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Transformer neural networks have revolutionized structural biology with the ability to predict protein structures at unprecedented high accuracy. Here, we report the predictive modeling performance of the state-of-the-art protein structure prediction methods built on transformers for 69 protein targets from the recently concluded 15th Critical Assessment of Structure Prediction (CASP15) challenge. Our study shows the power of transformers in protein structure modeling and highlights future areas of improvement.
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The landscape of RNA 3D structure modeling with transformer networks
Abstract Transformers are a powerful subclass of neural networks catalyzing the development of a growing number of computational methods for RNA structure modeling. Here, we conduct an objective and empirical study of the predictive modeling accuracy of the emerging transformer-based methods for RNA structure prediction. Our study reveals multi-faceted complementarity between the methods and underscores some key aspects that affect the prediction accuracy.
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- Award ID(s):
- 2208679
- PAR ID:
- 10523065
- Publisher / Repository:
- Oxford University Press
- Date Published:
- Journal Name:
- Biology Methods and Protocols
- Volume:
- 9
- Issue:
- 1
- ISSN:
- 2396-8923
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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