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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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Exploring the genomic and transcriptomic profiles of glycemic traits and drug repurposing

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Journal of Biomedical Science 2025年第1期32卷 1-14页

作者： Lin, Min-Rou Tsai, Cheng-Lin Liao, Cai-Sian Wei, Chun-Yu Chou, Wan-Hsuan Hsiao, Tzu-Hung Chang, Wei-Chiao Department of Clinical Pharmacy School of Pharmacy Taipei Medical 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 Core Laboratory of Neoantigen Analysis for Personalized Cancer Vaccine Office of R&D Taipei Medical University Taipei Taiwan Department of Medical Research Taichung Veterans General Hospital Taichung Taiwan Department of Public Health Fu Jen Catholic University New Taipei City Taiwan Institute of Genomics and Bioinformatics National Chung Hsing University Taichung Taiwan Master Program in Clinical Genomics and Proteomics School of Pharmacy Taipei Medical University Taipei Taiwan Integrative Research Center in Critical Care Wan Fang Hospital Taipei Medical University Taipei Taiwan

Type 2 diabetes is an increasingly prevalent metabolic disorder with moderate to high heritability. Glycemic indices are crucial for diagnosing and monitoring the disease. Previous genome-wide association study (GWAS) have identified several risk loci associated with type 2 diabetes, but data from the taiwanese population remain relatively sparse and primarily focus on type 2 diabetes status rather than glycemic trait levels. We conducted a comprehensive genome-wide meta-analysis to explore the genetics of glycemic traits. The study incorporated a community-based cohort of 145,468 individuals and a hospital-based cohort of 35,395 individuals. The study integrated genetics, transcriptomics, biological pathway analyses, polygenic risk score calculation, and drug repurposing for type 2 diabetes. This study assessed hemoglobin A1c and fasting glucose levels, validating known loci (FN3K, SPC25, MTNR1B, and FOXA2) and discovering new genes, including MAEA and PRC1. Additionally, we found that diabetes, blood lipids, and liver- and kidney-related traits share genetic foundations with glycemic traits. A higher PRS was associated with an increased risk of type 2 diabetes. Finally, eight repurposed drugs were identified with evidence to regulate blood glucose levels, offering new avenues for the management and treatment of type 2 diabetes. This research illuminates the unique genetic landscape of glucose regulation in taiwanese Han population, providing valuable insights to guide future treatment strategies for type 2 diabetes.

关键词： Drug repurposing Fasting glucose Genome-wide association study Glycemic traits Hemoglobin A1c Polygenic risk score Transcriptome-wide association study Type 2 diabetes

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Air-liquid microfluidics-integrated surface-enhanced Raman spectroscopy for selective molecular adsorption and detection to achieve bacterial discrimination

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Biosensors and Bioelectronics 2025年 285卷 117576页

作者： Ku, Chi-Yao Chiang, Yu-Wei Hsu, Huai-Yuan Cheng, Ho-Wen Chen, Ko-Lun Han, Yin-Yi Wang, Juen-Kai Wang, Yuh-Lin Huang, Nien-Tsu Graduate Institute of Biomedical Electronics and Bioinformatics National Taiwan University Taipei Taiwan Institute of Atomic and Molecular Sciences Academia Sinica Taipei Taiwan Molecular Science and Technology Taiwan International Graduate Program Academia Sinica Taipei Taiwan International Graduate Program of Molecular Science and Technology National Taiwan University Taipei Taiwan Department of Anesthesiology National Taiwan University Hospital Taipei100229 Taiwan Department of Trauma National Taiwan University Hospital Taipei100229 Taiwan Center for Condensed Matter Sciences National Taiwan University Taipei Taiwan Center for Atomic Initiative for New Materials National Taiwan University Taipei Taiwan Department of Electrical Engineering National Taiwan University Taipei Taiwan

