PSR-MAPMS: A new approach for the interpretable prediction of myelin autoantigenic peptides in multiple sclerosis
Phasit Charoenkwan1, Nalini Schaduangrat2, Pramote Chumnanpuen3,4
1Modern Management and Information Technology, College of Arts, Media and Technology, Chiang Mai University, Chiang Mai, Thailand.
Abstract:
Within the central nervous system, the myelin sheath is composed of elements known as myelin autoantigens that are mistakenly targeted by the immune system in multiple sclerosis (MS). This autoimmune attack leads to the destruction of myelin, resulting in the neurological symptoms characteristic of MS. Identifying myelin autoantigenic peptides is crucial for understanding the pathogenesis of MS and developing targeted therapies. Traditional approaches often struggle with the complexity and heterogeneity of biological data, making it challenging to achieve accurate predictions in a cost-effective manner. Alternatively, computational approaches that utilize sequence information can aid in the biological elucidation of peptides. In this study, we present a novel propensity score-based approach, termed PSR-MAPMS, to predict and characterize T cell-specific myelin autoantigenic peptides in MS (MAPMSs). To the extent of our knowledge, PSR-MAPMS is the first machine learning (ML)-based approach that can predict and analyze MAPMSs based solely on sequence information. In PSR-MAPMS, we generated multiple aspects of propensity scores for MAPMSs. Important propensity scores were then chosen and applied to create the final hybrid model using an ensemble learning strategy. Extensive experiments results showed that PSR-MAPMS surpasses several conventional ML-based classifiers for MAPMS prediction in both cross-validation and independent tests. In the independent test results, the accuracy, MCC, and F1 scores of PSR-MAPMS were within the ranges of 0.899-0.949, 0.800-0.899, and 0.903-949, respectively. Moreover, our estimated propensity scores can identify crucial biochemical and physicochemical properties of MAPMSs, providing valuable revelations of the fundamental biological mechanisms, which facilitates the development of more effective and personalized treatments for MS. In addition, we created a simple-to-navigate web server for PSR-MAPMS, which is publicly accessible at https://pmlabqsar.pythonanywhere.com/PSR-MAPMS.
Insights
A new machine learning method, PSR-MAPMS, accurately predicts myelin autoantigenic peptides in multiple sclerosis (MS) using only sequence data. This advances understanding of MS pathogenesis and aids targeted therapy development.
Area of Science:
- Neuroimmunology
- Computational Biology
- Bioinformatics
Background:
- Multiple sclerosis (MS) involves an autoimmune attack on myelin autoantigens in the central nervous system, leading to myelin destruction and neurological symptoms.
- Identifying specific myelin autoantigenic peptides is critical for understanding MS pathogenesis and developing targeted therapies.
- Traditional methods for peptide identification face challenges with biological data complexity and cost-effectiveness.
Purpose of the Study:
- To develop a novel computational approach for predicting and characterizing T cell-specific myelin autoantigenic peptides in MS (MAPMSs).
- To establish the first machine learning (ML)-based method utilizing solely sequence information for MAPMS prediction and analysis.
- To provide insights into the biochemical and physicochemical properties of MAPMSs for better understanding of MS mechanisms.
Main Methods:
- Introduction of PSR-MAPMS, a propensity score-based machine learning approach.
- Generation of multiple propensity scores for MAPMSs.
- Application of an ensemble learning strategy using selected important propensity scores to create a hybrid model.
Main Results:
- PSR-MAPMS demonstrated superior performance compared to conventional ML classifiers in predicting MAPMSs.
- In independent tests, PSR-MAPMS achieved high accuracy (0.899-0.949), MCC (0.800-0.899), and F1 scores (0.903-0.949).
- The propensity scores identified key biochemical and physicochemical properties of MAPMSs, offering insights into MS biological mechanisms.
Conclusions:
- PSR-MAPMS is a highly effective ML tool for predicting MAPMSs based on sequence data.
- The method provides valuable biological insights, potentially facilitating the development of personalized MS treatments.
- A publicly accessible web server for PSR-MAPMS has been developed to aid researchers.
