ArticleCombinatorial chemistry & high throughput screening2026
20D-Dynamic Representation of Protein Sequences Combined with K-means Clustering.
Article in Combinatorial chemistry & high throughput screening, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Euro-Asian hybrids of Echinococcus multilocularis from red foxes in northern and northeastern Poland result from secondary contact between long-isolated populations.Scientific reports · 2026Article
- Evaluating the significance of embedding-based protein sequence alignment with clustering and double dynamic programming for remote homology.Scientific reports · 2025Article
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveThe objective of this research is to demonstrate that alignment-free bioinformatics approaches are effective tools for analyzing the similarity and dissimilarity of protein sequences. All numerical parameters representing sequences are expressed analytically, ensuring precision, clarity, and efficient processing, even for large datasets and long sequences. Additionally, a novel approach for identifying previously unknown virus strains is introduced.
methodsA novel approach is proposed, integrating the unique features of our newly developed method, the 20D-Dynamic Representation of Protein Sequences, with the K-means clustering algorithm. The sequences are represented as clouds of material points in a 20-dimensional space (20D-dynamic graphs), with their spatial distribution being unique to each protein sequence. The numerical parameters, referred to as descriptors in molecular similarity theory, represent quantities characteristic of dynamic systems and serve as input data for the K-means clustering algorithm.
resultsExamples of the application of the approach are presented, including projections of the 20D-dynamic graphs onto 3D spaces, which serve as a visual tool for comparing sequences. Additionally, cluster plots for the analyzed sequences are provided using the proposed method. DISCUSSION: Combining the 20D-Dynamic Representation of Protein Sequences with an unsupervised machine learning algorithm (K-means clustering) enhances its scalability. This approach is applicable to large datasets without restrictions on sequence length.
conclusionIt has been demonstrated that the 20D-Dynamic Representation of Protein Sequences, combined with the K-means clustering algorithm, successfully classifies subtypes of influenza A virus strains.
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