Department of Chemical Engineering and Materials Science, University of Minnesota, Minneapolis, Minnesota 55455, United States.
Center for Soft Matter Research, Department of Physics, New York University, New York, New York 10003, United States.
ACS Synth Biol. 2023 Sep 15;12(9):2600-2615. doi: 10.1021/acssynbio.3c00196. Epub 2023 Aug 29.
Engineered proteins have emerged as novel diagnostics, therapeutics, and catalysts. Often, poor protein developability─quantified by expression, solubility, and stability─hinders utility. The ability to predict protein developability from amino acid sequence would reduce the experimental burden when selecting candidates. Recent advances in screening technologies enabled a high-throughput (HT) developability dataset for 10 of 10 possible variants of protein ligand scaffold Gp2. In this work, we evaluate the ability of neural networks to learn a developability representation from a HT dataset and transfer this knowledge to predict recombinant expression beyond observed sequences. The model convolves learned amino acid properties to predict expression levels 44% closer to the experimental variance compared to a non-embedded control. Analysis of learned amino acid embeddings highlights the uniqueness of cysteine, the importance of hydrophobicity and charge, and the unimportance of aromaticity, when aiming to improve the developability of small proteins. We identify clusters of similar sequences with increased recombinant expression through nonlinear dimensionality reduction and we explore the inferred expression landscape via nested sampling. The analysis enables the first direct visualization of the fitness landscape and highlights the existence of evolutionary bottlenecks in sequence space giving rise to competing subpopulations of sequences with different developability. The work advances applied protein engineering efforts by predicting and interpreting protein scaffold expression from a limited dataset. Furthermore, our statistical mechanical treatment of the problem advances foundational efforts to characterize the structure of the protein fitness landscape and the amino acid characteristics that influence protein developability.
工程蛋白已成为新型诊断试剂、治疗剂和催化剂。通常，较差的蛋白可开发性——通过表达、溶解度和稳定性来量化——会阻碍其应用。如果能够根据氨基酸序列预测蛋白可开发性，那么在选择候选物时就可以减少实验负担。最近筛选技术的进步使得能够对蛋白配体支架 Gp2 的 10 种可能变体中的 10 种进行高通量（HT）可开发性数据集筛选。在这项工作中，我们评估了神经网络从 HT 数据集学习可开发性表示并将该知识转移到预测重组表达超出观察序列的能力。该模型将学习到的氨基酸属性进行卷积，以预测表达水平，与非嵌入式对照相比，更接近实验方差的 44%。对学习到的氨基酸嵌入的分析强调了半胱氨酸的独特性、疏水性和电荷的重要性以及芳香性的不重要性，这对于提高小蛋白的可开发性很重要。我们通过非线性降维识别具有增加重组表达的相似序列簇，并通过嵌套抽样探索推断的表达景观。该分析能够首次直接可视化适应度景观，并突出了序列空间中进化瓶颈的存在，导致具有不同可开发性的序列的竞争亚群出现。这项工作通过从有限的数据集预测和解释蛋白支架的表达来推进应用蛋白工程的努力。此外，我们对该问题的统计力学处理推进了对蛋白适应度景观结构以及影响蛋白可开发性的氨基酸特征的基础研究。
