In this article, we explore a new method for predicting protein structure using deep learning. Our approach combines two key components: ESM embedding and CDConv embedding. The former captures the intricate patterns in the data, while the latter helps to refine the model’s performance. We conduct an ablation study to demonstrate the importance of each component, showing that ESM embedding plays a pivotal role in the model’s efficacy. Our proposed method outperforms existing methods on two distinct datasets, demonstrating its potential for accurately predicting protein structure.
Introduction
Protein structure prediction is a fundamental problem in biochemistry and biophysics. Existing methods often rely on simplistic models or heuristics, limiting their accuracy. In this work, we propose a novel deep learning approach that leverages two key components to improve the prediction of protein structure.
Method
Our method combines ESM embedding and CDConv embedding. ESM embedding captures the intricate patterns in the data, providing a rich representation of the sequential information. CDConv embedding refines the model’s performance by incorporating the spatial relationships between amino acids. We perform an ablation study to demonstrate the importance of each component and show that our proposed method outperforms existing methods on two distinct datasets.
Ablation Study
We compare the performance of our proposed method with a baseline model, PROSTATA, on two datasets: S669 and Ssym. Our results show that ESM embedding plays a crucial role in the model’s efficacy, while CDConv embedding contributes to its overall performance. When we remove the ESM embedding, the performance of our method degrades significantly, particularly in the PCC metric. On the other hand, excluding the CDConv embedding results in a more subtle performance degradation, but still notable, particularly in the PCC metric.
Conclusion
In summary, we propose a novel deep learning approach for predicting protein structure that combines ESM embedding and CDConv embedding. Our method outperforms existing methods on two distinct datasets, demonstrating its potential for accurately predicting protein structure. The ablation study reveals the importance of each component, highlighting the pivotal role of ESM embedding in the model’s efficacy. This work has important implications for understanding the complex relationships between amino acids in protein structures and could lead to further advances in protein engineering and drug design.
