A popular approach to protein design is to combine a generative model with a discriminative model for conditional sampling. The generative model samples plausible sequences while the discriminative model guides a search for sequences with high fitness. Given its broad success in conditional sampling, classifier-guided diffusion modeling is a promising foundation for protein design, leading many to develop guided diffusion models for structure with inverse folding to recover sequences. In this work, we propose diffusioN Optimized Sampling (NOS), a guidance method for discrete diffusion models that follows gradients in the hidden states of the denoising network. NOS makes it possible to perform design directly in sequence space, circumventing significant limitations of structure-based methods, including scarce data and challenging inverse design. Moreover, we use NOS to generalize LaMBO, a Bayesian optimization procedure for sequence design that facilitates multiple objectives and edit-based constraints. The resulting method, LaMBO-2, enables discrete diffusions and stronger performance with limited edits through a novel application of saliency maps. We apply LaMBO-2 to a real-world protein design task, optimizing antibodies for higher expression yield and binding affinity to a therapeutic target under locality and liability constraints, with 97% expression rate and 25% binding rate in exploratory in vitro experiments.

翻译：蛋白质设计的流行方法是将生成模型与判别模型结合进行条件采样。生成模型采样合理的序列，而判别模型引导搜索高适应度的序列。鉴于分类器引导扩散模型在条件采样中的广泛成功，它为蛋白质设计提供了有前景的基础，促使许多研究者开发针对结构的引导扩散模型，并通过反向折叠恢复序列。在本工作中，我们提出扩散优化采样（NOS），这是一种针对离散扩散模型的引导方法，它沿着去噪网络隐状态的梯度进行优化。NOS使得直接在序列空间中进行设计成为可能，规避了基于结构方法的显著局限性，包括数据稀缺和逆向设计困难。此外，我们利用NOS推广了LaMBO（一种用于序列设计的贝叶斯优化程序），它支持多目标和基于编辑的约束。由此产生的方法LaMBO-2通过新颖的显著性图应用，实现了离散扩散，并在有限编辑下展现出更强的性能。我们将LaMBO-2应用于实际蛋白质设计任务，在定位和毒性约束下优化抗体的高表达产量和对治疗靶点的结合亲和力，在探索性体外实验中实现了97%的表达率和25%的结合率。