jens Meiler Center for Structural Biology Vanderbilt University Nashville Tennessee United States of America Chemical and Physical Biology Program Vanderbilt University Nashville Tennessee United States of America Department of Chemistry Vanderbilt University Nashville Tennessee United States of America. Institute of Medical Physics and Biophysics Medical Faculty Leipzig University 04107 Leipzig Germany. Institute for Drug Discovery Medical Faculty Leipzig University 04103 Leipzig Germany Center for Scalable Data Analytics and Artificial Intelligence ScaDS.AI Leipzig University 04105 Leipzig Germany Interdisciplinary Center for Bioinformatics Leipzig University 04107 Leipzig Germany. Bioinformatics Group Institute of Computer Science Interdisciplinary Center of Bioinformatics Leipzig University Härtelstraße 16-18 Leipzig D-04107 Germany. Bioinformatics Group Institute of Computer Science Interdisciplinary Center of Bioinformatics Leipzig University Härtelstraße 16-18 Leipzig D-04107 Germany Max-Planck-Institute for Mathematics in the Sciences Inselstrße 22 D-04103 Leipzig Germany Inst. f. Theoretical Chemistry University of Vienna Währingerstraße 17 A-1090 Wien Austria Facultad de Ciencias Universidad National de Colombia Sede Bogota Colombia Santa Fe Institute 1399 Hyde Park Rd. Santa Fe New Mexico United States of America. Interdisciplinary Center for Bioinformatics Leipzig University 04107 Leipzig Germany

G-protein coupled receptors (GPCRs) are critical components in cellular signaling, mediating various physiological responses to external stimuli. Here, we investigate the intricate relationship between cholesterol and the oxytocin receptor (OXTR), focusing on the binding mechanisms and the allosteric crosstalk of bound cholesterol to the orthosteric ligand binding pocket. Utilizing molecular docking and molecular dynamics simulations, we identify cholesterol binding sites both on the agonist-bound and antagonist-bound state, which show differing distributions and residence times of the cholesterol molecules. Importantly, both methods converge on several key sites, demonstrating strong predictive overlap. Notably, one such site, and several sites detected by our MD approach, also coincides with electron density observed in an experimental cryo-EM map, providing orthogonal validation for computational predictions. Allosteric network analysis uncovers the distinct pathways through which cholesterol may affect ligand mediated receptor signaling, highlighting the significance of one site on the extracellular leaflet between TM4 and TM5, and two sites on the intracellular leaflet between TM2, TM3, and TM4 and between TM4 and TM5 in transmitting allosteric signals to the orthosteric pocket. These findings provide insights into the impact of cholesterol on OXTR function, emphasizing specific binding sites and signaling paths for further experimental exploration.

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A normalization method for multiple reaction monitoring (MRM) data

A normalization method for multiple reaction monitoring (MRM...

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IEEE International Conference on bioinformatics and Biomedicine

作者： Jungsoo Gim Taesung Park Interdisciplinary Program for Bioinformatics and Department of Statistics Seoul National University

ISBN: (纸本)9781479956708

High accuracy and reproducibility provided by selected or multiple reaction monitoring (SRM/MRM) experiments suggest that it is likely to become the platform of choice for identifying and verifying candidate biomarkers. Although several methods have been developed to quantify and discover the significant changes in SRM/MRM expression, we show normalization continues to be an essential step in the differential expression analysis. Here, we first investigate what makes normalization across the samples difficult in SRM assay. Then, we suggest a simple but effective normalization method to overcome the difficulty. Both real and simulated data sets are examined and improved results, compared to an existing method, are shown for inferring differential expression.

关键词： Proteomics Selected reaction monitoring (SRM) Normalization Differential expression test

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Elastic restricted Boltzmann machines for cancer data analysis

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Frontiers of Electrical and Electronic Engineering in China 2017年第2期5卷 159-172页

作者： Sai Zhang Muxuan Liang Zhongjun Zhou Chen Zhang Ning Chen Ting Chen Jianyang Zeng Institute for Interdisciplinary Information Sciences Tsinghua University Beijing 100084 China Department of Statistics University of Wisconsin-Madison Madison WI 53706-1685 USA Bioinformatics Division TNLIST Department of Computer Science and Technology Tsinghua University Beijing 100084 China Program in Computational Biology and Bioinformatics Univesity of Southern California Los Angeles CA 90089 USA

