DeepGO-SE: Protein function prediction as Approximate Semantic Entailment

Kulmanov, Maxat; Guzmán-Vega, Francisco J.; Roggli, Paula Duek; Lane, Lydie; Arold, Stefan T.; Hoehndorf, Robert

doi:10.1101/2023.09.26.559473

Cited by 4 publications

(3 citation statements)

References 51 publications

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“…A variety of compared methods are summarized in Table 4, from simplest naï ve methods to newest state-of-the-art method DeepGO-SE (43). AI-based methods mainly rely on protein features or protein sequences as inputs, supplemented with information like PPI or semantic sometimes.…”

Section: Competing Methodsmentioning

confidence: 99%

FAPM: Functional Annotation of Proteins using Multi-Modal Models Beyond Structural Modeling

Xiang,

Xiong,

Chen

et al. 2024

Preprint

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Assigning appropriate property labels, such as functional terms and catalytic activity, to proteins, remains a significant challenge, particularly for the non-homologous ones. In contrast to prior approaches that mostly focused on protein sequence features, we employ pretrained protein language model to encode the sequence features, and natural language model for the semantic information of property descriptions. Specifically, we present FAPM, a contrastive model between natural language and protein sequence language, which combines the pretrained protein sequence model with the pretrained large language model to generate labels such as GO functional terms and catalytic activity prediction in the format of natural language. Our result shows that FAPM has learned superior representations related to protein properties, outperforming protein sequence-based or structure-based models. Our model achieves state-of-the-art performance both on public benchmarks and on experimentally-annotated phage proteins that have limited known homologous sequences. Furthermore, we demonstrate the flexibility of our model to accept additional free text prompts as input, such as easily accessible taxonomy information, not only enhances its predictive performance but also improves the model's explainability. Our methodology presents a novel avenue for exploration, characterized by its potential to supersede current methodologies reliant on multiple sequence alignment for protein annotation.

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Section: Competing Methodsmentioning

confidence: 99%

FAPM: Functional Annotation of Proteins using Multi-Modal Models Beyond Structural Modeling

Xiang,

Xiong,

Chen

et al. 2024

Preprint

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“…Microbial samples are complex and contain many uncharacterized proteins. Previously, we developed DeepGO-SE [33], a method for protein function prediction using protein sequence embeddings generated by ESM2 [9] and approximate semantic entailment. We showed that DeepGO-SE can be applied to uncharacterized proteins; however, since it is trained on all experimentally annotated proteins form UniProt-KB/Swissprot database, many of the functions it predicts are not relevant to microbiomes and exist only in eukaryotic genomes.…”

Section: Deepgometamentioning

confidence: 99%

“…This is to ensure that our model is robust and effective in predicting the functions of these newly discovered proteins. We did this by comparing DeepGOMeta predictions on the newly annotated proteins with other state-of-the-art methods that predict functions based on protein language model embeddings and transformer-based deep learning models, including TALE [17], SPROF-GO [18] and DeepGO-SE [33]. We found that DeepGOMeta outperforms the DeepGO-SE method in all three sub-ontology evaluations and performs better than all the compared methods in the BPO and CCO evaluations in terms of F max and S min .…”

Section: Evaluation and Comparison On The Time-based Splitmentioning

confidence: 99%

DeepGOMeta: Predicting functions for microbes

Tawfiq,

Niu,

Hoehndorf

et al. 2024

Preprint

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Analyzing microbial samples remains computationally challenging due to their diversity and complexity. The lack of robustde novoprotein function prediction methods exacerbates the difficulty in deriving functional insights from these samples. Traditional prediction methods, dependent on homology and sequence similarity, often fail to predict functions for novel proteins and proteins without known homologs. Moreover, most of these methods have been trained on largely eukaryotic data, and have not been evaluated or applied to microbial datasets. This research introduces DeepGOMeta, a deep learning model designed for protein function prediction, as Gene Ontology (GO) terms, trained on a dataset relevant to microbes. The model is validated using novel evaluation strategies and applied to diverse microbial datasets. Data and code are available athttps://github.com/bio-ontology-research-group/deepgometa

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DeepGOMeta for functional insights into microbial communities using deep learning-based protein function prediction

Tawfiq,

Niu,

Hoehndorf

et al. 2024

Sci Rep

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Analyzing microbial samples remains computationally challenging due to their diversity and complexity. The lack of robust de novo protein function prediction methods exacerbates the difficulty in deriving functional insights from these samples. Traditional prediction methods, dependent on homology and sequence similarity, often fail to predict functions for novel proteins and proteins without known homologs. Moreover, most of these methods have been trained on largely eukaryotic data, and have not been evaluated on or applied to microbial datasets. This research introduces DeepGOMeta, a deep learning model designed for protein function prediction as Gene Ontology (GO) terms, trained on a dataset relevant to microbes. The model is applied to diverse microbial datasets to demonstrate its use for gaining biological insights. Data and code are available at https://github.com/bio-ontology-research-group/deepgometa

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DeepGO-SE: Protein function prediction as Approximate Semantic Entailment

Cited by 4 publications

References 51 publications

FAPM: Functional Annotation of Proteins using Multi-Modal Models Beyond Structural Modeling

FAPM: Functional Annotation of Proteins using Multi-Modal Models Beyond Structural Modeling

DeepGOMeta: Predicting functions for microbes

DeepGOMeta for functional insights into microbial communities using deep learning-based protein function prediction

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