Comparar métodos
Examine os métodos selecionados lado a lado; as linhas que diferem ficam destacadas.
| Busca de Perfil HMMER× | Acoplamento Molecular× | |
|---|---|---|
| Área | Bioinformática | Bioinformática |
| Família | Process / pipeline | Process / pipeline |
| Ano de origem≠ | 1994 | 1982 |
| Autor original≠ | Sean Eddy | Irwin Kuntz |
| Tipo≠ | Probabilistic sequence search pipeline | Binding prediction pipeline |
| Fonte seminal≠ | Krogh, A., Brown, M., Mian, I. S., Sjölander, K., & Haussler, D. (1994). Hidden Markov models in computational biology: applications to protein modeling. Journal of Molecular Biology, 235(5), 1501-1531. DOI ↗ | Kuntz, I. D., Blaney, J. M., Oatley, S. J., Langridge, R., & Ferrin, T. E. (1982). A geometric approach to macromolecule-ligand interactions. Journal of Molecular Biology, 161(2), 269-288. DOI ↗ |
| Outros nomes≠ | profile-hidden Markov model, HMM profile search, HMMER | protein-ligand docking, binding prediction |
| Relacionados≠ | 3 | 4 |
| Resumo≠ | HMMER profile search identifies distant protein sequence homologs using probabilistic models of protein families, known as profile Hidden Markov Models (HMMs). Developed by Eddy and colleagues, this method captures sequence variation patterns within protein families and detects homologs with far greater sensitivity than position-weight matrices or pairwise alignment. | Molecular docking predicts the preferred binding orientation and affinity of a ligand (small molecule) within a protein binding pocket. Pioneered by Kuntz and colleagues in 1982, this computational method searches conformational space to find energetically favorable ligand-protein complexes, enabling rapid screening of chemical libraries for drug discovery. |
| ScholarGateConjunto de dados ↗ |
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