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Carbon Capture Science & Technology 2025年 16卷

作者： Hocheol Lim Hyein Cho Jeonghoon Kim Kyoung Tai No Bioinformatics and Molecular Design Research Center (BMDRC) Incheon 21983 Republic of Korea The Interdisciplinary Graduate Program in Integrative Biotechnology & Translational Medicine Yonsei University Incheon 21983 Republic of Korea

Efficient CO 2 capture is vital for mitigating climate change, with amine-based solvents being widely used due to their strong reactivity with CO 2 . However, optimizing key properties such as basicity, viscosity, and absorption capacity remains challenging, as traditional methods rely on labor-intensive experimentation and predefined chemical databases, limiting the exploration of novel solutions. Here, SAGE-Amine was introduced, a generative modeling approach that integrates Scoring-Assisted Generative Exploration (SAGE) with quantitative structure-property relationship models to design new amines tailored for CO 2 capture. Unlike conventional virtual screening restricted to existing compounds, SAGE-Amine generates novel amines by leveraging autoregressive natural language processing models trained on amine datasets. SAGE-Amine identified known amines for CO 2 capture from scratch and successfully performed single-property optimization, increasing basicity or reducing viscosity or vapor pressure. Furthermore, it facilitated multi-property optimization, simultaneously achieving high basicity with low viscosity and vapor pressure. The 10 top-ranked amines were suggested using SAGE-Amine and their thermodynamic properties were further assessed using COSMO-RS simulations, confirming their potential for CO 2 capture. These results highlight the potential of generative modeling in accelerating the discovery of amine solvents and expanding the possibilities for industrial CO 2 capture applications.

关键词： Material discovery De novo molecular design Quantitative structure-property relationship Solvent design Amine design

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Bile acid synthesis impedes tumor-specific T cell responses during liver cancer

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Science (New York, N.Y.) 2025年第6730期387卷 192-201页

作者： Varanasi, Siva Karthik Chen, Dan Liu, Yingluo Johnson, Melissa A. Miller, Cayla M. Ganguly, Souradipta Lande, Kathryn LaPorta, Michael A. Hoffmann, Filipe Araujo Mann, Thomas H. Teneche, Marcos G. Casillas, Eduardo Mangalhara, Kailash C. Mathew, Varsha Sun, Ming Jensen, Isaac J. Farsakoglu, Yagmur Chen, Timothy Parisi, Bianca Deota, Shaunak Havas, Aaron Lee, Jin Chung, H Kay Schietinger, Andrea Panda, Satchidananda Williams, April E. Farber, Donna L. Dhar, Debanjan Adams, Peter D. Feng, Gen-Sheng Shadel, Gerald S. Sundrud, Mark S. Kaech, Susan M. NOMIS Center for Immunobiology and Microbial Pathogenesis Salk Institute for Biological Studies La Jolla CA United States Department of Pathology School of Medicine University of California San Diego La Jolla CA United States Sanford Burnham Prebys Medical Discovery Institute La Jolla CA United States Department of Medicine School of Medicine University of California San Diego CA United States Razavi Newman Integrative Genomics and Bioinformatics Core Facility Salk Institute for Biological Studies La Jolla CA United States Department of Microbiology and Immunology Columbia University Irving Medical Center New York NY USA Department of Surgery Columbia University Irving Medical Center New York NY USA Regulatory Biology Laboratory Salk Institute for Biological Studies La Jolla CA United States Immunology Program Memorial Sloan Kettering Cancer Center New York NY USA Immunology and Microbial Pathogenesis Program Weill Cornell Graduate School of Medical Sciences New York NY USA Parker Institute for Cancer Immunotherapy New York NY USA Department of Microbiology and Immunology Geisel School of Medicine at Dartmouth Hanover NH USA Department of Medicine Geisel School of Medicine at Dartmouth Hanover NH USA Center for Digestive Health Dartmouth Health NH United States Dartmouth Cancer Center Dartmouth Health NH United States

