Protein design requires a deep understanding of the inherent complexities of the protein universe. While many efforts lean towards conditional generation or focus on specific families of proteins, the foundational task of unconditional generation remains underexplored and undervalued. Here, we explore this pivotal domain, introducing DiMA, a model that leverages continuous diffusion on embeddings derived from the protein language model, ESM-2, to generate amino acid sequences. DiMA surpasses leading solutions, including autoregressive transformer-based and discrete diffusion models, and we quantitatively illustrate the impact of the design choices that lead to its superior performance. We extensively evaluate the quality, diversity, distribution similarity, and biological relevance of the generated sequences using multiple metrics across various modalities. Our approach consistently produces novel, diverse protein sequences that accurately reflect the inherent structural and functional diversity of the protein space. This work advances the field of protein design and sets the stage for conditional models by providing a robust framework for scalable and high-quality protein sequence generation.

翻译：蛋白质设计需要对蛋白质宇宙的内在复杂性有深刻理解。尽管许多研究倾向于条件生成或关注特定蛋白质家族，但作为基础任务的非条件生成仍未被充分探索和重视。在此，我们探索了这一关键领域，提出了DiMA模型，该模型利用基于蛋白质语言模型ESM-2所得嵌入的连续扩散技术生成氨基酸序列。DiMA超越了包括自回归Transformer和离散扩散模型在内的领先方案，我们定量分析了导致其优越性能的设计选择的影响。我们使用多种模态下的多个指标，全面评估了生成序列的质量、多样性、分布相似性及生物学相关性。我们的方法一致地产生新颖、多样的蛋白质序列，这些序列精准反映了蛋白质空间固有的结构和功能多样性。这项工作推进了蛋白质设计领域，并通过提供一个可扩展的高质量蛋白质序列生成框架，为条件模型奠定了基础。