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

Content Provider	Springer Nature : BioMed Central
Author	Giguère, Sébastien Marchand, Mario Laviolette, François Drouin, Alexandre Corbeil, Jacques
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. Conclusion 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/ .
Related Links	https://bmcbioinformatics.biomedcentral.com/counter/pdf/10.1186/1471-2105-14-82.pdf
Ending Page	16
Page Count	16
Starting Page	1
File Format	HTM / HTML
ISSN	14712105
DOI	10.1186/1471-2105-14-82
Journal	BMC Bioinformatics
Issue Number	1
Volume Number	14
Language	English
Publisher	BioMed Central
Publisher Date	2013-03-05
Access Restriction	Open
Subject Keyword	Bioinformatics Microarrays Computational Biology Computer Appl. in Life Sciences Algorithms Major Histocompatibility Complex Root Mean Square Error Major Histocompatibility Complex Class Support Vector Regression Weighted Degree Computational Biology/Bioinformatics
Content Type	Text
Resource Type	Article
Subject	Molecular Biology Biochemistry Computer Science Applications Applied Mathematics Structural Biology
Journal Impact Factor	2.9/2023
5-Year Journal Impact Factor	3.6/2023

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Predicting Class II MHC-Peptide binding: a kernel based approach using similarity scores

Quantitative prediction of mouse class I MHC peptide binding affinity using support vector machine regression (SVR) models

USMPep: universal sequence models for major histocompatibility complex binding affinity prediction

Predicting residue-wise contact orders in proteins by support vector regression

PepDist: A New Framework for Protein-Peptide Binding Prediction based on Learning Peptide Distance Functions

NN-align. An artificial neural network-based alignment algorithm for MHC class II peptide binding prediction

Predicting peptides binding to MHC class II molecules using multi-objective evolutionary algorithms

PeptX: Using Genetic Algorithms to optimize peptides for MHC binding

POPISK: T-cell reactivity prediction using support vector machines and string kernels

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

Similar Documents

Predicting Class II MHC-Peptide binding: a kernel based approach using similarity scores

Quantitative prediction of mouse class I MHC peptide binding affinity using support vector machine regression (SVR) models

USMPep: universal sequence models for major histocompatibility complex binding affinity prediction

Predicting residue-wise contact orders in proteins by support vector regression

PepDist: A New Framework for Protein-Peptide Binding Prediction based on Learning Peptide Distance Functions

NN-align. An artificial neural network-based alignment algorithm for MHC class II peptide binding prediction

Predicting peptides binding to MHC class II molecules using multi-objective evolutionary algorithms

PeptX: Using Genetic Algorithms to optimize peptides for MHC binding

POPISK: T-cell reactivity prediction using support vector machines and string kernels

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