The metabolic landscape of cancer greatly influences antitumor immunity, yet it remains unclear how organ-specific metabolites in the tumor microenvironment influence immunosurveillance. We found that accumulation of primary conjugated and secondary bile acids (BAs) are metabolic features of human hepatocellular carcinoma and experimental liver cancer models. Inhibiting conjugated BA synthesis in hepatocytes through deletion of the BA-conjugating enzyme bile acid-CoA:amino acid N-acyltransferase (BAAT) enhanced tumor-specific T cell responses, reduced tumor growth, and sensitized tumors to anti-programmed cell death protein 1 (anti-PD-1) immunotherapy. Furthermore, different BAs regulated CD8+ T cells differently;primary BAs induced oxidative stress, whereas the secondary BA lithocholic acid inhibited T cell function through endoplasmic reticulum stress, which was countered by ursodeoxycholic acid. We demonstrate that modifying BA synthesis or dietary intake of ursodeoxycholic acid could improve tumor immunotherapy in liver cancer model systems.

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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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Plant biotic interactions

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Journal of Integrative Plant Biology 2016年第4期58卷 282-283页

作者： Hailing Jin Special Issue EditorUC President's Chair Director of Genetics Genomics and Bioinformatics Graduate ProgramCenter for Plant Cell BiologyInstitute for Integrative Genome BiologyUniversity of CaliforniaRiversideUSA

Plants are constantly under attack by pathogens,pests,and parasites,resulting in severe consequences on global food production and human *** pathogens and pests find their ways to invade and communicate with their hos... 详细信息

关键词： communicate constantly sophisticated consequences microbial fight biotic evolved pathogen volatile

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Identification of novel TMEM16A blockers through integrated virtual screening, molecular dynamics, and experimental studies

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Scientific Reports 2025年第1期15卷 1-11页

作者： Sangkhawasi, Mattanun Pitaktrakul, Wichuda Khumjiang, Rungtiwa Shigeta, Yasuteru Muanprasat, Chatchai Hengphasatporn, Kowit Rungrotmongkol, Thanyada Center of Excellence in Biocatalyst and Sustainable Biotechnology Department of Biochemistry Faculty of Science Chulalongkorn University Bangkok 10330 Thailand Chakri Naruebodindra Medical Institute Faculty of Medicine Ramathibodi Hospital Mahidol University Bang Pla Bang Pli Samut Prakan 10540 Thailand Center for Computational Sciences University of Tsukuba 1-1-1 Tennodai Ibaraki Tsukuba 305-8577 Japan Program in Bioinformatics and Computational Biology Graduate School Chulalongkorn University Bangkok 10330 Thailand Division of Biochemistry Department of Preclinical Science Faculty of Medicine Thammasat University Pathumthani 12120 Thailand

The calcium-activated chloride channel TMEM16A is a promising drug target for treating hypertension, secretory diarrheas, and various cancers, including head and neck cancer. Despite its potential, no FDA-approved drugs have provided the structural basis for directly inhibiting TMEM16A. This study aims to identify a novel pore-blocker of TMEM16A by integrating virtual screening, molecular dynamics simulations, and in vitro studies. Using the calcium-bound structure of TMEM16A with and without the pore-blocker 1PBC, we performed virtual screening on nearly 90,000 compounds from the ChemDiv database. Approximately 67% of these compounds demonstrated better binding affinity than 1PBC. Among the top 20 compounds selected for short-circuit current assays using human lung adenocarcinoma cells (Calu-3), compounds N066-0059, N066-0060, and N066-0067 inhibited TMEM16A activity with IC₅₀ values of 0.24 µM, 0.41 µM, and 0.48 µM, respectively, which was lower than that of a positive control Ani9 (9 µM). Due to its highest potency in electrophysiological assays, N066-0059 was subjected to mechanistic studies. Molecular dynamics simulations elucidated its binding stability and strength, showing superior performance to 1PBC over 500 ns with 3 replicates. This study advances TMEM16A-targeted drug development, offering new insights for anticancer therapies. © The Author(s) 2025.

