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PDB-IHM: A System for Deposition, Curation, Validation, and Dissemination of Integrative Structures

Journal of Molecular Biology 2025年 168963页

作者： Vallat, Brinda Webb, Benjamin M. Zalevsky, Arthur Tangmunarunkit, Hongsuda Sekharan, Monica R. Voinea, Serban Shafaeibejestan, Aref Sagendorf, Jared Hoch, Jeffrey C. Kurisu, Genji Morris, Kyle L. Velankar, Sameer Kesselman, Carl Burley, Stephen K. Berman, Helen M. Sali, Andrej Research Collaboratory for Structural Bioinformatics Protein Data Bank and the Institute for Quantitative Biomedicine Rutgers The State University of New Jersey Piscataway 08854 NJ United States Rutgers Cancer Institute Rutgers The State University of New Jersey New Brunswick 08901 NJ United States Research Collaboratory for Structural Bioinformatics Protein Data Bank Department of Bioengineering and Therapeutic Sciences Quantitative Biosciences Institute (QBI) Department of Pharmaceutical Chemistry University of California San Francisco. San Francisco 94157 CA United States Information Sciences Institute Viterbi School of Engineering University of Southern California Los Angeles CA United States Biological Magnetic Resonance Data Bank Department of Molecular Biology and Biophysics University of Connecticut Farmington 06030-3305 CT United States Protein Data Bank Japan Institute for Protein Research Osaka University Osaka Suita 565-0871 Japan Electron Microscopy Data Bank European Molecular Biology Laboratory European Bioinformatics Institute Cambridge Hinxton CB10 1SD United Kingdom Protein Data Bank in Europe European Molecular Biology Laboratory European Bioinformatics Institute Cambridge Hinxton CB10 1SD United Kingdom Research Collaboratory for Structural Bioinformatics Protein Data Bank San Diego Supercomputer Center University of California La Jolla 92093 CA United States Department of Chemistry and Chemical Biology Rutgers The State University of New Jersey Piscataway 08854 NJ United States Rutgers Artificial Intelligence and Data Science (RAD) Collaboratory Rutgers The State University of New Jersey Piscataway 08854 NJ United States Department of Quantitative and Computational Biology University of Southern California Los Angeles 90089 CA United States

Structures of many large biomolecular assemblies are now being determined using integrative approaches. In these approaches, information derived from multiple experimental and computational methods is combined to compute three-dimensional structures of multi-protein complexes and other macromolecular machines. A standalone prototype data resource for integrative structures called PDB-Dev was built, based on recommendations of the Integrative and Hybrid Methods (IHM) Task Force of the Worldwide Protein data Bank (wwPDB). This effort included developing data standards and software tools for collecting, curating, validating, visualizing, archiving, and disseminating integrative structures that span diverse spatiotemporal scales and conformational states. Mechanisms have been created to validate integrative structures based on the experimental data underpinning them. Building upon this foundational framework, PDB-Dev has been further expanded to handle large dynamic macromolecular systems and integrative structures that combine, for example, experimental restraints with atomic coordinates computed by machine learning algorithms. data standards and supporting tools have also been extended to capture information about biomolecular dynamics, such as conformational transitions and related kinetic data derived from biophysical methods. Recently, PDB-Dev was unified with the PDB archive and rebranded as PDB-IHM (***), further promoting FAIR (Findable, Accessible, Interoperable, and Reusable) principles of data stewardship for integrative structural biology. © 2025 The Author(s)

关键词： IHMCIF integrative modeling PDB PDBx/mmCIF structure validation

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rcsb-api: Python Toolkit for Streamlining Access to RCSB Protein data Bank APIs: rcsb-api: Python Toolkit to Access *** APIs

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Journal of Molecular Biology 2025年 168970页

