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DOCKGROUND: The Resource Expands to Protein-RNA Interactome

Journal of Molecular biology 2025年 169014页

作者： Collins, Keeley W. Copeland, Matthew M. Kundrotas, Petras J. Vakser, Ilya A. Computational Biology Program The University of Kansas Lawrence 66045 KS United States Department of Molecular Biosciences The University of Kansas Lawrence 66045 KS United States

RNA is a master regulator of cellular processes and will bind to many different proteins throughout its life cycle. Dysregulation of RNA and RNA-binding proteins can lead to various diseases, including cancer. To better understand molecular mechanisms of the cellular processes, it is important to characterize protein-RNA interactions at the structural level. There is a lack of experimental structures available for protein-RNA complexes due to the RNA inherent flexibility, which complicates the experimental structure determination. The scarcity of structures can be made up for with computational modeling. DOCKGROUND is a resource for development and benchmarking of structure-based modeling of protein interactions. It contains datasets focusing on different aspects of protein recognition. The foundation of all the datasets is the database of experimentally determined protein complexes, which previously contained only protein–protein assemblies. To further expand the utility of the DOCKGROUND resource, we extended the database to protein-RNA interactions. The new functionalities are available on the DOCKGROUND website at https://***/. The database can be searched using a number of criteria, including removal of redundancies at various sequence and structure similarity thresholds. The database updates with new structures from the Protein Data Bank on a weekly basis. © 2025 The Author(s)

关键词： molecular databases molecular recognition protein docking protein-RNA complexes structural modeling

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PHYLOVAE: UNSUPERVISED LEARNING OF PHYLOGENETIC TREES VIA VARIATIONAL AUTOENCODERS

arXiv

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arXiv 2025年

作者： Xie, Tianyu Richman, Harry Gao, Jiansi Matsen, Frederick A. Zhang, Cheng School of Mathematical Sciences Peking University China Center for Statistical Science Peking University China Computational Biology Program Fred Hutchinson Cancer Research Center Howard Hughes Medical Institute United States

Learning informative representations of phylogenetic tree structures is essential for analyzing evolutionary relationships. Classical distance-based methods have been widely used to project phylogenetic trees into Euclidean space, but they are often sensitive to the choice of distance metric and may lack sufficient resolution. In this paper, we introduce phylogenetic variational autoencoders (PhyloVAEs), an unsupervised learning framework designed for representation learning and generative modeling of tree topologies. Leveraging an efficient encoding mechanism inspired by autoregressive tree topology generation, we develop a deep latent-variable generative model that facilitates fast, parallelized topology generation. PhyloVAE combines this generative model with a collaborative inference model based on learnable topological features, allowing for high-resolution representations of phylogenetic tree samples. Extensive experiments demonstrate PhyloVAE’s robust representation learning capabilities and fast generation of phylogenetic tree topologies. © 2025, CC BY.

关键词： Adversarial machine learning

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Learning Multi-cellular Representations of Single-Cell Transcriptomics Data Enables Characterization of Patient-Level Disease States 29th

Learning Multi-cellular Representations of Single-Cell Tran...

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

作者： Liu, Tianyu De Brouwer, Edward Kuo, Tony Diamant, Nathaniel Missarova, Alsu Wang, Hanchen Hao, Minsheng Bravo, Hector Corrada Scalia, Gabriele Regev, Aviv Heimberg, Graham Research and Early Development Genentech South San FranciscoCA94080 United States Interdepartmental Program in Computational Biology and Bioinformatics Yale University New HavenCT06511 United States Roche Informatics F. Hoffmann-La Roche Ltd. Mississauga Canada Department of Computer Science Stanford University Palo AltoCA94035 United States

ISBN: (纸本)9783031902512

Single-cell RNA-seq (scRNA-seq) has become a prominent tool for studying human biology and disease. The availability of massive scRNA-seq datasets and advanced machine learning techniques has recently driven the development of single-cell foundation models that provide informative and versatile cell representations based on expression profiles. However, to understand disease states, we need to consider entire tissue ecosystems, simultaneously considering many different interacting cells. Here, we tackle this challenge by generating patient-level representations derived from multi-cellular expression context measured with scRNA-seq of tissues. We develop PaSCient, a novel model that employs a multi-level representation learning paradigm and provides importance scores at the individual cell and gene levels for fine-grained analysis across multiple cell types and gene programs characteristic of a given disease. We apply PaSCient to learn a disease model across a large-scale scRNA-seq atlas of 12.5 million cells from over 2,700 patients. Comprehensive and rigorous benchmarking demonstrates the superiority of PaSCient in disease classification and its multiple downstream applications, including dimensionality reduction, gene/cell type prioritization, and patient subgroup discovery. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Biomolecules

