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Analysing protein complexes in plant science: insights and limitation with AlphaFold 3

Botanical Studies 2025年第1期66卷 1-12页

作者： Lin, Pei-Yu Huang, Shiang-Chin Chen, Kuan-Lin Huang, Yu-Chun Liao, Chia-Yu Lin, Guan-Jun Lee, HueyTyng Chen, Pao-Yang Institute of Plant and Microbial Biology Academia Sinica Taipei Taiwan Institute of Plant Biology National Taiwan University Taipei Taiwan Bioinformatics Program Institute of Statistical Science Taiwan International Graduate Program Academia Sinica Taipei Taiwan Bioinformatics Program Taiwan International Graduate Program National Taiwan University Taipei Taiwan Genome and Systems Biology Degree Program Academia Sinica and National Taiwan University Taipei Taiwan

AlphaFold 3 (AF3), an artificial intelligence (AI)-based software for protein complex structure prediction, represents a significant advancement in structural biology. Its flexibility and enhanced scalability have unlocked new applications in various fields, specifically in plant science, including improving crop resilience and predicting the structures of plant-specific proteins involved in stress responses, signalling pathways, and immune responses. Comparisons with existing tools, such as ClusPro and AlphaPulldown, highlight AF3’s unique strengths in sequence-based interaction predictions and its greater adaptability to various biomolecular structures. However, limitations persist, including challenges in modelling large complexes, protein dynamics, and structures from underrepresented plant proteins with limited evolutionary data. Additionally, AF3 encounters difficulties in predicting mutation effects on protein interactions and DNA binding, which can be improved with molecular dynamics and experimental validation. This review presents an overview of AF3’s advancements, using examples in plant and fungal research, and comparisons with existing tools. It also discusses current limitations and offers perspectives on integrating molecular dynamics and experimental validation to enhance its capabilities.

关键词： AlphaFold 3 Crop resilience Protein complex Protein structure Protein–protein interaction Structure biology

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Alignment-Free Estimation of Read to Genome Distances and Its Applications 29th

Alignment-Free Estimation of Read to Genome Distances and...

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

作者： Şapcı, Ali Osman Berk Mirarab, Siavash Bioinformatics and Systems Biology Graduate Program UC San Diego San DiegoCA92093 United States Department of Electrical and Computer Engineering UC San Diego San DiegoCA92093 United States

ISBN: (纸本)9783031902512

Searching reads from unknown origins in a reference database and finding evolutionarily similar genomes is central to many applications. Quantifying the similarity by estimating the distance between each read and matching references could further help downstream analyses such as taxonomic characterization or even placing the read on a reference phylogeny. Such distances can be computed using alignment. Since alignment becomes impractical for ultra-large reference databases, many use k-mer-based tools for downstream tasks, but these methods do not compute distances. We introduce krepp to compute the distance between a read and a genome using a maximum likelihood framework built around k-mers. We further introduce algorithms to place reads on a reference tree. Empirical evaluations show that krepp accurately estimates distances, scales well with large databases, and can place short reads coming from any part of the genome on a reference phylogeny. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Average nucleotide identity Metagenomics Phylogenetic placement

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Emerging Techniques in Spatial Multiomics: Fundamental Principles and Applications to Dermatology

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Journal of Investigative Dermatology 2025年第5期145卷 1017-1032页

作者： Jia, Bojing B. Sun, Bryan K. Lee, Ernest Y. Ren, Bing Bioinformatics and Systems Biology Graduate Program University of California San Diego La Jolla CA United States Medical Scientist Training Program University of California San Diego La Jolla CA United States Department of Dermatology University of California Irvine Irvine CA United States Department of Dermatology University of California San Francisco San Francisco CA United States Center for Epigenomics Department of Cellular & Molecular Medicine University of California San Diego La Jolla CA United States Institute of Genomic Medicine Moores Cancer Center School of Medicine University of California San Diego La Jolla CA United States

