Supplementary data are available at Bioinformatics online.

Anti-CRISPRs are widespread amongst bacteriophage and promote bacteriophage infection by inactivating the bacterial host's CRISPR–Cas defence system. Identifying and characterizing anti-CRISPR proteins opens an avenue to explore and control CRISPR–Cas machineries for the development of new CRISPR–Cas based biotechnological and therapeutic tools. Past studies have identified anti-CRISPRs in several model phage genomes, but a challenge exists to comprehensively screen for anti-CRISPRs accurately and efficiently from genome and metagenome sequence data. Here, we have developed an ensemble learning based predictor, PaCRISPR, to accurately identify anti-CRISPRs from protein datasets derived from genome and metagenome sequencing projects. PaCRISPR employs different types of feature recognition united within an ensemble framework. Extensive cross-validation and independent tests show that PaCRISPR achieves a significantly more accurate performance compared with homology-based baseline predictors and an existing toolkit. The performance of PaCRISPR was further validated in discovering anti-CRISPRs that were not part of the training for PaCRISPR, but which were recently demonstrated to function as anti-CRISPRs for phage infections. Data visualization on anti-CRISPR relationships, highlighting sequence similarity and phylogenetic considerations, is part of the output from the PaCRISPR toolkit, which is freely available at http://pacrispr.erc.monash.edu/.

Supplementary data are available at Bioinformatics online.

Flagellotropic bacteriophages engage flagella to reach the bacterial surface as an effective means to increase the capture radius for predation. Structural details of these viruses are of great interest given the substantial drag forces and torques they face when moving down the spinning flagellum. We show that the main capsid and auxiliary proteins form two nested chainmails that ensure the integrity of the bacteriophage head. Core stabilising structures are conserved in herpesviruses suggesting their ancestral origin. The structure of the tail also reveals a robust yet pliable assembly. Hexameric rings of the tail-tube protein are braced by the N-terminus and a β-hairpin loop, and interconnected along the tail by the splayed β-hairpins. By contrast, we show that the β-hairpin has an inhibitory role in the tail-tube precursor, preventing uncontrolled self-assembly. Dyads of acidic residues inside the tail-tube present regularly-spaced motifs well suited to DNA translocation into bacteria through the tail.

Motivation Type III secreted effectors (T3SEs) can be injected into host cell cytoplasm via type III secretion systems (T3SSs) to modulate interactions between Gram-negative bacterial pathogens and their hosts. Due to their relevance in pathogen–host interactions, significant computational efforts have been put toward identification of T3SEs and these in turn have stimulated new T3SE discoveries. However, as T3SEs with new characteristics are discovered, these existing computational tools reveal important limitations: (i) most of the trained machine learning models are based on the N-terminus (or incorporating also the C-terminus) instead of the proteins’ complete sequences, and (ii) the underlying models (trained with classic algorithms) employed only few features, most of which were extracted based on sequence-information alone. To achieve better T3SE prediction, we must identify more powerful, informative features and investigate how to effectively integrate these into a comprehensive model. Results In this work, we present Bastion3, a two-layer ensemble predictor developed to accurately identify type III secreted effectors from protein sequence data. In contrast with existing methods that employ single models with few features, Bastion3 explores a wide range of features, from various types, trains single models based on these features and finally integrates these models through ensemble learning. We trained the models using a new gradient boosting machine, LightGBM and further boosted the models’ performances through a novel genetic algorithm (GA) based two-step parameter optimization strategy. Our benchmark test demonstrates that Bastion3 achieves a much better performance compared to commonly used methods, with an ACC value of 0.959, F-value of 0.958, MCC value of 0.917 and AUC value of 0.956, which comprehensively outperformed all other toolkits by more than 5.6% in ACC value, 5.7% in F-value, 12.4% in MCC value and 5.8% in AUC value. Based on our proposed two-layer ensemble model, we further developed a user-friendly online toolkit, maximizing convenience for experimental scientists toward T3SE prediction. With its design to ease future discoveries of novel T3SEs and improved performance, Bastion3 is poised to become a widely used, state-of-the-art toolkit for T3SE prediction. Availability and implementation http://bastion3.erc.monash.edu/ Contact selkrig@embl.de or wyztli@163.com or or trevor.lithgow@monash.edu Supplementary information Supplementary data are available at Bioinformatics online.

Carbapenem-resistant, hypervirulent Klebsiella pneumoniae (CR-hvKP) has recently emerged as a significant threat to public health. In this study, 29 K. pneumoniae isolates were isolated from eight patients admitted to the intensive care unit (ICU) of a comprehensive teaching hospital located in China from March 2017 to January 2018. Clinical information of patients was the basis for the further analyses of the isolates including antimicrobial susceptibility tests, identification of antibiotic resistance and virulence gene determinants, multilocus sequence typing (MLST), XbaI-macrorestriction by pulsed-field gel electrophoresis (PFGE). Selected isolates representing distinct resistance profiles and virulence phenotypes were screened for hypervirulence in a Galleria mellonella larvae infection model. In the course of the outbreak, the overall mortality rate of patients was 100% (n = 8) attributed to complications arising from CR-hvKP infections. All isolates except one (28/29, 96.6%) were resistant to multiple antimicrobial agents, and harbored diverse resistance determinants that included the globally prevalent carbapenemase blaKPC−2. Most isolates had hypervirulent genotypes being positive for 19 virulence-associated genes, including iutA (25/29, 86.2%), rmpA (27/29, 93.1%), ybtA (27/29, 93.1%), entB (29/29, 100%), fimH (29/29, 100%), and mrkD (29/29, 100%). MLST revealed ST11 for the majority of isolates (26/29, 89,7%). Infection assays demonstrated high mortality in the Galleria mellonella model with the highest LD50 values for three isolates (<105 CFU/mL) demonstrating the degree of hypervirulence of these CR-hvKP isolates, and is discussed relative to previous outbreaks of CR-hvKP.

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.

Motivation 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 Light Gradient Boosting Machine (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 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. Availability and implementation http://pengaroo.erc.monash.edu/. Supplementary information Supplementary data are available at Bioinformatics online.