Superior Epitope Predicting Algorithm (SepaMatrix)

More and more epitope prediction algorithms are pouring into the field of vaccine research, but the principles of predicting and the accuracy of algorithms vary widely. Creative Biolabs has gathered experts in bioinformatics, immunology, and vaccine research to optimize and improve existing basic algorithms, establishing a simple, experimentally validated epitope predicting algorithm – SepaMatrix, helping a wide range of vaccine researchers to successfully carry out epitope-focused vaccine research, which is showing good immunogenicity.

Epitope Predicting Algorithms

The combination of bioinformatics and a large amount of experimental data has sparked the advent of immunoinformatics, and the rise and development of immunoinformatics have helped scientists gain a deeper understanding of the immune system. As a branch of bioinformatics, immunoinformatics uses computer analysis and modeling to study data and problems related to immunology. Specifically, it is to study and design algorithms to map possible cell epitopes to shorten and reduce the time and cost of determining epitopes through experimental analysis. Currently, epitope prediction methods include QSAR analysis, protein threading, structural binding motif determination, docking techniques, homology modeling, and machine learning algorithms design. These algorithms are either based on sequence prediction or based on structural prediction.

Several Basic Epitope Predicting Methods

Motif search is the earliest and most widely used method for epitope prediction. MHC-binding motifs can be identified by comparing the sequence of a given peptide to known binders and non-binding partners in the motif library. EPIPREDICT is a motif-based predictive tool that is capable of predicting epitopes binds to MHC class II molecules. Since not all of the binding peptides have already recognized motifs, the accuracy of predicting method of the motif search is only about 60-70%. In addition, some peptides predicted by this method were experimentally verified and found to have weak affinity. Artificial neural networks (ANNs) are commonly used to address asthma-related problems, assess drug solubility, investigate cardiac disease, and are now being used to predict epitopes and to analyze MHC haplotypes. Because the length of peptides bound to MHC class I molecules is just about, whereas the length of epitopes bound to MHC class II molecules varies a lot, the prediction of MHC class I epitopes is easier than MHCII for that the latter requires more major anchor amino acids.

SepaMatrix - Superior Epitope Predicting Algorithm

The superior epitope prediction algorithm (SepaMatrix) is based on the quantitative matrix-driven method, and was improved and optimized to enhance the accuracy of prediction and compatibility with different systems and simplify the user's operation. QMs assign coefficients to each amino acid at each position in the peptide and perform operations to achieve epitope prediction. Epitope prediction with QMs consists of four steps, first, extracting all possible peptide frames from a given protein sequence; second, assigning amino acid- and position-specific matrix values to each amino acid residue in the given peptide frame; third, plus or multiplying the side chain values of each peptide to obtain a score of each peptide; fourth, selecting possible epitopes based on the score of each peptide. QMs fully considers the relative importance of each amino acid residue in the peptide sequence, rather than simply calculating the number of anchored amino acids. In addition to its simple operation, QMs also covers a broad range of peptides with binding potential and define quantifiable scores for each peptide. In addition to the value assignment of amino acids and their positions, SepaMatrix adds a calculation of the interaction between each amino acid and the binding groove on the basis of QM, taking the QM calculation for each step of antigen processing into account, and the geometric and electrostatic complementarities issues in the formation of stable complexes are considered too. Experimental validation also demonstrates SepaMatrix's excellent predictive power.

Fig.1 Workflow of QMs-based algorithm.

Features of SepaMatrix

• Excellent and proven predictive performance
• Easy-to-implement
• High throughput applicable
• Time-saving and cost-effective
• Provide valuable guidance on vaccine design

SepaMatrix integrates the strengths of various epitope predicting algorithms and avoids or compensates for their deficiencies, with characteristics of robust predictive power and ease of manipulation. If you are interested in epitope-focused vaccine research, Creative Biolabs can provide you with quality products and reliable services from every aspect of vaccine preparation including epitope predicting, vaccine design, formulation optimization, immunogenicity evaluation as well as customized-scale vaccine manufacturing!

All of our products can only be used for research purposes. These vaccine ingredients CANNOT be used directly on humans or animals.