迈向基于SE(3)-离散扩散的核酸与蛋白质复合物序列-结构联合生成
Towards Joint Sequence-Structure Generation of Nucleic Acid and Protein Complexes with SE(3)-Discrete Diffusion
AI总结：
本文提出MMDiff，利用SE(3)-离散扩散噪声联合生成核酸与蛋白质复合物的序列和结构，并通过新基准验证其在micro-RNA、单链DNA及多链复合物建模中的有效性。
摘要：
大分子生成模型在蛋白质工程等工业和生物医学领域具有丰富而深远的影响。然而，现有方法目前仅限于独立或联合地建模蛋白质结构或序列，而忽略了蛋白质与其他大分子之间普遍存在的相互作用。在本工作中，我们提出了MMDiff，这是一种生成模型，利用联合的SE(3)-离散扩散噪声，对核酸和蛋白质复合物的序列与结构进行联合设计，既可独立建模，也可在复合物中进行联合设计。该模型对新兴的大分子设计领域具有重要意义，包括基于结构的转录因子设计和非编码RNA序列设计。我们通过在本工作中引入的一个严格的新的大分子复合物生成设计基准，展示了MMDiff的实用性。我们的结果表明，MMDiff能够成功生成micro-RNA和单链DNA分子，同时在DNA和RNA分子与多链蛋白质复合物相互作用的联合建模方面也具备一定的能力。源代码：https://github.com/Profluent-Internships/MMDiff。
英文摘要：
Generative models of macromolecules carry abundant and impactful implications for industrial and biomedical efforts in protein engineering. However, existing methods are currently limited to modeling protein structures or sequences, independently or jointly, without regard to the interactions that commonly occur between proteins and other macromolecules. In this work, we introduce MMDiff, a generative model that jointly designs sequences and structures of nucleic acid and protein complexes, independently or in complex, using joint SE(3)-discrete diffusion noise. Such a model has important implications for emerging areas of macromolecular design including structure-based transcription factor design and design of noncoding RNA sequences. We demonstrate the utility of MMDiff through a rigorous new design benchmark for macromolecular complex generation that we introduce in this work. Our results demonstrate that MMDiff is able to successfully generate micro-RNA and single-stranded DNA molecules while being modestly capable of joint modeling DNA and RNA molecules in interaction with multi-chain protein complexes. Source code: https://github.com/Profluent-Internships/MMDiff.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
