Prediction of Secondary Structure of Proteins Using Sliding Window and Backpropagation Algorithm

Agarwal, Shivani; Singh, Vijander; Agarwal, Pankaj; Rani, Asha

doi:10.1007/978-981-13-1819-1_50

Cited by 1 publication

(1 citation statement)

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“…Future research could explore alternative convolutional layer designs Bianchi et al (2021); Thekumparampil et al (2018); Xu et al (2019) used in graph neural networks (GNNs) to potentially boost RNA secondary structure prediction accuracy. Additionally, adapting algorithms like sliding windows Agarwal et al (2019); Chen et al (2006) or leveraging RNA's physical properties, as demonstrated in AlphaFold Senior et al (2020Senior et al ( , 2019, are avenues worth exploring for enhanced performance given the long RNA sequences.…”

Section: Discussionmentioning

confidence: 99%

Graph Convolutional Network for predicting secondary structure of RNA

Korkin,

ammartayakun,

Busaranuvong

et al. 2024

Preprint

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The prediction of RNA secondary structures is essential for understanding its underlying principles and applications in diverse fields, including molecular diagnostics and RNA-based therapeutic strategies. However, the complexity of the search space presents a challenge. This work proposes a Graph Convolutional Network (GCNfold) for predicting the RNA secondary structure. GCNfold considers an RNA sequence as graph-structured data and predicts posterior base-pairing probabilities given the prior base-pairing probabilities, calculated using McCaskill’s partition function. The performance of GCNfold surpasses that of the state-of-the-art folding algorithms, as we have incorpo-rated minimum free energy information into the richly parameterized network, enhancing its robustness in predicting non-homologous RNA secondary structures. A Symmetric Argmax Post-processing algorithm ensures that GCNfold formulates valid structures. To validate our algo-rithm, we applied it to the SARS-CoV-2 E gene and determined the secondary structure of the E-gene across the Betacoronavirus subgenera.

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