ASPIRER: a new computational approach for identifying non-classical secreted proteins based on deep learning

Abstract

Protein secretion has a pivotal role in many biological processes and is particularly important for intercellular communication, from the cytoplasm to the host or external environment. Gram-positive bacteria can secrete proteins through multiple secretion pathways. The non-classical secretion pathway has recently received increasing attention among these secretion pathways, but its exact mechanism remains unclear. Non-classical secreted proteins (NCSPs) are a class of secreted proteins lacking signal peptides and motifs. Several NCSP predictors have been proposed to identify NCSPs and most of them employed the whole amino acid sequence of NCSPs to construct the model. However, the sequence length of different proteins varies greatly. In addition, not all regions of the protein are equally important and some local regions are not relevant to the secretion. The functional regions of the protein, particularly in the N- and C-terminal regions, contain important determinants for secretion. In this study, we propose a new hybrid deep learning-based framework, referred to as ASPIRER, which improves the prediction of NCSPs from amino acid sequences. More specifically, it combines a whole sequence-based XGBoost model and an N-terminal sequence-based convolutional neural network model; 5-fold cross-validation and independent tests demonstrate that ASPIRER achieves superior performance than existing state-of-the-art approaches. The source code and curated datasets of ASPIRER are publicly available at https://github.com/yanwu20/ASPIRER/. ASPIRER is anticipated to be a useful tool for improved prediction of novel putative NCSPs from sequences information and prioritization of candidate proteins for follow-up experimental validation.

MLACP 2.0: An updated machine learning tool for anticancer peptide prediction

“…Construction of baseline models: We utilized seven different classifiers (RF, ERT, SVM GB, AB, LGB, and XGB) that have been extensively applied in Bioinformatics and computational biology [32] , [33] , [34] , [35] , [36] , [37] . For each classifier, there are a set of hyperparameters that determine the performance of the model during cross-validation.…”

Accelerating High-Performance Classification of Bacterial Proteins Secreted via Non-Classical Pathways: no needing for deepness

“…The PeNGaRoo (Zhang et al, 2020) and ASPIRER (X. Wang et al, 2022) software used the same independent validation datasets, ASPIRER, after PeNGaRoo. We directly downloaded 34 negatives and 34 positive validation proteins from the ASPIRER site (files 'data/pengaroo_independent_test_neg.faa' and 'data/pengaroo_independent_test_pos.faa').…”

“…We know prediction methods based on the functional classification of proteins (Yu et al, 2010)(Restrepo-Montoya et al, 2011) and more recently advanced machine-learning techniques exploring dozens or hundreds of features from protein sequences (Zhang et al, 2020)(X. Wang et al, 2022). Such methods use the analysis of physical-chemical characteristics present in the amino acid sequence on grouping proteins.…”

PreAcrs: a machine learning framework for identifying anti-CRISPR proteins

“…The five features could be categorized into three groups: sequence features, evolutionary features, and pre-trained model features. These features have been widely applied in feature encoding research [19,30,31] and have achieved a good performance in protein properties and function predictions [32][33][34][35][36][37][38]. The following are the five features adopted in this study.…”