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相关概念视频

Ionization Energy03:12

Ionization Energy

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The amount of energy required to remove the most loosely bound electron from a gaseous atom in its ground state is called its first ionization energy (IE1). The first ionization energy for an element, X, is the energy required to form a cation with 1+ charge:
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Biodiversity and Human Values01:24

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Human civilization relies on biodiversity in many ways. Sudden changes in species biodiversity result in environmental changes that can modify weather patterns and therefore human civilizations.
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Professional Values01:29

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Nurses are responsible for caring for patients during birth, death, illness, and healing. Professional values guide the decisions and actions that nurses make in their careers. If nurses know the decisions and actions to take, providing patients with exceptional care is possible.
The values that are the foundation of the nursing profession are altruism, autonomy, human dignity, and social justice.
First, altruism refers to the concern for the welfare and well-being of others without personal...
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Residual Stresses01:26

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Residual stresses reside in a structure even after removing the original stress inducer. This phenomenon often arises from varied plastic deformations across different parts of a structure. Consider a rod stretched beyond its yield point. It will not regain its original length due to permanent deformation. Even after load removal, the rod does not entirely lose stress because of uneven plastic deformations, resulting in residual stresses. The computation of these stresses in structures is...
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A residual plot is a statistical representation of data used to analyze correlation and regression results. It helps verify the requirements for drawing specific conclusions about correlation and regression. To obtain the residual plot, first, the residual for each data value is calculated, which is simply the vertical distance between the observed and the predicted value obtained from the regression equation.
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A critical value is a definite value obtained from a particular probability distribution at a predecided confidence level (or a predecided significance level) for a given population parameter. The critical value provides demarcation that separates the sample statistics that are likely to occur from the ones that are unlikely to occur based on the given probability distribution and the population parameter to be estimated. The critical value for normal distribution is obtained from the z...
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基于图形的深度学习模型用于通过物理启发的特征工程来预测蛋白质电离残留的pKa值.

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  • 1Department of Chemical and Biological Engineering, Villanova University, Villanova, Pennsylvania 19085, United States.

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预测蛋白质残留 pKa 值对于药物发现至关重要. 这项研究引入了一个结合分子动力学和深度学习的新框架,大大提高了对关键可电离残留物现有工具的预测准确度.

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科学领域:

  • 生物化学 生物化学
  • 计算生物学 计算生物学
  • 结构生物学 结构生物学

背景情况:

  • 蛋白质pKa值对于生物功能,如酶活性和联体结合至关重要.
  • 准确的pKa预测对于药物发现至关重要,但像PROPKA这样的当前方法存在局限性.

研究的目的:

  • 开发一个改进的计算框架来预测蛋白质残留 pKa 值.
  • 通过综合分子动力学和深度学习,提高pKa预测的准确性和通用性.

主要方法:

  • 使用AMOEBA极化力场的高通量分子建模来生成蛋白质结构数据集.
  • 工程物理启发的功能,包括原子静电学.
  • 在PKAD-2数据集中的实验确定的pKa值上训练了三个基于图形的神经网络模型.

主要成果:

  • 与PROPKA3.5.1.1.相比,所有开发的模型都显示了与PROPKA3.5.1.2相比,酸,谷氨酸,氨酸和氨酸的pKa预测准确度的显著改善.
  • 图表注意力网络模型与最近的机器学习基准相比,显示出卓越的准确性和通用性.
  • 特性重要性分析揭示了与蛋白质微环境和原子几何学相关的有物理意义的模式.

结论:

  • 综合框架在预测蛋白质残留 pKa 值方面取得了重大进展.
  • 开发的深度学习模型和精心策划的数据集为研究人员提供了宝贵的资源.
  • 潜在的应用包括早期药物标识和蛋白质工程.