Protein Sequence Estimation Using Natural Language Processing Technique

Proteins play an important role in various biological processes, and understanding protein sequences is essential for applications such as disease research, drug discovery, and molecular biology. Traditional bioinformatics methods for protein sequence analysis are computationally expensive and require significant processing time. This paper presents a protein sequence estimation approach using Natural Language Processing (NLP) models, including ProtBERT, ProtT5, and Ankh3. These transformer-based models learn contextual relationships between amino acids and enable efficient protein sequence prediction. The proposed approach aims to improve prediction accuracy while reducing computational complexity compared to conventional methods. By combining NLP with bioinformatics, this work provides a simple and scalable solution for protein sequence analysis and supports future research in computational biology.