在蛋白质适应性预测上理解语言模型缩放
Chao Hou1, Di Liu2, Aziz Zafar2
1Department of Systems Biology, Columbia University Irving Medical Center, New York, NY 10032.
bioRxiv : the preprint server for biology
|August 8, 2025
概括
蛋白质语言模型在健身预测中的表现随着尺寸的增加而下降. 最佳性能需要适度的序列概率,而不是极端值,挑战深度学习中的"越大越好"假设.
科学领域:
- 计算生物学 计算生物学
- 机器学习 机器学习
- 蛋白质工程是指蛋白质工程.
背景情况:
- 蛋白质语言模型估计序列概率 (p(序列)) 用于突变效应预测和蛋白质设计.
- 较大的深度学习模型通常被认为在各种任务中表现更好.
研究的目的:
- 为了研究蛋白质语言模型的可扩展性,以预测健康状况.
- 了解模型大小,训练数据和随机性如何影响预测的p 序列及其与实际蛋白质适应性的关系.
主要方法:
- 分析蛋白质语言模型在不同模型大小和训练数据集的健身预测上的表现.
- 评估预测的p ((序列) 与同源序列中的进化模式的相关性.
主要成果:
- 在健身预测中的模型性能下降超过一定的尺寸,与一般的深度学习趋势相反.
- 模型大小,训练数据和随机元素可以使预测的p (序列) 偏离实际的蛋白质适应性.
- 最佳的适应性预测发生在p(序列) 与中等水平的进化模式相匹配时;极端的可能性导致统一的预测,未能捕捉到适应性景观.
结论:
- 较大的蛋白质模型倾向于预测更高的p 序列,可能超过最佳的中等范围并降低健身预测的准确性.
- 结果澄清了蛋白质模型的扩展行为,以预测健康状况,并为模型开发和应用提供了实际指导方针.
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