用贝叶斯神经网络模拟噬菌体展示实验噪声，提升蛋白结合力预测可靠性。
Bayes-PD: Exploring a Sequence to Binding Bayesian Neural Network model trained on Phage Display data
- 构建贝叶斯神经网络，模拟噬菌体展示中的实验噪声与模型不确定性
- 在真实结合亲和力数据上验证，避免依赖代理指标
- 适合需要高可信度蛋白设计的生物医学研究者
噬菌体展示是一种强大的实验技术，用于研究蛋白质与其他分子（如肽、DNA或RNA）之间的相互作用。然而，该数据在蛋白设计中未被充分利用，原因包括实验噪声高、数据预处理复杂以及结果难以解释。本文提出一种新方法：在训练循环中引入贝叶斯神经网络，以模拟噬菌体展示实验及其相关噪声。目标是探究理解实验噪声与模型不确定性如何提升此类模型对噬菌体展示实验的可靠解释能力。我们采用实际结合亲和力测量值进行验证，而非仅依赖来自‘保留轮次’的代理值。
原文摘要 · Abstract (English)
Phage display is a powerful laboratory technique used to study the interactions between proteins and other molecules, whether other proteins, peptides, DNA or RNA. The under-utilisation of this data in conjunction with deep learning models for protein design may be attributed to; high experimental noise levels; the complex nature of data pre-processing; and difficulty interpreting these experimental results. In this work, we propose a novel approach utilising a Bayesian Neural Network within a training loop, in order to simulate the phage display experiment and its associated noise. Our goal is to investigate how understanding the experimental noise and model uncertainty can enable the reliable application of such models to reliably interpret phage display experiments. We validate our approach using actual binding affinity measurements instead of relying solely on proxy values derived from 'held-out' phage display rounds.
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