We describe herein topological mRNA capture using branched oligodeoxynucleotides (ODNs) with multiple reactive functional groups. These fragmented ODNs efficiently formed topological complexes on template mRNA . In cell-based experiments targeting AcGFP mRNA, the bifurcated reactive ODNs showed a much larger gene silencing effect than the corresponding natural antisense ODN.
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Mol Divers
January 2025
Key Laboratory of Prevention and Treatment of Cardiovascular and Cerebrovascular Diseases Ministry of Education, Jiangxi Province Key Laboratory of Biomaterials and Biofabrication for Tissue Engineering, Gannan Medical University, Ganzhou, 341000, Jiangxi, China.
Identifying drug-target binding affinity (DTA) plays a critical role in early-stage drug discovery. Despite the availability of various existing methods, there are still two limitations. Firstly, sequence-based methods often extract features from fixed length protein sequences, requiring truncation or padding, which can result in information loss or the introduction of unwanted noise.
View Article and Find Full Text PDFNeural Netw
January 2025
Chongqing Key Laboratory of Computational Intelligence, Chongqing University of Posts and Telecommunications, Chongqing, 400065, China; College of Computer and Information Science, Chongqing Normal University, Chongqing, 401331, China. Electronic address:
The production of expressive molecular representations with scarce labeled data is challenging for AI-driven drug discovery. Mainstream studies often follow a pipeline that pre-trains a specific molecular encoder and then fine-tunes it. However, the significant challenges of these methods are (1) neglecting the propagation of diverse information within molecules and (2) the absence of knowledge and chemical constraints in the pre-training strategy.
View Article and Find Full Text PDFMol Inform
January 2025
Faculty of Information Technology, HUTECH University, 700000, Ho Chi Minh City, Vietnam.
In recent times, graph representation learning has been becoming a hot research topic which has attracted a lot of attention from researchers. Graph embeddings have diverse applications across fields such as information and social network analysis, bioinformatics and cheminformatics, natural language processing (NLP), and recommendation systems. Among the advanced deep learning (DL) based architectures used in graph representation learning, graph neural networks (GNNs) have emerged as the dominant and highly effective framework.
View Article and Find Full Text PDFProc Natl Acad Sci U S A
January 2025
Department of Materials Science and Engineering, Stanford University, Stanford, CA 94305.
A central paradigm of nonequilibrium physics concerns the dynamics of heterogeneity and disorder, impacting processes ranging from the behavior of glasses to the emergent functionality of active matter. Understanding these complex mesoscopic systems requires probing the microscopic trajectories associated with irreversible processes, the role of fluctuations and entropy growth, and the timescales on which nonequilibrium responses are ultimately maintained. Approaches that illuminate these processes in model systems may enable a more general understanding of other heterogeneous nonequilibrium phenomena, and potentially define ultimate speed and energy cost limits for information processing technologies.
View Article and Find Full Text PDFGenes (Basel)
November 2024
Department of Orthopedic Surgery, The Second Affiliated Hospital of Harbin Medical University, Harbin 150081, China.
Background: The enhancer-promoter interaction (EPI) is a critical component of gene regulatory networks, playing a significant role in understanding the complexity of gene expression. Traditional EPI prediction methods focus on one-to-one interactions, neglecting more complex one-to-many and many-to-many patterns. To address this gap, we utilize graph neural networks to comprehensively explore all interaction patterns between enhancers and promoters, capturing complex regulatory relationships for more accurate predictions.
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