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Refinement of NMR-determined protein structures with database derived distance constraints

Journal of bioinformatics and computational biology 2005年第6期3卷 1315-1329页

作者： Cui, Feng Jernigan, Robert Wu, Zhijun Program on Bioinformatics and Computational Biology Iowa State University Ames IA 50011 United States Department of Biochemistry Biophysics and Molecular Biology Iowa State University Ames IA 50011 United States Department of Mathematics Program on Bioinformatics and Computational Biology Iowa State University Ames IA 50011 United States

The protein structures determined by NMR (Nuclear Magnetic Resonance Spectroscopy) are not as detailed and accurate as those by X-ray crystallography and are often underdetermined due to the inadequate distance data available from NMR experiments. The uses of NMR-determined structures in such important applications as homology modeling and rational drug design have thus been severely limited. Here we show that with the increasing numbers of high quality protein structures being determined, a computational approach to enhancing the accuracy of the NMR-determined structures becomes possible by deriving additional distance constraints from the distributions of the distances in databases of known protein structures. We show through a survey on 462 NMR structures that, in fact, many inter-atomic distances in these structures deviate considerably from their database distributions and based on the refinement results on 10 selected NMR structures that these structures can actually be improved significantly when a selected set of distances are constrained within their high probability ranges in their database distributions. © Imperial College Press.

关键词： NMR protein structure refinement Protein structure database Structural bioinformatics

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Network-based method to infer the contributions of proteins to the etiology of drug side effects

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Frontiers of Electrical and Electronic Engineering in China 2015年第3期10卷 124-134页

作者： Rui Li (1) Ting Chen (1) (2) Shao Li (1) 1.MOE Key Laboratory of Bioinformatics and Bioinformatics Division TNLIST Department of Automation Tsinghua University Beijing 100084 China 2.Program in Computational Biology and Bioinformatics University of Southern California Los Angeles CA 90089 USA

Studying the molecular mechanisms that underlie the relationship between drugs and the side effects they produce is critical for drug discovery and drug development. Currently, however, computational methods are still unavailable to assess drug-protein interactions with the aim of globally inferring the contributions of various classes of proteins toward the etiology of side effects. In this work, we integrated data reflecting drug-side effect relationships, drug- target relationships, and protein-protein interactions to develop a novel network-based probabilistic model, SidePro, to evaluate the contributions of proteins toward the etiology of side effects. For a given side effect, the method applies an expectation--maximization algorithm and a diffusion kernel-based approach to estimate each protein＇s contribution. We applied this method to a wide range of side effects and validated the results using cross-validation and records from the Side Effect Resource database. We also studied a specific side effect, nephrotoxicity, which is known to be associated with the irrational use of the Chinese herbal compound triptolide, a diterpenoid epoxide in the Thunder of God Vine, Tripterygium wilfordii （Lei-Gong-Teng）. Using triptolide as an example, we scored the target proteins of triptolide using our model and investigated the high-scoring proteins and their related biological processes. The results demonstrated that our model could differentiate between the potential side effect targets and therapeutic targets of triptolide. Overall, the proposed model could accurately pinpoint the molecular mechanisms of drug side effects, thus making contribution to safe and effective drug development.

关键词： network pharmacology drug targets side effects triptolide

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Characterizing Emergency Department Care for Patients With Histories of Incarceration

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JACEP Open 2025年第1期6卷 100022-100022页

作者： Huang, Thomas Socrates, Vimig Ovchinnikova, Polina Faustino, Isaac Kumar, Anusha Safranek, Conrad Chi, Ling Wang, Emily A. Puglisi, Lisa Wong, Ambrose H. Wang, Karen H. Taylor, R. Andrew Department of Emergency Medicine Yale School of Medicine New Haven CT United States Department for Biomedical Informatics and Data Science Yale University School of Medicine New Haven CT United States Program of Computational Biology and Bioinformatics Yale University New Haven CT United States SEICHE Center for Health and Justice Yale School of Medicine New Haven CT United States Equity Research and Innovation Center Yale School of Medicine Yale University New Haven CT United States

