Consensus algorithm for calculation and labeling of protein binding affinity using multiple models

Abstract

The major histocompatibility complex (MHC) molecules, which bind peptides for presentation on the cell surface, play an important role in cell-mediated immunity. In light of developing databases and technologies over the years, significant progress has been made in research on peptide binding affinity calculation. Several in techniques have been developed to predict peptide binding to MHC class I. Most of the research on MHC Class I due to its nature brings better performance and more. Considering the use of different methods and different technologies, and the approach of similar methods on different proteins, a classification was created according to the binding affinity of protein peptides. For this classification, MHC Class I was studied using the MHCflurry, NetMHCPan, NetMHC, NetMHCCons and ssm-pmbec. In these simulations conducted within the scope of this thesis, no overall superiority was observed between the models. It has been determined that they are superior to each other in various points. Getting the best results may vary depending on the multiple uses of models. The important thing is to recognize the data and act with the appropriate model. But even that doesn't make a huge difference. Since the consensus approach is directly related to the models, the better the models, the better.