Protein research is crucial in various fundamental disciplines, but
understanding their intricate structure-function relationships remains
challenging. Recent Large Language Models (LLMs) have made significant strides
in comprehending task-specific knowledge, suggesting the potential for
ChatGPT-like systems specialized in protein to facilitate basic research. In
this work, we introduce ProtChatGPT, which aims at learning and understanding
protein structures via natural languages. ProtChatGPT enables users to upload
proteins, ask questions, and engage in interactive conversations to produce
comprehensive answers. The system comprises protein encoders, a
Protein-Language Pertaining Transformer (PLP-former), a projection adapter, and
an LLM. The protein first undergoes protein encoders and PLP-former to produce
protein embeddings, which are then projected by the adapter to conform with the
LLM. The LLM finally combines user questions with projected embeddings to
generate informative answers. Experiments show that ProtChatGPT can produce
promising responses to proteins and their corresponding questions. We hope that
ProtChatGPT could form the basis for further exploration and application in
protein research. Code and our pre-trained model will be publicly available.

ProtChatGPT 通过自然语言学习和理解蛋白质结构，使用户能够上传蛋白质、提问和进行互动对话以生成全面答案，为蛋白质研究提供了新的可能性。

ProtChatGPT: 面向理解蛋白质的大型语言模型

ProtChatGPT: Towards Understanding Proteins with Large Language Models

Coarse-grained (CG) models play a crucial role in the study of protein
structures, protein thermodynamic properties, and protein conformation
dynamics. Due to the information loss in the coarse-graining process,
backmapping from CG to all-atom configurations is essential in many protein
design and drug discovery applications when detailed atomic representations are
needed for in-depth studies. Despite recent progress in data-driven backmapping
approaches, devising a backmapping method that can be universally applied
across various CG models and proteins remains unresolved. In this work, we
propose BackDiff, a new generative model designed to achieve generalization and
reliability in the protein backmapping problem. BackDiff leverages the
conditional score-based diffusion model with geometric representations. Since
different CG models can contain different coarse-grained sites which include
selected atoms (CG atoms) and simple CG auxiliary functions of atomistic
coordinates (CG auxiliary variables), we design a self-supervised training
framework to adapt to different CG atoms, and constrain the diffusion sampling
paths with arbitrary CG auxiliary variables as conditions. Our method
facilitates end-to-end training and allows efficient sampling across different
proteins and diverse CG models without the need for retraining. Comprehensive
experiments over multiple popular CG models demonstrate BackDiff's superior
performance to existing state-of-the-art approaches, and generalization and
flexibility that these approaches cannot achieve. A pretrained BackDiff model
can offer a convenient yet reliable plug-and-play solution for protein
researchers, enabling them to investigate further from their own CG models.

通过生成模型和扩散模型，我们提出了一种新的反映蛋白质映射问题的方法，该方法能够在各种粗粒化模型和蛋白质之间实现通用性和可靠性，并在多种流行的粗粒化模型上展现出卓越的性能，为蛋白质研究提供了一种便捷而可靠的即插即用解决方案。

Backdiff：一种用于广义可转移蛋白质逆向映射的扩散模型

Backdiff: a diffusion model for generalized transferable protein  backmapping

Recent advancements in specialized large-scale architectures for training
image and language have profoundly impacted the field of computer vision and
natural language processing (NLP). Language models, such as the recent ChatGPT
and GPT4 have demonstrated exceptional capabilities in processing, translating,
and generating human languages. These breakthroughs have also been reflected in
protein research, leading to the rapid development of numerous new methods in a
short time, with unprecedented performance. Language models, in particular,
have seen widespread use in protein research, as they have been utilized to
embed proteins, generate novel ones, and predict tertiary structures. In this
book chapter, we provide an overview of the use of protein generative models,
reviewing 1) language models for the design of novel artificial proteins, 2)
works that use non-Transformer architectures, and 3) applications in directed
evolution approaches.

该研究总结了在蛋白质研究中使用语言模型的应用，包括设计新型人工蛋白质、使用非 Transformer 结构以及应用于定向进化方面。这些成果已经快速提升了蛋白质研究的发展和性能。