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Das Hauptziel dieser Dissertation lag in der Verbesserung einzelner Schritte im Prozess der automatischen Proteinstrukturbestimmung mittels Kernmagnetischer Resonanz (NMR). Dieser Prozess besteht aus einer Reihe von sequenziellen Schritten, welche zum Teil bereits erfolgreich automatisiert wurden. CYANA ist ein Programmpaket, welches routinemäßig zur automatischen Zuordnung der chemischen Verschiebungen, der Nuclear Overhauser Enhancement (NOE) Signalen und der Strukturrechnung von Proteinen verwendet wird. Einer der Schritte, der noch nicht erfolgreich automatisiert wurde, stellt die Signalidentifizierung von NMR Spektren dar. Dieser Schritt ist besonders wichtig, da Listen von NMR-Signalen Grundlage aller Folgeschritte sind. Fehler in den Signallisten pflanzen sich in allen Folgeschritten der Datenauswertung fort und können am Ende in falschen Strukturen resultieren. Daher war ein Ziel dieser Arbeit, einen robusten und verlässlichen Algorithmus zur Signalidentifizierung von NMR Spektren in CYANA zu implementieren. Dieser Algorithmus sollte mit dem in FLYA implementierten Ansatz zur automatischen Resonanzzuordnung, der automatischen NOE-Zuordnung und der Strukturrechnung mit CYANA kombiniert werden. Der in CYANA implementierte CYPICK Algorithmus ahmt den von Hand durchgeführten Ansatz nach. Bei der manuellen Methode schaut sich der Wissenschaftler zweidimensionale Konturliniendarstellungen der NMR Spektren an und entscheidet anhand verschiedener Geomtrie- und Ähnlichkeitskriterien, ob es sich um ein Signal des Proteins oder um einen Artefakt handelt. Proteinsignale sind ähnlich zu konzentrischen Ellipsen und erfüllen bestimmte geometrische Kriterien, wie zum Beispiel ungefähr kreisförmiges Aussehen nach entsprechender Skalierung der spektralen Achsen und gänzlich konvexe Formen, die Artefakte nicht aufzeigen. CYPICK bewertet die Konturlinien lokaler Extrema nach diesen Bedingungen und entscheidet anhand dieser, ob es sich um ein echtes Signal handelt oder nicht. Das zweite Ziel dieser Arbeit war es ein Maß zur Quantifizierung der Information von strukturellen NMR Distanzeinschränkungen zu entwickeln. Der sogenannte Informationsgehalt (I) ist vergleichbar mit der Auflösung in der Röntgenkristallographie. Ein weiteres Projekt dieser Dissertation beschäftigte sich mit der strukturbasierten Medikamentenentwicklung (SBDD). SBDD wird meist von der Röntgenkristallographie durchgeführt. NMR hat jedoch einige Vorteile gegenüber der Röntgenkristallographie, welche interessant für SBDD sind. Daher wurden Strategien entwickelt, die NMR für SBDD zugänglicher machen sollen.
Small molecule drug discovery is strongly supported by biophysical data. In the reach of this thesis, cell free protein expression was used to produce human target proteins for ligand binding assays using Surface Plasmon Resonance spectroscopy (SPR). In the second step the binding and interaction characteristics of small molecules and fragments were analyzed using Nuclear Magnetic Resonance spectroscopy (NMR).
The first target protein was the human acid sensing channel 1 (ASIC1a). ASIC1a was expressed in a cell free expression system based on E.coli lysate. To optimize the expression, several parameters including fusion tags, ion concentrations and different hydrophobic environments were tested.
The adaption of the folding environment for ASIC1a needed more optimization, because it is a very challenging target to express in an in vitro system. Three different expression modes were employed to find a suitable folding environment.