作者： Piehl, Dennis W. Vallat, Brinda Truong, Ivana Morsy, Habiba Bhatt, Rusham Blaumann, Santiago Biswas, Pratyoy Rose, Yana Bittrich, Sebastian Duarte, Jose M. Segura, Joan Bi, Chunxiao Myers-Turnbull, Douglas Hudson, Brian P. Zardecki, Christine Burley, Stephen K. Research Collaboratory for Structural Bioinformatics Protein Data Bank and the Institute for Quantitative Biomedicine Rutgers The State University of New Jersey Piscataway 08854 NJ United States Rutgers Cancer Institute Rutgers The State University of New Jersey New Brunswick 08901 NJ United States Research Collaboratory for Structural Bioinformatics Protein Data Bank San Diego Supercomputer Center University of California San Diego La Jolla 92093 CA United States Department of Chemistry and Chemical Biology Rutgers The State University of New Jersey Piscataway 08854 NJ United States Rutgers Artificial Intelligence and Data Science (RAD) Collaboratory Rutgers The State University of New Jersey Piscataway 08854 NJ United States

The Protein data Bank (PDB) was founded in 1971 as the first open-access digital data resource in biology to serve as the single global archive for three-dimensional (3D) macromolecular structure data. Current PDB holdings exceed 230,000 experimentally determined structures of proteins, nucleic acids, viruses, and macromolecular machines. The RCSB Protein data Bank *** research-focused web portal facilitates search, analyses, and visualization of every PDB structure along with more than one million Computed Structure Models from AlphaFold DB and the ModelArchive. It is powered by a set of publicly available Application Programming Interfaces (APIs) that both support *** users and provide programmatic access to PDB data. Given the breadth and levels of granularity encompassed in this rich data collection, efficiently accessing the information programmatically may be challenging for new users. RCSB PDB has developed a Python software package, rcsb-api, that facilitates easy and efficient use of RCSB PDB APIs within a Python environment. This software tool is designed to streamline access to the extensive corpus of data housed within the PDB, enabling researchers to search, retrieve, and analyze 3D biostructure data seamlessly. Its use will accelerate research in structural biology, molecular biology and biochemistry, drug discovery, and bioinformatics by providing more efficient tools for data integration and analysis. The new toolkit is available on GitHub (***/rcsb/py-rcsb-api) and published to the public Python package repository (PyPI) to foster wider usage and support basic and applied research in fundamental biology, biomedicine, and the energy sciences. © 2025 The Author(s)

关键词： bioinformatics FAIR principles RCSB Protein data Bank *** structural biology

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Ibaqpy: A scalable Python package for baseline quantification in proteomics leveraging SDRF metadata

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Journal of Proteomics 2025年 317卷

作者： Zheng, Ping Audain, Enrique Webel, Henry Dai, Chengxin Klein, Joshua Hitz, Marc-Phillip Sachsenberg, Timo Bai, Mingze Perez-Riverol, Yasset Chongqing Key Laboratory of Big Data for Bio Intelligence Chongqing University of Posts and Telecommunications Chongqing China Institute of Medical Genetics University Medicine Oldenburg Carl von Ossietzky University Oldenburg Germany The Novo Nordisk Foundation Center for Biosustainability Technical University of Denmark Lyngby Denmark State Key Laboratory of Proteomics Beijing Proteome Research Center National Center for Protein Sciences (Beijing) Beijing Institute of Life Omics Beijing China Program for Bioinformatics Boston University Boston United States Department of Computer Science Applied Bioinformatics University of Tübingen Tübingen Germany Institute for Bioinformatics and Medical Informatics University of Tübingen Tübingen Germany European Molecular Biology Laboratory European Bioinformatics Institute Wellcome Genome Campus Cambridge United Kingdom

