title: 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 compounds for testing in silico prior to biological validation.
  
  
  RESULTS
  We propose a specialized string kernel for small bio-molecules, peptides and pseudo-sequences of binding interfaces. The kernel incorporates physico-chemical properties of amino acids and elegantly generalizes eight kernels, comprised of the Oligo, the Weighted Degree, the Blended Spectrum, and the Radial Basis Function. We provide a low complexity dynamic programming algorithm for the exact computation of the kernel and a linear time algorithm for it's approximation. Combined with kernel ridge regression and SupCK, a novel binding pocket kernel, the proposed kernel yields biologically relevant and good prediction accuracy on the PepX database. For the first time, a machine learning predictor is capable of predicting the binding affinity of any peptide to any protein with reasonable accuracy. The method was also applied to both single-target and pan-specific Major Histocompatibility Complex class II benchmark datasets and three Quantitative Structure Affinity Model benchmark datasets.
  
  
  CONCLUSIONS
  On all benchmarks, our method significantly (p-value ≤ 0.057) outperforms the current state-of-the-art methods at predicting peptide-protein binding affinities. The proposed approach is flexible and can be applied to predict any quantitative biological activity. Moreover, generating reliable peptide-protein binding affinities will also improve system biology modelling of interaction pathways. Lastly, the method should be of value to a large segment of the research community with the potential to accelerate the discovery of peptide-based drugs and facilitate vaccine development. The proposed kernel is freely available at http://graal.ift.ulaval.ca/downloads/gs-kernel/.
authors.affiliation: Department of Computer Science and Software Engineering, Université Laval, Québec, Canada. sebastien.giguere.8@ulaval.ca
authors.forename: Sébastien
authors.initials: S
authors.lastname: Giguère
authors.forename: Mario
authors.initials: M
authors.lastname: Marchand
authors.forename: François
authors.initials: F
authors.lastname: Laviolette
authors.forename: Alexandre
authors.initials: A
authors.lastname: Drouin
authors.forename: Jacques
authors.initials: J
authors.lastname: Corbeil
mesh_terms: D000465:Algorithms; D000483:Alleles; D001185:Artificial Intelligence; D001665:Binding Sites; D003198:Computer Simulation; D000949:Histocompatibility Antigens Class II / Q000737:chemistry / Q000235:genetics / Q000378:metabolism; D010455:Peptides / Q000737:chemistry* / Q000276:immunology / Q000378:metabolism; D054730:Protein Interaction Domains and Motifs; D025941:Protein Interaction Mapping / Q000379:methods*
chemical_list: D000949:Histocompatibility Antigens Class II; D010455:Peptides