Predicting network of drug–enzyme interaction based on machine learning method

Niu, Bing; Zhang, Yuchao; Ding, Juan; Yin, Ling; Miao, Wang; Lu, Wei; Yuan, Xiaochen; Yin, Jinyuan

doi:10.1016/j.bbapap.2013.07.008

Cited by 7 publications

(2 citation statements)

References 111 publications

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“…The SVM has been widely used in bioinformatics [17][18][19][20][21][22][23][24][25][26][27] and was adopted as the classification algorithm in this work. Its basic principle is to transform the input vector into a high-dimension Hilbert space and seek a separating hyperplane with the maximal margin in this space by using the decision function:…”

Section: Support Vector Machine (Svm)mentioning

confidence: 99%

Identification of bacteriophage virion proteins by the ANOVA feature selection and analysis

et al. 2014

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The bacteriophage virion proteins play extremely important roles in the fate of host bacterial cells. Accurate identification of bacteriophage virion proteins is very important for understanding their functions and clarifying the lysis mechanism of bacterial cells. In this study, a new sequence-based method was developed to identify phage virion proteins. In the new method, the protein sequences were initially formulated by the g-gap dipeptide compositions. Subsequently, the analysis of variance (ANOVA) with incremental feature selection (IFS) was used to search for the optimal feature set. It was observed that, in jackknife cross-validation, the optimal feature set including 160 optimized features can produce the maximum accuracy of 85.02%. By performing feature analysis, we found that the correlation between two amino acids with one gap was more important than other correlations for phage virion protein prediction and that some of the 1-gap dipeptides were important and mainly contributed to the virion protein prediction. This analysis will provide novel insights into the function of phage virion proteins. On the basis of the proposed method, an online web-server, PVPred, was established and can be freely accessed from the website (http://lin.uestc.edu.cn/server/PVPred). We believe that the PVPred will become a powerful tool to study phage virion proteins and to guide the related experimental validations.

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Section: Support Vector Machine (Svm)mentioning

confidence: 99%

Identification of bacteriophage virion proteins by the ANOVA feature selection and analysis

et al. 2014

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“…The CfsSubsetEval (CFS) method combined with Best-first (BF) search was employed to search the optimal feature subset in this study. CFS [33,34] is a heuristic feature-selection algorithm for evaluating the worth of a subset of attributes by considering the individual predictive ability of each feature along with the degree of redundancy between them. Therefore, feature-class and feature-feature correlations of training set were first calculated by CFS and the merit was calculated according to function (1):Sn=itruecitalicfc¯i+ii−1cfftrue¯where S n is the heuristic “merit” of a feature subset S , truecitalicfc¯ is the mean feature-class correlation, and truecitalicff¯ is the average feature-feature inter-correlation.…”

Section: Methodsmentioning

confidence: 99%

2D-SAR, Topomer CoMFA and molecular docking studies on avian influenza neuraminidase inhibitors

Niu

et al. 2019

Computational and Structural Biotechnology Journal

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Avian influenza is a serious zoonotic infectious disease with huge negative impacts on local poultry farming, human health and social stability. Therefore, the design of new compounds against avian influenza has been the focus in this field. In this study, computational methods were applied to investigate the compounds with neuraminidase inhibitory activity. First, 2D-SAR model was built to recognize neuraminidase inhibitors (NAIs). As a result, the accuracy of 10 cross-validation and independent tests is 96.84% and 98.97%, respectively. Then, the Topomer CoMFA model was constructed to predict the inhibitory activity and analyses molecular fields. Two models were obtained by changing the cutting methods. The second model is employed to predict the activity (q2 = 0.784 and r2 = 0.982). Molecular docking was also used to further analyze the binding sites between NAIs and neuraminidase from human and avian virus. As a result, it is found that same binding Total Score has some differences, but the binding sites are basically the same. At last, some potential NAIs were screened and some optimal opinions were taken. It is expected that our study can assist to study and develop new types of NAIs.

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2D-SAR and 3D-QSAR analyses for acetylcholinesterase inhibitors

Niu

Zhao

et al. 2017

Mol Divers

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Alzheimer's disease (AD) accounts for almost three quarters of dementia patients and interferes people's normal life. Great progress has been made recently in the study of Acetylcholinesterase (AChE), known as one of AD's biomarkers. In this study, acetylcholinesterase inhibitors (AChEI) were collected to build a two-dimensional structure-activity relationship (2D-SAR) model and three-dimensional quantitative structure-activity relationship (3D-QSAR) model based on feature selection method combined with random forest. After calculation, the prediction accuracy of the 2D-SAR model was 89.63% by using the tenfold cross-validation test and 87.27% for the independent test set. Three cutting ways were employed to build 3D-QSAR models. A model with the highest [Formula: see text] (cross-validated correlation coefficient) and [Formula: see text](non-cross-validated correlation coefficient) was obtained to predict AChEI activity. The mean absolute error (MAE) of the training set and the test set was 0.0689 and 0.5273, respectively. In addition, molecular docking was also employed to reveal that the ionization state of the compounds had an impact upon their interaction with AChE. Molecular docking results indicate that Ser124 might be one of the active site residues.

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Predicting network of drug–enzyme interaction based on machine learning method

Cited by 7 publications

References 111 publications

Identification of bacteriophage virion proteins by the ANOVA feature selection and analysis

Identification of bacteriophage virion proteins by the ANOVA feature selection and analysis

2D-SAR, Topomer CoMFA and molecular docking studies on avian influenza neuraminidase inhibitors

2D-SAR and 3D-QSAR analyses for acetylcholinesterase inhibitors

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