Intensity-based absolute quantification (iBAQ) is essential in proteomics as it allows for the assessment of a protein's absolute abundance in various samples or conditions. However, the computation of these values for increasingly large-scale and high-throughput experiments, such as those using DIA, TMT, or LFQ workflows, poses significant challenges in scalability and reproducibility. Here, we present ibaqpy (https://***/bigbio/ibaqpy), a Python package designed to compute iBAQ values efficiently for experiments of any scale. Ibaqpy leverages the Sample and data Relationship Format (SDRF) metadata standard to incorporate experimental metadata into the quantification workflow. This allows for automatic normalization and batch correction while accounting for key aspects of the experimental design, such as technical and biological replicates, fractionation strategies, and sample conditions. Designed for large-scale proteomics datasets, ibaqpy can also recompute iBAQ values for existing experiments when an SDRF is available. We showcased ibaqpy's capabilities by reanalyzing 17 public proteomics datasets from ProteomeXchange, covering HeLa cell lines with 4921 samples and 5766 MS runs, quantifying a total of 11,014 proteins. In our reanalysis, ibaqpy is a key component in automating reproducible quantification, reducing manual effort and making quantitative proteomics more accessible while supporting FAIR principles for data reuse. Significance: Proteomics studies often rely on intensity-based absolute quantification (iBAQ) to assess protein abundance across various biological conditions. Despite its widespread use, computing iBAQ values at scale remains challenging due to the increasing complexity and volume of proteomics experiments. Existing tools frequently lack metadata integration, limiting their ability to handle experimental design intricacies such as replicates, fractions, and batch effects. Our work introduces ibaqpy, a scalable Python package that lev

关键词： Big data bioinformatics data integration Proteomics Quantification

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uniLIVER: a human liver cell atlas for data-driven cellular state mapping

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Journal of Genetics and Genomics 2025年

作者： Wu, Yanhong Fan, Yuhan Miao, Yuxin Li, Yuman Du, Guifang Chen, Zeyu Diao, Jinmei Chen, Yu-Ann Ye, Mingli You, Renke Chen, Amin Chen, Yixin Li, Wenrui Guo, Wenbo Dong, Jiahong Zhang, Xuegong Wang, Yunfang Gu, Jin MOE Key Lab of Bioinformatics BNRIST Bioinformatics Division Department of Automation Tsinghua University Beijing 100084 China Hepato-Pancreato-Biliary Center Beijing Tsinghua Changgung Hospital Tsinghua University Beijing 102218 China Clinical Translational Science Center Beijing Tsinghua Changgung Hospital Tsinghua University Beijing 102218 China Fuzhou Institute of Data Technology Fujian Fuzhou 350207 China Center for Synthetic and Systems Biology School of Life Sciences and School of Medicine Tsinghua University Beijing 100084 China

The liver performs several vital functions such as metabolism, toxin removal, and glucose storage through the coordination of various cell types. With the recent breakthrough of the single-cell/single-nucleus RNA-seq (sc/snRNA-seq) techniques, there is a great opportunity to establish a reference cell map of the liver at single-cell resolution with transcriptome-wise features. In this study, we build a unified liver cell atlas uniLIVER (http://***/database/uniliver) by integrative analysis of a large-scale sc/snRNA-seq data collection of normal human liver with 331,125 cells and 79 samples from 6 datasets. Moreover, we introduce LiverCT, a novel machine learning based method for mapping any query dataset to the liver reference map by introducing the definition of “variant” cellular states analogy to the sequence variants in genomic analysis. Applying LiverCT on liver cancer datasets, we find that the “deviated” states of T cells are highly correlated with the stress pathway activities in hepatocellular carcinoma, and the enrichments of tumor cells with the hepatocyte-cholangiocyte “intermediate” states significantly indicate poor prognosis. Besides, we find that the tumor cells of different patients have different zonation tendencies and this zonation tendency is also significantly associated with the prognosis. This reference atlas mapping framework can also be extended to any other tissues. © 2025 Institute of Genetics and Developmental Biology, Chinese Academy of sciences, and Genetics Society of China

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Predicting the physiological effects of multiple drugs using electronic health record

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Computers in Biology and Medicine 2025年 184卷 109485-109485页

作者： Jeon, Junhyeok Hong, Eujin Kim, Jong-Yeup Lee, Suehyun Kim, Hyun Uk Daejeon34141 Korea Republic of Department of Biomedical Informatics College of Medicine Konyang University Daejeon35365 Korea Republic of Healthcare Data Science Center Konyang University Hospital Daejeon35365 Korea Republic of Department of Computer Engineering Gachon University Seongnam13120 Korea Republic of Graduate School of Engineering Biology KAIST Daejeon34141 Korea Republic of BioProcess Engineering Research Center and BioInformatics Research Center KAIST Daejeon34141 Korea Republic of

