Abstract
Gram-positive bacteria have developed secretion systems to transport proteins across their cell wall, a process that plays an important role during host infection. These secretion mechanisms have also been harnessed for therapeutic purposes in many biotechnology applications. Accordingly, the identification of features that select a protein for efficient secretion from these microorganisms has become an important task. Among all the secreted proteins, “non-classical” secreted proteins are difficult to identify as they lack discernable signal peptide sequences and can make use of diverse secretion pathways. Currently, several computational methods have been developed to facilitate the discovery of such non-classical secreted proteins; however, the existing methods are based on either simulated or limited experimental datasets. In addition, they often employ basic features to train the models in a simple and coarse-grained manner. The availability of more experimentally validated datasets, advanced feature engineering techniques and novel machine learning approaches creates new opportunities for the development of improved predictors of “non-classical” secreted proteins from sequence data.
Results
In this work, we first constructed a high-quality dataset of experimentally verified “non-classical” secreted proteins, which we then used to create benchmark datasets. Using these benchmark datasets, we comprehensively analyzed a wide range of features and assessed their individual performance. Subsequently, we developed a two-layer LightGBM ensemble model that integrates several single-feature based models into an overall prediction framework. At this stage, LightGBM, a gradient boosting machine, was used as a machine learning approach and the necessary parameter optimization was performed by a particle swarm optimization (PSO) strategy. All single feature-based LightGBM models were then integrated into a unified ensemble model to further improve the predictive performance. Consequently, the final ensemble model achieved a superior performance with an Accuracy of 0.900, an F-value of 0.903, Matthew’s correlation coefficient of 0.803, and an area under the curve value of 0.963, and outperforming previous state-of-the-art predictors on the independent test. Based on our proposed optimal ensemble model, we further developed an accessible online predictor, PeNGaRoo, to serve users’ demands. We believe this online web server, together with our proposed methodology, will expedite the discovery of non-classically secreted effector proteins in Gram-positive bacteria and further inspire the development of next-generation predictors.
Original language | English |
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Article number | btz629 |
Number of pages | 8 |
Journal | Bioinformatics |
DOIs | |
Publication status | Accepted/In press - 8 Aug 2019 |
Cite this
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PeNGaRoo, a combined gradient boosting and ensemble learning framework for predicting non-classical secreted proteins. / Zhang, Yanju; Yu, Sha; Xie, Ruopeng; Li, Jiahui; Leier, Andre; Marquez-Lago, Tatiana T.; Akutsu, Tatsuya; Smith, A. Ian; Ge, Zongyuan; Wang, Jiawei; Lithgow, Trevor; Song, Jiangning.
In: Bioinformatics, 08.08.2019.Research output: Contribution to journal › Article › Research › peer-review
TY - JOUR
T1 - PeNGaRoo, a combined gradient boosting and ensemble learning framework for predicting non-classical secreted proteins
AU - Zhang, Yanju
AU - Yu, Sha
AU - Xie, Ruopeng
AU - Li, Jiahui
AU - Leier, Andre
AU - Marquez-Lago, Tatiana T.
AU - Akutsu, Tatsuya
AU - Smith, A. Ian
AU - Ge, Zongyuan
AU - Wang, Jiawei
AU - Lithgow, Trevor
AU - Song, Jiangning
PY - 2019/8/8
Y1 - 2019/8/8
N2 - MotivationGram-positive bacteria have developed secretion systems to transport proteins across their cell wall, a process that plays an important role during host infection. These secretion mechanisms have also been harnessed for therapeutic purposes in many biotechnology applications. Accordingly, the identification of features that select a protein for efficient secretion from these microorganisms has become an important task. Among all the secreted proteins, “non-classical” secreted proteins are difficult to identify as they lack discernable signal peptide sequences and can make use of diverse secretion pathways. Currently, several computational methods have been developed to facilitate the discovery of such non-classical secreted proteins; however, the existing methods are based on either simulated or limited experimental datasets. In addition, they often employ basic features to train the models in a simple and coarse-grained manner. The availability of more experimentally validated datasets, advanced feature engineering techniques and novel machine learning approaches creates new opportunities for the development of improved predictors of “non-classical” secreted proteins from sequence data.ResultsIn this work, we first constructed a high-quality dataset of experimentally verified “non-classical” secreted proteins, which we then used to create benchmark datasets. Using these benchmark datasets, we comprehensively analyzed a wide range of features and assessed their individual performance. Subsequently, we developed a two-layer LightGBM ensemble model that integrates several single-feature based models into an overall prediction framework. At this stage, LightGBM, a gradient boosting machine, was used as a machine learning approach and the necessary parameter optimization was performed by a particle swarm optimization (PSO) strategy. All single feature-based LightGBM models were then integrated into a unified ensemble model to further improve the predictive performance. Consequently, the final ensemble model achieved a superior performance with an Accuracy of 0.900, an F-value of 0.903, Matthew’s correlation coefficient of 0.803, and an area under the curve value of 0.963, and outperforming previous state-of-the-art predictors on the independent test. Based on our proposed optimal ensemble model, we further developed an accessible online predictor, PeNGaRoo, to serve users’ demands. We believe this online web server, together with our proposed methodology, will expedite the discovery of non-classically secreted effector proteins in Gram-positive bacteria and further inspire the development of next-generation predictors.
AB - MotivationGram-positive bacteria have developed secretion systems to transport proteins across their cell wall, a process that plays an important role during host infection. These secretion mechanisms have also been harnessed for therapeutic purposes in many biotechnology applications. Accordingly, the identification of features that select a protein for efficient secretion from these microorganisms has become an important task. Among all the secreted proteins, “non-classical” secreted proteins are difficult to identify as they lack discernable signal peptide sequences and can make use of diverse secretion pathways. Currently, several computational methods have been developed to facilitate the discovery of such non-classical secreted proteins; however, the existing methods are based on either simulated or limited experimental datasets. In addition, they often employ basic features to train the models in a simple and coarse-grained manner. The availability of more experimentally validated datasets, advanced feature engineering techniques and novel machine learning approaches creates new opportunities for the development of improved predictors of “non-classical” secreted proteins from sequence data.ResultsIn this work, we first constructed a high-quality dataset of experimentally verified “non-classical” secreted proteins, which we then used to create benchmark datasets. Using these benchmark datasets, we comprehensively analyzed a wide range of features and assessed their individual performance. Subsequently, we developed a two-layer LightGBM ensemble model that integrates several single-feature based models into an overall prediction framework. At this stage, LightGBM, a gradient boosting machine, was used as a machine learning approach and the necessary parameter optimization was performed by a particle swarm optimization (PSO) strategy. All single feature-based LightGBM models were then integrated into a unified ensemble model to further improve the predictive performance. Consequently, the final ensemble model achieved a superior performance with an Accuracy of 0.900, an F-value of 0.903, Matthew’s correlation coefficient of 0.803, and an area under the curve value of 0.963, and outperforming previous state-of-the-art predictors on the independent test. Based on our proposed optimal ensemble model, we further developed an accessible online predictor, PeNGaRoo, to serve users’ demands. We believe this online web server, together with our proposed methodology, will expedite the discovery of non-classically secreted effector proteins in Gram-positive bacteria and further inspire the development of next-generation predictors.
U2 - 10.1093/bioinformatics/btz629
DO - 10.1093/bioinformatics/btz629
M3 - Article
JO - Bioinformatics
JF - Bioinformatics
SN - 1367-4803
M1 - btz629
ER -