Prediction of GFP spectral properties using artificial neural network

The prediction of the excitation and the emission maxima of green fluorescent protein (GFP) chromophores were investigated by a quantitative structure‐property relationship study. A data set of 19 GFP color variants and an additional data set consisting of 29 synthetic GFP chromophores were collecte...

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Bibliographic Details
Published in:Journal of computational chemistry 2007-05, Vol.28 (7), p.1275-1289
Main Authors: Nantasenamat, Chanin, Isarankura-Na-Ayudhya, Chartchalerm, Tansila, Natta, Naenna, Thanakorn, Prachayasittikul, Virapong
Format: Article
Language:English
Subjects:
absorbance
Chemistry
chromophore
Color
Fluorescence
green fluorescent protein
Green Fluorescent Proteins - chemistry
Green Fluorescent Proteins - genetics
Green Fluorescent Proteins - metabolism
Models, Molecular
Molecular Structure
Mutation - genetics
neural network
Neural networks
Neural Networks (Computer)
partial least squares
Predictions
Protein synthesis
Proteins
quantitative structure-property relationship
Spectrum Analysis
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Summary:The prediction of the excitation and the emission maxima of green fluorescent protein (GFP) chromophores were investigated by a quantitative structure‐property relationship study. A data set of 19 GFP color variants and an additional data set consisting of 29 synthetic GFP chromophores were collected from the literature. Artificial neural network implementing the back‐propagation algorithm was employed. The proposed computational approach reliably predicted the excitation and the emission maxima of GFP chromophores with correlation coefficient exceeding 0.9. The usefulness of quantum chemical descriptors was revealed by a comparative study with other molecular descriptors. Assignment of appropriate protonation state of the chromophore for the GFP color variants data set was shown to be necessary for good predictive performance. Results suggest that the confinement of the GFP chromophore has no significant influence on the predictive performance of the data set used. A comparative investigation with the traditional modeling methods, particularly multiple linear regression and partial least squares, reveals that artificial neural network is the most suitable modeling approach for the GFP spectral properties. It is anticipated that this methodology has great potential in accelerating the design and engineering of novel GFP color variants of scientific or industrial interest. © 2007 Wiley Periodicals, Inc. J Comput Chem, 2007
ISSN:0192-8651
1096-987X
DOI:10.1002/jcc.20656