Various computational models have been developed to understand the physiological effects of drug-drug interactions, which can contribute to more effective drug treatments. However, they mostly focus on interactions of only two drugs, and do not consider the patient information. To address this challenge, we use publicly available electronic health record (EHR), MIMIC-IV, to develop machine learning models that predict the physiological effects of two or more drugs. This study involves extensive preprocessing of laboratory measurement data, prescription data and patient data. The resulting machine learning models predict potential abnormalities across 20 selected measurement items (e.g., concentrations of metabolites and blood cells) in the form of a sentence. Analysis of the model predictions showed that age, specific active pharmaceutical ingredients, and male/female appeared to be the most influential features. The model development process showcased in this study can be extended to other measurement items for a target EHR. © 2024 Elsevier Ltd

关键词： Electronic health record

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The microcirculation: master in normal pregnancy, puppet in preeclampsia

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American Journal of Obstetrics and Gynecology 2025年

作者： Thevissen, Kristof Cornette, Jerome Bruckers, Liesbeth Gyselaers, Wilfried Department of Rheumatology Ziekenhuis Oost-Limburg Genk Belgium Faculty of Medicine and Life Sciences Hasselt University Diepenbeek Belgium Department of Obstetrics and Fetal Medicine Erasmus Medical Center Rotterdam Netherlands Data Science Institute Interuniversity Institute for Biostatistics and Statistical Bioinformatics Hasselt University Diepenbeek Belgium Department of Obstetrics and Gynaecology Ziekenhuis Oost-Limburg Genk Belgium

Background: The microcirculation is studied sparsely in the field of maternal hemodynamics. With nailfold video capillaroscopy, further insight is possible in this interesting field within maternal hemodynamics. Objective: This study aimed to investigate the association between functional parameters of the microcirculation and the systemic cardiovascular system in pregnant women at risk for gestational hypertension disorders. Study Design: For this observational study, women with high cardiovascular risk according to maternal anthropometrics and obstetrical and medical history were recruited at random gestational ages, depending on the time of referral to the outpatient clinic for high-risk prenatal care at Ziekenhuis Oost-Limburg, Genk, Belgium. After birth, data on maternal and neonatal outcomes were obtained from hospital records, and only women with normal pregnancy (n=142) and preeclampsia (n=34) were included in this analysis. Nailfold video capillaroscopy measurements were performed in the first, second, and/or third trimesters. Video magnification of 200× was used for all fingers except the thumbs, and the stored images were analyzed offline. Capillary density was quantified (n/mm²), mean capillary diameter measured (μm), and capillary bed surface calculated as density × diameter. Cardiac output and total peripheral resistance were measured using impedance cardiography, together with sphygmomanometric blood pressure measurement. A linear mixed model for repeated measures was used to investigate the association between the microvascular and macrovascular parameters. No corrections for multiple testing were applied. Results: In normal pregnancies, a positive association was observed between the capillary bed surface and total vascular resistance (1.807;P=.01) and a negative association between capillary density and cardiac output (−0.269;P=.037). In preeclampsia, a negative association was observed between capillary density and mean arterial pressur

关键词： capillary density capillary diameter capillary perfusion cardiac output gestational hypertension disorder hypertension maternal hemodynamics microcirculation nailfold video capillaroscopy preeclampsia systemic circulation vascular resistance

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Synthetic Control Removes Spurious Discoveries from Double Dipping in Single-Cell and Spatial Transcriptomics data Analyses

Synthetic Control Removes Spurious Discoveries from Double ...

