Deep learning approaches achieved significant progress in predicting protein
structures. These methods are often applied to protein-protein interactions
(PPIs) yet require Multiple Sequence Alignment (MSA) which is unavailable for
various interactions, such as antibody-antigen. Computational docking methods
are capable of sampling accurate complex models, but also produce thousands of
invalid configurations. The design of scoring functions for identifying
accurate models is a long-standing challenge. We develop a novel
attention-based Graph Neural Network (GNN), ContactNet, for classifying PPI
models obtained from docking algorithms into accurate and incorrect ones. When
trained on docked antigen and modeled antibody structures, ContactNet doubles
the accuracy of current state-of-the-art scoring functions, achieving accurate
models among its Top-10 at 43% of the test cases. When applied to unbound
antibodies, its Top-10 accuracy increases to 65%. This performance is achieved
without MSA and the approach is applicable to other types of interactions, such
as host-pathogens or general PPIs.

我们开发了一种基于注意力的图神经网络 (ContactNet)，用于将从对接算法中得到的蛋白质相互作用模型分类为准确和错误的模型，不需要多序列比对，适用于其他类型的相互作用。

ContactNet：基于几何的深度学习模型用于预测蛋白质 - 蛋白质相互作用

ContactNet: Geometric-Based Deep Learning Model for Predicting  Protein-Protein Interactions

Multiple Sequence Alignment (MSA) plays a pivotal role in unveiling the
evolutionary trajectories of protein families. The accuracy of protein
structure predictions is often compromised for protein sequences that lack
sufficient homologous information to construct high quality MSA. Although
various methods have been proposed to generate virtual MSA under these
conditions, they fall short in comprehensively capturing the intricate
coevolutionary patterns within MSA or require guidance from external oracle
models. Here we introduce MSAGPT, a novel approach to prompt protein structure
predictions via MSA generative pretraining in the low MSA regime. MSAGPT
employs a simple yet effective 2D evolutionary positional encoding scheme to
model complex evolutionary patterns. Endowed by this, its flexible 1D MSA
decoding framework facilitates zero or few shot learning. Moreover, we
demonstrate that leveraging the feedback from AlphaFold2 can further enhance
the model capacity via Rejective Fine tuning (RFT) and Reinforcement Learning
from AF2 Feedback (RLAF). Extensive experiments confirm the efficacy of MSAGPT
in generating faithful virtual MSA to enhance the structure prediction
accuracy. The transfer learning capabilities also highlight its great potential
for facilitating other protein tasks.

MSAGPT 是一种新颖的方法，通过在低 MSA 水平下进行 MSA 生成预训练来促进蛋白质结构预测，其通过简单而有效的 2D 进化位置编码方案来建模复杂的进化模式，并通过 AlphaFold2 的反馈进一步增强模型能力，证实了 MSAGPT 在生成忠实的虚拟 MSA 以提高结构预测准确性方面的功效，并突显了其促进其他蛋白质任务的巨大潜力。

MSAGPT: 利用 MSA 生成预训练进行神经启发蛋白质结构预测

MSAGPT: Neural Prompting Protein Structure Prediction via MSA Generative  Pre-Training

The field of protein folding research has been greatly advanced by deep
learning methods, with AlphaFold2 (AF2) demonstrating exceptional performance
and atomic-level precision. As co-evolution is integral to protein structure
prediction, AF2's accuracy is significantly influenced by the depth of multiple
sequence alignment (MSA), which requires extensive exploration of a large
protein database for similar sequences. However, not all protein sequences
possess abundant homologous families, and consequently, AF2's performance can
degrade on such queries, at times failing to produce meaningful results. To
address this, we introduce a novel generative language model, MSA-Augmenter,
which leverages protein-specific attention mechanisms and large-scale MSAs to
generate useful, novel protein sequences not currently found in databases.
These sequences supplement shallow MSAs, enhancing the accuracy of structural
property predictions. Our experiments on CASP14 demonstrate that MSA-Augmenter
can generate de novo sequences that retain co-evolutionary information from
inferior MSAs, thereby improving protein structure prediction quality on top of
strong AF2.

该研究利用语言生成模型 MSA-Augmenter 及大规模 MSA 技术，微调 AlphaFold2 蛋白质结构预测模型在缺乏同源家族数据库的情况下，生成补充性的未见过的蛋白质序列，从而提高蛋白质结构预测准确性。