Protein Secondary Structure Prediction

Protein secondary structure prediction is a key task in bioinformatics, aiming to determine the arrangement of amino acids in proteins, including α-helices, β-sheets, and coils. By analyzing amino acid sequences, computational algorithms and machine learning techniques can predict these structural elements, which are crucial for understanding protein function and interactions. Despite significant progress, challenges remain in predicting non-local interactions and in cases of low sequence homology. Recent advances in machine learning hold promise for improving prediction accuracy and further advancing research in protein biology.