CRISPR-Cas is a tool that is widely used for gene editing. However, unexpected off-target effects may occur as a result of long-term nuclease activity. Anti-CRISPR proteins, which are powerful molecules that inhibit the CRISPR–Cas system, may have the potential to promote better utilization of the CRISPR-Cas system in gene editing, especially for gene therapy. Additionally, more in-depth research on these proteins would help researchers to better understand the co-evolution of bacteria and phages. Therefore, it is necessary to collect and integrate data on various types of anti-CRISPRs. Herein, data on these proteins were manually gathered through data screening of the literatures. Then, the first online resource, anti-CRISPRdb, was constructed for effectively organizing these proteins. It contains the available protein sequences, DNA sequences, coding regions, source organisms, taxonomy, virulence, protein interactors and their corresponding three-dimensional structures. Users can access our database at http://cefg.uestc.edu.cn/anti-CRISPRdb/ without registration. We believe that the anti-CRISPRdb can be used as a resource to facilitate research on anti-CRISPR proteins and in related fields.

Inconsistent results on the association between evolutionary rates and amino acid composition of proteins have been reported in eukaryotes. However, there are few studies of how amino acid composition can influence evolutionary rates in bacteria. Thus, we constructed linear regression models between composition frequencies of amino acids and evolutionary rates for bacteria. Compositions of all amino acids can on average explain 21.5% of the variation in evolutionary rates among 273 investigated bacterial organisms. In five model organisms, amino acid composition contributes more to variation in evolutionary rates than protein abundance, and frequency of optimal codons. The contribution of individual amino acid composition to evolutionary rate varies among organisms. The closer the GC-content of genome to its maximum or minimum, the better the correlation between the amino acid content and the evolutionary rate of proteins would appear in that genome. The types of amino acids that significantly contribute to evolutionary rates can be grouped into GC-rich and AT-rich amino acids. Besides, the amino acid with high composition also contributes more to evolutionary rates than amino acid with low composition in proteome. In summary, amino acid composition significantly contributes to the rate of evolution in bacterial organisms and this in turn is impacted by GC-content.

Investigation of essential genes is significant to comprehend the minimal gene sets of cell and discover potential drug targets. In this study, a novel approach based on multiple homology mapping and machine learning method was introduced to predict essential genes. We focused on 25 bacteria which have characterized essential genes. The predictions yielded the highest area under receiver operating characteristic (ROC) curve (AUC) of 0.9716 through tenfold cross-validation test. Proper features were utilized to construct models to make predictions in distantly related bacteria. The accuracy of predictions was evaluated via the consistency of predictions and known essential genes of target species. The highest AUC of 0.9552 and average AUC of 0.8314 were achieved when making predictions across organisms. An independent dataset from Synechococcus elongatus, which was released recently, was obtained for further assessment of the performance of our model. The AUC score of predictions is 0.7855, which is higher than other methods. This research presents that features obtained by homology mapping uniquely can achieve quite great or even better results than those integrated features. Meanwhile, the work indicates that machine learning-based method can assign more efficient weight coefficients than using empirical formula based on biological knowledge.

Background: The number of biological Knowledge bases/databases storing metabolic pathway information and models has been growing rapidly. These resources are diverse in the type of information/data, the analytical tools, and objectives. Here we present a review of the most popular metabolic pathway databases and model repositories, focusing on their scope, content including reactions, enzymes, compounds, and genes, and applicability. The review aims to help researchers choose a suitable database or model repository according to the information and data required, by providing an insight look of each pathway resource. Results: Four pathways databases and three model repositories were selected on the basis of popularity and diversity. Our review showed that the pathway resources vary in many aspects, such as their scope, content, access to data and the tools. In addition, inconsistencies have been observed in nomenclature and representation of database entities. The three model repositories reviewed do not offer a brief description of the models' characteristics such as simulation conditions. Conclusions: The inconsistencies among the databases in representing their contents may hamper the maximal use of the knowledge accumulated in these databases in particular and the area of systems biology at large. Therefore, it is strongly recommended that the database creators and the metabolic network models developers should follow international standards for the nomenclature of reactions and metabolites. Besides, computationally generated models that could be obtained from model repositories should be utilized with manual curations as they lack some important components that are necessary for full functionality of the models.Keywords: metabolic pathway; database; model repository Author summary: Four metabolic pathway databases and three model repositories were reviewed with regard to their scope, content, and applicability. Despite their innumerable use in the fields of systems biology and metabolic engineering, these pathway databases and model repositories are not in harmony with each other due to the inconsistencies in the way they represent their contents. Besides, the automatically generated metabolic models that can be obtained from the model repositories are not accurate enough for further scientific usage without additional manual curation. Therefore, international standards such as IUBMB principles should be strictly obeyed in creating such metabolic pathway resources.