Incorporating HADAMAC experiment into NVR for NMR Structure-Based Assignments. Halit Erdogan, undefined & Apaydin, M. S. In Proc. of the 6th International Symposium on Health Informatics and Bioinformatics (HIBIT'11), 2011. N abstract bibtex Protein structure determination is crucial to understand a protein’s function and to develop drugs against diseases. Nuclear Magnetic Resonance (NMR) spectroscopy is an experimental technique that allows one to study protein structure in solution. In NMR Structure-based assignment problem, the aim is to assign experimentally observed peaks to the specific nuclei of the target molecule by using a template protein and it is an important computational challenge. NVR-BIP is a tool that utilizes a scoring function based on NVR’s framework and computes assignments for given NMR data. In this paper, we incorporate HADAMAC experiment—which helps predict an amino acid class for each peak— with NVRBIP’s scoring function. Experiments show that the new scoring function results in higher assignment accuracies compared to the previous approaches
@inproceedings{hibit11,
author = {Halit Erdogan, and Mehmet Serkan Apaydin},
title = {Incorporating HADAMAC experiment into NVR for NMR Structure-Based Assignments},
booktitle = {Proc. of the 6th International Symposium on Health
Informatics and
Bioinformatics (HIBIT'11)},
year = {2011},
urlN ={hibit11.pdf},
abstract = {Protein structure determination is crucial to
understand a
protein’s function and to develop drugs against diseases.
Nuclear Magnetic Resonance (NMR) spectroscopy is an
experimental technique that allows one to study protein
structure in solution. In NMR Structure-based assignment
problem, the aim is to assign experimentally observed
peaks to the specific nuclei of the target molecule
by using a template protein and it is an important computational
challenge. NVR-BIP is a tool that utilizes a
scoring function based on NVR’s framework and
computes assignments for given NMR data. In this paper,
we incorporate HADAMAC experiment—which helps
predict an amino acid class for each peak— with NVRBIP’s
scoring function. Experiments show that the new
scoring function results in higher assignment accuracies
compared to the previous approaches},
}
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