Bacterial discrimination is crucial for accurate microbiological diagnosis and timely antibiotic treatment. Surface-enhanced Raman spectroscopy (SERS) is an ideal technique due to its non-invasive, label-free molecular sensing capabilities. By analyzing bacterial supernatants, containing various purine derivatives, SERS can differentiate bacterial species based on their unique spectral distributions. However, the same bacterial species with different antibiotic resistance may secrete similar purine derivatives, differing only slightly in composition. Furthermore, each purine derivative may have a different molecular affinity to the SERS substrate, making it difficult to distinguish the exact molecular ratio. To improve SERS-based bacterial discrimination, we propose an air-liquid microfluidics-integrated SERS system (ALM-SERS) that selectively adsorbs and detects bacterial secretions. By taking features of precise microdroplet manipulation and contact area from microfluidics and features of selective molecular adsorption and fingerprints characterization from the SERS technique, we successfully perform a "sequential molecular adsorption" strategy to address the signal interference of complicated molecular mixtures in existing SERS methods. As a proof of concept, we first evaluate the molecular affinity of purine derivatives and then demonstrate the competitive analyte adsorption using adenine/cytosine and hypoxanthine/uracil sample mixtures. Finally, we tested six bacterial supernatants, including two Gram types and four strains with identical taxonomy but differing antibiotic resistance. Another six clinically isolated bacterial samples with different antibiotic resistance were also applied. Our results showed that a successful bacterial discrimination between similar species was not possible using a single SERS spectrum. In summary, the ALM-SERS system offers a powerful approach for bacterial discrimination, even when spectral differences are subtle. Beyond microb

关键词： Raman spectroscopy

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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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Protein subcellular localization prediction of eukaryotes using a knowledge-based approach

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BMC bioinformatics 2009年第SUPPL. 15期10卷 1-13页

作者： Lin, Hsin-Nan Chen, Ching-Tai Sung, Ting-Yi Ho, Shinn-Ying Hsu, Wen-Lian Bioinformatics Program Taiwan International Graduate Program Academia Sinica Taipei Taiwan Bioinformatics Lab. Institute of Information Science Academia Sinica Taipei Taiwan Institute of Bioinformatics National Chiao Tung University Hsinchu Taiwan

Background: The study of protein subcellular localization (PSL) is important for elucidating protein functions involved in various cellular processes. However, determining the localization sites of a protein through wet-lab experiments can be time-consuming and labor-intensive. Thus, computational approaches become highly desirable. Most of the PSL prediction systems are established for single-localized proteins. However, a significant number of eukaryotic proteins are known to be localized into multiple subcellular organelles. Many studies have shown that proteins may simultaneously locate or move between different cellular compartments and be involved in different biological processes with different roles. Results: In this study, we propose a knowledge based method, called KnowPredsite, to predict the localization site(s) of both single-localized and multi-localized proteins. Based on the local similarity, we can identify the "related sequences" for prediction. We construct a knowledge base to record the possible sequence variations for protein sequences. When predicting the localization annotation of a query protein, we search against the knowledge base and used a scoring mechanism to determine the predicted sites. We downloaded the dataset from ngLOC, which consisted of ten distinct subcellular organelles from 1923 species, and performed ten-fold cross validation experiments to evaluate KnowPredsite's performance. The experiment results show that KnowPredsite achieves higher prediction accuracy than ngLOC and Blast-hit method. For single-localized proteins, the overall accuracy of KnowPredsite is 91.7%. For multi-localized proteins, the overall accuracy of KnowPredsite is 72.1%, which is significantly higher than that of ngLOC by 12.4%. Notably, half of the proteins in the dataset that cannot find any Blast hit sequence above a specified threshold can still be correctly predicted by KnowPredsite. Conclusion: KnowPredsite demonstrates the power of identifying rel

关键词： Proteins

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Multiplexed Nanoscopy via Buffer Exchange

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ACS Nano 2024年第34期18卷 23445-23456页

作者： Chang, Ting-Jui Ben Yang, T. Tony Department of Electrical Engineering National Taiwan University Taipei10617 Taiwan Graduate Institute of Biomedical Electronics and Bioinformatics National Taiwan University Taipei10617 Taiwan Department of Physics National Taiwan University Taipei10617 Taiwan Nano Science and Technology Program Taiwan International Graduate Program Academia Sinica and National Taiwan University Taipei10617 Taiwan

