tetano
Editor, Senior Moderator
SAR QSAR Environ Res. 2019 Jan 14:1-12. doi: 10.1080/1062936X.2018.1558416. [Epub ahead of print]
[h=1]Prediction of the binding affinity of aptamers against the influenza virus.[/h] Yu X[SUP]1,[/SUP][SUP]2[/SUP], Wang Y[SUP]1[/SUP], Yang H[SUP]1[/SUP], Huang X[SUP]1[/SUP].
[h=3]Author information[/h]
[h=3]Abstract[/h] Thousands of investigations on quantitative structure-activity/property relationships (QSARs/QSPRs) have been reported. However, few publications can be found that deal with QSARs for aptamers, because calculating two-dimensional and three-dimensional descriptors directly from aptamers (typically with 15-45 nucleotides) is difficult. This paper describes calculating molecular descriptors from amino acid sequences that are translated from DNA aptamer sequences with DNAMAN software, and developing QSAR models for the aptamers' binding affinity to the influenza virus. General regression neural network (GRNN) based on Parzen windows estimation was used to build the QSAR model by applying six molecular descriptors. The optimal spreading factor σ of Gaussian function of 0.3 was obtained with the circulation method. The correlation coefficients r from the GRNN model were 0.889 for the training set and 0.892 for the test set. Compared with the existing model for aptamers' binding affinity to the influenza virus, our model is accurate and competes favourably. The feasibility of calculating molecular descriptors from an amino acid sequence translated from DNA aptamer sequences to develop a QSAR model for the anti-influenza aptamers was demonstrated.
[h=4]KEYWORDS:[/h] Aptamer; artificial neural network; binding affinity; influenza virus; molecular descriptors
PMID: 30638061 DOI: 10.1080/1062936X.2018.1558416
[h=1]Prediction of the binding affinity of aptamers against the influenza virus.[/h] Yu X[SUP]1,[/SUP][SUP]2[/SUP], Wang Y[SUP]1[/SUP], Yang H[SUP]1[/SUP], Huang X[SUP]1[/SUP].
[h=3]Author information[/h]
[h=3]Abstract[/h] Thousands of investigations on quantitative structure-activity/property relationships (QSARs/QSPRs) have been reported. However, few publications can be found that deal with QSARs for aptamers, because calculating two-dimensional and three-dimensional descriptors directly from aptamers (typically with 15-45 nucleotides) is difficult. This paper describes calculating molecular descriptors from amino acid sequences that are translated from DNA aptamer sequences with DNAMAN software, and developing QSAR models for the aptamers' binding affinity to the influenza virus. General regression neural network (GRNN) based on Parzen windows estimation was used to build the QSAR model by applying six molecular descriptors. The optimal spreading factor σ of Gaussian function of 0.3 was obtained with the circulation method. The correlation coefficients r from the GRNN model were 0.889 for the training set and 0.892 for the test set. Compared with the existing model for aptamers' binding affinity to the influenza virus, our model is accurate and competes favourably. The feasibility of calculating molecular descriptors from an amino acid sequence translated from DNA aptamer sequences to develop a QSAR model for the anti-influenza aptamers was demonstrated.
[h=4]KEYWORDS:[/h] Aptamer; artificial neural network; binding affinity; influenza virus; molecular descriptors
PMID: 30638061 DOI: 10.1080/1062936X.2018.1558416