Background： Restricted Boltzmann machines （RBMs） are endowed with the universal power of modeling （binary） joint distributions. Meanwhile, as a result of their confining network structure, training RBMs confronts less difficulties when dealing with approximation and inference issues. But little work has been developed to fully exploit the capacity of these models to analyze cancer data, e.g., cancer genomic, transcriptomic, proteomic and epigenomic data. On the other hand, in the cancer data analysis task, the number of features/predictors is usually much larger than the sample size, which is known as the ＇~ 〉〉 N＂ problem and is also ubiquitous in other bioinformatics and computational biology fields. The ＂p 〉〉 N＂ problem puts the bias-variance trade-off in a more crucial place when designing statistical learning methods. However, to date, few RBM models have been particularly designed to address this issue. Methods： We propose a novel RBMs model, called elastic restricted Boltzmann machines （eRBMs）, which incorporates the elastic regularization term into the likelihood function, to balance the model complexity and sensitivity. Facilitated by the classic contrastive divergence （CD） algorithm, we develop the elastic contrastive divergence （eCD） algorithm which can train eRBMs efficiently. Results： We obtain several theoretical results on the rationality and properties of our model. We further evaluate the power of our model based on a challenging task -- predicting dichotomized survival time using the molecular profiling of tumors. The test results show that the prediction performance of eRBMs is much superior to that of the state-of-the-art methods. Conclusions： The proposed eRBMs are capable of dealing with the ＂p 〉〉 N＂ problems and have superior modeling performance over traditional methods. Our novel model is a promising method for future cancer data analysis.

关键词： RBMs regularization cancer data analysis survival time prediction

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Mutation of Oryza sativa CORONATINE INSENSITIVE 1b(OsCOI1b) delays leaf senescence

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Journal of Integrative Plant Biology 2015年第6期57卷 562-576页

作者： Sang-Hwa Lee Yasuhito Sakuraba Taeyoung Lee Kyu-Won Kim Gynheung An Han Yong Lee Nam-Chon Paek Department of Plant Science Plant Genomics and Breeding InstituteResearch Institute of Agriculture and Life SciencesSeoul National University Interdisciplinary Program in Bioinformatics Seoul National University Department of Plant Molecular Systems Biotechnology Crop Biotech InstituteKyung Hee University Department of Agricultural Biotechnology Seoul National University

Jasmonic acid （JA） functions in plant development, including senescence and immunity. Arabidopsis thaliana CORONATINE INSENSITIVE 1 encodes a JA receptor and functions in the JA‐responsive signaling pathway. The Arabidopsis genome harbors a single COI gene, but the rice （Oryza sativa） genome harbors three COI homologs, OsCOI1a, OsCOI1b, and OsCOI2. Thus, it remains unclear whether each OsCOI has distinct, additive, synergistic, or redundant func-tions in development. Here, we use the oscoi1b‐1 knockout mutants to show that OsCOI1b mainly affects leaf senescence under senescence‐promoting conditions. oscoi1b‐1 mutants stayed green during dark‐induced and natural senescence, with substantial retention of chlorophylls and photosyn-thetic capacity. Furthermore, several senescence‐associated genes were downregulated in oscoi1b‐1 mutants, including homologs of Arabidopsis thaliana ETHYLENE INSENSITIVE 3 and ORESARA 1, important regulators of leaf senescence. These results suggest that crosstalk between JA signaling and ethylene signaling affects leaf senescence. The Arabidopsis coi1‐1 plants containing 35S：OsCOI1a or 35S：OsCOI1b rescued the delayed leaf senescence during dark incubation, sug-gesting that both OsCOI1a and OsCOI1b are required for promoting leaf senescence in rice. oscoi1b‐1 mutants showed significant decreases in spikelet fertility and grain weight, leading to severe reduction of grain yield, indicating that OsCOI1‐mediated JA signaling affects spikelet fertility and grain filling.

关键词： Jasmonic acid leaf senescence OsCOI1b rice stay-green

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Integrating immunoinformatics and computational epitope prediction for a vaccine candidate against respiratory syncytial virus

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Infectious Disease Modelling 2024年第3期9卷 763-774页

作者： Truc Ly Nguyen Heebal Kim Department of Agricultural Biotechnology and Research Institute of Agriculture and Life Sciences Seoul National UniversitySeoul08826Republic of Korea Interdisciplinary Program in Bioinformatics Seoul National UniversitySeoul08826Republic of Korea eGnome Inc.Seoul05836Republic of Korea

