NVR-BIP: nuclear vector replacement using binary integer programming for NMR structure-based assignments

Apaydın, Serkan Mehmet and Çatay, Bülent and Patrick, Nicholas and Donald, Bruce R. (2009) NVR-BIP: nuclear vector replacement using binary integer programming for NMR structure-based assignments. (Accepted/In Press)

Warning
There is a more recent version of this item available.
[thumbnail of This is a RoMEO yellow publisher -- author can archive pre-print (ie pre-refereeing)] PDF (This is a RoMEO yellow publisher -- author can archive pre-print (ie pre-refereeing))
tcj09_v2.pdf
Restricted to Registered users only

Download (206kB) | Request a copy

Abstract

Nuclear Magnetic Resonance (NMRa) spectroscopy is an important experimental technique that allows one to study protein structure and dynamics in solution and to observe unfolded or partially folded proteins [?]. An important bottleneck in NMR protein structure determination is the assignment of NMR peaks to the corresponding nuclei. Structure-based assignment (SBA) aims to solve this problem with the help of a template protein which is homologous to the target and has applications in the study of structure-activity relationship, protein-protein and protein-ligand interactions. We formulate SBA as a linear assignment problem with additional Nuclear Overhauser Effect (NOE) constraints, which can be solved within Nuclear Vector Replacement’s (NVR) ([1, 2]) framework. Our approach uses NVR’s scoring function and data types, and also gives the option of using CH and NH RDCs, instead of NH RDCs which NVR requires. We test our technique on NVR’s data set as well as on two new proteins. Our results are comparable to NVR’s assignment accuracy on NVR’s test set, but higher on novel proteins. Our approach allows partial assignments. It is also complete and can return the optimum as well as near-optimum assignments. Furthermore, it allows us to analyze the information content of each data type and is easily extendable to accept new forms of input data, such as additional RDCs.
Item Type: Article
Divisions: Faculty of Engineering and Natural Sciences > Academic programs > Biological Sciences & Bio Eng.
Faculty of Engineering and Natural Sciences > Academic programs > Computer Science & Eng.
Depositing User: Serkan Mehmet Apaydın
Date Deposited: 02 Dec 2009 12:54
Last Modified: 26 Apr 2022 08:33
URI: https://research.sabanciuniv.edu/id/eprint/13123

Available Versions of this Item

Actions (login required)

View Item
View Item