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A comparative analysis of amino acid encoding schemes for the prediction of flexible length linear B-cell epitopes

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Title A comparative analysis of amino acid encoding schemes for the prediction of flexible length linear B-cell epitopes
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Creator Tanmaya Kumar Sahu
Prabina Kumar Meher
Nalini Kanta Choudhury
Atmakuri Ramakrishna Rao
 
Subject epitope prediction
machine learning
peptide encoding
random forest
vaccine designing
linear B-cell epitopes
 
Description Not Available
Linear B-cell epitopes have a prominent role in the development of peptide-based vaccines and disease diagnosis. High variability in the length of these epitopes is a major reason for low accuracy in their prediction. Most of the B-cell epitope prediction methods considered fixed length of epitope sequences and achieved good accuracy. Though a number of tools are available for the prediction of flexible length linear B-cell epitopes with reasonable accuracy, further improvement in the prediction performance is still expected. Thus, here we made an attempt to analyze the performance of machine learning approaches (MLA) with 18 different amino acid encoding schemes in the prediction of flexible length linear B-cell epitopes. We considered B-cell epitope sequences of variable lengths (11–56 amino acids) from well-established public resources. The performances of machine learning algorithms with the encoded epitope sequence datasets were evaluated. Besides, the feasible combinations of encoding schemes were also explored and analyzed. The results revealed that amino-acid composition (AC) and distribution component of composition–transition–distribution encoding schemes are suitable for heterogeneous epitope data, whereas amino-acid-anchoring-pair-composition (APC), dipeptide-composition and amino-acids-pair-propensity-scale (APP) are more appropriate for homogeneous data. Further, two combinations of peptide encoding schemes, i.e. APC + AC and APC + APP with random forest classifier were identified to have improved performance over the state-of-the-art tools for flexible length linear B-cell epitope prediction. The study also revealed better performance of random forest over other considered MLAs in the prediction of flexible length linear B-cell epitopes.
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Date 2023-05-08T09:39:08Z
2023-05-08T09:39:08Z
2022-08-23
 
Type Research Paper
 
Identifier Not Available
Not Available
http://krishi.icar.gov.in/jspui/handle/123456789/76967
 
Language English
 
Relation Not Available;
 
Publisher Briefings in Bioinformatics