Application of Improved Three-Dimensional Kernel Approach to Prediction of Protein Structural Class

Abstract EN

Kernel methods, such as kernel PCA, kernel PLS, and support vector machines, are widely known machine learning techniques in biology, medicine, chemistry, and material science.

Based on nonlinear mapping and Coulomb function, two 3D kernel approaches were improved and applied to predictions of the four protein tertiary structural classes of domains (all-α, all-β, α/β, and α + β) and five membrane protein types with satisfactory results.

In a benchmark test, the performances of improved 3D kernel approach were compared with those of neural networks, support vector machines, and ensemble algorithm.

Demonstration through leave-one-out cross-validation on working datasets constructed by investigators indicated that new kernel approaches outperformed other predictors.

It has not escaped our notice that 3D kernel approaches may hold a high potential for improving the quality in predicting the other protein features as well.

Or at the very least, it will play a complementary role to many of the existing algorithms in this regard.