Institute for Drug Discovery, Leipzig University Medical Faculty, Leipzig, Germany.
Center for Scalable Data Analytics and Artificial Intelligence ScaDS.AI, Dresden/Leipzig, Germany.
PLoS Comput Biol. 2024 Mar 14;20(3):e1011939. doi: 10.1371/journal.pcbi.1011939. eCollection 2024 Mar.
Post-translational modifications (PTMs) of proteins play a vital role in their function and stability. These modifications influence protein folding, signaling, protein-protein interactions, enzyme activity, binding affinity, aggregation, degradation, and much more. To date, over 400 types of PTMs have been described, representing chemical diversity well beyond the genetically encoded amino acids. Such modifications pose a challenge to the successful design of proteins, but also represent a major opportunity to diversify the protein engineering toolbox. To this end, we first trained artificial neural networks (ANNs) to predict eighteen of the most abundant PTMs, including protein glycosylation, phosphorylation, methylation, and deamidation. In a second step, these models were implemented inside the computational protein modeling suite Rosetta, which allows flexible combination with existing protocols to model the modified sites and understand their impact on protein stability as well as function. Lastly, we developed a new design protocol that either maximizes or minimizes the predicted probability of a particular site being modified. We find that this combination of ANN prediction and structure-based design can enable the modification of existing, as well as the introduction of novel, PTMs. The potential applications of our work include, but are not limited to, glycan masking of epitopes, strengthening protein-protein interactions through phosphorylation, as well as protecting proteins from deamidation liabilities. These applications are especially important for the design of new protein therapeutics where PTMs can drastically change the therapeutic properties of a protein. Our work adds novel tools to Rosetta's protein engineering toolbox that allow for the rational design of PTMs.
蛋白质的翻译后修饰（PTMs）在其功能和稳定性中起着至关重要的作用。这些修饰影响蛋白质折叠、信号转导、蛋白质-蛋白质相互作用、酶活性、结合亲和力、聚集、降解等等。迄今为止，已经描述了超过 400 种 PTMs，代表了远超遗传编码氨基酸的化学多样性。这些修饰对蛋白质的成功设计构成了挑战，但也代表了使蛋白质工程工具多样化的主要机会。为此，我们首先训练人工神经网络（ANNs）来预测十八种最丰富的 PTMs，包括蛋白质糖基化、磷酸化、甲基化和脱酰胺。在第二步中，这些模型被实施在计算蛋白质建模套件 Rosetta 中，这允许灵活地与现有协议结合，以模拟修饰位点，并了解它们对蛋白质稳定性和功能的影响。最后，我们开发了一种新的设计方案，该方案要么最大化，要么最小化特定位点被修饰的预测概率。我们发现，这种 ANN 预测和基于结构的设计的组合可以实现现有 PTMs 的修饰，以及引入新的 PTMs。我们工作的潜在应用包括但不限于糖基化掩盖表位、通过磷酸化增强蛋白质-蛋白质相互作用，以及保护蛋白质免受脱酰胺缺陷的影响。这些应用在设计新的蛋白质治疗药物时尤为重要，因为 PTMs 可以极大地改变蛋白质的治疗特性。我们的工作为 Rosetta 的蛋白质工程工具箱添加了新的工具，允许对 PTMs 进行合理设计。
