Structure determination of protein-peptide complexes from NMR chemical shift data using MELD.

Intrinsically disordered regions of proteins often mediate important protein-protein interactions. However, the folding upon binding nature of many polypeptide-protein interactions limits the ability of modeling tools to predict structures of such complexes. To address this problem, we have taken a tandem approach combining NMR chemical shift data and molecular simulations to determine structures of peptide-protein complexes. Here, we demonstrate this approach for polypeptide com-plexes formed with the extraterminal (ET) domain of bromo and extraterminal domain (BET) proteins, which exhibit a high degree of binding plasticity. This system is particularly challenging as the binding process includes allosteric changes across the ET receptor upon binding, and the polypeptide binding partners can form different conformations (e.g., helices and hair-pins) in the complex. In a blind study, the new approach successfully modeled bound-state conformations and binding pos-es, using only backbone chemical shift data, in excellent agreement with experimentally-determined structures. The approach also predicts relative binding affinities of different peptides. This hybrid MELD-NMR approach provides a powerful new tool for structural analysis of protein-polypeptide complexes in the low NMR information content regime, which can be used successfully for flexible systems where one polypeptide binding partner folds upon complex formation.

Predicting Pt-195 NMR Chemical Shift and 1J(195Pt-31P) Coupling Constant for Pt(0) Complexes Using the NMR-DKH Basis Sets

Pt(0) complexes have been widely used as catalysts for important reactions, such as the hydrosilylation of olefins. In this context, nuclear magnetic resonance (NMR) spectroscopy plays an important role in characterising of new structures and elucidating reaction mechanisms. In particular, the Pt-195 NMR is fundamental, as it is very sensitive to the ligand type and the oxidation state of the metal. In the present study, quantum mechanics computational schemes are proposed for the theoretical prediction of the Pt-195 NMR chemical shift and 1J(195Pt–31P) in Pt(0) complexes. The protocols were constructed using the B3LYP/LANL2DZ/def2-SVP/IEF-PCM(UFF) level for geometry optimization and the GIAO-PBE/NMR-DKH/IEF-PCM(UFF) level for NMR calculation. The NMR fundamental quantities were then scaled by empirical procedures using linear correlations. For a set of 30 Pt(0) complexes, the results showed a mean absolute deviation (MAD) and mean relative deviation (MRD) of only 107 ppm and 2.3%, respectively, for the Pt-195 NMR chemical shift. When the coupling constant is taken into account, the MAD and MRD for a set of 33 coupling constants in 26 Pt(0) complexes were of 127 Hz and 3.3%, respectively. In addition, the models were validated for a group of 17 Pt(0) complexes not included in the original group that had MAD/MRD of 92 ppm/1.7% for the Pt-195 NMR chemical shift and 146 Hz/3.6% for the 1J(195Pt–31P).

Modeling the Structure of Crystalline Alamethicin and Its NMR Chemical Shift Tensors

Alamethicin (ALM) is an antimicrobial peptide that is frequently employed in studies of the mechanism of action of pore-forming molecules. Advanced techniques of solid-state NMR spectroscopy (SSNMR) are important in these studies, as they are capable of describing the alignment of helical peptides, such as ALM, in lipid bilayers. Here, it is demonstrated how an analysis of the SSNMR measurements can benefit from fully periodic calculations, which employ the plane-wave density-functional theory (PW DFT) of the solid-phase geometry and related spectral parameters of ALM. The PW DFT calculations are used to obtain the structure of desolvated crystalline ALM and predict the NMR chemical shift tensors (CSTs) of its nuclei. A variation in the CSTs of the amidic nitrogens and carbonyl carbons along the ALM backbone is evaluated and included in simulations of the orientation-dependent anisotropic 15N and 13C chemical shift components. In this way, the influence of the site-specific structural effects on the experimentally determined orientation of ALM is shown in models of cell membranes.

Evaluation of Multi-Objective Optimization Algorithms for NMR Chemical Shift Assignment

An automated NMR chemical shift assignment algorithm was developed using multi-objective optimization techniques. The problem is modeled as a combinatorial optimization problem and its objective parameters are defined separately in different score functions. Some of the heuristic approaches of evolutionary optimization are employed in this problem model. Both, a conventional genetic algorithm and multi-objective methods, i.e., the non-dominated sorting genetic algorithms II and III (NSGA2 and NSGA3), are applied to the problem. The multi-objective approaches consider each objective parameter separately, whereas the genetic algorithm followed a conventional way, where all objectives are combined in one score function. Several improvement steps and repetitions on these algorithms are performed and their combinations are also created as a hyper-heuristic approach to the problem. Additionally, a hill-climbing algorithm is also applied after the evolutionary algorithm steps. The algorithms are tested on several different datasets with a set of 11 commonly used spectra. The test results showed that our algorithm could assign both sidechain and backbone atoms fully automatically without any manual interactions. Our approaches could provide around a 65% success rate and could assign some of the atoms that could not be assigned by other methods.