Predicting Distance matrix with large language models

ArXiv ID: 2409.16333 “View on arXiv”

Authors: Unknown

Abstract

Structural prediction has long been considered critical in RNA research, especially following the success of AlphaFold2 in protein studies, which has drawn significant attention to the field. While recent advances in machine learning and data accumulation have effectively addressed many biological tasks, particularly in protein related research. RNA structure prediction remains a significant challenge due to data limitations. Obtaining RNA structural data is difficult because traditional methods such as nuclear magnetic resonance spectroscopy, Xray crystallography, and electron microscopy are expensive and time consuming. Although several RNA 3D structure prediction methods have been proposed, their accuracy is still limited. Predicting RNA structural information at another level, such as distance maps, remains highly valuable. Distance maps provide a simplified representation of spatial constraints between nucleotides, capturing essential relationships without requiring a full 3D model. This intermediate level of structural information can guide more accurate 3D modeling and is computationally less intensive, making it a useful tool for improving structural predictions. In this work, we demonstrate that using only primary sequence information, we can accurately infer the distances between RNA bases by utilizing a large pretrained RNA language model coupled with a well trained downstream transformer.

Keywords: RNA structure prediction, pretrained RNA language model, transformer, distance maps, biological sequence analysis, Non-financial (Biotech/Computational Biology)

Complexity vs Empirical Score

• Math Complexity: 7.0/10
• Empirical Rigor: 3.0/10
• Quadrant: Lab Rats
• Why: The paper involves advanced ML concepts like transformer architectures, attention mechanisms, and SVD-based matrix decomposition, but relies on a small downstream dataset (~2k sequences) without backtesting or statistical metrics for financial application.

  flowchart TD
    A["Research Goal: Predict RNA Distance Maps<br>Using Only Sequence Data"] --> B["Input Data: RNA Sequences"]
    B --> C["Methodology: Pretrained RNA Language Model"]
    C --> D["Computational Process: Fine-tune Downstream Transformer"]
    D --> E["Prediction: Nucleotide Distance Matrix"]
    E --> F["Outcome: Accurate Structural Constraints<br>for 3D Modeling"]
    E --> G["Outcome: Reduced Computational Cost<br>vs. Full 3D Prediction"]
    F & G --> H["Impact: Advances in RNA Research<br>via Bio-linguistic Analysis"]