iNSP-GCAAP: Identifying Non-classical Secreted Proteins using Global Composition of Amino Acid Properties
H. T. Pham, T-H Nguyen-Vo, Q. H. Trinh, T. T. T. Do*, and B. P. Nguyen∗
Non-classical secreted proteins refer to a group of proteins released into the extracellular environment under the facilitation of different biological transporting pathways apart from the Sec/Tat system. As experimental determination of non-classical secreted proteins is often costly and requires skilled handling techniques, computational approaches are necessary. In this study, we introduce iNSP-GCAAP, a computational prediction framework, to identify non-classical secreted proteins. We propose using global composition of a customized set of amino acid properties to encode sequence data and use the random forest algorithm for classification. We used the training dataset introduced by Zhang et al. (Bioinformatics, 36(3), 704–712, 2020) to develop our model and test it with the independent test set in the same study.
The area under the receiver operating characteristic curve (AUC) on that test set was 0.9256 which outperformed other state-of-the-art methods using the same datasets. Our framework is also deployed as a user-friendly web-based application to support the research community to predict non-classical secreted proteins.
Source code and data are available upon request.