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29th International Conference on Research in Computational Molecular Biology, RECOMB 2025

作者： Song, Dongyuan Chen, Siqi Lee, Christy Li, Kexin Ge, Xinzhou Li, Jingyi Jessica Department of Genetics and Genome Sciences University of Connecticut Health Center FarmingtonCT06032 United States Interdepartmental Program of Bioinformatics University of California Los Angeles Los AngelesCA90095-7246 United States Department of Statistics and Data Science University of California Los Angeles Los AngelesCA90095-1554 United States Department of Statistics Oregon State University CorvallisOR97331-4606 United States

ISBN: (纸本)9783031902512

Double dipping is a common analytical pitfall in single-cell and spatial transcriptomics data analysis: after a clustering algorithm finds clusters as putative cell types or spatial domains, statistical tests are applied to the same data to identify differentially expressed (DE) genes as potential cell-type or spatial-domain markers. Because the genes that contribute to clustering are inherently likely to be identified as DE genes, double dipping can result in false-positive markers, especially when clusters are spurious, leading to ambiguously defined cell types or spatial domains. To address this challenge, we propose ClusterDE, a statistical method designed to identify post-clustering DE genes as reliable markers of cell types and spatial domains, while controlling the false discovery rate (FDR) regardless of clustering quality. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Biomolecules

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Translation dysregulation in cancer as a source for targetable antigens

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Cancer Cell 2025年第5期43卷 823-840.e18,20页

作者： Weller, Chen Bartok, Osnat McGinnis, Christopher S. Palashati, Heyilimu Chang, Tian-Gen Malko, Dmitry Shmueli, Merav D. Nagao, Asuteka Hayoun, Deborah Murayama, Ayaka Sakaguchi, Yuriko Poulis, Panagiotis Khatib, Aseel Erlanger Avigdor, Bracha Gordon, Sagi Cohen Shvefel, Sapir Zemanek, Marie J. Nielsen, Morten M. Boura-Halfon, Sigalit Sagie, Shira Gumpert, Nofar Yang, Weiwen Alexeev, Dmitry Kyriakidou, Pelgia Yao, Winnie Zerbib, Mirie Greenberg, Polina Benedek, Gil Litchfield, Kevin Petrovich-Kopitman, Ekaterina Nagler, Adi Oren, Roni Ben-Dor, Shifra Levin, Yishai Pilpel, Yitzhak Rodnina, Marina Cox, Jürgen Merbl, Yifat Satpathy, Ansuman T. Carmi, Yaron Erhard, Florian Suzuki, Tsutomu Buskirk, Allen R. Olweus, Johanna Ruppin, Eytan Schlosser, Andreas Samuels, Yardena Department of Molecular Cell Biology Weizmann Institute of Science Rehovot 7610001 Israel Department of Pathology Stanford University Stanford 94305 CA United States Parker Institute for Cancer Immunotherapy San Francisco 94129 CA United States Department of Cancer Immunology Institute for Cancer Research Oslo University Hospital Oslo 0379 Norway Precision Immunotherapy Alliance University of Oslo Oslo Norway Cancer Data Science Laboratory Center for Cancer Research National Cancer Institute National Institutes of Health Bethesda 20892 MD United States Department of Systems Immunology Weizmann Institute of Science Rehovot 7610001 Israel Department of Chemistry and Biotechnology of Engineering The University of Tokyo Tokyo 113-8656 Japan Department of Physical Biochemistry Max Planck Institute for Multidisciplinary Sciences Göttingen 37077 Germany Department of Pathology Sackler School of Medicine Tel Aviv University Tel Aviv 69978 Israel Department of Immunology and Regenerative Biology Weizmann Institute of Science Rehovot 7610001 Israel Computational Systems Biochemistry Research Group Max-Planck Institute of Biochemistry Martinsried 82152 Germany Department of Veterinary Resources Weizmann Institute of Science Rehovot 7610001 Israel Tissue Typing and Immunogenetics Unit Hadassah Hebrew University Hospital Jerusalem 9112102 Israel CRUK Lung Cancer Centre of Excellence University College London Cancer Institute London WC1E 6DD United Kingdom Tumour Immunogenomics and Immunosurveillance Laboratory University College London Cancer Institute London WC1E 6DD United Kingdom Department of Life Science Core Facilities Weizmann Institute of Science Rehovot 7610001 Israel Bioinformatics Unit Department of Life Sciences Core Facilities Weizmann Institute of Science Rehovot 7610001 Israel de Botton Institute for Protein Profiling the Nancy and Stephen Grand Israel National Center for Personalized Medicine Weizmann Institute of Science Rehovot

