融合结构知识的蛋白质预训练模型进展

汤天一; 熊翊名; 张睿格; 张建; 李文飞; 王骏; 王炜

doi:10.7498/aps.73.20240811

摘要

自然语言和图像处理领域引发的人工智能革命给蛋白质计算领域带来了新的思路和研究范式. 其中一个重大的进展是从海量蛋白质序列通过自监督学习得到预训练的蛋白质语言模型. 这类预训练模型编码了蛋白质的序列、进化、结构乃至功能等多种信息, 可方便地迁移至多种下游任务, 并展现了强大的泛化能力. 在此基础上, 人们正进一步发展融合更多种类数据的多模态预训练模型. 考虑到蛋白质结构是决定其功能的主要因素, 融合了结构信息的蛋白质预训练模型可更好地支持下游多种任务, 本文对这一方向的研究工作进行了介绍和总结. 此外, 还简介了融合先验知识的蛋白质预训练模型、RNA语言模型、蛋白质设计等方面的工作, 讨论了这些领域目前的现状、困难及可能的解决方案.

关键词:

Abstract

The AI revolution, sparked by natural language and image processing, has brought new ideas and research paradigms to the field of protein computing. One significant advancement is the development of pre-training protein language models through self-supervised learning from massive protein sequences. These pre-trained models encode various information about protein sequences, evolution, structures, and even functions, which can be easily transferred to various downstream tasks and demonstrate robust generalization capabilities. Recently, researchers have further developed multimodal pre-trained models that integrate more diverse types of data. The recent studies in this direction are summarized and reviewed from the following aspects in this paper. Firstly, the protein pre-training models that integrate protein structures into language models are reviewed: this is particularly important, for protein structure is the primary determinant of its function. Secondly, the pre-trained models that integrate protein dynamic information are introduced. These models may benefit downstream tasks such as protein-protein interactions, soft docking of ligands, and interactions involving allosteric proteins and intrinsic disordered proteins. Thirdly, the pre-trained models that integrate knowledge such as gene ontology are described. Fourthly, we briefly introduce pre-trained models in RNA fields. Finally, we introduce the most recent developments in protein designs and discuss the relationship of these models with the aforementioned pre-trained models that integrate protein structure information.

Keywords:

作者及机构信息

1.
南京大学物理学院, 南京　210093

2.
南京大学脑科学研究院, 南京　210093

通信作者: 张建, jzhang@nju.edu.cn ; 王炜, wangwei@nju.edu.cn

基金项目: 科技部科技创新项目(批准号: 2030-2021ZD0201300)和国家自然科学基金(批准号: 11934008)资助的课题.

Authors and contacts

1.
School of Physics, Nanjing University, Nanjing 210093, China

2.
Institute of Brain Science, Nanjing University, Nanjing 210093, China

Corresponding author: Zhang Jian, jzhang@nju.edu.cn ; Wang Wei, wangwei@nju.edu.cn

Funds: Project supported by the Science and Technology Innovation Project of the Ministry of Science and Technology (Grant No. 2030-2021ZD0201300) and the National Natural Science Foundation of China (Grant No. 11934008).

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施引文献

图 1 蛋白质多模态基础(预训练)模型及其应用 (只示意性列出若干下游任务)

Fig. 1. Protein multi-modal foundation (pre-trained) models and the downstream tasks.

下载: 全尺寸图片幻灯片

表 1 多模态蛋白质预训练模型

Table 1. Multimodal protein pre-trained models.

