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<title cf:type="text"><![CDATA[Progress in Biochemistry and Biophysics -->Interpretation of the Nobel Prize 2024]]></title>
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<title xmlns:cf="http://www.microsoft.com/schemas/rss/core/2005" cf:type="text"><![CDATA[The Discovery of microRNA and Its Significance: The Enlightenment of The Nobel Prize in Physiology or Medicine of 2024]]></title>
<link><![CDATA[http://www.pibb.ac.cn/pibben/article/abstract/20240471]]></link>
<description xmlns:cf="http://www.microsoft.com/schemas/rss/core/2005" cf:type="html"><![CDATA[The 2024 Nobel Prize in Physiology or Medicine was awarded to American scientists Victor Ambros and Gary Ruvkun in recognition of their discovery of microRNA (miRNA) and its role in regulating gene expression at the post-transcriptional level. miRNA is a type of small non-coding RNA (ncRNA) that regulates gene expression by binding to messenger RNA (mRNA). It exists not only in model organisms such as <i>Caenorhabditis elegans </i>(<i>C. elegans</i>) but also plays an important role in multicellular organisms, including humans, participating in regulating key life activities such as the cell cycle, cell death, and tissue differentiation. miRNAs have also been found in viruses, where they are involved in the process of viral infection. The discovery of miRNA has not only opened up a new research field in ncRNA but also challenged the classic “central dogma” of molecular biology. This dogma traditionally transcribes the linear transmission of genetic information: from DNA to mRNA, then translated into proteins, which ultimately carry out biological functions. However, due to the competitive binding of miRNAs with mRNA and other ncRNAs in cells, such as long non-coding RNA (lncRNA) and circular RNA (circRNA), a vast and complex gene expression regulatory network, known as the competing endogenous RNA (ceRNA) network, has emerged. The complexity and sophistication of the ceRNA regulatory network offer new perspectives for transcriptome research, aid in the exploration of gene functions and regulatory mechanisms at a deeper level, and then enable a more comprehensive understanding of various biological phenomena. Moreover, a complex interaction and regulatory network exists between miRNA and other ncRNAs. miRNA and other ncRNAs may also be generated through the splicing of the same genes, which have complex transcripts capable of simultaneously producing multiple types of ncRNAs, including miRNA, lncRNA, circRNA, <i>etc</i>., all of which are involved in a variety of biological processes. Meanwhile, miRNA itself is encoded by genes in the genome, and its expression is also regulated by other coding or ncRNAs. Together with mRNA and other ncRNAs, miRNA finely regulates the life activities of cells and affects the physiological and pathological functions of the body. The dysregulation of miRNA expression is closely linked to the onset and progression of many diseases, particularly cancers, cardiovascular diseases, and neurodegenerative disordors. Furthermore, miRNA provides new molecular markers and targets for the diagnosis and treatment of these diseases. In terms of disease diagnosis, miRNA can stably exist in body fluids and serve as a biomarker for many diseases. The research and development of miRNA drugs is currently advancing rapidly. At present, the research and development of miRNA drugs mainly includes endogenous miRNA analogs and inhibitors targeting endogenous miRNA. Although challenges such as stability, immunogenicity, and permeability remain, advances in chemical modification and delivery technologies are gradually overcoming these obstacles, promoting the clinical translation of miRNA-based drugs. This article summarizes the discovery, mechanism of action, and biological functions of miRNAs, as well as their interaction networks with other ncRNAs, and explores the future prospects of miRNA applications.]]></description>
<pubDate>2024/12/17 0:00:00</pubDate>
<category><![CDATA[Interpretation of the Nobel Prize 2024]]></category>
<author><![CDATA[LI Wen-Chao,XIAO Cheng-Feng,ZENG Zhao-Yang,XIONG Wei and QU Hong-Ke]]></author>
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<atom:name>LI Wen-Chao,XIAO Cheng-Feng,ZENG Zhao-Yang,XIONG Wei and QU Hong-Ke</atom:name>
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<title xmlns:cf="http://www.microsoft.com/schemas/rss/core/2005" cf:type="text"><![CDATA[Breakthrough of AlphaFold Structure Prediction and Its Impact and Challenges on Protein Research]]></title>