Molecular pathology, such as high-throughput genomic and proteomic profiling, identifies precise disease targets from biopsies but require tissue dissociation, losing valuable histologic and spatial context. Emerging spatial multi-omic technologies now enable multiplexed visualization of genomic, proteomic, and epigenomic targets within a single tissue slice, eliminating the need for labeling multiple adjacent slices. Although early work focused on RNA (spatial transcriptomics), spatial technologies can now concurrently capture DNA, genome accessibility, histone modifications, and proteins with spatially-resolved single-cell resolution. This review outlines the principles, advantages, limitations, and potential for spatial technologies to advance dermatologic research. By jointly profiling multiple molecular channels, spatial multiomics enables novel studies of copy number variations, clonal heterogeneity, and enhancer dysregulation, replete with spatial context, illuminating the skin's complex heterogeneity. © 2024 The Authors

关键词： Dermatopathology Digital pathology Multiomics Multiplexed imaging Spatial sequencing

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OXidative Stress PREDictor: A Supervised Learning Approach for Annotating Cellular Oxidative Stress States in Inflammatory Cells

Advanced Intelligent Systems

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Advanced Intelligent systems 2025年第3期7卷

作者： Chen, Po-Yuan Ko, Tai-Ming Institute of Bioinformatics and Systems Biology College of Engineering Bioscience National Yang Ming Chiao Tung University Hsinchu300 Taiwan Bioinformatics Program Institute of Statistical Science Taiwan International Graduate Program Academia Sinica Taipei115 Taiwan Institute of Biomedical Sciences Academia Sinica Taipei115 Taiwan Institute of Statistical Science Academia Sinica Taipei115 Taiwan Department of Biological Science and Technology College of Engineering Bioscience National Yang Ming Chiao Tung University Hsinchu300 Taiwan National Yang Ming Chiao Tung University Hsinchu300 Taiwan School of Pharmacy College of Pharmacy Drug Development and Value Creation Research Center Kaohsiung Medical University Kaohsiung807 Taiwan

Oxidative stress, characterized by an imbalance between reactive oxygen species (ROS) and antioxidants, plays a pivotal role in inflammatory responses associated with both chronic diseases and acute injuries. In this study, OXidative Stress PREDictor (OxSpred), a supervised learning model tailored to accurately annotate the oxidative stress state of innate immune cells at the single-cell level, is introduced. Compared to the traditional gene-set-variation-analysis-based enrichment method, OxSpred demonstrates superior accuracy with an area under the receiver operating characteristic curve of 0.89 and offers interpretable embeddings with significant biological relevance. Using the predicted ROS states, precise elucidation and interpretation of the roles of novel innate immune cell subtypes can be achieved. Overall, OxSpred enhances the utility of single-cell transcriptomic datasets by providing a robust in silico method for determining intracellular oxidative stress states, thereby enriching the understanding of innate immune cell functions during inflammation. © 2024 The Author(s). Advanced Intelligent systems published by Wiley-VCH GmbH.

关键词： Cells

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

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

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

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Correction to: Droplet Hi-C enables scalable, single-cell profiling of chromatin architecture in heterogeneous tissues (Nature Biotechnology, (2024), 10.1038/s41587-024-02447-1)

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Nature Biotechnology 2025年

作者： Chang, Lei Xie, Yang Taylor, Brett Wang, Zhaoning Sun, Jiachen Armand, Ethan J. Mishra, Shreya Xu, Jie Tastemel, Melodi Lie, Audrey Gibbs, Zane A. Indralingam, Hannah S. Tan, Tuyet M. Bejar, Rafael Chen, Clark C. Furnari, Frank B. Hu, Ming Ren, Bing Department of Cellular and Molecular Medicine University of California San Diego La JollaCA United States Biomedical Sciences Graduate Program University of California San Diego La JollaCA United States Medical Scientist Training Program University of California San Diego La JollaCA United States Department of Quantitative Health Sciences Lerner Research Institute Cleveland Clinic Foundation ClevelandOH United States Systems Biology and Bioinformatics PhD Program Case Western Reserve University School of Medicine ClevelandOH United States Bioinformatics and Systems Biology Program University of California San Diego La JollaCA United States Moores Cancer Center UC San Diego La JollaCA United States Department of Neurosurgery University of Minnesota MinneapolisMN United States Department of Medicine University of California San Diego School of Medicine La JollaCA United States Center for Epigenomics Institute for Genomic Medicine School of Medicine University of California San Diego La JollaCA United States

Correction to: Nature Biotechnologyhttps://***/10.1038/s41587-024-02447-1, published online 18 October 2024. In the version of the article initially published, in the Methods section, both mentions of "MboII" should have read "MboI (NEB, R0147M)" and have now been corrected in the HTML and PDF versions of the article. © The Author(s) 2025.

