De novo atomic protein structure modeling for cryoEM density maps using 3D transformer and HMM

Giri, Nabin; Cheng, Jianlin

doi:10.1038/s41467-024-49647-6

Cited by 7 publications

References 27 publications

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Deep learning methods for protein function prediction

Boadu,

Lee,

Cheng

2024

Proteomics

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Predicting protein function from protein sequence, structure, interaction, and other relevant information is important for generating hypotheses for biological experiments and studying biological systems, and therefore has been a major challenge in protein bioinformatics. Numerous computational methods had been developed to advance protein function prediction gradually in the last two decades. Particularly, in the recent years, leveraging the revolutionary advances in artificial intelligence (AI), more and more deep learning methods have been developed to improve protein function prediction at a faster pace. Here, we provide an in‐depth review of the recent developments of deep learning methods for protein function prediction. We summarize the significant advances in the field, identify several remaining major challenges to be tackled, and suggest some potential directions to explore. The data sources and evaluation metrics widely used in protein function prediction are also discussed to assist the machine learning, AI, and bioinformatics communities to develop more cutting‐edge methods to advance protein function prediction.

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Deep learning methods for protein function prediction

Boadu,

Lee,

Cheng

2024

Proteomics

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Single particle cryo-EM map and model validation: It's not crystal clear

Lander

2024

Current Opinion in Structural Biology

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Deep learning model for protein multi-label subcellular localization and function prediction based on multi-task collaborative training

Bai,

Li,

Luo

et al. 2024

Briefings in Bioinformatics

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The functional study of proteins is a critical task in modern biology, playing a pivotal role in understanding the mechanisms of pathogenesis, developing new drugs, and discovering novel drug targets. However, existing computational models for subcellular localization face significant challenges, such as reliance on known Gene Ontology (GO) annotation databases or overlooking the relationship between GO annotations and subcellular localization. To address these issues, we propose DeepMTC, an end-to-end deep learning-based multi-task collaborative training model. DeepMTC integrates the interrelationship between subcellular localization and the functional annotation of proteins, leveraging multi-task collaborative training to eliminate dependence on known GO databases. This strategy gives DeepMTC a distinct advantage in predicting newly discovered proteins without prior functional annotations. First, DeepMTC leverages pre-trained language model with high accuracy to obtain the 3D structure and sequence features of proteins. Additionally, it employs a graph transformer module to encode protein sequence features, addressing the problem of long-range dependencies in graph neural networks. Finally, DeepMTC uses a functional cross-attention mechanism to efficiently combine upstream learned functional features to perform the subcellular localization task. The experimental results demonstrate that DeepMTC outperforms state-of-the-art models in both protein function prediction and subcellular localization. Moreover, interpretability experiments revealed that DeepMTC can accurately identify the key residues and functional domains of proteins, confirming its superior performance. The code and dataset of DeepMTC are freely available at https://github.com/ghli16/DeepMTC.

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De novo atomic protein structure modeling for cryoEM density maps using 3D transformer and HMM

Cited by 7 publications

References 27 publications

Deep learning methods for protein function prediction

Deep learning methods for protein function prediction

Single particle cryo-EM map and model validation: It's not crystal clear

Deep learning model for protein multi-label subcellular localization and function prediction based on multi-task collaborative training

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