SPR binding studies with ASIC1a were performed with chicken ASIC1a expressed in insect cells. The immobilization of cASIC1a and the used buffer conditions were tested using Psalmotoxin 1, a naturally occurring peptide venom which binds strong to the trimeric form of ASIC1a. Compound characterization experiments were performed with a variety of different ligands including amiloride, a general blocker of the whole ENaC protein family. None of the used ligands showed titration curves that would match a simple 1:1 binding model. The experiments either show no binding signal or signal that could be interpreted as unspecific binding. Even amiloride that should be binding the protein shows no signals that fit a simple binding model.
Another target protein that was investigated is the soluble prolyl cis/trans isomerase Cyclophilin D (or peptidyl prolyl isomerase F – PPIF). This protein is involved in the regulation of the mitochondrial permeability transition pore and therefore a potential drug target to treat neurodegenerative diseases. Small molecule binding was tested with CypD using SPR. Following the kinetic analysis of small molecule ligands, the binding position of different binding fragments was analyzed. These fragments originated from a SPR based fragment screen and gave no co-crystal structures with CypD. Therefore NMR was used to investigate the binding position of these fragments. An analysis of the chemical shift perturbations upon ligand addition revealed that the NMR analysis was in line with the results gathered by x-ray crystallography. The fragments with unknown binding position however, all bind to a specific patch slightly outside the binding pocket.
The ligand CL1 showed a special behavior in the NMR experiments. Upon addition to CypD, it produced large shifts on many signals of the protein, accompanied by a severe line broadening. The shift perturbations were so numerous and large that the spectrum had to be reassigned in complex with the ligand. Triple selective labeling was applied to allow a fast and nearly complete signal assignment. The possibility to use highly sophisticated labeling schemes, is one of the advantages of cell free protein expression. After the assignment of the complex spectrum, the chemical shift perturbations were analyzed and quantified. The residues showing the strongest CSPs are also identified in the crystal structure to be involved in the binding of CL1, giving a consistent picture. The numerous and large shift perturbations, produced by CL1 led to the assumption, that the ligand induces a conformational change in CypD, which is not represented in the co-crystal structure. This conformational change was characterized by a NMR based structure determination. CypD apo yielded a defined bundle, whose folded regions overlap well with the corresponding crystal structure.
For the calculation of the CypD-CL1 complex structure, the sidechain resonances were assigned using an automated assignment approach with the software FLYA. The calculation of the CypD-CL1 complex structure did not result in a defined bundle. While parts of the protein converge in a well folded state, the region around the active site shows no defined folding. Careful analysis of the structure calculation suggests that the problems during structure calculation did not originate from an incorrect resonance assignment, but rather from a lack of NOE crosspeaks. This might be due to a broadening of the corresponding NOE crosspeaks or the coexistence of many different conformations. This leads to the conclusion, that the protein conformation is not defined by the NMR data and could be in a dynamic interchange between multiple structures.
This hypothesis is supported by other observations. The line broadening of the signals in the complex is pronounced in the area around the active site and the substrate binding pocket, hinting to a connection between catalytic activity and protein dynamics. In addition many NMR signals are sensitive to changes in the measurement field strength and the temperature. This field dependent signal splitting suggests dynamic conformational changes in the protein between at least two different conformations on a millisecond timescale.
The current working model is that CL1 binds to CypD and induces the catalytic cycle and the connected conformational changes in CypD. As a result the proline like moiety in CL1 is constantly switching between the cis and the trans conformation. Due to the high affinity of CL1, the inhibitor does not leave the binding pocket after successful catalysis, but stays bound in the pocket stimulating further catalytic cycles. These findings as well as the working model are well in line with data published for Cyclophilin A, another member of the cyclophilin family, thereby supporting the model.
This thesis is concerned with protein structures determined by nuclear magnetic resonance (NMR), and the text focuses on their analysis in terms of accuracy, gauged by the correspondence between the structural model and the experimental data it was calculated from, and in terms of precision, i.e. the degree of uncertainty of the atomic positions. Additionally, two protein structure calculation projects are described...