Understanding cellular functions, particularly in their intricate complexity, can greatly benefit from the spatial mapping of diverse molecules through multitarget single-molecule localization microscopy (SMLM). Existing methodologies, primarily restricting the encoding dimensions to color and lifetime or requiring cyclic staining, often involve broad chromatic detection, specialized optical configurations, or sophisticated labeling techniques. Here, we propose a simple approach called buffer-exchange stochastic optical reconstruction microscopy (beSTORM), which introduces an additional dimension to differentiate between single molecules irrespective of their spectral properties. This method leverages the distinguishable photoblinking responses to distinct buffer conditions, offering a straightforward yet effective means of fluorophore discrimination. Through buffer exchanges, beSTORM achieves multitarget SMLM imaging with minimal crosstalk. Direct integration with expansion microscopy (ExM) demonstrates its capability to resolve up to six proteins at the molecular level within a single emission color without chromatic aberration. Overall, beSTORM presents a highly compatible imaging platform, promising significant advancements in highly multiplexed nanoscopy for exploring multiple targets in biological systems with nanoscale precision. © 2024 The Authors. Published by American Chemical Society.

关键词： Stochastic systems

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Noise response in monomolecular closed systems

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Journal of the Chinese Chemical Society 2023年第3期70卷 747-758页

作者： Fajiculay, Erickson Hsu, Chao-Ping Institute of Chemistry Academia Sinica Taipei Taiwan Bioinformatics Program Institute of Statistical Science Taiwan International Graduate Program Academia Sinica Taipei Taiwan Institute of Bioinformatics and Structure Biology National Tsinghua University Hsinchu City Taiwan Physics Division National Center for Theoretical Sciences Taipei Taiwan Genome and Systems Biology Degree Program National Taiwan University Taipei Taiwan

One of the primary interests in understanding biological systems is the interaction between noise and cellular regulation. Noise levels can be altered by rate constant perturbations, which may result in an imbalance of important biological functions. Although the theory of noise localization can estimate noise perturbation response, it is currently limited to open systems, where the majority of biological networks belong. However, during their lifetime, some components of biological systems participate in reactions that can be categorized under a closed system. Therefore, a counterpart theory for closed systems is desirable. In this work, steady-state perturbations of monomolecular closed systems and ways to estimate the response of noise as (co)variances are explored. We extend the structural sensitivity analysis for analyzing noise response patterns by using additional clues derived from multinomial assumptions. We identified scenarios in which this combination might fail in noise prediction, which we call noise flip, arising from the nonlinear dependence of variance upon composition. Nevertheless, theoretical intuition and simulation show that noise flip diminishes with increasing system size. This work provides an efficient and scalable means to estimate noise responses with potential applications in model discrimination, network-wide response scanning, and reconstruction. © 2023 The Chemical Society Located in Taipei & Wiley-VCH GmbH.

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Target genes discovery through copy number alteration analysis in human hepatocellular carcinoma

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World Journal of Gastroenterology 2013年第47期19卷 8873-8879页

作者： De-Leung Gu Yen-Hsieh Chen Jou-Ho Shih Chi-Hung Lin Yuh-Shan Jou Chian-Feng Chen Institute of Biomedical Sciences Academia Sinica Institute of Microbiology and Immunology School of Life ScienceNational Yang-Ming University Bioinformatics Program Taiwan International Graduate ProgramAcademia Sinica Genome and Systems Biology Degree Program National Taiwan University VYM Genome Research Center National Yang-Ming University

High-throughput short-read sequencing of exomes and whole cancer genomes in multiple human hepatocellular carcinoma(HCC)cohorts confirmed previously identified frequently mutated somatic genes,such as TP53,CTNNB1 and AXIN1,and identified several novel genes with moderate mutation frequencies,including ARID1A,ARID2,MLL,MLL2,MLL3,MLL4,IRF2,ATM,CDKN2A,FGF19,PIK3CA,RPS6KA3,JAK1,KEAP1,NFE2L2,C16orf62,LEPR,RAC2,and *** classification of these mutated genes suggested that alterations in pathways participating in chromatin remodeling,Wnt/β-catenin signaling,JAK/STAT signaling,and oxidative stress play critical roles in HCC ***,because there are few druggable genes used in HCC therapy,the identification of new therapeutic targets through integrated genomic approaches remains an important *** a large amount of HCC genomic data genotyped by high density single nucleotide polymorphism arrays is deposited in the public domain,copy number alteration(CNA)analyses of these arrays is a cost-effective way to reveal target genes through profiling of recurrent and overlapping amplicons,homozygous deletions and potentially unbalanced chromosomal translocations accumulated during HCC ***,integration of CNAs with other high-throughput genomic data,such as aberrantly coding transcriptomes and non-coding gene expression in human HCC tissues and rodent HCC models,provides lines of evidence that can be used to facilitate the identification of novel HCC target genes with the potential of improving the survival of HCC patients.

