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通过机器学习和视觉生理学Opsin数据库 (VPOD) 发现基因型-表型关系

Seth A Frazer¹, Mahdi Baghbanzadeh², Ali Rahnavard²

¹Ecology, Evolution, and Marine Biology, University of California, Santa Barbara, California 93106, USA.

|October 26, 2024

概括

我们创建了视觉生理奥普辛数据库 (VPOD),以将奥普辛基因变异与它们的光吸收 (λmax) 表型联系起来. 机器学习模型现在可以从序列中预测opssin功能,帮助蛋白质设计和进化研究.

科学领域:

基因组学就是基因组学.
分子生物学分子生物学
生物信息学是一种生物信息学.

背景情况:

从遗传变异中预测表型对于生物工程和全球变化生物学至关重要.
作为色彩视觉的关键,素蛋白质的基因型-表型关系得到了广泛的研究,但数据是分散的.
了解opsin基因型-表型联系需要可访问的,编译的数据.

研究的目的:

编制一个全面的opsin基因型及其λmax表型的数据库.
开发和验证机器学习模型,用于从遗传序列中预测opsin λmax.
为了促进基因型-表型关系的系统分析在opsins.

主要方法:

从73个出版物中编制了视觉生理奥普辛数据库 (VPOD),其中包括864种奥普辛基因型及其λmax表型.
使用了deepBreaks和基于回归的机器学习 (ML) 模型.
分析VPOD数据以训练和测试ML模型以预测λmax.

主要成果:

该VPOD (1.0版本) 包含864个独特的素基因型和相关的λmax表型.
机器学习模型可靠地预测λmax,捕捉非添加性突变效应.
在opsin序列中确定了功能关键的氨基酸位点.

结论:

ML 能够从单独的基因序列中可靠地预测 opsin 功能.
这便于探索分子进化,生态利基连接和新型蛋白质设计.
数据库和预测模型为未来对可量化的表型的研究提供了基础.

关键词:

颜色视觉 - 颜色视觉是一种视觉.基因型表型关系的关系归算是指指责一个人.机器学习是机器学习.选择了自己的选择.预测现象类型的预测回归,编译数据库数据库.频谱灵敏度频谱灵敏度频谱灵敏度

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