ArticlePeerJ. Computer science2025
Protein-protein interaction prediction using enhanced features with spaced conjoint triad and amino acid pairwise distance.
Article in PeerJ. Computer science, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- A multi-label cascade flexible neural forest model for predicting the subcellular location of multi-site bacterial proteins.Scientific reports · 2026Article
- ProtAttn-QuadNet: An attention-based deep learning framework for protein-protein interaction prediction using ProtBERT embeddings.PloS one · 2026Article
- Recent Advances and Application of Machine Learning for Protein-Protein Interaction Prediction in Rice: Challenges and Future Perspectives.Proteomes · 2025Review
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Authors and funding
1 author.
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Abstract
Protein-protein interactions (PPIs) are pivotal in cellular processes, influencing a wide range of functions, from metabolism to immune responses. Despite the advancements in experimental techniques for PPI detection, their inherent limitations, such as high false-positive rates and significant resource demands, necessitate the development of computational approaches. This study presents a novel computational model named MFPIC (Multi-Feature Protein Interaction Classifier) for predicting PPIs, integrating enhanced sequence-based features, including a novel spaced conjoint triad (SCT) and amino acid pairwise distance (AAPD), with existing methods such as position-specific scoring matrices (PSSM) and AAindex-based features. The SCT captures complex sequence motifs by considering non-adjacent amino acid interactions, while AAPD provides critical spatial information about amino acid residues within protein sequences. The proposed model was evaluated across three benchmark datasets-
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