Aberrant peptides presented by major histocompatibility complex (MHC) molecules are targets for tumor eradication, as these peptides can be recognized as foreign by T cells. Protein synthesis in malignant cells is dysregulated, which may result in the generation and presentation of aberrant peptides that can be exploited for T cell-based therapies. To investigate the role of translational dysregulation in immunological tumor control, we disrupt translation fidelity by deleting tRNA wybutosine (yW)-synthesizing protein 2 (TYW2) in tumor cells and characterize the downstream impact on translation fidelity and immunogenicity using immunopeptidomics, genomics, and functional assays. These analyses reveal that TYW2 knockout (KO) cells generate immunogenic out-of-frame peptides. Furthermore, Tyw2 loss increases tumor immunogenicity and leads to anti-programmed cell death 1 (PD-1) checkpoint blockade sensitivity in vivo. Importantly, reduced TYW2 expression is associated with increased response to checkpoint blockade in patients. Together, we demonstrate that defects in translation fidelity drive tumor immunogenicity and may be leveraged for cancer immunotherapy. © 2025 The Author(s)

关键词： cancer immunopeptidome immune checkpoint blockade neoantigens non-canonical peptides translation fidelity

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A Bayesian-based approach for extracting the pion charge radius from electron-electron scattering data

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Chinese Physics C 2021年第8期45卷 12-27页

作者： Alam A Hidayat Bens Pardamean Bioinformatics and Data Science Research Center Bina Nusantara UniversityJakarta 11480Indonesia Computer Science Department BINUS Graduate Program-Master of Computer ScienceBina Nusantara UniversityJakarta 11480Indonesia

In this study,we utilize a potentially versatile Bayesian parameter approach to compute the value of the pion charge radius and quantify its uncertainty from several experimental e^(+)e^(-) datasets for the pion vector form *** employ dispersion relations to model the pion vector form factor to extract the *** model selection is used to determine the order of polynomial appearing in the form factor formulation that can be supported by the data,adapting the computation of Bayes evidence and Bayesian effective complexity based on Occam's *** findings indicate that five out of six used datasets favor the nine-parameter model for radius extraction,and accordingly,we average the radii from the *** some inconsistencies with the most updated radius values,our approach may serve as a more intuitive method of addressing parameter estimations in dispersion theory.

关键词： Bayesian complexity dispersion hadron pion

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Aggregating Structural Features for Computational Analysis of Mutations in BRCT Domains of BRCA1 Protein

Aggregating Structural Features for Computational Analysis o...

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2023 International Conference on Informatics, Multimedia, Cyber and Information Systems, ICIMCIS 2023

作者： Hidayat, Alam Ahmad Nirwantono, Rudi Isnan, Mahmud Trinugroho, Joko Pebrianto Pardamean, Bens Bina Nusantara University School of Computer Science Bioinformatics and Data Science Research Center Mathematics Department Jakarta11480 Indonesia Bina Nusantara University Faculty of Engineering Bioinformatics and Data Science Research Center Biotechnology Department Jakarta11480 Indonesia Bina Nusantara University Bioinformatics and Data Science Research Center Jakarta11480 Indonesia Binus Graduate Program Bioinformatics and Data Science Research Center Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350307306

Understanding the mechanistic interpretability of mutation effects in a protein can help predict the clinical implications of the genetic variants. Hence, computational variant effect predictions that involve protein structural features of the protein mutations might be suitable in this case. In this work, we focus on BRCT domains of BRCA1 gene that is widely studied in breast cancer studies. We retrieved 88 selected missense variants found in BRCT domains annotated in both ClinVar and gnomAD databases. To computationally characterize the pathogenic property of the mutations we used two types of features extracted from protein structures: a change in free Gibbs energy and a set of features derived from molecular dynamics simulations of each mutant. Using a dimensional reduction and Gaussian mixture model (GMM)-based clustering we demonstrate that the variants are segregated into two regions that may correspond to their pathogenic status. This method can be a potential computational pipeline for providing the preliminary mechanistic interpretation of mutation effects in terms of their thermodynamic and structural features. © 2023 IEEE.

关键词： Proteins

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