最近在计算预测二级和超二级结构从蛋白质序列的进展
Jian Zhang1,2, Jingjing Qian3, Quan Zou4
1School of Computer and Information Technology, Xinyang Normal University, Xinyang, China. jianzhang@xynu.edu.cn.
Methods in molecular biology (Clifton, N.J.)
|November 14, 2024
概括
这项研究审查了45种蛋白质二次结构 (SS) 和32种超二次结构 (SSS) 预测工具. 大多数现代预测器使用机器学习和进化数据来进行增强的蛋白质结构分析.
科学领域:
- * 计算生物学和生物信息学.
- * 结构生物信息学和蛋白质结构预测.
背景情况:
- * 蛋白质二次结构 (SSs) 和超二次结构 (SSSs) 是蛋白质三维结构的基础.
- *准确预测SS和SSS对于开发生物信息学工具和了解蛋白质功能至关重要.
- *已经开发了许多基于序列的预测工具,需要对最近的进展进行全面审查.
研究的目的:
- *为了调查和分析自2018年以来发布的二次结构 (SS) 预测指标.
- * 审查基于序列的超次结构 (SSS) 预测器,重点关注卷状卷和β-hairpins.
- *评估这些蛋白质结构预测工具的输入,预测模型,范围和可用性.
主要方法:
- *对45个SSS预测因素和32个SSS预测因素进行了全面的文献调查和分析.
- *专注于自2018年以来发表的方法,重点是基于序列的方法.
- * 检查机器学习模型,特别是深度神经网络,以及使用进化序列配置文件.
主要成果:
- *确定了45个二次结构 (SS) 预测器和32个超二次结构 (SSS) 预测器,其中大量是最近发布的.
- * 发现机器学习,特别是深度神经网络和进化序列概况在现代预测者中占主导地位.
- *强调了几个最先进的SS和SSS预测工具的可用性和特性.
结论:
- * 蛋白质结构预测领域正在迅速发展,受到机器学习和深度学习的推动.
- * 现代基于序列的预测器为分析蛋白质二级和超二级结构提供了强大的功能.
- *这些工具的持续开发和可访问性对于生物信息学研究和应用至关重要.
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