Prediction of Nucleotide Binding Peptides Using Star Graph Topological Indices

Abstract

The nucleotide binding proteins are involved in many important cellular processes, such as transmission of genetic information or energy transfer and storage. Therefore, the screening of new peptides for this biological function is an important research topic. The current study proposes a mixed methodology to obtain the first classification model that is able to predict new nucleotide binding peptides, using only the amino acid sequence. Thus, the methodology uses a Star graph molecular descriptor of the peptide sequences and the Machine Learning technique for the best classifier. The best model represents a Random Forest classifier based on two features of the embedded and non-embedded graphs. The performance of the model is excellent, considering similar models in the field, with an Area Under the Receiver Operating Characteristic Curve (AUROC) value of 0.938 and true positive rate (TPR) of 0.886 (test subset). The prediction of new nucleotide binding peptides with this model could be useful for drug target studies in drug development.

Molecular Descriptor Analysis of Certain Isomeric Natural Polymers

“…Liu et al [33] investigated and identified proteins having nucleotide-binding activity using star graph TIs. Ali et al [34,35] and Du et al [36] studied and applied some degreebased TIs such as the first Zagreb connection index, ordinary generalized geometric-arithmetic index, general Platt index, and general sum-connectivity index to establish extremal results for alkanes.…”