关键词： Copy number alteration High-density single nucleotide polymorphism arrays Driver genes Hepatocellular carcinoma

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The crystal structure of a replicative hexameric helicase DnaC and its complex with single-stranded DNA

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Nucleic Acids Research 2009年第3期37卷 804-814页

作者： Lo, Yu-Hua Tsai, Kuang-Lei Sun, Yuh-Ju Chen, Wei-Ti Huang, Cheng-Yang Hsiao, Chwan-Deng Institute of Molecular Biology Academia Sinica Taipei 115 Taiwan Institute of Bioinformatics and Structural Biology National Tsing Hua University Hsinchu 300 Taiwan Taiwan International Graduate Program Academia Sinica Taipei 115 Taiwan

DNA helicases are motor proteins that play essential roles in DNA replication, repair and recombination. In the replicative hexameric helicase, the fundamental reaction is the unwinding of duplex DNA;however, our understanding of this function remains vague due to insufficient structural information. Here, we report two crystal structures of the DnaB-family replicative helicase from Geobacillus kaustophilus HTA426 (Gk DnaC) in the apo-form and bound to single-stranded DNA (ssDNA). The Gk DnaC-ssDNA complex structure reveals that three symmetrical basic grooves on the interior surface of the hexamer individually encircle ssDNA. The ssDNA-binding pockets in this structure are directed toward the N-terminal domain collar of the hexameric ring, thus orienting the ssDNA toward the DnaG primase to facilitate the synthesis of short RNA primers. These findings provide insight into the mechanism of ssDNA binding and provide a working model to establish a novel mechanism for DNA translocation at the replication fork. © 2008 The Author(s).

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Structural insights into TDP-43 in nucleic-acid binding and domain interactions

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Nucleic Acids Research 2009年第6期37卷 1799-1808页

作者： Kuo, Pan-Hsien Doudeva, Lyudmila G. Wang, Yi-Ting Shen, Che-Kun James Yuan, Hanna S. Institute of Bioinformatics and Structural Biology National Tsing Hua University Taipei Taiwan Institute of Molecular Biology National Taiwan University Taipei Taiwan Taiwan International Graduate Program Chemical Biology and Molecular Biophysics National Taiwan University Taipei Taiwan Graduate Institute of Biochemistry and Molecular Biology National Taiwan University Taipei Taiwan

TDP-43 is a pathogenic protein: its normal function in binding to UG-rich RNA is related to cystic fibrosis, and inclusion of its C-terminal fragments in brain cells is directly linked to frontotemporal lobar degeneration (FTLD) and amyotrophic lateral sclerosis (ALS). Here we report the 1.65 Å crystal structure of the C-terminal RRM2 domain of TDP-43 in complex with a single-stranded DNA. We show that TDP-43 is a dimeric protein with two RRM domains, both involved in DNA and RNA binding. The crystal structure reveals the basis of TDP-43's TG/ UG preference in nucleic acids binding. It also reveals that RRM2 domain has an atypical RRM-fold with an additional β-strand involved in making protein-protein interactions. This self association of RRM2 domains produced thermal-stable RRM2 assemblies with a melting point greater than 85°C as monitored by circular dichroism at physiological conditions. These studies thus characterize the recognition between TDP-43 and nucleic acids and the mode of RRM2 self association, and provide molecular models for understanding the role of TDP-43 in cystic fibrosis and the neurodegenerative diseases related to TDP-43 proteinopathy. © 2009 The Author(s).

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