Paper Title
An Empirical Analysis of Different Protein Folding Algorithms on Cytokine Protein Structure

Abstract
Numerous applications, including identifying protein misfolding, protein docking, and comprehending the function of proteins, depend on accurate prediction of the three-dimensional structure of proteins. Cytokines, a type of protein involved in intercellular communication in the immune system and inflammation, are of special interest due to their medicinal significance. Accurate prediction of these protein types can be very resourceful in medical studies. Several deep learning-based algorithms for predicting protein structures from amino acid sequences, such as Alpha Fold 2, Omega Fold, and ESM Fold, have been developed in recent years. There has been significant research in this field, such as benchmark studies of nanobodies[6], comparative study of deep learning based model ESM2 and Homology modeling based Swiss Model[9], evaluating protein docking using Alpha Fold 2[7] and so on. But there has not been a study that analyzes the performance of these models on Cytokines. In this work, we assessed how well these models performed at predicting the Cytokine structures. Overall, these results highlight the importance of considering both accuracy and speed when selecting a protein structure prediction model. Keywords - Protein Folding, Cytokines, Structure Prediction, Alpha Fold, Omega Fold, ESM Fold.