With the rise of Transformers and Large Language Models (LLMs) in Chemistry
and Biology, new avenues for the design and understanding of therapeutics have
opened up to the scientific community. Protein sequences can be modeled as
language and can take advantage of recent advances in LLMs, specifically with
the abundance of our access to the protein sequence datasets. In this paper, we
developed the GPCR-BERT model for understanding the sequential design of G
Protein-Coupled Receptors (GPCRs). GPCRs are the target of over one-third of
FDA-approved pharmaceuticals. However, there is a lack of comprehensive
understanding regarding the relationship between amino acid sequence, ligand
selectivity, and conformational motifs (such as NPxxY, CWxP, E/DRY). By
utilizing the pre-trained protein model (Prot-Bert) and fine-tuning with
prediction tasks of variations in the motifs, we were able to shed light on
several relationships between residues in the binding pocket and some of the
conserved motifs. To achieve this, we took advantage of attention weights, and
hidden states of the model that are interpreted to extract the extent of
contributions of amino acids in dictating the type of masked ones. The
fine-tuned models demonstrated high accuracy in predicting hidden residues
within the motifs. In addition, the analysis of embedding was performed over 3D
structures to elucidate the higher-order interactions within the conformations
of the receptors.

本研究使用 Transformers 和 Large Language Models (LLMs) 开发了 GPCR-BERT 模型，用于理解 G 蛋白偶联受体 (GPCRs) 的顺序设计，揭示了氨基酸序列、配体选择性和构象模体之间的关系，并通过对模型的注意权重和隐藏状态的解释，实现了在掩蔽氨基酸类型方面的精确预测，同时分析了三维结构中的高阶相互作用。

GPCR-BERT：使用蛋白质语言模型解释 G 蛋白偶联受体的连续设计

GPCR-BERT: Interpreting Sequential Design of G Protein Coupled Receptors  Using Protein Language Models

Deep learning is catalyzing a scientific revolution fueled by big data,
accessible toolkits, and powerful computational resources, impacting many
fields including protein structural modeling. Protein structural modeling, such
as predicting structure from amino acid sequence and evolutionary information,
designing proteins toward desirable functionality, or predicting properties or
behavior of a protein, is critical to understand and engineer biological
systems at the molecular level. In this review, we summarize the recent
advances in applying deep learning techniques to tackle problems in protein
structural modeling and design. We dissect the emerging approaches using deep
learning techniques for protein structural modeling, and discuss advances and
challenges that must be addressed. We argue for the central importance of
structure, following the "sequence -> structure -> function" paradigm. This
review is directed to help both computational biologists to gain familiarity
with the deep learning methods applied in protein modeling, and computer
scientists to gain perspective on the biologically meaningful problems that may
benefit from deep learning techniques.

该综述总结了近年来将深度学习技术应用于蛋白质结构建模及设计问题的最新进展，讨论了已经出现和需解决的挑战，针对该领域提出了许多有益的见解。