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Life Sciences Commons

Open Access. Powered by Scholars. Published by Universities.®

2006

Genetics and Genomics

Genetics, Development and Cell Biology Presentations, Posters and Proceedings

Articles 1 - 2 of 2

Full-Text Articles in Life Sciences

Comparing Kernels For Predicting Protein Binding Sites From Amino Acid Sequence, Feihong Wu, Byron Olson, Drena Dobbs, Vasant Honavar Jan 2006

Comparing Kernels For Predicting Protein Binding Sites From Amino Acid Sequence, Feihong Wu, Byron Olson, Drena Dobbs, Vasant Honavar

Genetics, Development and Cell Biology Presentations, Posters and Proceedings

The ability to identify protein binding sites and to detect specific amino acid residues that contribute to the specificity and affinity of protein interactions has important implications for problems ranging from rational drug design to analysis of metabolic and signal transduction networks. Support vector machines (SVM) and related kernel methods offer an attractive approach to predicting protein binding sites. An appropriate choice of the kernel function is critical to the performance of SVM. Kernel functions offer a way to incorporate domain-specific knowledge into the classifier. We compare the performance of 3 types of kernels functions: identity kernel, sequence-alignment kernel, and ...


Identifying Interaction Sites In "Recalcitrant" Proteins: Predicted Protein And Rna Binding Sites In Rev Proteins Of Hiv-1 And Eiav Agree With Experimental Data, Michael Terribilini, Jae-Hyung Lee, Changhui Yan, Robert L. Jernigan, Susan Carpenter, Vasant Honavar, Drena Dobbs Jan 2006

Identifying Interaction Sites In "Recalcitrant" Proteins: Predicted Protein And Rna Binding Sites In Rev Proteins Of Hiv-1 And Eiav Agree With Experimental Data, Michael Terribilini, Jae-Hyung Lee, Changhui Yan, Robert L. Jernigan, Susan Carpenter, Vasant Honavar, Drena Dobbs

Genetics, Development and Cell Biology Presentations, Posters and Proceedings

Protein-protein and protein nucleic acid interactions are vitally important for a wide range of biological processes, including regulation of gene expression, protein synthesis, and replication and assembly of many viruses. We have developed machine learning approaches for predicting which amino acids of a protein participate in its interactions with other proteins and/or nucleic acids, using only the protein sequence as input. In this paper, we describe an application of classifiers trained on datasets of well-characterized protein-protein and protein-RNA complexes for which experimental structures are available. We apply these classifiers to the problem of predicting protein and RNA binding sites ...