In silico identification of Gram-negative bacterial secreted proteins from primary sequence

Yu, Lezheng; Luo, Jiesi; Guo, Yanzhi; Li, Yizhou; Pu, Xuemei; Li, Menglong

doi:10.1016/j.compbiomed.2013.06.001

Cited by 13 publications

(20 citation statements)

References 59 publications

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“…These results are achieved by using only 220 features in total. Note that these enhancements achieved by using evolutionary-based features extracted from PSSM compared to the results reported using features extracted from GO (Wan et al, 2013;Yu et al, 2013;Pacharawongsakda and Theeramunkong, 2013;Marcin et al, 2012). It highlights the importance of our method to explore potential discriminatory information embedded in PSSM and introduce reliable features to tackle the protein subcellular localization prediction problem (Table 5).…”

Section: Feature Vector Subcellular Locations Overallmentioning

confidence: 59%

“…To provide more information about the statistical significance of our achieved results, we will report Sensitivity, Specificity, and Matthew's Correlation Coefficient (MCC) for each subcellular location as well as for the overall benchmark (Hu et al, 2012;Yu et al, 2013;Dehzangi et al, 2014b;Marcin et al, 2012). Sensitivity, which is also referred to as the true positive rate, is a criterion used to evaluate the model as a metric of its ability to identify the correct samples.…”

Section: Evaluation Methodsmentioning

confidence: 98%

“…To tackle this problem, a wide range of classification techniques have been implemented and used Shen, 2007a, 2006a;Chou and Shen, 2008;Wan et al, 2013;Chou and Cai, 2002;Yu et al, 2013;Nanni et al, 2013aNanni et al, , 2013bShen and Chou, 2007;Huang and Yuan, 2013). Among these classifiers, Support Vector Machine (SVM) (Wan et al, 2013;Yu et al, 2013;Pierleoni et al, 2011;Du and Yu, 2013;Matsuda et al, 2005) or K-Nearest Neighbor (KNN) based classifiers Shen and Chou, 2010a;Chen et al, 2013aChen et al, , 2012 have attained the most promising results.…”

Section: Introductionmentioning

confidence: 98%

“…Among these classifiers, Support Vector Machine (SVM) (Wan et al, 2013;Yu et al, 2013;Pierleoni et al, 2011;Du and Yu, 2013;Matsuda et al, 2005) or K-Nearest Neighbor (KNN) based classifiers Shen and Chou, 2010a;Chen et al, 2013aChen et al, , 2012 have attained the most promising results. However, the most significant enhancements in protein subcellular localization prediction have been achieved by improving the feature extraction techniques rather than improving on the classification techniques being used (Tantoso and Li, 2008).…”

Section: Introductionmentioning

confidence: 99%

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Gram-positive and Gram-negative protein subcellular localization by incorporating evolutionary-based descriptors into Chou׳s general PseAAC

Dehzangi

Heffernan

Sharma

et al. 2015

Journal of Theoretical Biology

230

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Protein subcellular localization is defined as predicting the functioning location of a given protein in the cell. It is considered an important step towards protein function prediction and drug design. Recent studies have shown that relying on Gene Ontology (GO) for feature extraction can improve protein subcellular localization prediction performance. However, relying solely on GO, this problem remains unsolved. At the same time, the impact of other sources of features especially evolutionary-based features has not been explored adequately for this task. In this study, we aim to extract discriminative evolutionary features to tackle this problem. To do this, we propose two segmentation based feature extraction methods to explore potential local evolutionary-based information for Gram-positive and Gram-negative subcellular localizations. We will show that by applying a Support Vector Machine (SVM) classifier to our extracted features, we are able to enhance Gram-positive and Gram-negative subcellular localization prediction accuracies by up to 6.4% better than previous studies including the studies that used GO for feature extraction.

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Section: Feature Vector Subcellular Locations Overallmentioning

confidence: 59%

Section: Evaluation Methodsmentioning

confidence: 98%

Section: Introductionmentioning

confidence: 98%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Gram-positive and Gram-negative protein subcellular localization by incorporating evolutionary-based descriptors into Chou׳s general PseAAC

Dehzangi

Heffernan

Sharma

et al. 2015

Journal of Theoretical Biology

230

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“…Based on our previous research [20], this work is intended to further improve the efficiency of recognition among different types of Gram-negative bacterial secreted proteins. Firstly, two different substitution models are developed based on AAC, PSSM and N-terminal signal peptides.…”

Section: Introductionmentioning

confidence: 99%

An Improved Approach for Rapidly Identifying Different Types of Gram-Negative Bacterial Secreted Proteins

Yu¹,

Liu²,

Du³

et al. 2018

Self Cite

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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 Se-cretP v.2.2 is proposed to rapidly and accurately distinguish different types of Gram-negative bacterial 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.

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Protein Sorting Prediction

Nielsen

2023

Methods in Molecular Biology

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In silico identification of Gram-negative bacterial secreted proteins from primary sequence

Cited by 13 publications

References 59 publications

Gram-positive and Gram-negative protein subcellular localization by incorporating evolutionary-based descriptors into Chou׳s general PseAAC

Gram-positive and Gram-negative protein subcellular localization by incorporating evolutionary-based descriptors into Chou׳s general PseAAC

An Improved Approach for Rapidly Identifying Different Types of Gram-Negative Bacterial Secreted Proteins

Protein Sorting Prediction

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