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SProtFP:一种基于机器学习的方法,用于对 prokaryotes 中的小 ORF 的功能分类

Akshay Khanduja¹, Debasisa Mohanty¹

¹National Institute of Immunology, Aruna Asaf Ali Marg, New Delhi 110067, India.

NAR genomics and bioinformatics

|January 9, 2025

概括

我们开发了SProtFP,这是一种用于注释小细菌蛋白和识别抗菌的机器学习工具. 这种方法有助于了解微生物群落中未表征的蛋白质的功能.

科学领域:

微生物学微生物学
生物信息学是一种生物信息学.
计算生物学计算生物学

背景情况:

小蛋白质 (≤100氨基酸) 在所有生命形式中都至关重要.
这些小蛋白质的功能性注释,特别是在 prokaryotes 中,仍然具有挑战性.
识别新的小蛋白及其功能对于理解微生物生物学至关重要.

研究的目的:

开发一种基于机器学习的方法,SProtFP,用于 prokaryotic 小蛋白的功能注释.
为了能够识别小型开放式读取框架 (smORF) 编码的抗微生物 (AMP).
为了促进微生物组数据集的大规模功能注释,特别是那些具有低序列同质性的数据集.

主要方法:

SProtFP使用在物理化学描述器上训练的独立人工神经网络 (ANN).
分类类别包括抗毒素2型,细菌素,DNA结合,金属结合,核糖体蛋白,RNA结合,1型毒素和2型毒素蛋白.
一个单独的模型被训练用于识别smORF编码的抗微生物 (AMP).

主要成果:

在试验组中,SProtFP实现了高性能,平均ROC-AUC为0.92 (10倍交叉验证) 和0.94/0.93.
该方法成功识别了成千上万的远程同类物质,并在人类肠道微生物组数据中为未表征的蛋白质分配了功能.
对于微生物组数据集的大规模注释的实用性已被证明,即使具有低序列同质性.

结论:

SProtFP 是一种有效的工具,用于对 prokaryotic 小蛋白和 smORF 编码的 AMP 的功能注释.
该方法显著提高了新型小蛋白及其在微生物群落中的功能的发现.
SProtFP 在线可用,可与其他基因组注释工具集成,用于全面分析.

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