Despite breakthroughs achieved in protein sequence-to-structure and function-to-sequence predictions, the affinity-to-mutation prediction problem remains unsolved. Such a problem is of exponential complexity deemed to find a mutated protein or protein complex having a guaranteed binding-affinity change. Here we introduce an adversarial learning-based mutation method that creates optimal amino acid substitutions and changes the mutant’s affinity change significantly in a preset direction. The key aspect in our method is the adversarial training process that dynamically labels the real side of the protein data and generates fake pseudo-data accordingly to construct a deep learning architecture for guiding the mutation. The method is sufficiently flexible to generate both single- and multipointed mutations at the adversarial learning step to mimic the natural circumstances of protein evolution. Compared with random mutants, our mutated sequences have in silico exhibited more than one order of change in magnitude of binding free energy change towards stronger complexes in the case study of Novavax–angiotensin-converting enzyme-related carboxypeptidase vaccine construct optimization. We also applied the method iteratively each time, using the output as the input sequence of the next iteration, to generate paths and a landscape of mutants with affinity-increasing monotonicity to understand SARS-CoV-2 Omicron’s spike evolution. With these steps taken for effective generation of protein mutants of monotone affinity, our method will provide potential benefits to many other applications including protein bioengineering, drug design, antibody reformulation and therapeutic protein medication.

尽管在蛋白质序列到结构和功能到序列预测方面取得了突破，但突变亲和力预测问题仍未解决。这样的问题具有指数复杂性，被认为是找到具有保证的结合亲和力变化的突变蛋白质或蛋白质复合物。在这里，我们介绍了一种基于对抗性学习的突变方法，该方法可以创建最佳的氨基酸替换，并在预设方向上显着改变突变体的亲和力变化。我们方法的关键方面是对抗性训练过程，动态标记蛋白质数据的真实一面并相应地生成假伪数据，以构建用于指导突变的深度学习架构。该方法足够灵活，可以在对抗性学习步骤中生成单点和多点突变，以模拟蛋白质进化的自然环境。与随机突变体相比，在 Novavax-血管紧张素转换酶相关羧肽酶疫苗构建体优化的案例研究中，我们的突变序列在计算机模拟中表现出向更强复合物结合自由能变化的幅度超过一个数量级的变化。我们还每次迭代应用该方法，使用输出作为下一次迭代的输入序列，生成具有亲和力增加单调性的突变体路径和景观，以了解 SARS-CoV-2 Omicron 的尖峰进化。通过采取这些步骤来有效生成单调亲和力的蛋白质突变体，我们的方法将为许多其他应用提供潜在的好处，包括蛋白质生物工程、药物设计、抗体重新配制和治疗性蛋白质药物。