Combinatorial Chemistry & High Throughput Screening - Volume 21, Issue 6, 2018

Background: Accurately recognizing nitrated tyrosine residues from protein sequences would pave a way for understanding the mechanism of nitration and the screening of the tyrosine residues in sequences. Results: In this study, we proposed a prediction model that used the extreme learning machine (ELM) algorithm as the prediction engine to identify nitrated tyrosine residues. To encode each tyrosine residue, a sliding window technique was adopted to extract a peptide segment for each tyrosine residue, from which a number of features were extracted. These features were analyzed by a popular feature selection method, Minimum Redundancy Maximum Relevance (mRMR) method, producing a feature list, in which all features were ranked in a rigorous way. Then, the Incremental Feature Selection (IFS) method was utilized to discover the optimal features, on which the optimal ELM-based prediction model was built. This model produced satisfactory results on the training dataset with a Matthews correlation coefficient of 0.757. The model was also evaluated by an independent test dataset that contained only positive samples, yielding a sensitivity of 0.938. Conclusion: Compared to other prediction models that use classic machine learning algorithms as prediction engines on the same datasets with their own optimal features, the optimal ELM-based prediction model produced much better results, indicating the superiority of the proposed model for the identification of nitrated tyrosine residues from protein sequences.

Aims and Objective: Chemical toxicity effect is one of the major reasons for declining candidate drugs. Detecting the toxicity effects of all chemicals can accelerate the procedures of drug discovery. However, it is time-consuming and expensive to identify the toxicity effects of a given chemical through traditional experiments. Designing quick, reliable and non-animal-involved computational methods is an alternative way. Method: In this study, a novel integrated multi-label classifier was proposed. First, based on five types of chemical-chemical interactions retrieved from STITCH, each of which is derived from one aspect of chemicals, five individual classifiers were built. Then, several integrated classifiers were built by integrating some or all individual classifiers. Result and Conclusion: By testing the integrated classifiers on a dataset with chemicals and their toxicity effects in Accelrys Toxicity database and non-toxic chemicals with their performance evaluated by jackknife test, an optimal integrated classifier was selected as the proposed classifier, which provided quite high prediction accuracies and wide applications.

Aims and Objective: A large number of experimental evidences report that the oscillatory dynamics of p53 would regulate the cell fate decisions. Moreover, multiple time delays are ubiquitous in gene expression which have been demonstrated to lead to important consequences on dynamics of genetic networks. Although delay-driven sustained oscillation in p53-based networks is commonplace, the precise roles of such delays during the processes are not completely known. Method: Herein, an integrated model with five basic components and two time delays for the network is developed. Using such time delays as the bifurcation parameter, the existence of Hopf bifurcation is given by analyzing the relevant characteristic equations. Moreover, the effects of such time delays are studied and the expression levels of the main components of the system are compared when taking different parameters and time delays. Result and Conclusion: The above theoretical results indicated that the transcriptional and translational delays can induce oscillation by undergoing a super-critical Hopf bifurcation. More interestingly, the length of these delays can control the amplitude and period of the oscillation. Furthermore, a certain range of model parameter values is essential for oscillation. Finally, we illustrated the main results in detail through numerical simulations.

Aims and Objective: Redundant information of microarray gene expression data makes it difficult for cancer classification. Hence, it is very important for researchers to find appropriate ways to select informative genes for better identification of cancer. This study was undertaken to present a hybrid feature selection method mRMR-ICA which combines minimum redundancy maximum relevance (mRMR) with imperialist competition algorithm (ICA) for cancer classification in this paper. Materials and Methods: The presented algorithm mRMR-ICA utilizes mRMR to delete redundant genes as preprocessing and provide the small datasets for ICA for feature selection. It will use support vector machine (SVM) to evaluate the classification accuracy for feature genes. The fitness function includes classification accuracy and the number of selected genes. Results: Ten benchmark microarray gene expression datasets are used to test the performance of mRMR-ICA. Experimental results including the accuracy of cancer classification and the number of informative genes are improved for mRMR-ICA compared with the original ICA and other evolutionary algorithms. Conclusion: The comparison results demonstrate that mRMR-ICA can effectively delete redundant genes to ensure that the algorithm selects fewer informative genes to get better classification results. It also can shorten calculation time and improve efficiency.

Aims and Objective: In order to understand the dynamic mechanisms of tumor growth and make a contribution to develop anti-cancer treatment strategies, a mathematical model for tumor growth with two-time delays is proposed in this article. Materials and Methods: First, the relationships among host cells, tumor cells and effector cells, and the biological meaning of two-time delays are explained. Moreover, the system stability is discussed by analyzing the characteristic equation of the model. In addition, the existence and properties of oscillatory dynamic are also researched by using normative theory and central manifold method. Finally, the numerical simulations are performed to further illustrate and support the theoretical results. Results: Both two-time delays in the model can affect the dynamics of tumor growth. Meanwhile, the system can experience a Hopf bifurcation when the delay crosses a series of critical values. Further, a clear formula is deduced to determine the Hopf bifurcation and the direction of stability of the periodic solution. Finally, these results are verified by using numerical simulation. Conclusion: The results demonstrated that the time from identifying tumor cells to making the appropriate response for the immune system and the time needed for competition between host cells and tumor cells for natural resources and living space is significant for tumor growth. These findings in this paper may help us better understand the behaviors of tumors and develop better anti-cancer treatment strategies.

Background: The diverse anticancer measures display varied efficacy in different patients. Thus, appropriate therapy should be chosen for individual patients, and prognostic prediction, based on biomarkers, is a prerequisite for personalized therapy. Objective: In this study, the prognostic model was established based on the genes that were significantly correlated with the survival time for patient death risk evaluation. Method: Univariate Cox proportional hazards regression analysis was utilized for screening the genes significantly correlated with the patients' survival time. Multivariate Cox proportional hazards regression analysis was utilized for establishing the model. Kaplan-Meier and ROC analyses were used for the validation of the prognostic prediction potential of the constructed model. Results: ROC analysis was conducted in the training and validation datasets, and their AUROC values were 0.774 and 0.723, respectively. In comparison to the known prognostic biomarkers, our prognostic biomarker model constituted by the combination of 6 genes displayed superiority in prediction capability. Conclusions: These results indicated that our biomarker model could effectively stratify the risks in gastric adenocarcinoma patients with high prognostic prediction accuracy and sensitivity.

Background: Rifampin resistance has dampened the existing efforts being made to control the global crisis of Tuberculosis and antimicrobial resistance in general. Previous studies that attempted to provide insights into the structural mechanism of Rifampin resistance did not utilize the X-ray crystal structure of Mycobacterium tuberculosis RNA polymerase due to its unavailability. Methods/Results: We provide an atomistic mechanism of Rifampin resistance in a single active site mutating Mycobacterium tuberculosis RNA polymerase, using a recently resolved crystal structure. We also unravel the structural interplay of this mutation upon co-binding of Rifampin with a novel inhibitor, D-AAP1. Mutation distorted the overall conformational landscape of Mycobacterium tuberculosis RNA polymerase, reduced binding affinity of Rifampin and shifted the overall residue interaction network of the enzyme upon binding of only Rifampin. Interestingly, co-binding with DAAP1, though impacted by the mutation, exhibited improved Rifampin binding interactions amidst a distorted residue interaction network. Conclusion: Findings offer vital conformational dynamics and structural mechanisms of mutant enzyme-single ligand and mutant enzyme-dual ligand interactions which could potentially shift the current therapeutic protocol of Tuberculosis infections.