No 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.

In oriented‐sample (OS) solid‐state NMR of membrane proteins, the angular‐dependent dipolar couplings and chemical shifts provide a direct input for structure calculations. However, so far only 1H–15N dipolar couplings and 15N chemical shifts have been routinely assessed in oriented 15N‐labeled samples. The main obstacle for extending this technique to membrane proteins of arbitrary topology has remained in the lack of additional experimental restraints. We have developed a new experimental triple‐resonance NMR technique, which was applied to uniformly doubly (15N, 13C)‐labeled Pf1 coat protein in magnetically aligned DMPC/DHPC bicelles. The previously inaccessible 1Hα–13Cα dipolar couplings have been measured, which make it possible to determine the torsion angles between the peptide planes without assuming α‐helical structure a priori. The fitting of three angular restraints per peptide plane and filtering by Rosetta scoring functions has yielded a consensus α‐helical transmembrane structure for Pf1 protein.

No abstract

Recent experimental studies revealed that charge carriers harvested by bulk heterojunction organic photovoltaics can be collected on ultrafast time scales. To investigate ultrafast exciton mobility, we construct simple, nonatomistic models of a common polymeric electron donor material. We first explore the relationship between the magnitude of energetic noise in the model Hamiltonian and the spatial extent of resulting eigenstates. We then employ a quantum master equation approach to simulate migration of chromophore-localized initial excited states. Excitons initially localized on a single chromophore at the center of the model delocalize down polymer chains and across pi-stacked chromophores through a coherent, wavelike mechanism during the first few tens of femtoseconds. We explore the dependence of this coherent delocalization on coupling strength and on the magnitude of energetic noise. At longer times we observe continued migration toward a uniform population distribution that proceeds through an incoherent, diffusive mechanism. A series of simulations modeling exciton harvesting in domains of varying size demonstrates that smaller domains enhance ultrafast exciton harvesting yield. Our nonatomistic model falls short of quantitative accuracy but demonstrates that excitons are mobile within electron donor domains on ultrafast time scales and that coherent exciton transport can enhance ultrafast exciton harvesting.

In oriented‐sample (OS) solid‐state NMR of membrane proteins, the angular‐dependent dipolar couplings and chemical shifts provide a direct input for structure calculations. However, so far only 1H–15N dipolar couplings and 15N chemical shifts have been routinely assessed in oriented 15N‐labeled samples. The main obstacle for extending this technique to membrane proteins of arbitrary topology has remained in the lack of additional experimental restraints. We have developed a new experimental triple‐resonance NMR technique, which was applied to uniformly doubly (15N, 13C)‐labeled Pf1 coat protein in magnetically aligned DMPC/DHPC bicelles. The previously inaccessible 1Hα–13Cα dipolar couplings have been measured, which make it possible to determine the torsion angles between the peptide planes without assuming α‐helical structure a priori. The fitting of three angular restraints per peptide plane and filtering by Rosetta scoring functions has yielded a consensus α‐helical transmembrane structure for Pf1 protein.

No abstract

The unbounded permutations of biological molecules, including proteins and their constituent peptides, present a dilemma in identifying the components of complex biosamples. Sequence search algorithms used to identify peptide spectra can be expanded to cover larger classes of molecules, including more modifications, isoforms, and atypical cleavage, but at the cost of false positives or false negatives due to the simplified spectra they compute from sequence records. Spectral library searching can help solve this issue by precisely matching experimental spectra to library spectra with excellent sensitivity and specificity. However, compiling spectral libraries that span entire proteomes is pragmatically difficult. Neural networks that predict complete spectra containing a full range of annotated and unannotated ions can be used to replace these simplified spectra with libraries of fully predicted spectra, including modified peptides. Using such a network, we created predicted spectral libraries that were used to rescore matches from a sequence search done over a large search space, including a large number of modifications. Rescoring improved the separation of true and false hits by 82%, yielding an 8% increase in peptide identifications, including a 21% increase in nonspecifically cleaved peptides and a 17% increase in phosphopeptides.