Pulsed electron-electron double resonance (PELDOR), also called Double Electron-Electron Resonance, (DEER) is a pulsed EPR technique that can provide structural information of biomolecules, such as proteins or nucleic acids, complementary to other structure determination methods by measuring long distances (from 1.5 up to 10 nm) between two paramagnetic labels. Incorporation of the rigid Ç-label pairwise into DNA or RNA molecules enables the determination not only of the distance but also of the mutual orientation between the two Ç-labels by multi-frequency orientation-selective PELDOR data (X-, Q- and G-band frequencies). Thus, information about the orientation of secondary structure elements of nucleic acids can be revealed and used as additional angular information for structure determination. Since Ç does not have motion independent from the helix where it resides, the conformational flexibility of the nucleic acid molecule can be directly determined. This thesis demonstrates the advancement of PELDOR spectroscopy, beyond its original scope of distance measurements, to determine the mutual orientation between two rigid spin labels towards the characterization of the conformational space sampled by highly flexible nucleic acid molecules. Applications of the methodology are shown on two systems: a three-way junction, namely a cocaine aptamer in its bound-state, and a two-way junction, namely a bent DNA.
More in detail, the conformational changes of the cocaine aptamer upon cocaine binding were investigated by analysis of the distance distributions. The cocaine-bound and the unbound states could be differentiated by their conformational flexibility, which decreases in the presence of the ligand. Moreover, the obtained distance distributions revealed a small change in the mean distance between the two spin labels upon cocaine binding. This indicates a ligand-induced conformational change, which presumably originates at the junction where cocaine is known to bind. The investigation of the relative orientation between the two spin-labeled helices of the aptamer revealed further structural insights into the conformational dynamics of the cocaine-bound state. The angular information from the orientation-selective PELDOR data and the a priori knowledge about the secondary structure of the aptamer were helpful in obtaining a molecular model describing its global folding and flexibility. In spite of a large flexible aptamer, the kink angle between the Ç-labeled helices was found to be rather well-defined.
As for the bent DNA molecule, a two-step protocol was proposed to investigate the conformational flexibility. In the first step, a database with all the possible conformers was created, using available restraints from NMR and distance restraints derived from PELDOR. In a second step, a weighted ensemble of these conformers fitting the multi-frequency PELDOR data was built. The uniqueness of the obtained structural ensemble was checked by validation against an independent PELDOR data set recorded at a higher magnetic field strength. In addition, the kink and twist angle pairs were determined and the resulting structural ensemble was compared with the conformational space deduced both from FRET experiments and from the structure determined by the NMR restraints alone.
Overall, this thesis underlines the potential of using PELDOR spectroscopy combined with rigid spin labels in the context of structure determination of nucleic acids in order to determine the relative orientation between two helices, the conformational flexibility and the conformational changes of nucleic acid molecules upon ligand binding.
Alzheimer’s disease (AD), which was first reported more than a century ago by Alhzeimer, is one of the commonest forms of dementia which affects >30 million people globally (>8 million in Europe). The origin and pathogenesis of AD is poorly understood and there is no cure available for the disease. AD is characterized by the accumulation of senile plaques composed of amyloid beta peptides (Ab 37-43) which is formed by the gamma secretase (GS) complex by cleaving amyloid precursor protein. Therefore GS can be an attractive drug target. Since GS processes several other substrates like Notch, CD44 and Cadherins, nonspecific inhibition of GS has many side effects. Due to the lack of crystal structure of GS, which is attributed to the extreme difficulties in purifying it, molecular modeling can be useful to understand its architecture. So far only low resolution cryoEM structures of the complex has been solved which only provides a rough structure of the complex at low 12-15 A resolution Furthermore the activity of GS in vitro can be achieved by means of cell-free (CF) expression.
GS comprises catalytic subunits namely presenilins and supporting elements containing Pen-2, Aph-1 and Nicastrin. The origin of AD is hidden in the regulated intramembrnae proteolysis (RIP) which is involved in various physiological processes and also in leukemia. So far growth factors, cytokines, receptors, viral proteins, cell adhesion proteins, signal peptides and GS has been shown to undergo RIP. During RIP, the target proteins undergo extracellular shredding and intramembrane proteolysis.