关键词： And electrophysiological assays Cytotoxicity Molecular dynamics Pore-blocker inhibitor TMEM16A Virtual screening

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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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Advantages of two quantum programming platforms in quantum computing and quantum chemistry

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Journal of cheminformatics 2025年第1期17卷 77页

作者： Pei-Hua Wang Wei-Yeh Wu Che-Yu Lee Jia-Cheng Hong Yufeng Jane Tseng Undergraduate Program in Intelligent Computing and Big Data Chung Yuan Christian University No. 200 Zhongbei Road Taoyuan 320314 Taiwan. Quantum Information Center Chung Yuan Christian University No. 200 Zhongbei Road Taoyuan 320314 Taiwan. Department of Computer Science and Information Engineering National Taiwan University No. 1 Sec. 4 Roosevelt Road Taipei 10617 Taiwan. Graduate Institute of Biomedical Electronics and Bioinformatics College of Electrical Engineering and Computer Science National Taiwan University No. 1 Sec. 4 Roosevelt Road Taipei 10617 Taiwan. Department of Computer Science and Information Engineering National Taiwan University No. 1 Sec. 4 Roosevelt Road Taipei 10617 Taiwan. yjtseng@csie.ntu.edu.tw. Graduate Institute of Biomedical Electronics and Bioinformatics College of Electrical Engineering and Computer Science National Taiwan University No. 1 Sec. 4 Roosevelt Road Taipei 10617 Taiwan. yjtseng@csie.ntu.edu.tw. Physics Division National Center for Theoretical Sciences No. 1 Sec. 4 Roosevelt Road Taipei 10617 Taiwan. yjtseng@csie.ntu.edu.tw. Center for Quantum Science and Engineering National Taiwan University No. 1 Sec. 4 Roosevelt Road Taipei 10617 Taiwan. yjtseng@csie.ntu.edu.tw.

Quantum computing is at the forefront of technological advancement and has the potential to revolutionize various fields, including quantum chemistry. Choosing an appropriate quantum programming language becomes critical as quantum education and research increase. In this paper, we comprehensively compare two leading quantum programming languages, Qiskit and PennyLane, focusing on their suitability for teaching and research. We delve into their basic and advanced usage, examine their learning curves, and evaluate their capabilities in quantum computing experiments. We also demonstrate using a quantum programming language to build a half adder and a machine learning model. Our study reveals that each language has distinct advantages. While PennyLane excels in research applications due to its flexibility to adjust parameters in detail and access multiple sources of real quantum devices, Qiskit stands out in education because of its web-based graphical user interface and smaller code size. The codes and the dataset used in the studies are available at https://***/wangpeihua1231/quantum-programming-platform .

关键词： PennyLane Qiskit Quantum chemistry Quantum computing Quantum programming language

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Evolving hypernetwork classifiers for microRNA expression profile analysis

Evolving hypernetwork classifiers for microRNA expression pr...

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2007 IEEE Congress on Evolutionary Computation, CEC 2007

作者： Kim, Sun Kim, Soo-Jin Zhang, Byoung-Tak Center for Bioinformatics Technology Graduate Program in Bioinformatics Seoul National University Seoul 151-742 Korea Republic of School of Computer Science and Engineering Seoul National University Seoul 151-744 Korea Republic of

ISBN: (纸本)1424413400

High-throughput microarrays inform us on different outlooks of the molecular mechanisms underlying the function of cells and organisms. While computational analysis for the microarrays show good performance, it is still difficult to infer modules of multiple co-regulated genes. Here, we present a novel classification method to identify the gene modules associated with cancers from microarray data. The proposed approach is based on 'hypernetworks', a hypergraph model consisting of vertices and weighted hyperedges. The hypernetwork model is inspired by biological networks and its learning process is suitable for identifying interacting gene modules. Applied to the analysis of microRNA (miRNA) expression profiles on multiple human cancers, the hypernetwork classifiers identified cancer-related miRNA modules. The results show that our method performs better than decision trees and naive Bayes. The biological meaning of the discovered miRNA modules has been examined by literature search. © 2007 IEEE.

关键词： Microarrays

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Regulation of the SQUAMOSA PROMOTER-BINDING PROTEIN-LIKE genes/microRNA 156 Module by the Homeodomain Proteins PENNYWISE and POUND-FOOLISH in Arabidopsis

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Molecular Plant 2011年第6期4卷 1123-1132页

作者： Shruti Lal Leo Bryan Pacis Harley M.S. Smith Genetics Genomics and Bioinformatics Graduate Program University of California Riverside CA 92521 USA Center for Plant Cell Biology Institute of Integrative Genome Biology Department of Botany and Plant Sciences University of California Riverside CA 92521 USA

