Prediction of human protein functions with protein mapping techniques and deep learning model

Abstract

Protein functions are important for understanding the molecular mechanism of living organisms. Protein structures are used when determining the functions of proteins. Protein functions are mostly used to determine the annotations of uncharacterized protein sequences, to understand the cellular mechanisms of living things, to identify functional changes in genes or proteins that cause disease, and to develop new approaches to prevent, treat and diagnose diseases. Protein functions can be determined effectively by experimental methods. However, experimental methods take time and go through many chemical processes, causing these stages to be slow and costly. In addition to these, the annotations of some proteins whose functional structure and sequence are known cannot be specified due to experimental processes. Due to such reasons and disadvantages, computational-based approaches are needed. Artificial intelligence algorithms are generally used for computational-based applications. In order to predict protein functions with artificial intelligence methods, protein sequences must be mapped with certain mapping methods. In this study, prediction of gene ontology-based protein functions was performed using certain protein mapping techniques. The study consists of four different stages; obtaining protein data, mapping protein sequences, classifying protein functions, and determining the performance of protein mapping techniques. At the end of the study, the best accuracy and AUC score in the biological process category was obtained by the PAM250 protein mapping technique and was calculated as 69% and 88%, respectively. In the cellular component category, the best accuracy and AUC value were obtained by FIBHASH protein mapping technique with 64% and 89%, respectively. In the molecular function category, the best result was obtained with FIBHASH with 64% AUC score and 89% accuracy. It has been observed that the combined use of the proposed artificial intelligence method and protein numerical mapping techniques have an effective role in predicting protein functions.