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Genomic analysis of progenitors in viral infection implicates glucocorticoids as suppressors of plasmacytoid dendritic cell generation

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Proceedings of the National Academy of Sciences of the United States of America 2025年第18期122卷 e2410092122页

作者： Jo, Yeara Greene, Trever T. Chiale, Carolina Zhang, Kai Fang, Ziyan Dallari, Simone Marooki, Nuha Wang, Wei Zuniga, Elina I. Division of Biological Sciences University of California San Diego La Jolla 92093 CA United States Department of Chemistry and Biochemistry University of California San Diego La Jolla 92093 CA United States Bioinformatics and Systems Biology Graduate Program University of California San Diego La Jolla 92093 CA United States Department of Cellular and Molecular Medicine University of California San Diego La Jolla 92093 CA United States

Plasmacytoid Dendritic cells (pDCs) are the most potent producers of interferons, which are critical antiviral cytokines. pDC development is, however, compromised following a viral infection, and this phenomenon, as well as its relationship to conventional (c)DC development is still incompletely understood. By using lymphocytic choriomeningitis virus (LCMV) infection in mice as a model system, we observed that DC progenitors skewed away from pDC and toward cDC development during in vivo viral infection. Subsequent characterization of the transcriptional and epigenetic landscape of fms-like tyrosine kinase 3+ (Flt3+) DC progenitors and follow-up studies revealed increased apoptosis and reduced proliferation in different individual DC-progenitors as well as a profound type I interferon (IFN-I)-dependent ablation of pre-pDCs, but not pre-DC precursors, after both acute and chronic LCMV infections. In addition, integrated genomic analysis identified altered activity of 34 transcription factors in Flt3+ DC progenitors from infected mice, including two regulators of Glucocorticoid (GC) responses. Subsequent studies demonstrated that addition of GCs to DC progenitors led to downregulated pDC-primed-genes while upregulating cDC-primed-genes, and that endogenous GCs selectively decreased pDC, but not cDC, numbers upon in vivo LCMV infection. These findings demonstrate a significant ablation of pre-pDCs in infected mice and identify GCs as suppressors of pDC generation from early progenitors. This provides a potential explanation for the impaired pDC development following viral infection and links pDC numbers to the hypothalamic–pituitary–adrenal axis. Copyright © 2025 the Author(s).

关键词： dendritic cell development glucocorticoids interferon plasmacytoid dendritic cells viral infection

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Modeling and analyzing three-dimensional structures of human disease proteins

Modeling and analyzing three-dimensional structures of human...

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11th Pacific Symposium on Biocomputing 2006, PSB 2006

作者： Ye, Yuzhen Li, Zhanwen Godzik, Adam Bioinformatics and Systems Biology Program Burnham Institute San Diego CA 92037 United States

ISBN: (纸本)9812564632

Three-dimensional structures of proteins, experimental or predicted, show us how these molecular machines actually work. With the help of information on disease-related mutations, they can also show us how they malfunction in diseases. Such understanding, currently lacking for most human diseases, is an important first step before designing drugs or therapies to cure specific diseases. Here we used homology modeling to model human disease-related proteins, and studied structural characteristics of disease related' mutations and compared them with non synonymous SNPs. 1484 domains from 874 proteins were modeled, and together with experimentally determined structures of 369 domains they provided the structural coverage of 48% of total residues in 1237 human disease proteins. We found that disease-related mutations have statistically significantly preference to form clusters on protein surfaces. In contrast, the non-synonymous SNPs appear to be randomly distributed on the surface. We interpret these results as an indication that disease mutations affect protein-protein interaction interfaces. This interpretation is supported by the analysis of 8 experimentally determined complexes between disease proteins, where disease-related mutations are clearly located in the binding interface of proteins, while SNPs are not. The non-uniform distribution of disease mutations indicates that we can use this feature as guidance in modeling and evaluating human disease proteins and their complexes. We set up a resource for Disease Protein Models (DPM at http://***/DPM>. which can be used for studying the relation between disease and mutation / polymorphism sites in the context of protein 3D structures and complexes.