This thesis is based on molecular modeling, molecular dynamics (MD) simulations, cell-free (CF) expression, mass spectrometry, NMR, crystallization, activity assay etc of the components of GS complex and G-protein coupled receptors (GPCRs).
First I validated the NMR structure of PS1 CTF in detergent micelles and lipid bilayers using coarse-grained MD simulations using MARTINI forcefield implemented in Gromacs. CTF was simulated in DPC micelles, DPPC and DLPC lipid bilayer. Starting from random configuration of detergent and lipids, micelle and lipid bilyer were formed respectively in presence of CTF and it was oriented properly to the micelle and bilyer during the simulation. Around DPC molecules formed micelle around CTF in agreement of the experimental results in which 80-85 DPC molecules are required to form micelles. The structure obtained in DPC was similar to that of NMR structure but differed in bilayer simulations showed the possibility of substrate docking in the conserved PAL motif. Simulations of CTF in implicit membrane (IMM1) in CHAMM yielded similar structure to that from coarse grained MD.
I performed cell-free expression optimization, crystallization and NMR spectroscopy of Pen-2 in various detergent micelles. Additionally Pen-2 was modeled by a combination of rosetta membrane ab-initio method, HHPred distant homology modeling and incorporating NMR constraints. The models were validated by all atom and coarse grained MD simulations both in detergent micelles and POPC/DPPC lipid bilayers using MARTINI forcefield.
GS operon consisting of all four subunits was co-expressed in CF and purified. The presence of of GS subunits after pull-down with Aph-1 was determined by western blotting (Pen-2) and mass spectrometry (Presenilin-1 and Aph-1). I also studied interactions of especially PS1 CTF, APP and NTF by docking and MD.
I also made models and interfaces of Pen-2 with PS1 NTF and checked their stability by MD simulations and compared with experimental results. The goal is to model the interfaces between GS subunits using molecular modeling approaches based on available experimental data like cross-linking, mutations and NMR structure of C-terminal fragment of PS1 and transmembrane part of APP. The obtained interfaces of GS subunits may explain its catalysis mechanism which can be exploited for novel lead design. Due to lack of crystal/NMR structure of the GS subunits except the PS1 CTF, it is not possible to predict the effect of mutations in terms of APP cleavage. So I also developed a sequence based approach based on machine learning using support vector machine to predict the effect of PS1 CTF L383 mutations in terms of Aβ40/Aβ42 ratio with 88% accuracy. Mutational data derived from the Molgen database of Presenilin 1 mutations was using for training.
GPCRs (also called 7TM receptors) form a large superfamily of membrane proteins, which can be activated by small molecules, lipids, hormones, peptides, light, pain, taste and smell etc. Although 50% of the drugs in market target GPCRs , only few are targeted therapeutically. Such wide range of targets is due to involvement of GPCRs in signaling pathways related to many diseases i.e. dementia (like Alzheimer's disease), metabolic (like diabetes) including endocrinological disorders, immunological including viral infections, cardiovascular, inflammatory, senses disorders, pain and cancer.
Cannabinoid and adrenergic receptors belong to the class A (similar to rhodopsin) GPCRs. Docking of agonists and antagonists to CB1 and CB2 cannabinoid receptors revealed the importance of a centrally located rotamer toggle switch, and its possible role in the mechanism of agonist/antagonist recognition. The switch is composed of two residues, F3.36 and W6.48, located on opposite transmembrane helices TM3 and TM6 in the central part of the membranous domain of cannabinoid receptors. The CB1 and CB2 receptor models were constructed based on the adenosine A2A receptor template. The two best scored conformations of each receptor were used for the docking procedure. In all poses (ligand-receptor conformations) characterized by the lowest ligand-receptor intermolecular energy and free energy of binding the ligand type matched the state of the rotamer toggle switch: antagonists maintained an inactive state of the switch, whereas agonists changed it. In case of agonists of β2AR, the (R,R) and (S,S) stereoisomers of fenoterol, the molecular dynamics simulations provided evidence of different binding modes while preserving the same average position of ligands in the binding site. The (S,S) isomer was much more labile in the binding site and only one stable hydrogen bond was created. Such dynamical binding modes may also be valid for ligands of cannabinoid receptors because of the hydrophobic nature of their ligand-receptor interactions. However, only very long molecular dynamics simulations could verify the validity of such binding modes and how they affect the process of activation.