Objectives: Patients with a history of incarceration experience bias from health care team members, barriers to privacy, and a multitude of health care disparities. We aimed to assess care processes delivered in emergency departments (EDs) for people with histories of incarceration. Methods: We utilized a fine-tuned large language model to identify patient incarceration status from 480,374 notes from the ED setting. We compared socio-demographic characteristics, comorbidities, and care processes, including disposition, restraint use, and sedation, between individuals with and without a history of incarceration. We then conducted multivariable logistic regression to assess the independent correlation of incarceration history and management in the ED while adjusting for demographic characteristics, health behaviors, presentation, and past medical history. Results: We found 1734 unique patient encounters with a history of incarceration from a total of 177,987 encounters. Patients with history of incarceration were more likely to be male, Black, Hispanic, or other race/ethnicity, currently unemployed or disabled, and had smoking and substance use histories, compared with those without. This cohort demonstrated higher odds of elopement (OR: 3.59 [95% CI: 2.41–5.12]), leaving against medical advice (OR: 2.39 [95% CI: 1.46–3.67]), and being subjected to sedation (OR: 3.89 [95% CI: 3.19–4.70]) and restraint use (OR: 3.76 [95% CI: 3.06–4.57]). After adjusting for covariates, the association between incarceration and elopement remained significant (adjusted odds ratio: 1.65 [95% CI: 1.08–2.43]), while associations with other dispositions, restraint use, and sedation did not persist. Conclusion: This study identified differences in patient characteristics and care processes in the ED for patients with histories of incarceration and demonstrated the potential of using natural language processing in measuring care processes in populations that are largely hidden, but highly prev

关键词： artificial intelligence emergency medicine incarceration large language models quality of health care

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Convergent Usage of Amino Acids in Human Cancers as A Reversed Process of Tissue Development

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Genomics, Proteomics & bioinformatics 2022年第1期20卷 147-162页

作者： Yikai Luo Han Liang Graduate Program in Quantitative and Computational Biosciences Baylor College of MedicineHoustonTX 77030USA Department of Bioinformatics and Computational Biology The University of Texas MD Anderson Cancer CenterHoustonTX 77030USA Department of Systems Biology The University of Texas MD Anderson Cancer CenterHoustonTX 77030USA

Genome-and transcriptome-wide amino acid usage preference across different species is a well-studied phenomenon in molecular evolution,but its characteristics and implication in cancer evolution and therapy remain largely ***,we analyzed large-scale transcriptome/proteome profiles,such as The Cancer Genome Atlas(TCGA),the Genotype-Tissue Expression(GTEx),and the Clinical Proteomic Tumor Analysis Consortium(CPTAC),and found that compared to normal tissues,different cancer types showed a convergent pattern toward using biosynthetically low-cost amino *** a pattern can be accurately captured by a single index based on the average biosynthetic energy cost of amino acids,termed energy cost per amino acid(ECPA).With this index,we further compared the trends of amino acid usage and the contributing genes in cancer and tissue development,and revealed their reversed ***,focusing on the liver,a tissue with a dramatic increase in ECPA during development,we found that ECPA represents a powerful biomarker that could distinguish liver tumors from normal liver samples consistently across 11 independent patient cohorts and outperforms any index based on single *** study reveals an important principle underlying cancer evolution and suggests the global amino acid usage as a system-level biomarker for cancer diagnosis.

关键词： Amino acid usage Tissue development Biosynthetic energy Diagnostic biomarker

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A multi-omic approach implicates novel protein dysregulation in post-traumatic stress disorder

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Genome Medicine 2025年第1期17卷 1-19页

