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Abstract：
In silico validation of de novo designed proteins with deep learning (DL)-based structure prediction algorithms has become mainstream. However, formal evidence of the relationship between a high-quality predicted model and the chance of experimental success is lacking. We used experimentally characterized de novo water-soluble and transmembrane β-barrel designs to show that AlphaFold2 and ESMFold excel at different tasks. ESMFold can efficiently identify designs generated based on high-quality (designable) backbones. However, only AlphaFold2 can predict which sequences have the best chance of experimentally folding among similar designs. We show that ESMFold can generate high-quality structures from just a few predicted contacts and introduce a new approach based on incremental perturbation of the prediction (\"in silico melting\"), which can reveal differences in the presence of favorable contacts between designs. This study provides a new insight on DL-based structure prediction models explainability and on how they could be leveraged for the design of increasingly complex proteins; in particular membrane proteins which have historically lacked basic in silico validation tools.
摘要：
使用基于深度学习（DL）的结构预测算法对从头设计的蛋白质进行计算机验证已成为主流。然而,缺乏高质量预测模型与实验成功机会之间关系的正式证据。我们使用了经过实验表征的从头水溶性和跨膜β桶设计，以表明AlphaFold2和ESMFold在不同任务中表现出色。ESMFold可以有效地识别基于高质量（可设计）主干生成的设计。然而,只有AlphaFold2可以预测哪些序列在相似设计中具有实验折叠的最佳机会。我们证明了ESMFold可以从几个预测的接触中生成高质量的结构，并引入了一种基于预测的增量扰动的新方法（“硅熔化”），这可以揭示设计之间存在有利接触的差异。这项研究为基于DL的结构预测模型的可解释性以及如何利用它们来设计日益复杂的蛋白质提供了新的见解;特别是历史上缺乏基本的计算机验证工具的膜蛋白。
