Protein function prediction stands as a pivotal challenge within the realms of modern biology and bioinformatics, crucial for understanding protein roles and interactions in biological systems. This understanding is essential for identifying drug targets, deciphering disease mechanisms, and enhancing biotechnological applications. Although recent years have seen significant improvements in predicting protein structures, accurately predicting protein functions has remained a more complex endeavor. This complexity arises from the limited number of known functions, their intricate nature, and the interactions between proteins.
Gene Ontology and Protein Function Prediction
The Gene Ontology (GO) plays a foundational role in describing protein functions, encompassing molecular functions, biological processes, and cellular components. Traditional methods for function prediction have leveraged various data sources, including protein sequences, interactions, structures, and literature. Despite the variety of approaches, a common limitation has been the reliance on sequence similarity, which may not always be reliable, especially for proteins lacking sequence similarity to known functional domains.
Introducing DeepGO-SE
DeepGO-SE emerges as a novel method that transcends traditional limitations by combining a pretrained large language model with a neuro-symbolic model. This approach leverages the axioms of GO for knowledge-enhanced machine learning, specifically targeting proteins with no significant similarity to those in training datasets. By exploiting the formal theory and over 100,000 axioms of GO, DeepGO-SE aims to improve function prediction through a form of approximate semantic entailment, thereby addressing the nuanced differences between the sub-ontologies of GO.
The development of DeepGO-SE is a collaborative effort, with contributions from researchers across prestigious institutions, including the King Abdullah University of Science and Technology (KAUST) and the SIB Swiss Institute of Bioinformatics. The team’s work signifies a step forward in the utilization of machine learning for protein function prediction, underscoring the potential of integrating ontological knowledge into computational models.
DeepGO-SE not only enhances the prediction of molecular functions but also offers new avenues for understanding complex biological processes and cellular components. By incorporating information about an organism’s proteome and interactome, DeepGO-SE demonstrates substantial improvements in predicting annotations to biological processes and cellular components, leveraging protein-protein interactions to expand the predictive capabilities beyond molecular functions alone.
Incorporation of Protein Sequence Features and Background Knowledge:
- Protein Sequence Features: DeepGO-SE utilizes features generated by a pre-trained protein language model. These features capture the complex patterns in amino acid sequences that are indicative of the protein’s function. A protein language model is trained on vast databases of known protein sequences, learning to predict properties and functions based on sequence patterns.
- Background Knowledge from GO: The Gene Ontology (GO) provides a comprehensive framework categorizing protein functions into a hierarchical structure, covering biological processes, molecular functions, and cellular components. DeepGO-SE integrates this structured knowledge to enhance prediction accuracy. This integration allows the model to leverage existing information about protein functions and relationships.
- Interactions Between Proteins: Protein-protein interactions (PPIs) are crucial for understanding protein functions, as proteins often work together in networks to perform biological activities. DeepGO-SE incorporates data on these interactions, improving its ability to predict the functions of proteins by understanding their context within cellular processes.
Three Main Conclusions:
- Knowledge-Enhanced Machine Learning Methods: These methods, which integrate background knowledge (like GO annotations and PPI data), outperform traditional approaches that rely solely on raw sequence data. This highlights the value of incorporating rich, structured biological knowledge into machine learning models.
- Hierarchical Prediction Approach: The prediction of GO functions is most effective when using a separate, hierarchical approach. This method respects the structured nature of GO, allowing for more precise and context-aware predictions.
- Generalization Capability of ESM2-Based Models: Models based on ESM2 (a type of protein language model) embeddings can generalize well to proteins that have not been seen during training. This means they can predict functions for novel or less-studied proteins with a high degree of accuracy.
Prediction of Biological Processes and Cellular Components:
- DeepGO-SE can predict a protein’s role in biological processes and its cellular component based solely on its amino acid sequence. However, predictions improve significantly when the sequence data is combined with information on protein-protein interactions (PPIs). The limitation here is that many novel proteins lack known PPIs, which restricts the effectiveness of this combined approach.
PPI Prediction:
- There’s an identified need for methods capable of accurately predicting PPIs for novel proteins, using only their sequence data. Future iterations of DeepGO-SE plan to integrate predictors that can infer these interactions from protein sequences and structures, potentially overcoming the current limitations faced by novel proteins without known interactions.
Zero-shot Predictions and Speed:
- Zero-shot Predictions: DeepGO-SE can make accurate predictions for proteins without requiring any direct example of the protein’s function, known as zero-shot learning. This capability is crucial for studying newly discovered or poorly characterized proteins.
- Speed Advantage: Unlike methods that depend on multiple sequence alignments (a time-consuming process), DeepGO-SE uses ESM2 embeddings, which are quicker to compute. This makes DeepGO-SE faster in obtaining predictions compared to other methods, enhancing its utility for large-scale protein function analysis.
Conclusion and Future Directions
The advent of DeepGO-SE represents a significant advancement in the field of protein function prediction, highlighting the importance of integrating diverse sources of information and ontological knowledge into computational models. As protein function prediction continues to evolve, methods like DeepGO-SE will play a crucial role in unlocking new biological insights and fostering developments across drug discovery, disease understanding, and biotechnology.
This innovative approach, detailed in their publications on bioRxiv and Research Square, has been recognized for its contribution to computational biology and bioinformatics, receiving publication in Nature Machine Intelligence in February 2024. As the field progresses, DeepGO-SE and similar methodologies will undoubtedly pave the way for more accurate, efficient, and comprehensive predictions of protein functions, further bridging the gap between computational predictions and biological realities.
reference link:https://www.nature.com/articles/s42256-024-00795-w
