Determining Optimal Features for Predicting Type IV Secretion System Effector Proteins for
            <i>Coxiella burnetii</i>

Ashari, Zhila Esna; Brayton, Kelly A.; Broschat, Shira L.

doi:10.1145/3107411.3107416

Cited by 6 publications

(5 citation statements)

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“…The total number of features, including elements of vector features, was 1,027. The complete list of these features and the tools and software needed for their computation are presented in Esna Ashari et al (2017, 2018).…”

Section: Methodsmentioning

confidence: 99%

“…We suspect that the use of these differing feature sets explains the differences in effector predictions by the different algorithms. As a result of the disparities between the results of earlier methods, we assembled all the features used in prior studies and used a multi-level, statistical approach to determine which were the most effective in predicting effector proteins (Esna Ashari et al, 2017, 2018). Because of the number of validated effectors available for L. pneumophila , we then ran a number of experiments on the whole genome of L. pneumophila using our optimal set of features (Esna Ashari et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

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Prediction of T4SS Effector Proteins for Anaplasma phagocytophilum Using OPT4e, A New Software Tool

2019

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Type IV secretion systems (T4SS) are used by a number of bacterial pathogens to attack the host cell. The complex protein structure of the T4SS is used to directly translocate effector proteins into host cells, often causing fatal diseases in humans and animals. Identification of effector proteins is the first step in understanding how they function to cause virulence and pathogenicity. Accurate prediction of effector proteins via a machine learning approach can assist in the process of their identification. The main goal of this study is to predict a set of candidate effectors for the tick-borne pathogen Anaplasma phagocytophilum , the causative agent of anaplasmosis in humans. To our knowledge, we present the first computational study for effector prediction with a focus on A. phagocytophilum . In a previous study, we systematically selected a set of optimal features from more than 1,000 possible protein characteristics for predicting T4SS effector candidates. This was followed by a study of the features using the proteome of Legionella pneumophila strain Philadelphia deduced from its complete genome. In this manuscript we introduce the OPT4e software package for Optimal-features Predictor for T4SS Effector proteins. An earlier version of OPT4e was verified using cross-validation tests, accuracy tests, and comparison with previous results for L. pneumophila . We use OPT4e to predict candidate effectors from the proteomes of A. phagocytophilum strains HZ and HGE-1 and predict 48 and 46 candidates, respectively, with 16 and 18 deemed most probable as effectors. These latter include the three known validated effectors for A. phagocytophilum .

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Prediction of T4SS Effector Proteins for Anaplasma phagocytophilum Using OPT4e, A New Software Tool

2019

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“…This proteome contains 2,942 protein sequences and was used as our 134 test set [S2 File]. We calculated the feature values for all the protein sequences in L. pneumophila using different tools 135 and programming languages as described in [11]. We then used our three models for de novo prediction of effector 136 proteins in the L. pneumophila proteome.…”

Section: (C) Machine Learning Models and Validationmentioning

confidence: 99%

“…As a result, recent studies have 25 focused on using prediction approaches such as scoring effector proteins based on their characteristics or using machine 26 learning algorithms [5][6][7][8][9][10]. Because these methods considered different sets of features, we examined their effectiveness 27 in an earlier study and determined a set of optimal features for prediction of T4SS effector proteins [11][12]. By features, 28 we refer here to the characteristics and properties of protein sequences that can be measured and thus assigned binary or 29 continuous numerical values.…”

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Using an Optimal Set of Features with a Machine Learning-Based Approach to Predict Effector Proteins forLegionella pneumophila

