.png){kind=icon}
返回
GPSFun: geometry-aware protein sequence function predictions with language models
.png){kind=icon}
OA
AI
DOI：10.1093/nar/gkae381.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Knowledge of protein function is essential for elucidating disease mechanisms and discovering new drug targets. However, there is a widening gap between the exponential growth of protein sequences and their limited function annotations. In our prior studies, we have developed a series of methods including GraphPPIS, GraphSite, LMetalSite and SPROF-GO for protein function annotations at residue or protein level. To further enhance their applicability and performance, we now present GPSFun, a versatile web server for Geometry-aware Protein Sequence Function annotations, which equips our previous tools with language models and geometric deep learning. Specifically, GPSFun employs large language models to efficiently predict 3D conformations of the input protein sequences and extract informative sequence embeddings. Subsequently, geometric graph neural networks are utilized to capture the sequence and structure patterns in the protein graphs, facilitating various downstream predictions including protein-ligand binding sites, gene ontologies, subcellular locations and protein solubility. Notably, GPSFun achieves superior performance to state-of-the-art methods across diverse tasks without requiring multiple sequence alignments or experimental protein structures. GPSFun is freely available to all users at https://bio-web1.nscc-gz.cn/app/GPSFun with user-friendly interfaces and rich visualizations. Graphical Abstract
Keyword:
BINDING-PROTEINS
STRUCTURE ALIGNMENT
DOMAIN ANTIBODIES
RECEPTOR
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
13.1
论文数:
3.6W
被引数:
29.0W
机构
引用论文
GraphBind: protein structural context embedded rules learned by hierarchical graph neural networks for recognizing nucleic-acid-binding residuesGraphBind: 通过层次图神经网络学习的蛋白质结构上下文嵌入规则，用于识别核酸结合残基.png)
NUCLEIC ACIDS RESEARCHOAAI
NUCLEIC ACIDS RESEARCHIF13.1
OAAI分享收藏Fast and accurate protein function prediction from sequence through pretrained language model and homology-based label diffusion通过预训练的语言模型和基于同源性的标签扩散从序列中快速准确地预测蛋白质功能
OAAI
OAAI分享收藏NetGO: improving large-scale protein function prediction with massive network informationNUCLEIC ACIDS RESEARCHOAAI
NUCLEIC ACIDS RESEARCHIF13.1
OAAI分享收藏Mol* Viewer: modern web app for 3D visualization and analysis of large biomolecular structuresMol * Viewer: 用于3D可视化和分析大型生物分子结构的现代web应用程序NUCLEIC ACIDS RESEARCHOAAI
NUCLEIC ACIDS RESEARCHIF13.1
OAAI分享收藏
查看更多内容