NDLI: Learning to Translate Sequence and Structure to Function: Identifying DNA Binding and Membrane Binding Proteins

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Learning to Translate Sequence and Structure to Function: Identifying DNA Binding and Membrane Binding Proteins

Content Provider	Springer Nature Link
Author	Langlois, Robert E. Carson, Matthew B. Bhardwaj, Nitin Lu, Hui
Copyright Year	2007
Abstract	A protein’s function depends in a large part on interactions with other molecules. With an increasing number of protein structures becoming available every year, a corresponding structural annotation approach identifying such interactions grows more expedient. At the same time, machine learning has gained popularity in bioinformatics providing robust annotation of genes and proteins without sequence homology. Here we have developed a general machine learning protocol to identify proteins that bind DNA and membrane. In general, there is no theory or even rule of thumb to pick the best machine learning algorithm. Thus, a systematic comparison of several classification algorithms known to perform well is investigated. Indeed, the boosted tree classifier is found to give the best performance, achieving 93% and 88% accuracy to discriminate non-homologous proteins that bind membrane and DNA, respectively, significantly outperforming all previously published works. We also attempted to address the importance of the attributes in function prediction and the relationships between relevant attributes. A graphical model based on boosted trees is applied to study the important features in discriminating DNA-binding proteins. In summary, the current protocol identified physical features important in DNA and membrane binding, rather than annotating function through sequence similarity.
Starting Page	1043
Ending Page	1052
Page Count	10
File Format	PDF
ISSN	00906964
Journal	Annals of Biomedical Engineering
Volume Number	35
Issue Number	6
e-ISSN	15739686
Language	English
Publisher	Kluwer Academic Publishers-Plenum Publishers
Publisher Date	2007-04-13
Publisher Place	New York
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	AdaBoost Support vector machines C4.5 Feature interactions Biochemistry Mechanics Biophysics/Biomedical Physics Biomedical Engineering Biomedicine general
Content Type	Text
Resource Type	Article
Subject	Biomedical Engineering

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