Protein–protein interaction and non-interaction predictions using gene sequence natural vector

• Published in: Communications biology 2022-07, Vol.5 (1), p.652-652, Article 652
• Main Authors: Zhao, Nan, Zhuo, Maji, Tian, Kun, Gong, Xinqi
• Format: Article
• Language: English
• Subjects: 631/114/1386; 631/114/2397; Amino acid sequence; Biology; Biomedical and Life Sciences; Computer applications; DNA sequencing; Homo sapiens; Life Sciences; Mus musculus; Nucleotide sequence; Predictions; Protein interaction; Proteins
• ISSN: 2399-3642; 2399-3642
• DOI: 10.1038/s42003-022-03617-0

Predicting protein–protein interaction and non-interaction are two important different aspects of multi-body structure predictions, which provide vital information about protein function. Some computational methods have recently been developed to complement experimental methods, but still cannot effectively detect real non-interacting protein pairs. We proposed a gene sequence-based method, named NVDT (Natural Vector combine with Dinucleotide and Triplet nucleotide), for the prediction of interaction and non-interaction. For protein–protein non-interactions (PPNIs), the proposed method obtained accuracies of 86.23% for Homo sapiens and 85.34% for Mus musculus , and it performed well on three types of non-interaction networks. For protein-protein interactions (PPIs), we obtained accuracies of 99.20, 94.94, 98.56, 95.41, and 94.83% for Saccharomyces cerevisiae , Drosophila melanogaster , Helicobacter pylori , Homo sapiens, and Mus musculus , respectively. Furthermore, NVDT outperformed established sequence-based methods and demonstrated high prediction results for cross-species interactions. NVDT is expected to be an effective approach for predicting PPIs and PPNIs. Protein-protein non-interactions and interactions are distinguished and predicted by gene sequence using single nucleotide and contiguous nucleotides combined with machine learning models.