Related ArticlesA polynomial-time nuclear vector replacement algorithm for automated NMR resonance assignments.
J Comput Biol. 2004;11(2-3):277-98
Authors: Langmead CJ, Yan A, Lilien R, Wang L, Donald BR
High-throughput NMR structural biology can play an important role in structural genomics. We report an automated procedure for high-throughput NMR resonance assignment for a protein of known structure, or of a homologous structure. These assignments are a prerequisite for probing protein-protein interactions, protein-ligand binding, and dynamics by NMR. Assignments are also the starting point for structure determination and refinement. A new algorithm, called Nuclear Vector Replacement (NVR) is introduced to compute assignments that optimally correlate experimentally measured NH residual dipolar couplings (RDCs) to a given a priori whole-protein 3D structural model. The algorithm requires only uniform( 15)N-labeling of the protein and processes unassigned H(N)-(15)N HSQC spectra, H(N)-(15)N RDCs, and sparse H(N)-H(N) NOE's (d(NN)s), all of which can be acquired in a fraction of the time needed to record the traditional suite of experiments used to perform resonance assignments. NVR runs in minutes and efficiently assigns the (H(N),(15)N) backbone resonances as well as the d(NN)s of the 3D (15)N-NOESY spectrum, in O(n(3)) time. The algorithm is demonstrated on NMR data from a 76-residue protein, human ubiquitin, matched to four structures, including one mutant (homolog), determined either by x-ray crystallography or by different NMR experiments (without RDCs). NVR achieves an assignment accuracy of 92-100%. We further demonstrate the feasibility of our algorithm for different and larger proteins, using NMR data for hen lysozyme (129 residues, 97-100% accuracy) and streptococcal protein G (56 residues, 100% accuracy), matched to a variety of 3D structural models. Finally, we extend NVR to a second application, 3D structural homology detection, and demonstrate that NVR is able to identify structural homologies between proteins with remote amino acid sequences using a database of structural models.
[NMR paper] GANA--a genetic algorithm for NMR backbone resonance assignment.
GANA--a genetic algorithm for NMR backbone resonance assignment.
Related Articles GANA--a genetic algorithm for NMR backbone resonance assignment.
Nucleic Acids Res. 2005;33(14):4593-601
Authors: Lin HN, Wu KP, Chang JM, Sung TY, Hsu WL
NMR data from different experiments often contain errors; thus, automated backbone resonance assignment is a very challenging issue. In this paper, we present a method called GANA that uses a genetic algorithm to automatically perform backbone resonance assignment with a high degree of precision and recall....
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[NMR paper] An expectation/maximization nuclear vector replacement algorithm for automated NMR re
An expectation/maximization nuclear vector replacement algorithm for automated NMR resonance assignments.
Related Articles An expectation/maximization nuclear vector replacement algorithm for automated NMR resonance assignments.
J Biomol NMR. 2004 Jun;29(2):111-38
Authors: Langmead CJ, Donald BR
We report an automated procedure for high-throughput NMR resonance assignment for a protein of known structure, or of an homologous structure. Our algorithm performs Nuclear Vector Replacement (NVR) by Expectation/Maximization (EM) to compute...
Automated sequence-specific protein NMR assignment using the memetic algorithm MATCH
Automated sequence-specific protein NMR assignment using the memetic algorithm MATCH
Jochen Volk, Torsten Herrmann and Kurt Wüthrich
Journal of Biomolecular NMR; 2008; 41(3); pp 127 - 138
Abstract:
MATCH (Memetic Algorithm and Combinatorial Optimization Heuristics) is a new memetic algorithm for automated sequence-specific polypeptide backbone NMR assignment of proteins. MATCH employs local optimization for tracing partial sequence-specific assignments within a global, population-based search environment, where the simultaneous application of local and global optimization heuristics...