Abstract
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.
Highlights
Due to the rapid development of genome and protein science, the biological information has expanded dramatically
Levitt and Chothia proposed to classify protein tertiary structures into the following four structural classes based on the secondary structural content of the domains
The first goal of this paper is to illustrate the application of 3D kernel approach as a relatively new tool in proteins domains field for classification purposes
Summary
Due to the rapid development of genome and protein science, the biological information has expanded dramatically. It is very important and highly desirable for computers to manage, organize, and interpret the information. Two classification problems, protein’s tertiary structure classes of domains and membrane protein types, were researched with some machine learning techniques. The details of proteins domains structures are extremely complicated and irregular. Their overall structural frames are simple, regular, and truly elegant [11,12,13]. This class is dominated by small folds, many of which form a simple bundle with helices running up and down. (2) All-β: this class has a core composed of antiparallel β-sheets, usually two sheets pack against each other. (3) α/β: this class contains both α-helices and β-strands that are largely interspersed in forming mainly parallel β-sheet; (4) α + β: this class contains both of the two secondary structure elements that, are largely segregated in forming mainly antiparallel β-sheets
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