模型名	时间	模型	数据模态	预训练方法	训练集	参数量	算力要求	下游任务	文献
融合了结构信息的通用蛋白质预训练模型
Bepler &Berger	2019	Bi-LSTM	Sequence, structure	MLM for sequences, supervised learning for 3D structures	76M sequences, 28K structures	—	1X 32G-V100, 13 to 51 days	Fold classification transmembrane region prediction	[19,42]
Guo model	2022	CNN	Structure	Self-supervised pre-training on noised pair-distance	73K structures	—	—	QA, PPI	[43]
New IEConv	2022	GCN	Sequence, structure	Contrastive learning between randomly sampled 3D substructures	476K chains	30M	—	protein function prediction, protein fold classification, structural similarity prediction, protein-ligand binding affinity prediction	[44]
GearNet	2023	ESM-1b, GearNet	Sequence, structure	PLM, contrastive learning	805K structures from AlphaFoldDB	—	4X A100	Fold classification, EC, GO
STEPS	2023	BERT, GCN	Sequence, structure	PLM, supervised learning from 3D structures	40K structures	—	—	Membrane protein classification, cellular location prediction, EC
UNI-MOL	2023	Transformer	Sequence, structure	Atom 3D position denoise, masked atom type prediction	209M molecule conformations, 3.2M protein pockets structure	—	8X 32G-V100, 3 days	molecular property prediction, molecular conformation generation, pocket property prediction, protein-ligand binding pose prediction
SaProt	2023	BERT	Sequence, structure	Convert structures to structure-aware vocabulary, MLM	40M sequences and structures from PDB/AlphaFoldDB	650M	64X 80G-A100, 3 months	Thermostability, HumanPPI, Metal Ion Binding, EC, GO, DeepLoc, contact prediction	[51]
融合了结构信息的非通用蛋白质预训练模型
Evoformer	2021	Evoformer	Sequence, structure	MLM, Supervised learning	BPD+Uniclust30, PDB	—	128TPU-v3, 11 days	Structure prediction	[2]
DeepFRI	2021	LSTM+GCN	Sequence, structure	PLM(pretrained, frozen), supervised learning for 3D structures	10M sequences for pre-training	—	—	GO, EC, PPI interaction sites	[47]
LM-GVP	2022	Transformer +GVP	Sequence, structure	PLM(changeable), supervised learning for 3D structures	—	—	8X 32G-V100	fluorescence, protease stability, GO, mutational effects	[48]
ProNet	2023	GCN	Sequence, structure	Supervised learning	—	—	—	Fold classification, reaction classification, binding affinity, PI
HoloProt	2022	MPN	Sequence, structure surface	Supervised learning	—	1.8M	1X 1080Ti, 1 day	Ligand binding affinity, EC	[56]
编码动态三维结构信息的预训练模型
ProtMD	2022	E(3)-Equivariant Graph Matching Network	Sequence, structure trajectory	Self-supervised learning, atom-level prompt-based denoising generative task, conformation-level snapshot ordering task	62.8K snapshots from MD for 64 protein-ligand pairs	5.2M	4X V100	Binding affinity prediction, binary classification of ligand efficacy	[58]
融合了知识的蛋白质预训练模型
OntoProtein	2022	ProtBert, Gu-model	Sequence, knowledge	MLM, contrastive learning	ProteinKG25 with 5M knowledge triples	—	V100	TAPE, PPI, Protein function prediction	[60]
KeAP	2023	ProtBert, Gu-model	Sequence, knowledge	MLM	ProteinKG25	—	—	TAPE, PPI, Protein function prediction	[62]
ProtST	2023	ProtBert, ESM-1b, ESM-2, PubMedBert	Sequence, knowledge	MLM, Multimodal Representation Alignment, Multimodal Mask Prediction	ProtDescribe with 553K sequence-property pairs	—	4X V100	Protein localization prediction, Fitness landscape prediction, Protein function annotation	[63]
RNA语言模型
RNA-FM	2022.8	BERT	Sequence	MLM	RNAcentral, 23.7M ncRNA sequences	—	8X A100 80G, 1 month	SS prediction, 3D contact/distance map, 3D reconstruction, evolution study, RNA-protein interaction, MRL prediction	[78]
RNABert	2022	BERT	Sequence	MLM	RNAcentral (762K) & Rfam 14.3 dataset	—	V100	structural alignment, clustering	[86]
SpliceBERT	2023	BERT	Sequence	MLM	Pre-mRNA of 72 vertebrates, 2M sequences, 64B nucleotides	19.4M	8X V100, 1 week	multi-species splice site prediction, human branch point prediction	[79]
RNA-MSM	2023	MSA-transformer	Sequence	MLM	4069 RNA families from Rfam 14.7	—	8X V100 32G	SS prediction, solvent accessibility prediction	[83]
Uni-RNA	2023	BERT	Sequence	MLM	RNAcentral & nt & GWH (1billion sequences)	25—400M	128X A100	SS prediction, 3D structure prediction, MRL, Isoform percentage prediction on 3’UTR, splice site prediction, classification of ncRNA functional families, modification site prediction	[84]
RNAErnie	2024	ERNIE	Sequence, motif information	MLM at base/subsequence/motif level masking	RNAcentral, 23M ncRNA sequences	105M	4X V100 32G, 250 hours	sequence classification, RNA–RNA interaction, SS prediction	[85]
*PLM, protein language model; MLM, masked language model; GCN, graph convolutional network; GVP, geometric vector perceptrons; EC, enzyme commission number prediction; GO, gene ontology term prediction; PPI, protein-protein interaction; TAPE, the tasks assessing protein embeddings database; QA, quality assessment of structures; SS, secondary structure; MRL, mean ribosome load prediction in mRNA.

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