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Engineering Mice to Study Human Immunity

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Annual Review of Immunology 2025年第1期43卷 451-487页

作者： Sefik, Esen Xiao, Tianli Chiorazzi, Michael Odell, Ian Zhang, Fengrui Agrawal, Kriti Micevic, Goran Flavell, Richard A. Department of Immunobiology Yale School of Medicine New Haven CT United States Howard Hughes Medical Institute Yale School of Medicine New Haven CT United States Department of Internal Medicine Section of Medical Oncology Yale School of Medicine New Haven CT United States Department of Dermatology Yale School of Medicine New Haven CT United States Computational Biology and Bioinformatics Program Yale University New Haven CT United States Department of Pathology Yale School of Medicine New Haven CT United States

Humanized mice, which carry a human hematopoietic and immune system, have greatly advanced our understanding of human immune responses and immunological diseases. These mice are created via the transplantation of human hematopoietic stem and progenitor cells into immunocompromised murine hosts further engineered to support human hematopoiesis and immune cell growth. This article explores genetic modifications in mice that enhance xeno-tolerance, promote human hematopoiesis and immunity, and enable xenotransplantation of human tissues with resident immune cells. We also discuss genetic editing of the human immune system, provide examples of how humanized mice with humanized organs model diseases for mechanistic studies, and highlight the roles of these models in advancing knowledge of organ biology, immune responses to pathogens, and preclinical drugs tested for cancer treatment. The integration of multi-omics and state-of-the art approaches with humanized mouse models is crucial for bridging existing human data with causality and promises to significantly advance mechanistic studies. Copyright © 2025 by the author(s).

关键词： cancer human hematopoietic stem and progenitor cells human immunity human organ modeling humanized mice immunological diseases infectious diseases xenotransplantation

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Engineering mtDNA deletions by reconstituting end joining in human mitochondria

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Cell 2025年第10期188卷 2778-2793.e21,24页

作者： Fu, Yi Land, Max Kavlashvili, Tamar Cui, Ruobing Kim, Minsoo DeBitetto, Emily Lieber, Toby Ryu, Keun Woo Choi, Elim Masilionis, Ignas Saha, Rahul Takizawa, Meril Baker, Daphne Tigano, Marco Lareau, Caleb A. Reznik, Ed Sharma, Roshan Chaligne, Ronan Thompson, Craig B. Sfeir, Agnel Molecular Biology Program Sloan Kettering Institute Memorial Sloan Kettering Cancer Center New York NY United States Computational and Systems Biology Program Sloan Kettering Institute Memorial Sloan Kettering Cancer Center New York NY United States Cancer Biology and Genetics Program Memorial Sloan Kettering Cancer Center New York NY United States Computational Oncology Department of Epidemiology and Biostatistics Memorial Sloan Kettering Cancer Center New York NY United States Department of Pathology and Genomic Medicine Thomas Jefferson University Philadelphia PA United States Howard Hughes Medical Institute New York NY United States

Recent breakthroughs in the genetic manipulation of mitochondrial DNA (mtDNA) have enabled precise base substitutions and the efficient elimination of genomes carrying pathogenic mutations. However, reconstituting mtDNA deletions linked to mitochondrial myopathies remains challenging. Here, we engineered mtDNA deletions in human cells by co-expressing end-joining (EJ) machinery and targeted endonucleases. Using mitochondrial EJ (mito-EJ) and mito-ScaI, we generated a panel of clonal cell lines harboring a ∼3.5 kb mtDNA deletion across the full spectrum of heteroplasmy. Investigating these cells revealed a critical threshold of ∼75% deleted genomes, beyond which oxidative phosphorylation (OXPHOS) protein depletion, metabolic disruption, and impaired growth in galactose-containing media were observed. Single-cell multiomic profiling identified two distinct nuclear gene deregulation responses: one triggered at the deletion threshold and another progressively responding to heteroplasmy. Ultimately, we show that our method enables the modeling of disease-associated mtDNA deletions across cell types and could inform the development of targeted therapies. © 2025 Elsevier Inc.

