JRSeek:人工智能与病毒中的果卷折叠分类相遇
bioRxiv : the preprint server for biology
|February 20, 2025
概括
在使用大型语言模型 (LLM) 嵌入的病毒中预测常见的果卷 (JR) 折叠实现了超过95%的准确性. 这种基于序列的方法为分析病毒结构提供了一个有希望的策略,特别是当实验数据有限时.
科学领域:
- * * 结构生物学 结构生物学
- * 病毒学 病毒学
- * 生物信息学是一门学科.
背景情况:
- * 果卷 (JR) 折叠是各种病毒家族的病毒囊体和核囊体中普遍存在的结构图案.
- *从蛋白质序列预测JR折叠对于理解病毒结构-功能关系至关重要,特别是对于结构未解决的病毒.
研究的目的:
- * 开发和评估基于序列的计算工具,用于预测病毒蛋白中JR折叠的存在.
- * 评估大型语言模型 (LLM) 嵌入组合与物流回归 (LR) 的准确性和通用性,用于JR折叠预测.
主要方法:
- * 训练后勤回归 (LR) 模型使用六种不同的大型语言模型 (LLM) 嵌入在病毒和非病毒蛋白序列的精选数据集上.
- * 利用主要成分分析 (PCA) 可视化序列嵌入并评估不同蛋白质类型的分离性.
- *使用AlphaFold3用于未分类的病毒序列的验证预测.
主要成果:
- * LR模型在区分JR与非JR序列方面实现了超过95%的准确性,独立于所使用的特定LLM嵌入.
- *PCA揭示了一些序列的固有分离性,而LR模型对于分类模两可的序列至关重要.
- *该模型证明了在一些病毒家族中双重JR折叠的概括性,并成功预测了未分类病毒中的JR折叠,由AlphaFold3.3证实.
结论:
- *基于序列的LLM嵌入加上LR提供了一个非常准确和高效的方法来预测病毒JR折叠.
- *这种方法对于分析实验结构数据稀缺的病毒序列特别有价值.
- *未来的工作应该集中在专门为更复杂的折叠开发模型,如双JR折叠,以提高所有病毒家族的概括性.
相关概念视频
Classification of Systems-I
167
Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
167
Classification of Systems-II
133
Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
133
Aggregates Classification
298
Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...
298
Classification of Leukocytes
1.6K
Leukocytes are classified into two groups based on the presence or absence of cytoplasmic granules. Granular leukocytes, which contain granules, belong to the myeloid lineage and are divided into three subtypes: neutrophils, eosinophils, and basophils. These cells are roughly spherical and characterized by the granules in their cytoplasm.
Neutrophils are the most abundant type of granular leukocytes, comprising 50-70% of all leukocytes. They feature small, evenly distributed granules and a...
Neutrophils are the most abundant type of granular leukocytes, comprising 50-70% of all leukocytes. They feature small, evenly distributed granules and a...
1.6K
Intracellular Movement of Viruses and Bacteria
2.8K
Intracellular bacteria and viruses often comprise a group of highly infectious pathogens that can cause several diseases. Bacterial pathogens include those belonging to the genus Rickettsia responsible for conditions such as rocky mountain spotted fever and the Mediterranean spotted fever; Chlamydia, a genus responsible for a sexually transmitted disease; Coxiella burnetii, an agent responsible for Q fever. Viral pathogens include vaccinia—a poxvirus, and herpes simplex virus—a...
2.8K
Viral Structure
61.6K
Viruses are extraordinarily diverse in shape and size, but they all have several structural features in common. All viruses have a core that contains a DNA- or RNA-based genome. The core is surrounded by a protective coat of proteins called the capsid. The capsid is composed of subunits called capsomeres. The capsid and genome-containing core are together known as the nucleocapsid.
61.6K


