Automated assignment of NMR spectra of macroscopically oriented proteins using simulated annealing

Abstract

An automated technique for the sequential assignment of NMR backbone resonances of oriented protein samples has been developed and tested based on N-N homonuclear exchange and spin-exchanged separated local-field spectra. By treating the experimental spectral intensity as a pseudopotential, the Monte-Carlo Simulated Annealing algorithm has been employed to seek lowest-energy assignment solutions over a large sampling space where direct enumeration would be unfeasible. The determined sequential assignments have been scored based on the positions of the crosspeaks resulting from the possible orders for the main peaks. This approach is versatile in terms of the parameters that can be specified to achieve the best-fit result. At a minimum the algorithm requires a continuous segment of the main-peak chemical shifts obtained from a uniformly labeled sample and a spin-exchanged experimental spectrum represented as a 2D matrix array. With selective labeling experiments, groups of chemical shifts corresponding to specific locations in the protein backbone can be fixed, thereby decreasing the sampling space. The output from the program consists of a list of top-score main peak assignments, which can be subjected to further scoring criteria until a consensus solution is found. The algorithm has first been tested on a synthetic spectrum with randomly generated chemical shifts and dipolar couplings for the main peaks. The original assignments have been successfully recovered for as many as 100 main peaks when residue-type information was used even in the presence of substantial spectral peak overlap. The algorithm was then applied to assigning two sets of experimental spectra to recover and confirm the previously established assignments in an automated fashion. For the 20-residue transmembrane domain of Pf1 coat protein reconstituted in magnetically aligned bicelles, the original assignment by Park et al. (2010) was recovered by the automated algorithm with additional input from 5 selectively labeled amino acid spectra. The second case considered was the 46 residue Pf1 bacteriophage from Thiriot et al. (2005) and Knox et al. (2010), of which 38 residues were fit. Automated fitting resulted in several possible assignments but not exactly the original assignment. By using a post-fitting filtering procedure based on the number of missed cross peaks and Pf1 helical structure, a consensus spectroscopic assignment is proposed covering 84% of the original assignment. While the automated assignment works best in spectra with well-resolved crosspeaks, it also tolerates substantial spectral crowding to yield reasonable assignments in the cases where ambiguity and degeneracy of possible assignment solutions are inevitable.

NMR “Crystallography” for Uniformly (13C, 15N)‐Labeled Oriented Membrane Proteins

“…Pf1 coat protein, the major protein of Pf1 bacteriophage, represents an ideal test system for method development in OS‐NMR, because its transmembrane domain consists of a well‐characterized α‐helix, and its OS‐NMR spectra have been unambiguously assigned using both selective labeling and the PISA wheel patterns and spectroscopic techniques . Moreover, recently, the spectroscopic assignment of Pf1 has been confirmed by an automated assignment algorithm . Our developed NMR pulse sequence shown in Figure consists of three blocks: i) Evolution of 1 H– 13 C couplings under both 1 H– 1 H and 13 C– 13 C homonuclear decoupling; ii) proton‐mediated magnetization transfer of 13 C dipolar evolution to the proximal 15 N sites; iii) detection at the 15 N spin sites.…”

“…[40,41] Moreover, recently, the spectroscopic assignment of Pf1 has been confirmed by an automated assignment algorithm. [42] Our developed NMR pulse sequence shown in Figure 1 consists of three blocks: i) Evolution of 1 H-13 C couplings under both 1 H-1 H and 13 C-13 C homonuclear decoupling;…”

Coalitional game theory based local power exchange algorithm for networked microgrids

“…The matching problem was first introduced by Gale and Shapley [21] in 1962. After them, different matching models have been proposed and applied in many areas, such as biological computation [22,23], economics [24], computer vision [25], and climate [26,27]. However, to authors' knowledge, none of the famous existing matching models [28][29][30] are suitable for this networked MG coalitional local power exchange situation.…”

Estudo da extração hidrometalúrgica de matais de transição com nanopartículas superparamagnéticas funcionalizadas

“…A proposta de utilização das nanopartículas com íons metálicos imobilizados foi devidamente comprovada na comparação da captura do citocromo-C e da mioglobina, abrindo muitas perspectivas no desenvolvimento da Cromatografia Magnética por Afinidade, ampliando bastante o campo de atuação da técnica IMAC, em sistemas biológicos e oferecendo uma alternativa perante as pesquisas desenvolvidas na área de separação de proteínas [104][105][106][107][108][109][110][111][112][113][114][115][116][117] .…”