<link><![CDATA[http://www.pibb.ac.cn/pibben/article/abstract/20240374]]></link>
<description xmlns:cf="http://www.microsoft.com/schemas/rss/core/2005" cf:type="html"><![CDATA[In recent years, deep learning-based methods have achieved significant breakthroughs in protein structure prediction. The open-source release of AlphaFold 2 (AF2) in 2021 enabled high-precision prediction of three-dimensional structures for both individual proteins and protein complexes, allowing researchers to rapidly obtain reliable structural information and greatly accelerating advancements in protein structure and function studies. The release of AlphaFold 3 (AF3) in 2024 took this further by achieving accurate predictions of three-dimensional structures for protein-nucleic acid and protein-small molecule complexes. With improved algorithms and a more efficient model, AF3 significantly enhanced prediction accuracy, especially demonstrating outstanding performance in antigen-antibody and protein-small molecule complexes. The success of AlphaFold has not only brought revolutionary progress to structural biology but also showcased immense application potential in fields such as drug development, protein design, and molecular function research, driving innovation in biomedical studies. This article will review the development history of AlphaFold and related protein structure prediction methods, summarize their key technologies and current applications, and, by considering their limitations, provide an outlook on future research directions and applications.]]></description>
<pubDate>2024/10/25 0:00:00</pubDate>
<category><![CDATA[Interpretation of the Nobel Prize 2024]]></category>
<author><![CDATA[GONG Wei-Bin]]></author>
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<atom:name>GONG Wei-Bin</atom:name>
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<title xmlns:cf="http://www.microsoft.com/schemas/rss/core/2005" cf:type="text"><![CDATA[Intelligent Protein Engineering]]></title>
<link><![CDATA[http://www.pibb.ac.cn/pibben/article/abstract/20240402]]></link>
<description xmlns:cf="http://www.microsoft.com/schemas/rss/core/2005" cf:type="html"><![CDATA[Proteins are essential fundamental substance for life processes, performing a variety of key roles in organisms, such as constructing cell structures, participating in metabolism and energy transformation, regulating physiological functions, providing immune protection, and transmitting signals. The diverse functions of proteins are achieved through their unique amino acid sequences and corresponding three-dimensional structures. Protein engineering involves altering or designing protein sequences and structures to achieve specific functions, and these efforts enhance our knowledge of proteins and offer powerful tools and technical support for research in biomedicine, biomaterials, bioengineering, and related fields. In recent years, with the advancements in algorithms, the accumulation of big data, and improvements in hardware computational power, artificial intelligence technology has rapidly developed and gradually been applied in the field of protein engineering, leading to the emergence of intelligent protein engineering. By utilizing biological big data such as genomics, proteomics, protein structure databases, and establishing various advanced deep learning models based on the data, intelligent protein engineering can achieve efficient, precise, and predictable protein design and modification. This article primarily focuses on four aspects of intelligent protein engineering, including structure design, backbone-free sequence design, backbone-based sequence design, and other auxiliary design approaches, summarizing the latest progress in the artificial intelligence technologies employed in these fields, and compiling the practical results achieved in recent years using intelligent protein engineering technology. As an emerging technology and method, intelligent protein engineering demonstrates significant potential and prospects, bringing substantial impacts on future scientific research and technological innovation, and providing new solutions and tools for addressing global challenges.]]></description>
<pubDate>2024/12/3 0:00:00</pubDate>
<category><![CDATA[Interpretation of the Nobel Prize 2024]]></category>
<author><![CDATA[WANG Kai-Yue and YE Sheng]]></author>
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<atom:name>WANG Kai-Yue and YE Sheng</atom:name>
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