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Updated: May 7, 2025

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通过几何图形学习和语言模型模型准确预测微生物蛋白质的最佳条件

Mingming Zhu1, Yidong Song1, Qianmu Yuan1,2

  • 1School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, 510006, China.

Communications biology
|December 31, 2024
PubMed
概括

预测极端性蛋白质的最佳条件对于工业酶工程至关重要. 一个新的几何图形学习模型GeoPoc使用结构和序列数据准确预测蛋白质的最佳温度,pH值和盐度.

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An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
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A proteome is the entire set of proteins that a cell type produces. We can study proteomes using the knowledge of genomes because genes code for mRNAs, and the mRNAs encode proteins. Although mRNA analysis is a step in the right direction, not all mRNAs are translated into proteins.
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Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
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