Respiratory syncytial virus(RSV)poses a significant global health threat,especially affecting infants and the *** this,the present study proposes an innovative approach to vaccine design,utilizing immunoinformatics and computational *** analyzed RSV's structural proteins across both subtypes A and B,identifying potential helper T lymphocyte,cytotoxic T lymphocyte,and linear B lymphocyte *** such as antigenicity,allergenicity,toxicity,and cytokine-inducing potential were rigorously ***,we evaluated the conservancy of these epitopes and their population coverage across various RSV *** comprehensive analysis identified six major histocompatibility complex class I(MHC-I)binding,five MHC-II binding,and three B-cell *** were integrated with suitable linkers and adjuvants to form the ***,molecular docking and molecular dynamics simulations demonstrated stable interactions between the vaccine candidate and human Toll-like receptors(TLR4 and TLR5),with a notable preference for *** simulation analysis underscored the vaccine's potential to elicit a strong immune *** study presents a promising RSV vaccine candidate and offers theoretical support,marking a significant advancement in vaccine development ***,the promising in silico findings need to be further validated through additional in vivo studies.

关键词： Respiratory syncytial virus Multiepitope vaccine Immunoinformatics Docking molecular Molecular dynamics simulation Immune simulation

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Finding pathway regulators: gene set approach using peak identification algorithms

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BMC proceedings 2007年第1期1 Suppl 1卷 S90页

作者： Eunjee Lee Jung Hoon Woo Ji Wan Park Taesung Park Interdisciplinary Program in Bioinformatics Seoul National University Seoul South Korea 151-747. eunjee01@***

Recently, a number of different approaches have been used to examine variation in gene expression and to identify genes whose level of transcript differed greatly among unrelated individuals. Previous studies have commonly focused on identifying determinants that regulate gene expressions by targeting individual genes. However, it is difficult to detect true differences in the level of gene expression among genotypes from noise due to issues such as multiple testing and limited sample size. To increase the statistical power for detecting this difference, we consider a 'gene set' approach by focusing on subtle but coordinated changes in gene expression across multiple genes rather than individual genes. We defined a 'gene set' as a set of genes in the same biological pathway and focused on identifying common regulators based on an assumption that the genes within the same pathway are controlled by common regulators. We applied the gene set approach to the expression data of mRNA in Centre d'Etude du Polymorphisme Humain lymphoblast cells to identify regulators controlling the genes in a biological pathway. Our gene set approach successfully identified potent regulators controlling gene expression in an inflammatory response pathway.

关键词： Significant Marker Common Regulator Common Peak Multipoint Linkage Analysis Significant eQTL

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VizEpis: A visualization and mapping tool for interpreting epistasis

VizEpis: A visualization and mapping tool for interpreting e...

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IEEE International Conference on bioinformatics and Biomedicine

作者： Min-Seok Kwon Sungyoung Lee Yongkang Kim Taesung Park Interdisciplinary program in Bioinformatics Seoul National University Department of Statistics Seoul National University

ISBN: (纸本)9781467368001

An important issue in genetic association studies is gene-gene interaction (epistasis) underlying common complex diseases that are affected by multiple genetic factors. Although many methods have been proposed to analyze epistasis, the interpretation of the identified gene-gene interactions is not straightforward. In order to efficiently provide the statistical interpretation and biological evidences of gene-gene interactions, we developed the VizEpis, a tool for visualizing of gene-gene interactions in genetic association analysis and mapping of epistatic interaction to the biological evidence from public interaction databases. Using interaction network and circular plot, the VizEpis provides to explore the interaction network integrated with biological evidences in epigenetic regulation, splicing, transcription, translation and post-translation level. To aid statistical interaction in genotype level, the VizEpis provides pairwise checkerboard, forest, and funnel. VizEpis provides for the user-specified variants with statistical interaction to find biological evidences.

关键词： Vizualization Gene-gene interaction Genome-wide association study

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Diversity and Evolution of RNA Recognition Motifs: A Case Study on the Utility of RdRp in the phylum Duplornaviricota

Diversity and Evolution of RNA Recognition Motifs: A Case St...