作者： Wang, Jiawei Liu, Yujing Li, Hongyu Nguyen, Tuan P. Soto-Vargas, John Lee Wilson, Rashaun Wang, Weiwei Lam, TuKiet T. Zhang, Chi Lin, Chen Wolf, Erika J. Williamson, Douglas E. Ursano, Robert Stein, Thor DiSano, Krista Schnurr, Paula Scott, William K. Pierce, Meghan Montalvo-Ortiz, Janitza Noller, Crystal Miller, Mark W. Marx, Brian McKee, Ann Logue, Mark W. Keane, Terence M. Labadorf, Adam T. Kaye, Alfred Huber, Bertrand R. Hoffman, Ellen Davis, David A. Cruz, Dianne A. Che, Alicia Benedek, David Alvarez, Victor E. Lewis, David A. Glausier, Jill Holtzheimer, Paul E. Friedman, Matthew J. Williams, Kenneth R. Picciotto, Marina R. Nairn, Angus C. Krystal, John H. Duman, Ronald S. Young, Keith A. Zhao, Hongyu Girgenti, Matthew J. Program of Computational Biology & Bioinformatics Yale University New Haven 06511 CT United States Department of Psychiatry Yale School of Medicine New Haven 06511 CT United States Department of Biostatistics Yale School of Public Health New Haven 06510 CT United States NIDA Neuroproteomics Center Yale School of Medicine New Haven 06511 CT United States Department of Molecular Biophysics and Biochemistry Yale School of Medicine New Haven 06511 CT United States Keck MS & Proteomics Resource Yale School of Medicine New Haven 06510 CT United States Department of Psychiatry University of Pittsburgh Pittsburgh 15213 PA United States Department of Psychiatry Geisel School of Medicine at Dartmouth Lebanon 03756 NH United States Central Texas Veterans Health Care System Research Service Temple 76504 TX United States Department of Psychiatry and Behavioral Sciences Texas A & M University School of Medicine Bryan 77807 TX United States National Center for PTSD United States Department of Veterans Affairs White River Junction 05009 VT United States

Background : Post-traumatic stress disorder (PTSD) is a common and disabling psychiatric disorder. PTSD involves multiple brain regions and is often comorbid with other psychiatric disorders, such as major depressive disorder (MDD). Recent genome-wide association studies (GWASs) have identified many PTSD risk loci and transcriptomics studies of postmortem brain have found differentially expressed genes associated with PTSD cases. In this study, we integrated genome-wide measures across modalities to identify convergent molecular effects in the PTSD brain. Methods: We performed tandem mass spectrometry (MS/MS) on a large cohort of donors (N = 66) in two prefrontal cortical areas, dorsolateral prefrontal cortex (DLPFC), and subgenual prefrontal cortex (sgPFC). We also coupled the proteomics data with transcriptomics and microRNA (miRNA) profiling from RNA-seq and small-RNA sequencing, respectively for the same cohort. Additionally, we utilized published GWAS results of multiple psychiatric disorders for integrative analysis. Results: We found differentially expressed proteins and co-expression protein modules disrupted by PTSD. Integrative analysis with transcriptomics and miRNA data from the same cohort pointed to hsa-mir-589 as a regulatory miRNA responsible for dysregulation of neuronal protein networks for PTSD, including the gamma-aminobutyric acid (GABA) vesicular transporter, SLC32A1. In addition, we identified significant enrichment of risk genes for other psychiatric disorders, such as autism spectrum disorder (ASD) and major depressive disorder (MDD) within PTSD protein co-expression modules, suggesting shared molecular pathology. Conclusions: We integrated genome-wide measures of mRNA and miRNA expression and proteomics profiling from PTSD, MDD, and control (CON) brains to identify convergent and divergent molecular processes across genomic modalities. We substantially expand the number of differentially expressed genes and proteins in PTSD and identify dow

关键词： Major depressive disorder MicroRNAs Multi-omics Prefrontal cortex PTSD

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Incorporating gene annotations as node metadata to improve network centrality measures for better node ranking 13

Incorporating gene annotations as node metadata to improve n...

