Comparar métodos

	Topología de Redes de Interacción Proteína-Proteína (PPI)	Reconstrucción por crio-microscopía electrónica	Búsqueda de Perfiles HMMER
Campo	Bioinformática	Bioinformática	Bioinformática
Familia	Process / pipeline	Process / pipeline	Process / pipeline
Año de origen	2000	1975	1994
Autor original	Peter Uetz	Joachim Frank	Sean Eddy
Tipo	Network analysis pipeline	Image reconstruction pipeline	Probabilistic sequence search pipeline
Fuente seminal	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.	Frank, J. (2002). Single-particle imaging of macromolecules by cryo-electron microscopy. Annual Review of Biophysics and Biomolecular Structure, 31, 303-319.	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.
Alias	protein interaction networks, interactome analysis, network topology	cryo-electron microscopy, cryo-EM, single-particle cryo-EM	profile-hidden Markov model, HMM profile search, HMMER
Relacionados	3	3	3
Resumen	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.	Cryo-electron microscopy (cryo-EM) determines three-dimensional macromolecular structures at atomic or near-atomic resolution by imaging proteins frozen in vitreous ice. Pioneered by Frank, Henderson, and others, this technique has revolutionized structural biology by enabling visualization of large, non-crystallizable complexes and capturing functional conformational states.	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.