突变
蛋白质设计
计算机科学
理论(学习稳定性)
蛋白质功能预测
序列(生物学)
蛋白质结构
计算生物学
蛋白质结构预测
蛋白质功能
人工智能
遗传学
生物
机器学习
基因
生物化学
作者
Sanaa Mansoor,Minkyung Baek,David Juergens,J. P. Watson,David Baker
摘要
Predicting the effects of mutations on protein function and stability is an outstanding challenge. Here, we assess the performance of a variant of RoseTTAFold jointly trained for sequence and structure recovery, RFjoint , for mutation effect prediction. Without any further training, we achieve comparable accuracy in predicting mutation effects for a diverse set of protein families using RFjoint to both another zero-shot model (MSA Transformer) and a model which requires specific training on a particular protein family for mutation effect prediction (DeepSequence). Thus, although the architecture of RFjoint was developed to address the protein design problem of scaffolding functional motifs, RFjoint acquired an understanding of the mutational landscapes of proteins during model training that is equivalent to that of recently developed large protein language models. The ability to simultaneously reason over protein structure and sequence could enable even more precise mutation effect predictions following supervised training on the task. These results suggest that RFjoint has a quite broad understanding of protein sequence-structure landscapes, and can be viewed as a joint model for protein sequence and structure which could be broadly useful for protein modeling. This article is protected by copyright. All rights reserved.
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