(192bb) Multi Metric 3D Protein Descriptors: The Correlation Impact of Algebraic Forms and Its Analysis
AIChE Annual Meeting
2017
2017 Annual Meeting
Computational Molecular Science and Engineering Forum
Poster Session: Computational Molecular Science and Engineering Forum (CoMSEF)
Monday, October 30, 2017 - 3:15pm to 4:45pm
To assess the utility of global and local indices, a classification model for the prediction of the major four protein structural classes was built with the Linear Discriminant Analysis (LDA) technique. The developed model correctly 92.6% and 92.7% of the proteins on the training and test sets, respectively. The model yield high values of the generalized square correlation coefficient (GC2) on both the training and test series. The statistical parameters derived from the validation procedures endorse the strength, stability and the high predictive power of the proposed model. The performance of the LDA-model demonstrates the competence of the proposed indices not only to codify relevant biochemical information related to the structural classes of proteins, but also to yield suitable interpretability. It is anticipated that the current method will benefit the prediction of other protein attributes or functions.