关键词： DOGMA-seq end joining mitochondrial pathologies mtDNA mtDNA deletion

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

SSRN

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SSRN 2025年

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

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

关键词： Genome

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Catalysis-Induced Highly-Stable Interface on Porous Silicon for High-Rate Lithium-Ion Batteries

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Nano-Micro Letters 2025年第8期17卷 548-563页

作者： Zhuobin Han Phornphimon Maitarad Nuttapon Yodsin Baogang Zhao Haoyu Ma Kexin Liu Yongfeng Hu Siriporn Jungsuttiwong Yumei Wang Li Lu Liyi Shi Shuai Yuan Yongyao Xia Yingying Lv Research Centre of Nanoscience and Nanotechnology Shanghai UniversityShanghai 200444People’s Republic of China Department of Chemistry Faculty of ScienceSilpakorn UniversityNakhon Pathom 73000Thailand Sinopec Shanghai Research Institute of Petrochemical Technology Co. Ltd.Shanghai 201208People’s Republic of China Department of Chemistry and Center of Excellence for Innovation in Chemistry Faculty of Science Ubon Ratchathani UniversityUbon Ratchathani 34190Thailand National University of Singapore(Chongqing)Research Institute Chongqing 401123People’s Republic of China Department of Chemistry Fudan UniversityShanghai 200433People’s Republic of China Emerging Industries Institute Shanghai University Jiaxing 314006ZhejiangPeople’s Republic of China Program in Bioinformatics and Computational Biology Graduate SchoolChulalongkorn UniversityBangkok 10330Thailand

Silicon stands as a key anode material in lithium-ion battery ascribing to its high energy ***,the poor rate performance and limited cycling life remain unresolved through conventional approaches that involve carbon composites or nanostructures,primarily due to the un-controllable effects arising from the substantial formation of a solid electrolyte interphase(SEI)during the ***,an ultra-thin and homogeneous Ti doping alumina oxide catalytic interface is meticulously applied on the porous Si through a synergistic etching and hydrolysis *** defect-rich oxide interface promotes a selective adsorption of fluoroethylene carbonate,leading to a catalytic reaction that can be aptly described as“molecular concentration-in situ conversion”.The resultant inorganic-rich SEI layer is electrochemical stable and favors ion-transport,particularly at high-rate cycling and high *** robustly shielded porous Si,with a large surface area,achieves a high initial Coulombic efficiency of 84.7%and delivers exceptional high-rate performance at 25 A g^(−1)(692 mAh g^(−1))and a high Coulombic efficiency of 99.7%over 1000 *** robust SEI constructed through a precious catalytic layer promises significant advantages for the fast development of silicon-based anode in fast-charging batteries.

关键词： Catalytic interface Mesoporous Inorganic-rich SEI Silicon anode Lithium-ion batteries

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The complex interplay between risk tolerance and the spread of infectious diseases

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Journal of the Royal Society Interface 2025年第225期22卷 20240486页

作者： Nguyen, Maximilian M. Freedman, Ari S. Cheung, Matthew A. Saad-Roy, Chadi M. Espinoza, Baltazar Grenfell, Bryan T. Levin, Simon A. Lewis-Sigler Institute Princeton University PrincetonNJ United States Department of Ecology and Evolutionary Biology Princeton University PrincetonNJ United States Program in Applied and Computational Mathematics Princeton University PrincetonNJ United States Miller Institute for Basic Research in Science University of California Berkeley BerkeleyCA United States Department of Integrative Biology University of California BerkeleyCA United States Biocomplexity Institute University of Virginia CharlottesvilleVA United States

Risk-driven behaviour provides a feedback mechanism through which individuals both shape and are collectively affected by an epidemic. We introduce a general and flexible compartmental model to study the effect of heterogeneity in the population with regard to risk tolerance. The interplay between behaviour and epidemiology leads to a rich set of possible epidemic dynamics. Depending on the behavioural composition of the population, we find that increasing heterogeneity in risk tolerance can either increase or decrease the epidemic size. We find that multiple waves of infection can arise due to the interplay between transmission and behaviour, even without the replenishment of susceptibles. We find that increasing protective mechanisms such as the effectiveness of interventions, the fraction of risk-averse people in the population and the duration of intervention usage reduce the epidemic overshoot. When the protection is pushed past a critical threshold, the epidemic dynamics enter an underdamped regime where the epidemic size exactly equals the herd immunity threshold and overshoot is eliminated. Finally, we can find regimes where epidemic size does not monotonically decrease with a population that becomes increasingly risk-averse. © 2025 The Author(s).

