Proteins perform much of the work in living organisms, and consequently the
development of efficient computational methods for protein representation is
essential for advancing large-scale biological research. Most current
approaches struggle to efficiently integrate the wealth of information
contained in the protein sequence and structure. In this paper, we propose a
novel framework for embedding protein graphs in geometric vector spaces, by
learning an encoder function that preserves the structural distance between
protein graphs. Utilizing Graph Neural Networks (GNNs) and Large Language
Models (LLMs), the proposed framework generates structure- and sequence-aware
protein representations. We demonstrate that our embeddings are successful in
the task of comparing protein structures, while providing a significant
speed-up compared to traditional approaches based on structural alignment. Our
framework achieves remarkable results in the task of protein structure
classification; in particular, when compared to other work, the proposed method
shows an average F1-Score improvement of 26% on out-of-distribution (OOD)
samples and of 32% when tested on samples coming from the same distribution as
the training data. Our approach finds applications in areas such as drug
prioritization, drug re-purposing, disease sub-type analysis and elsewhere.

本文提出了一种利用图神经网络和自然语言大模型生成结构和序列感知型蛋白质表示的新框架，并将其与传统的基于结构对齐的方法相比较，证明了该方法在比较蛋白质结构和蛋白质结构分类任务中的优越性能。

蛋白质图形的神经嵌入

Neural Embeddings for Protein Graphs

We reformulate minimalist grammars as partial functions on term algebras for
strings and trees. Using filler/role bindings and tensor product
representations, we construct homomorphisms for these data structures into
geometric vector spaces. We prove that the structure-building functions as well
as simple processors for minimalist languages can be realized by piecewise
linear operators in representation space. We also propose harmony, i.e. the
distance of an intermediate processing step from the final well-formed state in
representation space, as a measure of processing complexity. Finally, we
illustrate our findings by means of two particular arithmetic and fractal
representations.

本文探讨了极简主义语法的改进与应用，通过创建同态关系将语言结构实现为几何向量空间中的线性算子，并使用中间状态与最终结构复杂度的距离来度量处理复杂度。