Structure-based prediction of host-pathogen protein interactions.
The discovery, validation, and characterization of protein-based interactions from different species are crucial for translational research regarding a variety of pathogens, ranging from the malaria parasite Plasmodium falciparum to HIV-1. Here, we review recent advances in the prediction of host-pathogen protein interfaces using structural information. In particular, we observe that current methods chiefly perform machine learning on sequence and domain information to produce large sets of candidate interactions that are further assessed and pruned to generate final, highly probable sets. Structure-based studies have also emphasized the electrostatic properties and evolutionary transformations of pathogenic interfaces, supplying crucial insight into antigenic determinants and the ways pathogens compete for host protein binding. Advancements in spectroscopic and crystallographic methods complement the aforementioned techniques, permitting the rigorous study of true positives at a molecular level. Together, these approaches illustrate how protein structure on a variety of levels functions coordinately and dynamically to achieve host takeover.
Brigham & Women's Hospital, Harvard Medical School, Harvard University, Cambridge, MA, United States. Electronic address: rmariano@g.harvard.edu.
Rachelle
R
Mariano
Dept. of Computer Science, Univ. of Miami, Coral Gables, FL, United States; Center for Computational Science, Univ. of Miami, Coral Gables, FL, United States; Sylvester Cancer Center, Univ. of Miami, Miami, FL, United States.
Stefan
S
Wuchty
D054884:Host-Pathogen Interactions; D006801:Humans; D011485:Protein Binding; D011487:Protein Conformation; D025941:Protein Interaction Mapping / Q000379:methods*; D011506:Proteins / Q000737:chemistry* / Q000378:metabolism*
D011506:Proteins