Human N-formyl peptide receptors (FPRs) are G protein-coupled receptors (GPCRs) involved in many physiological processes, including host defense against bacterial infection and resolving inflammation. The three human FPRs (FPR1, FPR2 and FPR3) share significant sequence homology and perform their action via coupling to Gi protein. Activation of FPRs induces a variety of responses, which are dependent on the agonist, cell type, receptor subtype, and also species involved. FPRs are expressed mainly by phagocytic leukocytes. Together, these receptors bind a large number of structurally diverse groups of agonistic ligands, including N-formyl and nonformyl peptides of different composition, that chemoattract and activate phagocytes. For example, N-formyl-Met-Leu-Phe (fMLF), an FPR1 agonist, activates human phagocyte inflammatory responses, such as intracellular calcium mobilization, production of cytokines, generation of reactive oxygen species, and chemotaxis. This ligand can efficiently activate the major bactericidal neutrophil functions and it was one of the first characterized bacterial chemotactic peptides. Whereas fMLF is by far the most frequently used chemotactic peptide in studies of neutrophil functions, atomistic descriptions for fMLF-FPR1 binding mode are still scarce mainly because of the absence of a crystal structure of this receptor. Elucidating the binding modes may contribute to designing novel and more efficient non-peptide FPR1 drug candidates. Molecular modeling of FPR1, on the other hand, can provide an efficient way to reveal details of ligand binding and activation of the receptor. However, recent modelings of FPRs were confined only to bovine rhodopsin as a template.
To locate specific ligand-receptor interactions based on a more appropriate template than rhodopsin we generated the homology models of FPR1 using the crystal structure of the chemokine receptor CXCR4, which shares over 30% sequence identity with FPR1 and is located in the same γ branch of phylogenetic tree of GPCRs (rhodopsin is located in α branch). Docking and model refinement procedures were pursued afterward. Finally, 40 ns full-atom MD simulations were conducted for the Apo form as well as for complexes of fMLF (agonist) and tBocMLF (antagonist) with FPR1 in the membrane. Based on locations of the N- and C-termini of the ligand the FPR1 extracellular pocket can be divided into two zones, namely, the anchor and activation regions. The formylated M1 residue of fMLF bound to the activation region led to a series of conformational changes of conserved residues. Internal water molecules participating in extended hydrogen bond networks were found to play a crucial role in transmitting the agonist-receptor interactions. A mechanism of initial steps of the activation concurrent with ligand binding is proposed.
I accurately predicted the structure and ligand binding pose of dopamine receptor 3 (RMSD to the crystal structure: 2.13 Å) and chemokine receptor 4 (CXCR4, RMSD to the crystal structure 3.21 Å) in GPCR-Dock 2010 competition. The homology model of the dopamine receptor 3 was 8 th best overall in the competition.