The morphology of inflorescences is regulated in part by the temporal and spatial events that regulate flower specification. In Arabidopsis, an endogenous flowering time pathway mediated by a subset of SQUAMOSA PROMOTER- BINDING PROTEIN-LIKE （SPL） transcription factors, including SPL3, SPL4, and SPL5, function to specify flowers by activating floral meristem identity genes. During shoot development, SPL3, SPL4, and SPL5 are post-transcriptionally regulated by microRNA156 （miR156）. The photoperiod regulated florigenic signal, FLOWERING LOCUS T （FT）, promotes floral induction, in part by activating SPL3, SPL4, and SPL5. In turn, these SPLs function in parallel with FT to specify flower meristems. Two related BELLl-like homeobox genes PENNYWISE （PNY） and POUND-FOOLISH （PNF） expressed in the shoot apical meristem are absolutely required for the specification of floral meristems. Genetic studies show that the floral specification function of FT depends upon PNYand PNF; however, the interplay between these homeodomain proteins and SPLs is not known. In this manuscript, we show that the photoperiodic floral induction of SPL3, SPL4, and SPL5 is dependent upon PNY and PNE Further, PNY and PNF also control SPL3, SPL4, and SPL5 expression by negatively regulating miR156. Lastly, ectopic expres- sion of SPL4 partially rescues the pny pnf non-flower-producing phenotype, while overexpression of SPL3 or SPL5 in pny pnf plants was unable to restore flower specification. These results suggest that：（1） SPL3, SPL4, and SPL5 function is dependent upon PNY and PNF, or （2） expression of multiple SPLs is required for floral specification in pny pnf plants.

关键词： Development meristem homeodomain flowering microRNA.

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Biomedical data and AI

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Science China Life Sciences 2025年第5期68卷 1536-1540页

作者： Hao Xu Shibo Zhou Zefeng Zhu Vincenzo Vitelli Liangyi Chen Ziwei Dai Ning Yang Luhua Lai Shengyong Yang Sergey Ovchinnikov Zhuoran Qiao Sirui Liu Chen Song Jianfeng Pei Han Wen Jianfeng Feng Yaoyao Zhang Zhengwei Xie Yang-Yu Liu Zhiyuan Li Fulai Jin Hao Li Mohammad Lotfollahi Xuegong Zhang Ge Yang Shihua Zhang Ge Gao Pulin Li Qi Liu Jing-Dong Jackie Han Peking-Tsinghua Center for Life Sciences (CLS) Academy for Advanced Interdisciplinary StudiesPeking University Center for Quantitative Biology (CQB) Academy for Advanced Interdisciplinary StudiesPeking University Peking University-Tsinghua University-National Institute of Biological Sciences Joint Graduate Program Academy for Advanced Interdisciplinary StudiesPeking University Department of Physics University of Chicago School of Life Sciences Southern University of Science and Technology Peking University Chengdu Academy for Advanced Interdisciplinary Biotechnologies College of Chemistry and Molecular Engineering Peking University Department of Biotherapy Cancer Center and State Key Laboratory of BiotherapyWest China HospitalSichuan University Department of Biology Massachusetts Institute of Technology Lambic Therapeutics Inc. Changping Laboratory Al for Science Institute Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence Fudan University Department of Obstetrics and Gynecology West China Second University HospitalSichuan University Peking University International Cancer Institute and Peking University-Yunnan Baiyao International Medical Institute and State Key Laboratory of Natural and Biomimetic Drugs Department of Molecular and Cellular PharmacologySchool of Pharmaceutical SciencesPeking University Health Science CenterPeking University Channing Division of Network Medicine Department of MedicineBrigham and Women's Hospital and Harvard Medical School Center for Artificial Intelligence and Modeling the Carl R.Woese Institute for Genomic BiologyUniversity of Illinois Urbana-Champaign Department of Genetics and Genome Sciences School of Medicine and Department of Computer and Data Sciences and Department of Population and Quantitative Health SciencesCase Western Reserve University Department of Biochemistry and Biophysics University of California Sanger Institute Department of Automation Tsinghua University State Key Laboratory of Multimodal Artificial Intelligence Systems I

The development of artificial intelligence(AI) and the mining of biomedical data complement each other. From the direct use of computer vision results to analyze medical images for disease screening, to now integrating biological knowledge into models and even accelerating the development of new AI based on biological discoveries, the boundaries of both are constantly expanding, and their connections are becoming closer.

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