PreAcrs: a machine learning framework for identifying anti-CRISPR proteins

Published online: 25 October 2022Background: Anti-CRISPR proteins are potent modulators that inhibit the CRISPRCas immunity system and have huge potential in gene editing and gene therapy as a genome-editing tool. Extensive studies have shown that anti-CRISPR proteins are essential for modifying endogenous genes, promoting the RNA-guided binding and cleavage of DNA or RNA substrates. In recent years, identifying and characterizing anti-CRISPR proteins has become a hot and significant research topic in bioinformatics. However, as most anti-CRISPR proteins fall short in sharing similarities to those currently known, traditional screening methods are time-consuming and inefficient. Machine learning methods could fill this gap with powerful predictive capability and provide a new perspective for anti-CRISPR protein identification. Results: Here, we present a novel machine learning ensemble predictor, called PreAcrs, to identify anti-CRISPR proteins from protein sequences directly. Three features and eight different machine learning algorithms were used to train PreAcrs. PreAcrs outperformed other existing methods and significantly improved the prediction accuracy for identifying anti-CRISPR proteins. Conclusions: In summary, the PreAcrs predictor achieved a competitive performance for predicting new anti-CRISPR proteins in terms of accuracy and robustness. We anticipate PreAcrs will be a valuable tool for researchers to speed up the research process. The source code is available at: https://github.com/Lyn-666/anti_CRISPR.git.Lin Zhu, Xiaoyu Wang, Fuyi Li and Jiangning Son

Systematic analysis and prediction of type IV secreted effector proteins by machine learning approaches

In the course of infecting their hosts, pathogenic bacteria secrete numerous effectors, namely, bacterial proteins that pervert host cell biology. Many Gram-negative bacteria, including context-dependent human pathogens, use a type IV secretion system (T4SS) to translocate effectors directly into the cytosol of host cells. Various type IV secreted effectors (T4SEs) have been experimentally validated to play crucial roles in virulence by manipulating host cell gene expression and other processes. Consequently, the identification of novel effector proteins is an important step in increasing our understanding of host–pathogen interactions and bacterial pathogenesis. Here, we train and compare six machine learning models, namely, Naïve Bayes (NB), K-nearest neighbor (KNN), logistic regression (LR), random forest (RF), support vector machines (SVMs) and multilayer perceptron (MLP), for the identification of T4SEs using 10 types of selected features and 5-fold cross-validation. Our study shows that: (1) including different but complementary features generally enhance the predictive performance of T4SEs; (2) ensemble models, obtained by integrating individual single-feature models, exhibit a significantly improved predictive performance and (3) the ‘majority voting strategy’ led to a more stable and accurate classification performance when applied to predicting an ensemble learning model with distinct single features. We further developed a new method to effectively predict T4SEs, Bastion4 (Bacterial secretion effector predictor for T4SS), and we show our ensemble classifier clearly outperforms two recent prediction tools. In summary, we developed a state-of-the-art T4SE predictor by conducting a comprehensive performance evaluation of different machine learning algorithms along with a detailed analysis of single- and multi-feature selections.ISSN:1467-5463ISSN:1477-405

Systematic analysis and prediction of type IV secreted effector proteins by machine learning approaches

In the course of infecting their hosts, pathogenic bacteria secrete numerous effectors, namely, bacterial proteins that pervert host cell biology. Many Gram-negative bacteria, including context-dependent human pathogens, use a type IV secretion system (T4SS) to translocate effectors directly into the cytosol of host cells. Various type IV secreted effectors (T4SEs) have been experimentally validated to play crucial roles in virulence by manipulating host cell gene expression and other processes. Consequently, the identification of novel effector proteins is an important step in increasing our understanding of host-pathogen interactions and bacterial pathogenesis. Here, we train and compare six machine learning models, namely, Naive Bayes (NB), K-nearest neighbor (KNN), logistic regression (LR), random forest (RF), support vector machines (SVMs) and multilayer perceptron (MLP), for the identification of T4SEs using 10 types of selected features and 5-fold cross-validation. Our study shows that: (1) including different but complementary features generally enhance the predictive performance of T4SEs; (2) ensemble models, obtained by integrating individual single-feature models, exhibit a significantly improved predictive performance and (3) the majority voting strategy' led to a more stable and accurate classification performance when applied to predicting an ensemble learning model with distinct single features. We further developed a new method to effectively predict T4SEs, Bastion4 (Bacterial secretion effector predictor for T4SS), and we show our ensemble classifier clearly outperforms two recent prediction tools. In summary, we developed a state-of-the-art T4SE predictor by conducting a comprehensive performance evaluation of different machine learning algorithms along with a detailed analysis of single- and multi-feature selections

ICR ANNUAL REPORT 2019 (Volume 26)[All Pages]

This Annual Report covers from 1 January to 31 December 201

The panoply of Brucella: search for Type IV Secretion System effectors and characterization of lysozyme inhibitors

RESUMEN: Brucella es un patógeno intracelular que ha desarrollado una serie de mecanismos que permiten a la bacteria enfrentarse a las adversidades con las que se encuentra dentro del hospedador. Entre todos los mecanismos desarrollados por Brucella nos centramos en los efectores del Sistema de Secreción de tipo IV y en los inhibidores de lisozima. Hemos establecido un nuevo método de cribado de efectores basado en un ensayo de interferencia con la replicación del virus de la fiebre amarilla (YFV). Usando este nuevo método se han encontrado 8 proteínas de Brucella que incrementan la replicación del YFV y otras 9 proteínas que la disminuyen. Por otro lado, se ha visto que la proteína BAB1_0466 de Brucella inhibe la actividad lítica de la lisozima y que esta proteína es necesaria para la supervivencia de Brucella abortus durante la infección de sangre completa, donde los monocitos podrían estar jugando un papel fundamental.ABSTRACT: Brucella is an intracellular pathogen which has developed some mechanisms to face the adversities found inside the host. Among all the mechanisms of Brucella, we centered our study on the translocation of effector proteins to subvert the host cell and the inhibition of the host lysozyme activity. We have set up a new effector screening method based on assaying the interference with replication of the YFV. The screening showed 8 Brucella proteins that upregulate YFV and other 9 proteins that downregulate YFV. Moreover, it was showed that the Brucella protein named BAB1_0466 inhibits the lytic activity of lysozyme and is necessary for Brucella abortus survival in whole blood. In fact, BAB1_0466 could be important for B. abortus survival inside monocytes.La financiación para la realización de esta Tesis doctoral ha sido proporcionada por el Ministerio de Ciencia e Innovación (proyecto BFU2011-25658) y por la Universidad de Cantabria (proyectos 55.VP23.64005 y 55.JU07.64661)