HyperAttention and Linformer-Based β-catenin Sequence Prediction For Bone Formation.

Introduction Beta (β)-catenin, a pivotal protein in bone development and homeostasis, is implicated in various bone disorders. Peptide-based therapeutics offer a promising approach due to their specificity and potential for reduced side effects. Attention networks are widely used for peptide sequence prediction, specifically sequence-to-sequence models. Hence, the current study aims to develop a HyperAttention and informatics-based β-catenin sequence prediction for bone formation. Methods β-catenin protein sequences were downloaded and quality-checked using UniProt and FASTA sequences using DeepBio (Deep Bio Inc., Seoul, South Korea) for predictive analysis. Data was analyzed for duplicates, outliers, and missing values. The data was then split into training and testing sets, with 80% of the data used for training and 20% for testing, and peptide sequences were encoded and subjected to algorithms. Results The HyperAttention and Linformer models perform well in predictive sequence, with HyperAttention correctly predicting 87% of instances and Linformer predicting 89%. Both models have higher sensitivity and specificity, with Linformer showing better identification of 91% of negative instances and slightly better sensitivity. Conclusion The HyperAttention and Linformer models effectively predict peptide sequences with high specificity and sensitivity. Further optimization and development are needed for optimal application and balance between positive and negative instances.

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Human subjects: All authors have confirmed that this study did not involve human participants or tissue. Animal subjects: All authors have confirmed that this study did not involve animal subjects or tissue. Conflicts of interest: In compliance with the ICMJE uniform disclosure form, all authors declare the following: Payment/services info: All authors have declared that no financial support was received from any organization for the submitted work. Financial relationships: All authors have declared that they have no financial relationships at present or within the previous three years with any organizations that might have an interest in the submitted work. Other relationships: All authors have declared that there are no other relationships or activities that could appear to have influenced the submitted work.