Prediction of Protein–Ligand Interaction Based on the Positional Similarity Scores Derived from Amino Acid Sequences
| Autor: | Vladimir Poroikov, Dmitry Filimonov, Alexey Lagunin, B. N. Sobolev, Dmitry Karasev |
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| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
0301 basic medicine
Models Molecular prediction of protein targets Protein family Protein Conformation Computational biology Ligands Catalysis Article Inorganic Chemistry local sequence similarity 03 medical and health sciences Local sequence Naive Bayes classifier Phylogenetics Pairwise sequence alignment Humans Amino Acid Sequence Physical and Theoretical Chemistry Databases Protein Molecular Biology Spectroscopy Phylogeny Mathematics chemistry.chemical_classification Multiple sequence alignment Binding Sites 030102 biochemistry & molecular biology Organic Chemistry Computational Biology Proteins General Medicine Computer Science Applications Amino acid 030104 developmental biology chemistry ROC Curve protein–ligand interaction Area Under Curve Algorithms Protein ligand Protein Binding |
| Zdroj: | International Journal of Molecular Sciences Volume 21 Issue 1 |
| ISSN: | 1422-0067 |
| DOI: | 10.3390/ijms21010024 |
| Popis: | The affinity of different drug-like ligands to multiple protein targets reflects general chemical&ndash biological interactions. Computational methods estimating such interactions analyze the available information about the structure of the targets, ligands, or both. Prediction of protein&ndash ligand interactions based on pairwise sequence alignment provides reasonable accuracy if the ligands&rsquo specificity well coincides with the phylogenic taxonomy of the proteins. Methods using multiple alignment require an accurate match of functionally significant residues. Such conditions may not be met in the case of diverged protein families. To overcome these limitations, we propose an approach based on the analysis of local sequence similarity within the set of analyzed proteins. The positional scores, calculated by sequence fragment comparisons, are used as input data for the Bayesian classifier. Our approach provides a prediction accuracy comparable or exceeding those of other methods. It was demonstrated on the popular Gold Standard test sets, presenting different sequence heterogeneity and varying from the group, including different protein families to the more specific groups. A reasonable prediction accuracy was also found for protein kinases, displaying weak relationships between sequence phylogeny and inhibitor specificity. Thus, our method can be applied to the broad area of protein&ndash ligand interactions. |
| Databáze: | OpenAIRE |
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