Proteins are essential for almost all biological processes and derive their
diverse functions from complex 3D structures, which are in turn determined by
their amino acid sequences. In this paper, we exploit the rich biological
inductive bias of amino acid sequences and introduce FoldFlow-2, a novel
sequence-conditioned SE(3)-equivariant flow matching model for protein
structure generation. FoldFlow-2 presents substantial new architectural
features over the previous FoldFlow family of models including a protein large
language model to encode sequence, a new multi-modal fusion trunk that combines
structure and sequence representations, and a geometric transformer based
decoder. To increase diversity and novelty of generated samples -- crucial for
de-novo drug design -- we train FoldFlow-2 at scale on a new dataset that is an
order of magnitude larger than PDB datasets of prior works, containing both
known proteins in PDB and high-quality synthetic structures achieved through
filtering. We further demonstrate the ability to align FoldFlow-2 to arbitrary
rewards, e.g. increasing secondary structures diversity, by introducing a
Reinforced Finetuning (ReFT) objective. We empirically observe that FoldFlow-2
outperforms previous state-of-the-art protein structure-based generative
models, improving over RFDiffusion in terms of unconditional generation across
all metrics including designability, diversity, and novelty across all protein
lengths, as well as exhibiting generalization on the task of equilibrium
conformation sampling. Finally, we demonstrate that a fine-tuned FoldFlow-2
makes progress on challenging conditional design tasks such as designing
scaffolds for the VHH nanobody.

利用氨基酸序列的丰富生物归纳偏差，FoldFlow-2 是一种新颖的序列条件的 SE (3)- 等变流匹配模型，用于蛋白质结构生成，并通过在一个比以前的 PDB 数据集大一个数量级的新数据集上进行规模训练，改进了无条件生成的设计能力、多样性和新颖性，同时在平衡构象抽样任务上具有泛化性能。