Sequence specific resonance assignment via Multicanonical Monte Carlo search using an ABACUS approach
ABACUS [Grishaev et al. (2005) Proteins 61:36–43] is a novel protocol for automated protein structure determination via NMR. ABACUS starts from molecular fragments defined by unassigned J-coupled spin-systems and involves a Monte Carlo stochastic search in assignment space, probabilistic sequence selection, and assembly of fragments into structures that are used to guide the stochastic search. Here, we report further development of the two main algorithms that increase the flexibility and robustness of the method. Performance of the BACUS [Grishaev and Llinás (2004) J Biomol NMR 28:1–101] algorithm was significantly improved through use of sequential connectivities available from through-bond correlated 3D-NMR experiments, and a new set of likelihood probabilities derived from a database of 56 ultra high resolution X-ray structures. A Multicanonical Monte Carlo procedure, Fragment Monte Carlo (FMC), was developed for sequence-specific assignment of spin-systems. It relies on an enhanced assignment sampling and provides the uncertainty of assignments in a quantitative manner. The efficiency of the protocol was validated on data from four proteins of between 68–116 residues, yielding 100% accuracy in sequence specific assignment of backbone and side chain resonances.
KeywordsBACUS NOE identification Fragment Monte Carlo Resonance assignment
We wish to thank Dr. Lilia Kaustov and Dr. Sampas Srisailam for providing us with resonance assignments of CPH domain of Cul7 and protein pa0128, respectively, which were performed by conventional manual procedure. We also thank Dr. Bin Wu for providing us with complete resonance assignment and NOESY peak lists of proteins mth1743 and mth0256. This work was supported by the Ontario Research and Development Challenge Fund, the US National Institute of Health Protein Structure Initiative (P50-GM62413-01 and GM67965) through the Northeast Structural Genomics Consortium, Genome Canada through the Ontario Genomics Institute, and The Canadian Institutes of Health Research.
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