Shotgun EM of mycobacterial protein complexes during stationary phase stress

There is little structural information about the protein complexes conferring resistance in Mycobacterium tuberculosis (Mtb) to anti-microbial oxygen and nitrogen radicals in the phagolysosome. Here, we expose the model Mycobacterium, Mycobacterium smegmatis, to simulated oxidative-stress conditions and apply a shotgun EM method for the structural detection of the resulting protein assemblies. We identified: glutamine synthetase I, essential for Mtb virulence; bacterioferritin A, critical for Mtb iron regulation; aspartyl aminopeptidase M18, a protease; and encapsulin, which produces a cage-like structure to enclose cargo proteins. After further investigation, we found that encapsulin carries dye-decolourising peroxidase, a protein antioxidant, as its primary cargo under the conditions tested.

TooT-T: discrimination of transport proteins from non-transport proteins

Background

Membrane transport proteins (transporters) play an essential role in every living cell by transporting hydrophilic molecules across the hydrophobic membranes. While the sequences of many membrane proteins are known, their structure and function is still not well characterized and understood, owing to the immense effort needed to characterize them. Therefore, there is a need for advanced computational techniques takes sequence information alone to distinguish membrane transporter proteins; this can then be used to direct new experiments and give a hint about the function of a protein.

Results

This work proposes an ensemble classifier TooT-T that is trained to optimally combine the predictions from homology annotation transfer and machine-learning methods to determine the final prediction. Experimental results obtained by cross-validation and independent testing show that combining the two approaches is more beneficial than employing only one.

Conclusion

The proposed model outperforms all of the state-of-the-art methods that rely on the protein sequence alone, with respect to accuracy and MCC. TooT-T achieved an overall accuracy of 90.07% and 92.22% and an MCC 0.80 and 0.82 with the training and independent datasets, respectively.

A Machine Learning-Based QSAR Model for Benzimidazole Derivatives as Corrosion Inhibitors by Incorporating Comprehensive Feature Selection

BACKGROUND

Computational prediction of inhibition efficiency (IE) for inhibitor molecules is a crucial supplementary way to design novel molecules that can efficiently inhibit corrosion onto metallic surfaces.

PURPOSE

Here we are dedicated to developing a new machine learning-based predictor for the inhibition efficiency (IE) of benzimidazole derivatives.

METHODS

First, a comprehensively numerical representation was given on inhibitor molecules from all aspects of energy, electronic, topological, physicochemical and spatial properties based on 3-D structures and 150 valid structural descriptors were obtained. Then, a thorough investigation of these structural descriptors was implemented. The multicollinearity-based clustering analysis was performed to remove the linear correlated feature variables, so 47 feature clusters were produced. Meanwhile, Gini importance by random forest (RF) was used to further measure the contributions of the descriptors in each cluster and 47 non-linear descriptors were selected with the highest Gini importance score in the corresponding cluster. Further, considering the limited number of available inhibitors, different feature subsets were constructed according to the Gini importance score ranking list of 47 descriptors.

RESULTS

Finally, support vector machine (SVM) models based on different feature subsets were tested by leave-one-out cross validation. Through comparisons, the optimal SVM model with the top 11 descriptors was achieved based on Poly kernel. This model yields a promising performance with the correlation coefficient (R) and root-mean-square error (RMSE) of 0.9589 and 4.45, respectively, which indicates that the method proposed by us gives the best performance for the current data.

CONCLUSION

Based on our model, 6 new benzimidazole molecules were designed and their IE values predicted by this model indicate that two of them have high potential as outstanding corrosion inhibitors.

Distinguishing the disease-associated SNPs based on composition frequency analysis

Single-nucleotide polymorphism (SNP) is a basical variation in genome. When SNPs occur at the binding sites of microRNA, they can influence the binding efficiency, cause a fluctuation of the mRNA in vivo, and thus arouse posttranscriptional level abnormality. Therefore, SNP has a strong correlation with diseases. Although enormous SNPs have been experimentally identified, only a tiny proportion of them are truly disease-associated SNPs (dSNPs) that relate to microRNA modification and then are involved in disease causing process. Therefore, it is important to distinguish dSNPs from the usual SNPs. Analysis here shows that composition is different between sequence segments centered by dSNP and SNP. Inspired by the composition, transition and distribution features which are meaningful and effective in characterizing proteins' sequence information, we improved and applied it to represent the frequency and physicochemical properties of a gene sequence. Binary encoding scheme was also used for further labelling four nucleic acids (A, T, C, and G). First, clustering analysis was performed to gain reasonable negative samples. Then, optimization tests were implemented on different ratios of positive vs negative samples and different feature subsets retrieved by evaluation method of F score. The optimal model constructed by random forest achieves an accuracy of more than 90% on the testing data set. Moreover, the promising results of the external validation also demonstrate the practical applicability of our method. Finally, principal component analysis on the features indicates that all features in our method gain the gross contribution to the prediction model.

Effective prediction of bacterial type IV secreted effectors by combined features of both C-termini and N-termini

Various bacterial pathogens can deliver their secreted substrates also called as effectors through type IV secretion systems (T4SSs) into host cells and cause diseases. Since T4SS secreted effectors (T4SEs) play important roles in pathogen-host interactions, identifying them is crucial to our understanding of the pathogenic mechanisms of T4SSs. A few computational methods using machine learning algorithms for T4SEs prediction have been developed by using features of C-terminal residues. However, recent studies have shown that targeting information can also be encoded in the N-terminal region of at least some T4SEs. In this study, we present an effective method for T4SEs prediction by novelly integrating both N-terminal and C-terminal sequence information. First, we collected a comprehensive dataset across multiple bacterial species of known T4SEs and non-T4SEs from literatures. Then, three types of distinctive features, namely amino acid composition, composition, transition and distribution and position-specific scoring matrices were calculated for 50 N-terminal and 100 C-terminal residues. After that, we employed information gain represent to rank the importance score of the 150 different position residues for T4SE secretion signaling. At last, 125 distinctive position residues were singled out for the prediction model to classify T4SEs and non-T4SEs. The support vector machine model yields a high receiver operating curve of 0.916 in the fivefold cross-validation and an accuracy of 85.29% for the independent test set.