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相关概念视频

End Point Prediction: Gran Plot01:07

End Point Prediction: Gran Plot

A Gran plot is used to predict the equivalence volume or endpoint of a potentiometric or acid-base titration without reaching the endpoint. Typically, titration data is collected as a function of the titrant's volume up to a point less than the equivalence volume and then transformed into a linear format. The straight line is extended to the x-axis, indicating the necessary titrant volume to achieve the equivalence point.
For potentiometric titration, the Gran plot is created by plotting the...

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Sitetack:一个深度学习模型,通过使用已知的PTM来改进PTM预测.

Clair S Gutierrez, Alia A Kassim, Benjamin D Gutierrez

    bioRxiv : the preprint server for biology
    |June 19, 2024
    PubMed
    概括

    纳入已知的翻译后修改 (PTM) 站点显著改善了PTM位置的深度学习预测. 这种方法提高了准确性,并突出了PTM在规范进一步修改中的作用.

    科学领域:

    • 蛋白质组学和生物信息学
    • 计算生物学 计算生物学
    • 机器学习在生物学中的应用

    背景情况:

    • 翻译后修改 (PTM) 对蛋白质组多样性,生物生命和治疗发展至关重要.
    • 使用深度学习预测PTM位置是有希望的,但受到数据集限制和分析挑战的阻碍.
    • 目前用于PTM预测的基于序列的深度学习模型在准确性和范围方面存在局限性.

    研究的目的:

    • 评估将已知的PTM站点纳入基于序列的深度学习算法以改善PTM位置预测的影响.
    • 调查一个PTM的位置是否能提高对其他PTM的预测.

    主要方法:

    • 在蛋白质序列中,PTM位置被编码为不同的氨基酸残留物.
    • 然后使用文字嵌入技术编码蛋白质序列.
    • 使用卷积神经网络 (CNN) 预测每个部位的修改概率.

    主要成果:

    • 该模型的性能与已知的PTM没有明确标记时的现有方法相比.
    • 与当前模型相比,标记已知的PTM站点导致预测准确度显著提高.
    • 该模型表明,对现有的PTM位置的了解可以提高其他PTM的可预测性.

    结论:

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    • 这些发现强调了PTM在蛋白质上连续安装额外的PTM中的关键作用.
    • 包括关于已知的PTM位置的信息是提高蛋白质学机器学习算法的性能的一个有价值的策略.
    • 这种方法为更准确的PTM预测提供了途径,推进了蛋白质组研究和治疗应用.