An optimal set of features for predicting type IV secretion system effector proteins for a subset of species based on a multi-level feature selection approach

Abstract

Type IV secretion systems (T4SS) are multi-protein complexes in a number of bacterial pathogens that can translocate proteins and DNA to the host. Most T4SSs function in conjugation and translocate DNA; however, approximately 13% function to secrete proteins, delivering effector proteins into the cytosol of eukaryotic host cells. Upon entry, these effectors manipulate the host cell’s machinery for their own benefit, which can result in serious illness or death of the host. For this reason recognition of T4SS effectors has become an important subject. Much previous work has focused on verifying effectors experimentally, a costly endeavor in terms of money, time, and effort. Having good predictions for effectors will help to focus experimental validations and decrease testing costs. In recent years, several scoring and machine learning-based methods have been suggested for the purpose of predicting T4SS effector proteins. These methods have used different sets of features for prediction, and their predictions have been inconsistent. In this paper, an optimal set of features is presented for predicting T4SS effector proteins using a statistical approach. A thorough literature search was performed to find features that have been proposed. Feature values were calculated for datasets of known effectors and non-effectors for T4SS-containing pathogens for four genera with a sufficient number of known effectors, Legionella pneumophila, Coxiella burnetii, Brucella spp, and Bartonella spp. The features were ranked, and less important features were filtered out. Correlations between remaining features were removed, and dimensional reduction was accomplished using principal component analysis and factor analysis. Finally, the optimal features for each pathogen were chosen by building logistic regression models and evaluating each model. The results based on evaluation of our logistic regression models confirm the effectiveness of our four optimal sets of features, and based on these an optimal set of features is proposed for all T4SS effector proteins.

EffHunter: A Tool for Prediction of Effector Protein Candidates in Fungal Proteomic Databases

“…These databases comprise, to date, more than 4000 proteins involved in pathogenicity, from more than 260 plant and animal pathogens; 70% of them being phytopathogens [19,20]. The search for protein effectors in the PHI base for the positive data set was done using the following criteria: length ≤400 amino acids [5,[21][22][23][24][25], ≥4 cysteine residues [26,27], presence of signal peptide and lack of transmembrane domains [1,27,28].…”

T4SE-XGB: interpretable sequence-based prediction of type IV secreted effectors using eXtreme gradient boosting algorithm

“…The frequencies of dipeptides at the C-terminus, like SS, KE, EE, EK, AA, AG and LL involved in former studies have shown variances between effectors and the non-effectors were also calculated (Zou and Chen, 2016;Zou, et al, 2013). (iii) We also searched for several types of protein motifs including nuclear localization signals (NLS), E-Block (EEXXE motif), conserved EPIYA motifs (EPIYA_CON), hypothetical EPIYA motifs (EPIYA_HYS) and Prenylation Domain (CaaX motif) that have been proposed and extracted before Esna Ashari, et al, 2018;Noroy, et al, 2019).…”

“…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;Noroy, et al, 2019;Sankarasubramanian, et al, 2016) 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 (Acici, et al, 2019;Ashari, et al, 2017;Burstein, et al, 2009;Chao, et al, 2019;Esna Ashari, et al, 2018;Guo, et al, 2018;Hong, et al, 2019;Li, et al, 2020;Lifshitz, et al, 2013;Wang, et al, 2014;Xiong, et al, 2018;Xue, et al, 2018;Yan, et al, 2020;Zou, et al, 2013). In the latter group of methods, Burstein et al (Burstein, et al, 2009) worked on Legionella pneumophila to identify T4SEs and validated 40 novel effectors which were predicted by machine learning algorithms.…”

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

“…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.…”