Using Neural Networks to Forecast the Configuration of Proteins

Predicting the secondary structure of proteins continues to be a significant hurdle in the field of bioinformatics. This anticipation plays a crucial role as an intermediary stage in addressing the challenge of predicting the tertiary structure of proteins, which is instrumental in determining their functions. This prediction holds the potential to facilitate drug development and contribute to the identification of viral diseases. One can forecast the secondary structure of a protein by examining its primary components, including the amino acid sequence and various additional factors. Through the examination of established sequences and recognized protein types, it becomes feasible to anticipate unfamiliar sequences. The objective of this article is to enhance the forecast accuracy of protein secondary structure by adjusting the current code, aiming to reach an 80% accuracy rate.