Identification of coenzyme-binding proteins with machine learning algorithms

Abstract

The coenzyme-binding proteins play a vital role in the cellular metabolism processes, such as fatty acid biosynthesis, enzyme and gene regulation, lipid synthesis, particular vesicular traffic, and β-oxidation donation of acyl-CoA esters. Based on the theory of Star Graph Topological Indices (SGTIs) of protein primary sequences, we proposed a method to develop a first classification model for predicting protein with coenzyme-binding properties. To simulate the properties of coenzyme-binding proteins, we created a dataset containing 2897 proteins, among 456 proteins functioned as coenzyme-binding activity. The SGTIs of peptide sequence were calculated with Sequence to Star Network (S2SNet) application. We used the SGTIs as inputs to several classification techniques with a machine learning software - Weka. A Random Forest classifier based on 3 features of the embedded and non-embedded graphs was identified as the best predictive model for coenzyme-binding proteins. This model developed was with the true positive (TP) rate of 91.7%, false positive (FP) rate of 7.6%, and Area Under the Receiver Operating Characteristic Curve (AUROC) of 0.971. The prediction of new coenzyme-binding activity proteins using this model could be useful for further drug development or enzyme metabolism researches.