Biological membranes separate the cell interior from the outside and have diverse functions from signal transduction, apoptosis to transportations of ions and small molecules in and out of the cell. Most of these functions are fulfilled by proteins incorporated in the membrane. However, lipids as the main component of membrane not only serve as structural element for bilayer formation but they are also directly involved e.g. signalling processes and bilayer properties are important to mediate protein interactions. To fully understand the role of lipids, it is necessary to develop a molecular understanding of how certain membrane components modify bulk bilayer structure and dynamics. Membranes are known to have many different motions in different conditions and time scales. Temperature, pH, water content and many other conditions change membrane dynamics in a high degree. In addition to this, time scales of motions in membranes vary from ns to ms range corresponding to fast motion and slow motion, respectively. Therefore, membranes are needed to be studied systematically by varying the conditions and using methods to investigate motions in various time scales separately. The aim of this study was therefore perform a combined solid-state NMR / molecular dynamics study on model membranes. Different substrates, such as potential drugs, polarizing agents and signaling lipids were incorporated into bilayers and their location within the membrane and their effect onto the membrane was probed. NSAIDs (non-steroidal anti-inflammatory drugs), pirinixic acid derivatives, ceramides and polarizing agents were the substrates for membranes in this study. There were several experimental methods that were applied in order to investigate effects of these substrates on membrane dynamics. Different kind of phospholipids including POPC, DMPC and DPPC were used. In addition to experimental work, with the information gathered from solid state NMR experiments molecular dynamics simulations were performed to obtain more information about the membranes at the molecular level. As a result, combination of solid-state NMR with molecular dynamics simulations provides very systematic way of investigating membrane dynamics in a large range of time scales.
Pirinixic acid derivatives were special interest of this study because of their activity on peroxisome proliferator-activated receptor (PPAR) as an agonist as well as on enzymes of microsomal prostaglandin E2 synthase-1 (PGE2s) -1 and 5-lipoxygenase (5-LO) as dual inhibitor. Two potent pirinixic acid derivatives, 2-(4-chloro-6-(quinolin-6-ylamino)pyrimidin-2-ylthio)octanoic acid (compound 2) and 2-(4-chloro-6-(quinolin-6-ylamino)pyrimidin-2-ylthio)octanoate (compound 3), have been worked and their insertion depts were investigated by combining of solid state NMR and molecular dynamics simulations. Both experimental and theoretical results pointed out that compound 3 was inserted the phospholipid bilayer more deeply than 2. NSAIDs – lipid mixtures have been also studied here. It is known that consumption of NSAIDs as in mixture with lipids results much fewer side effects than consumption of the drugs alone. Thus, it is crucial to understand interactions of NSAIDs with lipids and investigate the possible complex formation of drugs with lipids. In this study, interactions of three widely used NSAIDs, ibuprofen, diclofenac and piroxicam, with DPPC were investigated by solid-state NMR. 1H and 31P NMR results depicted that ibuprofen and diclofenac had interactions with lipids, which is an indication of drug-lipid complex formation whereas piroxicam didn’t show any interactions with lipids suggesting that no complex formation occurred in the case of piroxicam. Ceramides are known to play key roles in many cell processes and many studies showed that the functions of ceramides are related with the ceramide effects on biological membranes. Therefore, in this study, influences of ceramides on biophysics of lipid bilayers were investigated by using various solid state NMR techniques and molecular dynamics simulations. Results from molecular dynamics simulations clearly showed that ceramide and lipids have strong interactions. More evidences about ceramide-lipid interactions were provided from 1H and 14N NMR results. In addition, it was indicated by both simulation and experimental methods that ceramide increased the rigidity of DMPC by increasing chain order parameters. BTbk is a biradical, which is used as polarizing agent for dynamic nuclear polarization (DNP) experiments and found to be more efficient than other widely used polarizing agents such as TOTAPOL. Since it is a hydrophobic compound, which prefers to stay inside lipid bilayer it is important to investigate the location and orientation of bTbk along the bilayer in order to understand its enhancement profile in DNP measurements. In this study, both NMR relaxation time measurements and molecular dynamics simulations revealed that bTbk tends to stay more close to hydrophobic chain of lipids than the interfacial part of lipids at bilayer surface.
In the first part of this work, a brief introduction on lipid membranes as well as a theoretical summary on both methods of solid-state NMR and molecular dynamics simulations is given. Then, in the second part methodology is introduced for both solid-state NMR spectrometer and theoretical calculations. Afterwards, results of different membrane systems are discussed in the following parts for both solid state NMR and MD. Finally, in the last part, a summary and the conclusion of the overall results together with some future plans are explained.
