Abstract
BackgroundThe 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.ResultsWe 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.ConclusionOn 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/.
Highlights
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
For the machine learning algorithm itself, we show that kernel ridge regression [14] (KRR) is generally preferable to the support vector regression (SVR) algorithm [17] because kernel ridge regression [14] (KRR) has less hyperparameters to tune than SVR, making the learning time smaller
PepX database To our knowledge, this is the first kernel method attempt at learning a predictor which takes the protein crystal and the peptide sequence as input to predict the binding energy of the complex. Many consider this task as a major challenge with important consequences for molecular biology
Summary
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 an interesting class of therapeutics since they display strong activity, high selectivity, low toxicity and few drug-drug interactions. Peptides represent templates of choice for mimicking a secondary structure in order to modulate protein-protein interactions [5,6] They are a very interesting class of therapeutics since they display strong activity, high selectivity, low toxicity and fewer drug-drug interactions. They can serve as investigative tools to gain insight into the role of a protein, by binding to distinct regulatory regions to inhibit specific functions
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have