Ashari

Brayton

Broschat

2018

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2 Type IV secretion systems exist in a number of bacterial pathogens and are used to secrete effector proteins directly into 3 host cells in order to change their environment making the environment hospitable for the bacteria. In recent years, 4 several machine learning algorithms have been developed to predict effector proteins, potentially facilitating experimental 5 verification. However, inconsistencies exist between their results. Previously we analysed the disparate sets of predictive 6 features used in these algorithms to determine an optimal set of 370 features for effector prediction. This work focuses on 7 the best way to use these optimal features by designing three machine learning classifiers, comparing our results with 8 those of others, and obtaining de novo results. We chose the pathogen Legionella pneumophila strain Philadelphia-1, a 9 cause of Legionnaires' disease, because it has many validated effector proteins and others have developed machine 10 learning prediction tools for it. While all of our models give good results indicating that our optimal features are quite 11 robust, Model 1, which uses all 370 features with a support vector machine, has slightly better accuracy. Moreover, 12 Model 1 predicted 760 effector proteins, more than any other study, 315 of which have been validated. Although the 13 results of our three models agree well with those of other researchers, their models only predicted 126 and 311 candidate 14 effectors. 15 16 Introduction 17 Bacterial pathogens can use secretion systems to deliver proteins to the host cell. There are nine known secretion systems, 18 but the focus of this work is on the type IV secretion system (T4SS). The T4SS is composed of multiple proteins 102 features were divided among our three classifiers as follows: i) features related to PSSM composition, ii) features related 103 to the auto-covariance correlation of PSSM, and iii) chemical, structural, and compositional features [S1 Table] (e.g., 104 amino acid composition, dipeptide composition, average hydropathy, total hydropathy, hydropathy of C terminal,105 hydropathy of N terminal, number of coiled coil regions, signal peptide probability, polarity, molecular mass, length, and 106 homology to known effectors). For our second ensemble classifier, Model 3, the three groups of features divided among 107 our classifiers were as follows: i) PSSM-related features (PSSM composition and auto covariance correlation of PSSM), 108 ii) features related to the composition of amino acids in protein sequences (amino acid composition and dipeptide 109 composition), and iii) chemical and structural features (average hydropathy, total hydropathy, hydropathy of C terminal, 110 hydropathy of N terminal, number of coiled coil regions, signal peptide probability, polarity, molecular mass, length, and 111 homology to known effectors). 112After building our dataset and designing our machine learning classifiers, we used 10-fold cross-validation to 113 validate our models and to test for overfitting in the results. The...

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“…Instead, a large number of computational methods have been developed for prediction of T4SEs in the last decade, which successfully speed up the process in terms of time and efficiency. These computational approaches can be categorized into two main groups: the first group of approaches infer new effectors based on sequence similarity with currently known effectors (Chen et al, 2010 ; Lockwood et al, 2011 ; Marchesini et al, 2011 ; Meyer et al, 2013 ; Sankarasubramanian et al, 2016 ; Noroy et al, 2019 ) or phylogenetic profiling analysis (Zalguizuri et al, 2019 ), and the second group of approaches involve learning the patterns of known secreted effectors that distinguish them from non-secreted proteins based on machine learning and deep learning techniques (Burstein et al, 2009 ; Lifshitz et al, 2013 ; Zou et al, 2013 ; Wang et al, 2014 ; Ashari et al, 2017 ; Wang Y. et al, 2017 ; Esna Ashari et al, 2018 , 2019a , b ; Guo et al, 2018 ; Xiong et al, 2018 ; Xue et al, 2018 ; Acici et al, 2019 ; Chao et al, 2019 ; Hong et al, 2019 ; Wang J. et al, 2019 ; Li J. et al, 2020 ; Yan et al, 2020 ). In the latter group of methods, Burstein et al ( 2009 ) worked on Legionella pneumophila to identify T4SEs and validated 40 novel effectors which were predicted by machine learning algorithms.…”

Section: Introductionmentioning

confidence: 99%

T4SE-XGB: Interpretable Sequence-Based Prediction of Type IV Secreted Effectors Using eXtreme Gradient Boosting Algorithm

et al. 2020

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Type IV secreted effectors (T4SEs) can be translocated into the cytosol of host cells via type IV secretion system (T4SS) and cause diseases. However, experimental approaches to identify T4SEs are time-and resource-consuming, and the existing computational tools based on machine learning techniques have some obvious limitations such as the lack of interpretability in the prediction models. In this study, we proposed a new model, T4SE-XGB, which uses the eXtreme gradient boosting (XGBoost) algorithm for accurate identification of type IV effectors based on optimal features based on protein sequences. After trying 20 different types of features, the best performance was achieved when all features were fed into XGBoost by the 5-fold cross validation in comparison with other machine learning methods. Then, the ReliefF algorithm was adopted to get the optimal feature set on our dataset, which further improved the model performance. T4SE-XGB exhibited highest predictive performance on the independent test set and outperformed other published prediction tools. Furthermore, the SHAP method was used to interpret the contribution of features to model predictions. The identification of key features can contribute to improved understanding of multifactorial contributors to host-pathogen interactions and bacterial pathogenesis. In addition to type IV effector prediction, we believe that the proposed framework can provide instructive guidance for similar studies to construct prediction methods on related biological problems. The data and source code of this study can be freely accessed at https://github.com/CT001002/T4SE-XGB.

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Determining Optimal Features for Predicting Type IV Secretion System Effector Proteins for Coxiella burnetii

Cited by 6 publications

References 20 publications

Prediction of T4SS Effector Proteins for Anaplasma phagocytophilum Using OPT4e, A New Software Tool

Prediction of T4SS Effector Proteins for Anaplasma phagocytophilum Using OPT4e, A New Software Tool

Using an Optimal Set of Features with a Machine Learning-Based Approach to Predict Effector Proteins forLegionella pneumophila

T4SE-XGB: Interpretable Sequence-Based Prediction of Type IV Secreted Effectors Using eXtreme Gradient Boosting Algorithm

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