Kernel Regressive Shuffling Shepherd Quantum Artificial Intelligence for Protein Structure Prediction

Protein structure identification is the process of finding the protein structure from the amino acid sequence in biomedical field. Protein structure prediction helps to learn how the protein interacts with other molecules and practitioners for finding the treatment process. Many researchers carried out their research on different protein structure prediction methods. But the accuracy level was not improved, and time complexity and root mean square error was not reduced. To address these issues, a new method called Kernel Regressive Shuffling Shepherd Quantum Artificial Intelligence (KRSSQAI) method is introduced. The quantitatively analyzed results demonstrate that the proposed method achieves higher protein structure prediction accuracy with reduced time consumption compared to existing methods.