关键词： Proteins

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Modeling the causal regulatory network by integrating chromatin accessibility and transcriptome data

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National Science Review 2016年第2期3卷 240-251页

作者： Yong Wang Rui Jiang Wing Hung Wong Department of Statistics Department of Biomedical Data ScienceBio-X ProgramStanford University Academy of Mathematics and Systems Science National Center for Mathematics and Interdisciplinary SciencesChinese Academy of Sciences MOE Key Laboratory of Bioinformatics Bioinformatics Division and Center for Synthetic and Systems BiologyTNLISTDepartment of AutomationTsinghua University

Cell packs a lot of genetic and regulatory information through a structure known as chromatin,*** is wrapped around histone proteins and is tightly packed in a remarkable *** express a gene in a specific coding region,the chromatin would open up and DNA loop may be formed by interacting enhancers and ***,the mediator and cohesion complexes,sequence-specific transcription factors,and RNA polymerase Ⅱ are recruited and work together to elaborately regulate the expression *** is in pressing need to understand how the information,about when,where,and to what degree genes should be expressed,is embedded into chromatin structure and gene regulatory *** to large consortia such as Encyclopedia of DNA Elements(ENCODE) and Roadmap Epigenomic projects,extensive data on chromatin accessibility and transcript abundance are available across many tissues and cell *** rich data offer an exciting opportunity to model the causal regulatory ***,we will review the current experimental approaches,foundational data,computational problems,interpretive frameworks,and integrative models that will enable the accurate interpretation of regulatory ***,we will discuss the efforts to organize,analyze,model,and integrate the DNA accessibility data,transcriptional data,and functional genomic regions *** believe that these efforts will eventually help us understand the information flow within the cell and will influence research directions across many fields.

关键词： gene regulatory network open chromatin DNA accessibility transcription factor colocalization statistical model data integration

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Mapping dihydropteroate synthase evolvability through identification of a novel evolutionarily critical substructure

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International Journal of Biological Macromolecules 2025年 311卷 143325页

作者： Sanyal, Dwipanjan Shivram, A. Pandey, Deeptanshu Banerjee, Suharto Uversky, Vladimir N. Muzata, Danny Chivukula, Aneesh Sreevallabh Jasuja, Ravi Chattopadhyay, Krishnananda Chowdhury, Sourav Structural Biology and Bioinformatics Division CSIR-Indian Institute of Chemical Biology Kolkata India Department of Computer Science and Information Systems Birla Institute of Technology and Science-Pilani Hyderabad India Department of Biological Sciences Birla Institute of Technology and Science-Pilani Hyderabad India Max Delbrück Center for Molecular Medicine Berlin Germany Research Program in Men's Health: Aging and Metabolism Brigham and Women's Hospital Harvard Medical School BostonMA United States USF Health Byrd Alzheimer's Research Institute Morsani College of Medicine University of South Florida TampaFL33612 United States

Protein evolution shapes pathogen adaptation-landscape, particularly in developing drug resistance. The rapid evolution of target proteins under antibiotic pressure leads to escape mutations, resulting in antibiotic resistance. A deep understanding of the evolutionary dynamics of antibiotic target proteins presents a plausible intervention strategy for disrupting the resistance trajectory. Mutations in Dihydropteroate synthase (DHPS), an essential folate pathway protein and sulfonamide antibiotic target, reduce antibiotic binding leading to anti-folate resistance. Deploying statistical analyses on the DHPS sequence-space and integrating deep mutational analysis with structure-based network-topology models, we identified critical DHPS subsequences. Our frustration landscape analysis suggests how conformational and mutational changes redistribute energy within DHPS substructures. We present an epistasis-based fitness prediction model that simulates DHPS adaptive walks, identifying residue positions that shape evolutionary constraints. Our optimality analysis revealed a substructure central to DHPS evolvability, and we assessed its druggability. Combining evolution and structure, this integrated framework identifies a DHPS substructure with significant evolutionary and structural impact. Targeting this region may constrain DHPS evolvability and slow resistance emergence, offering new directions for antibiotic development. © 2025

关键词： Topology

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