Nearly two decades have passed since the publication of the first study reporting the discovery of microRNAs (miRNAs). The key role of miRNAs in post-transcriptional gene regulation led to the performance of an increasing number of studies focusing on origins, mechanisms of action and functionality of miRNAs. In order to associate each miRNA to a specific functionality it is essential to unveil the rules that govern miRNA action. Despite the fact that there has been significant improvement exposing structural characteristics of the miRNA-mRNA interaction, the entire physical mechanism is not yet fully understood. In this respect, the development of computational algorithms for miRNA target prediction becomes increasingly important. This manuscript summarizes the research done on miRNA target prediction. It describes the experimental data currently available and used in the field and presents three lines of computational approaches for target prediction. Finally, the authors put forward a number of considerations regarding current challenges and future directions.
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http://dx.doi.org/10.1016/j.gpb.2012.10.001 | DOI Listing |
Front Biosci (Landmark Ed)
January 2025
Department of Obstetrics and Gynecology, Zhongda Hospital, School of Medicine, Southeast University, 210000 Nanjing, Jiangsu, China.
Background: Pre-eclampsia (PE) is a gestational disorder that significantly endangers maternal and fetal health. Transfer ribonucleic acid (tRNA)-derived small RNAs (tsRNAs) are important in the progression and diagnosis of various diseases. However, their role in the development of PE is unclear.
View Article and Find Full Text PDFJ Bone Miner Res
January 2025
San Francisco Coordinating Center, California Pacific Medical Center Research Institute and University of California, San Francisco, CA 94158, United States.
Bone mineral density (BMD) levels achieved on osteoporosis treatment are predictive of subsequent fracture risk, and T-score > -2.5 has been proposed as a minimum treatment target for women with osteoporosis. Knowing the likelihood of attaining target T-scores with different medications for different baseline BMD levels can help determine appropriate initial treatment for individual patients.
View Article and Find Full Text PDFViruses
January 2025
Department of Immunology and Microbiology, Scripps Research Institute, La Jolla, CA 92037, USA.
Lassa fever (LF), a viral hemorrhagic fever disease with a case fatality rate that can be over 20% among hospitalized LF patients, is endemic to many West African countries. Currently, no vaccines or therapies are specifically licensed to prevent or treat LF, hence the significance of developing therapeutics against the mammarenavirus Lassa virus (LASV), the causative agent of LF. We used in silico docking approaches to investigate the binding affinities of 2015 existing drugs to LASV proteins known to play critical roles in the formation and activity of the virus ribonucleoprotein complex (vRNP) responsible for directing replication and transcription of the viral genome.
View Article and Find Full Text PDFViruses
December 2024
Laboratory of Molecular and Cellular Virology, Institute of Biomedical Sciences, Faculty of Medicine, Universidad de Chile, Santiago 8380453, Chile.
RNA-binding proteins (RBPs) are cellular factors involved in every step of RNA metabolism. During HIV-1 infection, these proteins are key players in the fine-tuning of viral and host cellular and molecular pathways, including (but not limited to) viral entry, transcription, splicing, RNA modification, translation, decay, assembly, and packaging, as well as the modulation of the antiviral response. Targeted studies have been of paramount importance in identifying and understanding the role of RNA-binding proteins that bind to HIV-1 RNAs.
View Article and Find Full Text PDFViruses
December 2024
Beijing Youcare Kechuang Pharmaceutical Technology Co., Ltd., Beijing 100176, China.
Human respiratory syncytial virus (RSV) remains a significant global health threat, particularly for vulnerable populations. Despite extensive research, effective antiviral therapies are still limited. To address this urgent need, we present AVP-GPT2, a deep-learning model that significantly outperforms its predecessor, AVP-GPT, in designing and screening antiviral peptides.
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