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FaaPred: a SVM-based prediction method for fungal adhesins and adhesin-like proteins
Jayashree Ramana1, Dinesh Gupta
1Structural and Computational Biology Group, International Centre for Genetic Engineering and Biotechnology, Aruna Asaf Ali Marg, New Delhi, India.
Abstract:
Adhesion constitutes one of the initial stages of infection in microbial diseases and is mediated by adhesins. Hence, identification and comprehensive knowledge of adhesins and adhesin-like proteins is essential to understand adhesin mediated pathogenesis and how to exploit its therapeutic potential. However, the knowledge about fungal adhesins is rudimentary compared to that of bacterial adhesins. In addition to host cell attachment and mating, the fungal adhesins play a significant role in homotypic and xenotypic aggregation, foraging and biofilm formation. Experimental identification of fungal adhesins is labor- as well as time-intensive. In this work, we present a Support Vector Machine (SVM) based method for the prediction of fungal adhesins and adhesin-like proteins. The SVM models were trained with different compositional features, namely, amino acid, dipeptide, multiplet fractions, charge and hydrophobic compositions, as well as PSI-BLAST derived PSSM matrices. The best classifiers are based on compositional properties as well as PSSM and yield an overall accuracy of 86%. The prediction method based on best classifiers is freely accessible as a world wide web based server at http://bioinfo.icgeb.res.in/faap. This work will aid rapid and rational identification of fungal adhesins, expedite the pace of experimental characterization of novel fungal adhesins and enhance our knowledge about role of adhesins in fungal infections.
Insights
We developed a Support Vector Machine (SVM) method to predict fungal adhesins and adhesin-like proteins. This tool enhances the identification of these crucial proteins, aiding in understanding fungal infections and developing therapies.
Area of Science:
- Mycology
- Bioinformatics
- Computational Biology
Background:
- Adhesion is a critical initial step in microbial infections, mediated by adhesins.
- Fungal adhesins are vital for host cell attachment, mating, aggregation, foraging, and biofilm formation.
- Current knowledge of fungal adhesins lags behind bacterial adhesins, and experimental identification is resource-intensive.
Purpose of the Study:
- To develop a computational method for predicting fungal adhesins and adhesin-like proteins.
- To improve the efficiency and accuracy of identifying fungal adhesins compared to experimental methods.
Main Methods:
- Utilized Support Vector Machine (SVM) algorithms for classification.
- Trained SVM models using various sequence-derived features: amino acid, dipeptide, multiplet fractions, charge, hydrophobicity, and PSSM matrices.
- Evaluated model performance based on accuracy and other metrics.
Main Results:
- Achieved an overall accuracy of 86% with the best performing SVM classifiers.
- Identified that models combining compositional properties and PSSM features yielded the highest accuracy.
- Developed a publicly accessible web server (http://bioinfo.icgeb.res.in/faap) for predicting fungal adhesins.
Conclusions:
- The developed SVM-based method provides a rapid and rational approach for identifying fungal adhesins.
- This tool can accelerate experimental characterization of novel fungal adhesins.
- Enhanced identification of fungal adhesins will deepen our understanding of their role in fungal pathogenesis and therapeutic targeting.
