Protein function prediction is a challenging task in the field of bioinformatics. While protein structure prediction has made significant progress, accurately determining the function of a protein remains limited by the scarcity of available function annotations and the complexity of protein interactions. However, a recent study published in the journal Nature Machine Intelligence introduces a novel method called DeepGO-SE that aims to address these challenges.
DeepGO-SE leverages a large, pre-trained protein language model to predict gene ontology (GO) functions from protein sequences. Unlike traditional methods that rely on sequence similarity, DeepGO-SE incorporates background knowledge from formal axioms in GOs to improve predictions. By using semantic entailment, which considers the truthfulness of multiple approximate models, DeepGO-SE surpasses baseline methods in predicting protein functions.
The researchers evaluated DeepGO-SE using the UniProtKB/Swiss-Prot dataset and found that it outperformed other methods in all three sub-ontologies of GO. In terms of molecular functions, DeepGO-SE achieved a maximum F measure (F max) of 0.554, surpassing DeepGoZero and MLP methods. For biological processes, DeepGO-SE’s F max (0.432) was 8% higher than DeepGraphGO. In cellular components, DeepGO-SE attained an F max of 0.721.
To further enhance the predictions, the researchers modified the input vectors of DeepGO-SE by incorporating additional information about protein-protein interactions (PPIs). This integration improved the prediction of biological processes while slightly decreasing the accuracy of molecular functions. Notably, DeepGO-SE demonstrated superior performance when compared to other methods in predicting protein functions using the neXtPro dataset.
An ablation study was also conducted to assess the contribution of individual components to the models. The removal of specific loss functions resulted in varied impacts on performance, highlighting the importance of different components for accurate predictions.
In conclusion, DeepGO-SE presents a promising approach to protein function prediction by leveraging a pre-trained protein language model, GO background knowledge, and PPI information. This method demonstrates superior performance compared to existing methods and shows potential for further improvements in the field of bioinformatics. As the study emphasizes the need for methods that predict interactions for novel proteins, future research may focus on developing algorithms that rely solely on protein sequences to overcome this limitation.
FAQ Section:
1. What is protein function prediction?
Protein function prediction is the task of determining the specific role or function of a protein within a biological system.
2. What are the challenges in protein function prediction?
The scarcity of available function annotations and the complexity of protein interactions pose challenges in accurately determining protein function.
3. What is DeepGO-SE?
DeepGO-SE is a novel method that uses a large, pre-trained protein language model to predict gene ontology (GO) functions from protein sequences. It incorporates background knowledge from formal axioms in GOs to improve predictions.
4. How does DeepGO-SE differ from traditional methods?
Unlike traditional methods that rely on sequence similarity, DeepGO-SE leverages semantic entailment to consider the truthfulness of multiple approximate models, resulting in improved predictions.
5. How was DeepGO-SE evaluated?
DeepGO-SE was evaluated using the UniProtKB/Swiss-Prot dataset and outperformed other methods in all three sub-ontologies of GO, including molecular functions, biological processes, and cellular components.
6. What is the impact of incorporating protein-protein interaction information?
By incorporating additional information about protein-protein interactions (PPIs), DeepGO-SE improved the prediction of biological processes while slightly decreasing the accuracy of molecular functions.
7. How does DeepGO-SE compare to other methods?
DeepGO-SE demonstrated superior performance compared to other methods, including DeepGoZero, MLP, and DeepGraphGO, in predicting protein functions using the neXtPro dataset.
8. What was the result of the ablation study?
The ablation study revealed that the removal of specific loss functions had varied impacts on performance, highlighting the importance of different components for accurate predictions.
9. What is the potential of DeepGO-SE?
DeepGO-SE presents a promising approach to protein function prediction and has the potential for further improvements in the field of bioinformatics. It addresses the need for methods that predict interactions for novel proteins.
Definitions:
– Bioinformatics: The use of computational methods to analyze biological data, particularly in the field of genomics.
– Protein sequences: A series of amino acids that make up a protein.
– Gene Ontology (GO): A standardized system of defining and categorizing gene and protein functions.
– Semantic entailment: The relationship between two statements where the truth of one statement logically implies the truth of another statement.
– UniProtKB/Swiss-Prot: A comprehensive protein sequence database containing fully annotated and reviewed entries.
– Protein-protein interactions (PPIs): The physical interactions between two or more proteins within a cell.
Suggested related links:
– Nature Machine Intelligence
– UniProtKB
The source of the article is from the blog japan-pc.jp