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2013 4th ACM Conference on bioinformatics, computational biology and Biomedical Informatics, ACM-BCB 2013

作者： Mistry, Divya Dickerson, Julie Department of Electrical and Computer Engineering Bioinformatics and Computational Biology Program Iowa State University Ames IA United States

ISBN: (纸本)9781450324342

Network centrality measures allow ranking of nodes and edges based on their importance to the network topology. Closeness centrality [1] and shortest path betweenness centrality [2] are two of the most popular and well-utilized centrality measures that have provided good results [3,4,5,6]. Both of these centralities rely exclusively on topological features of the network [7] to calculate node importance. We propose an improvement to these path length based centrality measures that incorporate nodespecific metadata to provide biologically relevant node ranking. We choose gene annotations and gene ontology (GO) evidences as our metadata to highlight the new approach. Application of the newly proposed centrality measures to synthetic networks, and pathogen infected barley's gene co-expression networks resulted in a significantly better prioritization of the nodes. We compared our results against unmodified centrality measures applied to the same networks. Our proposed improvements provide a new avenue for tailoring centrality measures for biological networks, and hold great potential for further improvement of random walk based [8] and motif-based centrality [9] measures. Copyright © 2007 by the Association for Computing Machinery.

关键词： Metadata

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Dynamics of Cell Fate Decision Mediated by the Interplay of Autophagy and Apoptosis in Cancer Cells 1

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丛书名： Springer Theses

1000年

作者： Iman Tavassoly

ISBN: (数字)9783319149622

ISBN: (纸本)9783319149615;9783319365121

This interdisciplinary thesis introduces a systems biology approach to study the cell fate decision mediated by autophagy. A mathematical model of interaction between Autophagy and Apoptosis in mammalian cells is proposed. In this dynamic model autophagy acts as a gradual response to stress (Rheostat) that delays the initiation of bistable switch of apoptosis to give the cells an opportunity to survive. The author shows that his dynamical model is consistent with existing quantitative measurements of time courses of autophagic responses to cisplatin treatment. To understand the function of this response in cancer cells, he has provided a systems biology experimental framework to study quantitative and dynamical aspects of autophagy in single cancer cells using live-cell imaging and quantitative fluorescence microscopy. This framework can provide new insights on function of autophagic response in cancer cells.

关键词： Biomaterials computational biology/bioinformatics Cancer Research Cell biology Biological and Medical Physics, Biophysics

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Transcriptomic and epigenomic signatures of liver metabolism and insulin sensitivity in aging mice

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Mechanisms of Ageing and Development 2025年 225卷 112068页

作者： González, John T. Scharfman, Olivia H. Zhu, Wanling Kasamoto, Jessica Gould, Victoria Perry, Rachel J. Higgins-Chen, Albert T. Department of Pathology Yale University School of Medicine New Haven CT United States Department of Cellular & Molecular Physiology Yale School of Medicine New Haven CT United States Department of Endocrinology & Metabolism Yale School of Medicine New Haven CT United States Program in Computational Biology and Bioinformatics Yale University New Haven CT United States Altos Labs Institute of Computation San Diego 92114 CA United States Department of Psychiatry Yale School of Medicine New Haven CT United States

Age-related declines in insulin sensitivity and glucose metabolism contribute to metabolic disease. Despite the liver's central role in glucose homeostasis, a comprehensive phenotypic characterization and concurrent molecular analysis of insulin resistance and metabolic dysfunction in the aging liver is lacking. We characterized hepatic insulin resistance and mitochondrial metabolic defects through metabolic cage, hyperinsulinemic-euglycemic clamp, and tracer studies paired with transcriptomic and DNA methylation analyses in young and aged male mice. Aged mice exhibited benchmark measures of whole body and liver insulin resistance. Aged mice showed lower pyruvate dehydrogenase flux, decreased fatty acid oxidation and citrate synthase fluxes, and increased pyruvate carboxylase flux under insulin-stimulated conditions. Molecular analysis revealed age-related changes in metabolic genes Pck1, Socs3, Tbc1d4, and Enpp1. Unsupervised network analysis identified an intercorrelated phenotype module (ME-Glucose), RNA module, and DNA methylation module. The DNA methylation module was enriched for lipid metabolism pathways and TCF-1 binding, while the RNA module was enriched for MZF-1 binding and regulation by miR-155–5p. Protein-protein interaction network analysis revealed interactions between module genes and canonical metabolic pathways, highlighting genes including Ets1, Ppp1r3b, and Enpp3. This study reveals novel genes underlying age-related hepatic insulin resistance as potential targets for metabolic interventions to promote healthy aging. © 2025 Elsevier B.V.

