Consensus Algorithm for Calculation of Protein Binding Affinity using Multiple Models

Ayşenaz Ezgi Ergin, Deniz Turgay Altılar
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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 ssmpmbec. 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. Xix
多模型计算蛋白质结合亲和力的一致性算法
主要的组织相容性复合体(MHC)分子,结合多肽在细胞表面呈现,在细胞介导的免疫中发挥重要作用。近年来,随着数据库和技术的发展,肽结合亲和力计算的研究取得了重大进展。已经开发了几种预测MHC I类肽结合的技术,大多数针对MHC I类的研究由于其性质带来了更好的性能和更多的功能。考虑到不同的方法和技术的使用,以及不同蛋白质上相似方法的方法,根据蛋白质肽的结合亲和力建立了分类。为此,使用MHCflurry、NetMHCPan、NetMHC、NetMHCCons和ssmpmbec对MHC I类进行了研究。在本文范围内进行的这些模拟中,没有观察到模型之间的总体优势。已经确定他们在各方面都优于对方。获得最佳结果可能会因模型的多次使用而有所不同。重要的是要识别数据并使用适当的模型。但即使这样也不会有太大的不同。由于共识方法与模型直接相关,所以模型越好越好。第十九
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