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。

基于序列扩增的 SE (3)- 流匹配用于条件蛋白质背骨生成

Sequence-Augmented SE(3)-Flow Matching For Conditional Protein Backbone  Generation

Protein representation learning is a challenging task that aims to capture
the structure and function of proteins from their amino acid sequences.
Previous methods largely ignored the fact that not all amino acids are equally
important for protein folding and activity. In this article, we propose a
neural clustering framework that can automatically discover the critical
components of a protein by considering both its primary and tertiary structure
information. Our framework treats a protein as a graph, where each node
represents an amino acid and each edge represents a spatial or sequential
connection between amino acids. We then apply an iterative clustering strategy
to group the nodes into clusters based on their 1D and 3D positions and assign
scores to each cluster. We select the highest-scoring clusters and use their
medoid nodes for the next iteration of clustering, until we obtain a
hierarchical and informative representation of the protein. We evaluate on four
protein-related tasks: protein fold classification, enzyme reaction
classification, gene ontology term prediction, and enzyme commission number
prediction. Experimental results demonstrate that our method achieves
state-of-the-art performance.

蛋白质表示学习是一个具有挑战性的任务，旨在从其氨基酸序列中捕获蛋白质的结构和功能。本文提出了一种神经聚类框架，通过考虑蛋白质的一级和三级结构信息来自动发现蛋白质的关键部分，在四个蛋白质相关任务上取得了最先进的表现。

蛋白质表征学习的聚类

Clustering for Protein Representation Learning

Antibodies have become an important class of therapeutic agents to treat
human diseases. To accelerate therapeutic antibody discovery, computational
methods, especially machine learning, have attracted considerable interest for
predicting specific interactions between antibody candidates and target
antigens such as viruses and bacteria. However, the publicly available datasets
in existing works have notable limitations, such as small sizes and the lack of
non-binding samples and exact amino acid sequences. To overcome these
limitations, we have developed AVIDa-hIL6, a large-scale dataset for predicting
antigen-antibody interactions in the variable domain of heavy chain of heavy
chain antibodies (VHHs), produced from an alpaca immunized with the human
interleukin-6 (IL-6) protein, as antigens. By leveraging the simple structure
of VHHs, which facilitates identification of full-length amino acid sequences
by DNA sequencing technology, AVIDa-hIL6 contains 573,891 antigen-VHH pairs
with amino acid sequences. All the antigen-VHH pairs have reliable labels for
binding or non-binding, as generated by a novel labeling method. Furthermore,
via introduction of artificial mutations, AVIDa-hIL6 contains 30 different
mutants in addition to wild-type IL-6 protein. This characteristic provides
opportunities to develop machine learning models for predicting changes in
antibody binding by antigen mutations. We report experimental benchmark results
on AVIDa-hIL6 by using neural network-based baseline models. The results
indicate that the existing models have potential, but further research is
needed to generalize them to predict effective antibodies against unknown
mutants. The dataset is available at this https URL

利用 VHHs 的简单结构，作者建立了一个大规模的数据集 AVIDa-hIL6，以人源性细胞因子 IL-6 为抗原，包含 573,891 个抗原 - VHHs 氨基酸序列对，并且所有抗原 - VHHs 对都有可靠的标签进行分类，这个数据集可以用于预测抗体 - 抗原相互作用，研究人员进一步发现存在潜在的机器学习模型来预测有效的抗体。