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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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Isotopic Labeling-Enabled Chemical Proteomics Analysis Revealed Structural and Functional Features of Allysine Modifications in Mammalian Cells and Tissues

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Analytical Chemistry 2025年 2025 Apr 28页

作者： Sin, Yi-Cheng Hosny, Nora Batti Angulski, Addeli Bez Kim, Do-Hyung Metzger, Joseph M. Chen, Yue Department of Biochemistry Molecular Biology and Biophysics University of Minnesota at Twin Cities MinneapolisMN55455 United States Department of Integrative Biology and Physiology University of Minnesota at Twin Cities MinneapolisMN55455 United States Bioinformatics and Computational Biology Program University of Minnesota at Twin Cities MinneapolisMN55455 United States

Allysine is a pivotal protein post-translational modification that regulates protein interaction and activities. It is also recognized as a marker of oxidative stress under certain metabolic and physiological conditions. In this study, we developed a capture-and-release chemical proteomics workflow with heavy isotopic labeling that enables system-wide enrichment and site-specific identification of allysine as well as other carbonylated peptides, such as peptides containing glutamic semialdehyde derived from the oxidative damage of arginine and proline, with high confidence. The streamlined workflow led to the identification of 434 allysine sites on 349 proteins in human 293T and HCT116 cells and 317 allysine sites on 157 proteins in mouse muscle tissues without any treatment with an oxidative stress-inducing chemical reagent. We identified 48 histone allysine sites, including 38 sites on core histones in human 293T cells, many of which overlapped with well-characterized histone acetylation and methylation epigenetic marks. Bioinformatic analysis revealed notable characteristics of the amino acid preferences of allysine flanking sequences and the significant depletion of allysine sites in the protein secondary structure in cultured human cells. Pathway analysis showed that allysine substrates were involved in diverse cellular processes including translation, protein folding, and RNA processing in human cells and were enriched with muscle contractile fiber proteins and metabolic enzymes in mouse muscle tissue. Thus, our integrated chemical proteomics analysis revealed the structural and functional features of allysine targets under regular growth conditions in cultured human cells and mouse tissues. © 2025 American Chemical Society.

关键词： Proteolysis

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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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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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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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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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Pre-Training Genomic Language Model with Variants for Better Modeling Functional Genomics

SSRN

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SSRN 2025年

作者： Liu, Tianyu Zhang, Xiangyu Ying, Rex Zhao, Hongyu Interdepartmental Program in Computational Biology & Bioinformatics Yale University New HavenCT06511 United States Department of Biostatistics Yale University New HavenCT06511 United States Department of Computer Science Yale University New HavenCT06511 United States

Sequence-to-function models can predict gene expression from sequence data and be used to link genetic information with transcriptomics data to understand regulatory processes and their effects on complex phenotypes. The genomic language models are pre-trained with large-scale DNA sequences and can generate robust representations of these sequences by learning the genomic context. However, few studies can estimate the predictability of gene expression levels and bridge these two classes of models together to explore individualized gene expression prediction. In this manuscript, we propose UKBioBERT as a DNA language model pre-trained with genetic variants from UK BioBank. We demonstrate that UKBioBERT generates informative embeddings capable of identifying gene functions, and improving gene expression prediction in cell lines, thereby enhancing our understanding of gene expression predictability. Building upon these embeddings, we combine UKBioBERT with state-of-the-art sequence-to-function architectures, Enformer and Borzoi, to create UKBioFormer and UKBioZoi. These models exhibit better performance in predicting highly predictable gene expression levels and can be generalized across different cohorts. Furthermore, UKBioFormer effectively captures the relationship between genetic variants and expression variations, enabling in-silico mutation analyses. Collectively, our findings underscore the value of integrating genomic language models and sequence-to-function approaches for advancing functional genomics research. © 2025, The Authors. All rights reserved.

关键词： Genome

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