Learning a peptide-protein binding affinity predictor with kernel ridge regression.

ABSTRACT:

Background

The cellular function of a vast majority of proteins is performed through physical interactions with other biomolecules, which, most of the time, are other proteins. Peptides represent templates of choice for mimicking a secondary structure in order to modulate protein-protein interaction. They are thus an interesting class of therapeutics since they also display strong activity, high selectivity, low toxicity and few drug-drug interactions. Furthermore, predicting peptides that would bind to a specific MHC alleles would be of tremendous benefit to improve vaccine based therapy and possibly generate antibodies with greater affinity. Modern computational methods have the potential to accelerate and lower the cost of drug and vaccine discovery by selecting potential compou

SUBMITTER: Giguere S 

PROVIDER: S-EPMC3651388 | biostudies-literature | 2013 Mar

REPOSITORIES: biostudies-literature