The knowledge of three-dimensional structures of biomolecules is fundamental for the understanding of their function. Nuclear magnetic resonance (NMR) spectroscopy represents besides X-ray crystallography one of the two most widely used techniques to study macromolecules at atomic resolution. Its application has long been a laborious task that could take months and required the expertise of an experienced scientist, however, owing to the tremendous effort that has been put into the development of respective computer algorithms, structure determination by NMR spectroscopy of small- to medium sized proteins is nowadays routinely performed. CYANA is one widely used software package, which combines the majority of individual steps towards a three-dimensional structure. The most common application of the program, however, restricts to the combined automated NOE assignment and structure calculation based on NOESY peak lists and an existing chemical shift assignment. Completely automated structure determination starting from NMR spectra is to date technically possible with CYANA, however, not yet routinely applied. In order to achieve this long-term goal, the individual steps need to become more robust with regard to data imperfections such as peak overlap, spectral artifacts or a limited amount of NMR data. The work presented in this thesis should be placed within the context of increasing the reliability and improving the accuracy of structures determined by CYANA on the basis of solution- as well as solid-state NMR data.
The chapter “Systematic evaluation of combined automated NOE assignment and structure calculation with CYANA” comprises an extensive study on the robustness of the combined automated NOE assignment and structure calculation algorithm based on experimental solution NMR data sets that were modified in multiple ways to mimic different kinds of data imperfections. The results show that the algorithm is remarkably robust with regard to imperfections of the NOESY peak lists and the chemical shift tolerances but susceptible to lacking or erroneous resonance assignments, in particular for nuclei that are involved in many NOESY cross peaks.
In the chapter “Peakmatch – A simple and robust tool for peaklist matching” a method to achieve self-consistency of the chemical shift referencing among a set of peak lists is presented. The Peakmatch algorithm matches a set of peak lists to a specified reference peak list, neither of which have to be assigned, by optimizing an assignment-free match-score function. The algorithm has been extensively tested on the basis of experimental NMR data sets of five different proteins. The results show that peak lists from many different types of spectra can be matched reliably as long as they contain at least two corresponding dimensions.
NMR structures are represented by bundles of conformers whose spread indicates the precision of the atomic coordinates. However, there is as yet no reliable measure of structural accuracy, i.e. how close NMR conformers are to the “true” structure. Instead, the precision of structure bundles is widely (mis)interpreted as a measure of structural quality. Attempts to increase the precision thus often yield tight structure bundles where the precision overestimates the accuracy. To overcome this problem, the chapter “Increased reliability of NMR protein structures by consensus structure bundles” introduces a new protocol for NMR structure determination with the software package CYANA that produces bundles of conformers with a realistic precision that is throughout a large number of test data sets a much better estimate of the structural accuracy than the precision of conventional structure bundles.
Solid-state NMR is a powerful technique to study molecules which are not amenable to either solution NMR or X-ray crystallography. Despite the reporting of individual atomic resolution structures of membrane proteins and amyloid fibrils based on solid-state NMR data, the application is far from routine. One major obstacle that hinders structure determination by solid-state NMR is the overall lower quality of the solid-state NMR spectra. It is therefore necessary to increase the robustness of the computer algorithms in order to improve the results when using lower quality solid-state NMR spectra. The chapter “Structure calculations of the model protein GB1 from solid-state NMR data” presents structure calculations on the basis of a set of two-dimensional solid-state NMR experiments of the model protein GB1. The most important result obtained from these test calculations is that the limitation of structural accuracy can be attributed to inaccurate distance information resulting from the limited correlation between peak intensities and distance, which is especially severe in spin diffusion-based solid-state NMR experiments.
The chapter “Full relaxation matrix-based correction of relayed polarization transfer for solid-state NMR structure calculation” therefore introduces a method which corrects experimental peak intensities for spin diffusion in order to improve the distance information from solid-state NMR spectra. The results show that the structural accuracy can be significantly improved when using the corrected distance information, however, strongly dependent on the preliminary structural model that is required as input for the method.