Salīdzināt metodes

	PPI tīkla topoloģija	HMMER profila meklēšana	Molekulārā dokēšana
Nozare	Bioinformātika	Bioinformātika	Bioinformātika
Saime	Process / pipeline	Process / pipeline	Process / pipeline
Izcelsmes gads	2000	1994	1982
Autors	Peter Uetz	Sean Eddy	Irwin Kuntz
Tips	Network analysis pipeline	Probabilistic sequence search pipeline	Binding prediction pipeline
Pirmavots	Uetz, P., Giot, L., Cagney, G., Mansfield, T. A., Judson, R. S., Knight, J. R., ... & Lomax, J. (2000). A comprehensive analysis of protein-protein interactions in Saccharomyces cerevisiae. Nature, 403(6770), 623-627.	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.	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.
Citi nosaukumi	protein interaction networks, interactome analysis, network topology	profile-hidden Markov model, HMM profile search, HMMER	protein-ligand docking, binding prediction
Saistītās	3	3	4
Kopsavilkums	Protein-protein interaction network analysis identifies and characterizes the structural properties of cellular interaction networks. Pioneered by Uetz and colleagues through large-scale yeast two-hybrid screening, this approach reveals topological features like hubs, modules, and motifs that encode functional organization and disease associations.	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.