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2023 IEEE International Conference on bioinformatics and Biomedicine, BIBM 2023

作者： Je, Mikyung Cho, Myeongji Son, Hyeon S. Seoul National University Interdisciplinary Graduate Program in Bioinformatics College of Natural Science Seoul Korea Republic of Seoul National University Lab. of Computational Biology & Bioinformatics Graduate School of Public Health Seoul Korea Republic of Seoul National University Institute of Health and Environment Seoul Korea Republic of

ISBN: (纸本)9798350337488

Viruses possess specific conserved regions known as RNA recognition motifs, which are shared within taxonomic groups. Applying these to the Baltimore virus classification system, there are genes/proteins that serve as biomarkers for each of the seven groups. Mutations in conserved sequence regions can have a significant impact on the function of a virus, especially if these conserved regions are part of a major protein. In this study, we analyzed the RdRp gene, known to be a representative RRM of RNA viruses, from viruses of five families (Alternaviridae, Chrysoviridae, Totiviridae, Sedoreoviridae, and Spinareoviridae) within the phylum Duplornaviricota. Data were retrieved from the NCBI non-redundant (nr) database (https://***/protein) applying a cut-off of 300 amino acids in length. RdRp protein sequences were aligned using PROMALS3D, and PhyML (v3.0) was used for phylogenetic analysis. In the next step, we utilized the officially provided ICTV taxonomy and bioinformatics tools to assign the phylogenetic position of RdRp. Although this analysis focused solely on RdRp, it can be concluded that using this gene as a marker may be sufficient for taxonomic assignment. Subsequent analyses will target DNA viruses and RT viruses to perform a more comprehensive assessment. This extensive analysis aims to validate the usefulness of marker genes in the overall viral taxonomy. © 2023 IEEE.

关键词： classification marker gene RdRp RNA recognition motif taxonomy virus

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Iterative segmented least square method for functional microRNA-mRNA module discovery in breast cancer

Iterative segmented least square method for functional micro...

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IEEE International Conference on bioinformatics and Biomedicine

作者： Sungmin Rhee Sangsoo Lim Sun Kim Computer Science and Engineering Seoul National University Republic of Korea Interdisciplinary Program in Bioinformatics Seoul National University Republic of Korea Computer Science and Engineering Interdisciplinary Program in Bioinformatics Seoul National University Republic of Korea

ISBN: (纸本)9781509016129

MicroRNAs (miRNAs) have significant biological roles at the molecular level by regulating genes post-transcriptionally. To understand the functional effects of miRNAs in different biological contexts, it is essential to elucidate miRNA -mRNA regulatory modules (MRMs). The computational complexity for inferencing MRMs is very high due to the many-to-many relationships between miRNAs and mRNAs and inferencing MRMs is still a challenging unresolved problem. In this paper, we propose a novel iterative segmented least square method for functional MRM discovery. Our method operates in two steps: 1) grouping and ordering the miRNAs and mRNAs to build per-sample matrices representing miRNA-mRNA regulations, and 2) determining maximum sized modules from structured miRNA-mRNA matrices. In experiments with human breast cancer data sets from TCGA, we show that our method outperforms existing methods in terms of both GO similarity and cluster evaluation. In addition, we show that modules determined by our method can be used for breast cancer survival prediction and subtype classification.

关键词： Optimization microRNA Regulatory network inference

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Quality control plot for high dimensional omics data

Quality control plot for high dimensional omics data

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2016 IEEE International Conference on bioinformatics and Biomedicine, BIBM 2016

作者： Kim, Gyu-Tae Kim, Yongkang Kwon, Min-Seok Park, Taesung Department of Statistics Seoul National University Daehak-Dong Seoul Gwanak-gu08826 Korea Republic of Interdisciplinary Program in Bioinformatics Seoul National University Daehak-Dong Gwanak-gu Seoul08826 Korea Republic of

ISBN: (纸本)9781509016105

Quality control (QC) becomes more important in pre-processing analysis of high dimensional omics data. Several routine QC processes became a standard process in omics data analysis. The standard QC analysis includes calculating quality-related measures, checking the consistency among samples, detecting outlying observations and so forth. QC analysis tends to be more important in the era of high dimensional omics data. Although several QC analysis tools providing simple graphical display have been developed by many researchers, they usually require a subjective decision on QC. Here, we propose high-dimensional data quality control (HidQC) plot which is a simple and efficient QC tool for handling high dimensional omics data. HidQC plot primarily focuses on identifying samples of poor quality by conducting a contrast analysis for the between/within group distances and the summary distances. HidQC plot checks the quality by investigating the consistency of samples for each group. Unlike other QC plots, HidQC plot provides the p-value of each sample based on the permutation test, which can be used as a more objective criterion to determine whether to use the sample or not. We applied HidQC plot to MicroArray Quality Control (MAQC) project 1 data to demonstrate its usefulness. © 2016 IEEE.

关键词： Quality control

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