ArticleBMC bioinformatics2011
NClassG+: A classifier for non-classically secreted Gram-positive bacterial proteins.
Article in BMC bioinformatics, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed, 36 citations in OpenAlex.
- iNClassSec-ESM: Discovering potential non-classical secreted proteins through a novel protein language model.Computational and structural biotechnology journal · 2025Article
- ASPIRER: a new computational approach for identifying non-classical secreted proteins based on deep learning.Briefings in bioinformatics · 2022Article
- PncsHub: a platform for annotating and analyzing non-classically secreted proteins in Gram-positive bacteria.Nucleic acids research · 2022Article
- Protein Secretion Prediction Tools and Extracellular Vesicles Databases.Methods in molecular biology (Clifton, N.J.) · 2021Article
- A journey through thePeerJ · 2021Article
- NonClasGP-Pred: robust and efficient prediction of non-classically secreted proteins by integrating subset-specific optimal models of imbalanced data.Microbial genomics · 2020Article
- Article
- High-Throughput Identification of Mammalian Secreted Proteins Using Species-Specific Scheme and Application to Human Proteome.Molecules (Basel, Switzerland) · 2018Article
- Better Than Nothing? Limitations of the Prediction Tool SecretomeP in the Search for Leaderless Secretory Proteins (LSPs) in Plants.Frontiers in plant science · 2016Article
- Rule-based knowledge acquisition method for promoter prediction in human and Drosophila species.TheScientificWorldJournal · 2014Article
- Article
- The Corynebacterium pseudotuberculosis in silico predicted pan-exoproteome.BMC genomics · 2012Article
Corrections and comments
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Authors and funding
5 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundMost predictive methods currently available for the identification of protein secretion mechanisms have focused on classically secreted proteins. In fact, only two methods have been reported for predicting non-classically secreted proteins of Gram-positive bacteria. This study describes the implementation of a sequence-based classifier, denoted as NClassG+, for identifying non-classically secreted Gram-positive bacterial proteins.
resultsSeveral feature-based classifiers were trained using different sequence transformation vectors (frequencies, dipeptides, physicochemical factors and PSSM) and Support Vector Machines (SVMs) with Linear, Polynomial and Gaussian kernel functions. Nested k-fold cross-validation (CV) was applied to select the best models, using the inner CV loop to tune the model parameters and the outer CV group to compute the error. The parameters and Kernel functions and the combinations between all possible feature vectors were optimized using grid search.
conclusionsThe final model was tested against an independent set not previously seen by the model, obtaining better predictive performance compared to SecretomeP V2.0 and SecretPV2.0 for the identification of non-classically secreted proteins. NClassG+ is freely available on the web at http://www.biolisi.unal.edu.co/web-servers/nclassgpositive/.
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