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McDermott, J.E., Corrigan, A., Peterson, E., Oehmen, C., Niemann, G., Cambronne, E.D., Sharp, D., Adkins, J.N., Samudrala, R. and Heffron, F. (2011) Computational Prediction of Type III and IV Secreted Effectors in Gram-Negative Bacteria. Infection and Immunity, 79, 23-32. https://doi.org/10.1128/IAI.00537-10
has been cited by the following article:
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TITLE:
An Improved Approach for Rapidly Identifying Different Types of Gram-Negative Bacterial Secreted Proteins
AUTHORS:
Lezheng Yu, Fengjuan Liu, Lixiao Du, Yizhou Li
KEYWORDS:
Gram-Negative Bacteria, Secreted Protein, Position-Specific Scoring Matrix, Signal Peptide, Support Vector Machine
JOURNAL NAME:
Natural Science,
Vol.10 No.5,
May
18,
2018
ABSTRACT: Protein
secretion plays an important role in bacterial lifestyles. In Gram-negative
bacteria, a wide range of proteins are secreted to modulate the interactions of
bacteria with their environments and other bacteria via various secretion
systems. These proteins are essential for the virulence of bacteria, so it is
crucial to study them for the pathogenesis of diseases and the development of
drugs. Using amino acid composition (AAC), position-specific scoring matrix
(PSSM) and N-terminal signal peptides, two different substitution models are
firstly constructed to transform protein sequences into numerical vectors.
Then, based on support vector machine (SVM) and the “one to one”algorithm, a hybrid
multi-classifier named SecretP v.2.2 is proposed to rapidly and
accuratelydistinguish different types of Gram-negativebacterial
secreted proteins. When performed on the same test set for a comparison with
other methods, SecretP v.2.2 gets the highest total sensitivity of 93.60%. A
public independent dataset is used to further test the power of SecretP v.2.2
for predicting NCSPs, it also yields satisfactory results.