关键词： DNA methylation Insulin resistance Metabolic flux analysis Mitochondrial dysfunction Tricarboxylic acid (TCA) cycle

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Constructing a Boolean implication network to study the interactions between environmental factors and OTUs

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Frontiers of Electrical and Electronic Engineering in China 2014年第4期9卷 127-141页

作者： Congmin Zhu Rui Jiang Ting Chen MOE Key Laboratory of Bioinformatics and Bioinformatics Division Center for Synthetic & Systems Biology TNLIST/ Department of Automation Tsinghua University Beijing 100084 China Computational and Molecular Biology Program University of Southern California Loa Angles CA 90089 USA

Mining relationships between microbes and the environment they live in are crucial to understand the intrinsic mechanisms that govern cycles of carbon, nitrogen and energy in a microbial community. Building upon next- generation sequencing technology, the selective capture of 16S rRNA genes has enabled the study of co-occurrence patterns of microbial species from the viewpoint of complex networks, yielding successful descriptions of phenomena exhibited in a microbial community. However, since the effects of such environmental factors as temperature or soil conditions on microbes are complex, reliance on the analysis of co-occurrence networks alone cannot elucidate such complicated effects underlying microbial communities. In this study, we apply a statistical method, which is called Boolean implications for metagenomic studies （BIMS） for extracting Boolean implications （IF-THEN relationships） to capture the effects of environmental factors on microbial species based on 16S rRNA sequencing data. We first demonstrate the power and effectiveness of BIMS through comprehensive simulation studies and then apply it to a 16S rRNA sequencing dataset of real marine microbes. Based on a total of 6,514 pairwise relationships identified at a low false discovery rate （FDR） of 0.01, we construct a Boolean implication network between operational taxonomic units （OTUs） and environmental factors. Relationships in this network are supported by literature, and, most importantly, they bring biological insights into the effects of environmental factors on microbes. We next apply BIMS to detect three-way relationships and show the possibility of using this strategy to explain more complex relationships within a microbial community.

关键词： Boolean implication metagenome marine OTUs environmental factors

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Paralog-divergent Features May Help Reduce Off-target Effects of Drugs:Hints from Glucagon Subfamily Analysis

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Genomics, Proteomics & bioinformatics 2017年第4期15卷 246-254页

作者： Zhining Sa Jingqi Zhou Yangyun Zou Zhixi Su Xun Gu State Key Laboratory of Genetic Engineering and MOE Key Laboratory of Contemporary Anthropology School of LifeSciencesFudan UniversityShanghai 200433China Department of Genetics Development and Cell BiologyProgram of Bioinformatics and Computational BiologyIowa State UniversityAmesIA 50011USA

Side effects from targeted drugs remain a serious conccrn. One reason is the nonselective binding of a drug to unintended proteins such as its paralogs, which arc highly homologous in sequences and have similar structures and drug-binding pockets. To identify targctablc differences between paralogs, we analyzed two types （type-I and type-ll） of functional divergence between two paralogs in the known target protein receptor family G-protein coupled receptors （GPCRs） at the amino acid level. Paralogous protein receptors in glucagon-like subfamily, glucagon receptor （GCGR） and glucagon-like peptide-I receptor （GLP-I R）, exhibit divergence in ligands and are clinically validated drug targets for type 2 diabetes. Our data showed that type-ll alnino acids were significantly enriched in the binding sites of antagonist MK-0893 to GCGR. which had a radical shift in physicochemical properties between GCGR and GLP-1R. We also examined the role of type-I amino acids between GCGR and GLP-IR. The divergent features between GCGR and GLP-I R paralogs may be helpful in their discrimination, thus enabling the identification of binding sites to reduce undesirable side effects and increase the target specificity of drugs.

关键词： Paralog Functional divergence Functional site Drug specificity Evolutionary conservation

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