Abstract—Three dimensional (3D) protein structures
determine the function of a protein within a cell. Classification
of 3D structure of proteins is therefore crucial to inferring
protein functional information as well as the evolution of
interactions between proteins. In this paper, we utilized the
artificial neural network (ANN) paradigm to classify the
protein structures. The approach equally divides a 3D protein
structure into several parts and then extracts statistical
features from each part. The different parts in spatial domain
are thus viewed as the feature vectors which are input into the
neural network. The computational experiments achieve better
results in most cases than other currently existing approaches.
Index Terms—Protein structure; classification; ANN.
Hui Li, Chunmei Liu, and Legand Burge are with the Department of Systems and Computer Science, Howard University, Washington, DC 20059, USA (e-mail: email@example.com; firstname.lastname@example.org; email@example.com.).
William Southerland is with the Department of Biochemistry and Molecular Biology Howard University, Washington, DC 20059, USA (email: firstname.lastname@example.org).
Cite:Hui Li, Chunmei Liu, Legand Burge, and William Southerland, "Classification of Protein 3D Structures Using Artificial Neural Network," International Journal of Machine Learning and Computing vol.2, no. 6, pp. 791-793, 2012.