Large-Scale Multi-omic Biosequence Transformers for Modeling Protein-Nucleic Acid Interactions.

Area of Science:

• Bioinformatics
• Computational Biology
• Molecular Biology

Background:

• Transformer models have advanced biomolecule analysis, but typically focus on single data types (proteins or nucleic acids).
• This single-omic approach limits the models' ability to capture interactions between different biological molecules.
• Existing models struggle to integrate diverse biological sequence data effectively.

Purpose of the Study:

• To introduce OmniBioTE, the largest open-source multi-omic transformer model.
• To demonstrate the capability of a unified model to learn joint representations from mixed protein and nucleic acid sequences.
• To establish a new benchmark for multi-omic sequence analysis and prediction.

Main Methods:

• Trained OmniBioTE on over 250 billion tokens of mixed protein and nucleic acid sequence data.
• Evaluated OmniBioTE's ability to learn representations consistent with the central dogma of molecular biology.
• Assessed performance on predicting binding free energy changes (ΔG) and identifying protein-nucleic acid interaction sites.

Main Results:

• OmniBioTE learned joint representations reflecting the central dogma without explicit biological labels.
• Achieved state-of-the-art results in predicting protein-nucleic acid binding free energy (ΔG).
• Demonstrated emergent learning of structural information, predicting residues involved in binding interactions.
• Outperformed single-omic models in both multi-omic and single-omic tasks on a performance-per-FLOP basis.

Conclusions:

• Multi-omic transformer models like OmniBioTE offer a powerful unified approach for biological sequence analysis.
• Integrating diverse omics data enhances predictive accuracy and reveals deeper biological insights.
• OmniBioTE sets a new standard for open-source tools in multi-omic bioinformatics.