关键词： epidemic size heterogeneous behaviour intervention adoption and usage risk tolerance

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Rationally designed antimicrobial peptides with high selectivity and efficiency against phytopathogenic Ralstonia solanecearum

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Science of the Total Environment 2025年 976卷 179354页

作者： Manik, Melvalia Cristin Darai, Nitchakan Rungrotmongkol, Thanyada Duan, Lian Harada, Ryuhei Shigeta, Yasuteru Hengphasatporn, Kowit Vangnai, Alisa S. Biotechnology Program Faculty of Science Chulalongkorn University Bangkok10330 Thailand Futuristic Science Research Center School of Science Walailak University Nakhon Si Thammarat80160 Thailand Drug and Cosmetics Excellence Center Walailak University Thasala Nakhon Si Thammarat80160 Thailand Program in Bioinformatics and Computational Biology Chulalongkorn University Bangkok10330 Thailand Center for Computational Sciences University of Tsukuba 1-1-1 Tennodai Ibaraki Tsukuba305-8577 Japan Center of Excellence in Biocatalyst and Sustainable Biotechnology Faculty of Science Chulalongkorn University Bangkok10330 Thailand Department of Biochemistry Faculty of Science Chulalongkorn University Bangkok10330 Thailand

Ralstonia solanacearum, the causative agent of bacterial wilt, poses a global threat to agriculture, necessitating urgent and sustainable solutions as traditional methods lose efficacy. This study developed WRF-13, a synthetic antimicrobial peptide (AMP) designed to mimic natural AMPs, exhibiting potent antibacterial and anti-biofilm activity with high specificity against R. solanacearum. Mechanistic studies, including microscopy and computational analyses, demonstrated that WRF-13 disrupts the bacterial membrane. WRF-13 remained stable across a wide pH (5–8) and temperature (25–50 °C) range, essential for field applications, and showed no detectable toxicity to mammalian or plant cells at elevated concentrations. Greenhouse trials confirmed its efficacy in reducing bacterial wilt severity up to 65 %, highlighting its potential to protect crops from R. solanacearum infection. Overall, this study highlights WRF-13 as a targeted solution for managing bacterial wilt and advancing sustainable agriculture. © 2025 Elsevier B.V.

关键词： Antimicrobial peptides Bacterial wilt Crop protection In silico-peptide design Ralstonia solanacearum

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E2Former: A Linear-time Efficient and Equivariant Transformer for Scalable Molecular Modeling

arXiv

引用

arXiv 2025年

作者： Li, Yunyang Huang, Lin Ding, Zhihao Wang, Chu Wei, Xinran Yang, Han Wang, Zun Liu, Chang Shi, Yu Jin, Peiran Zhang, Jia Gerstein, Mark B. Qin, Tao Department of Computer Science Yale University New Haven United States Microsoft Research AI4Science The Hong Kong Polytechnic University Hong Kong Huazhong University of Science and Technology Wuhan China Program in Computational Biology and Biomedical Informatics Yale University New Haven United States

Equivariant Graph Neural Networks (EGNNs) have demonstrated significant success in modeling microscale systems, including those in chemistry, biology and materials science. However, EGNNs face substantial computational challenges due to the high cost of constructing edge features via spherical tensor products, making them impractical for large-scale systems. To address this limitation, we introduce E2Former, an equivariant and efficient transformer architecture that incorporates the Wigner 6j convolution (Wigner 6j Conv). By shifting the computational burden from edges to nodes, the Wigner 6j Conv reduces the complexity from O(|E|) to O(|V|) while preserving both the model’s expressive power and rotational equivariance. We show that this approach achieves a 7x–30x speedup compared to conventional SO(3) convolutions. Furthermore, our empirical results demonstrate that the derived E2Former mitigates the computational challenges of existing approaches without compromising the ability to capture detailed geometric information. This development could suggest a promising direction for scalable and efficient molecular modeling. © 2025, CC BY.

关键词： Large scale systems

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