Structure Determination of Protein−Protein Complexes Using NMR Chemical Shifts: Case of an Endonuclease Colicin−Immunity Protein Complex

Detection of structural and conformational changes in ALS-causing mutant profilin-1 with hydrogen/deuterium exchange mass spectrometry and bioinformatics techniques

The hydrogen/deuterium exchange (HDX) is a reliable method to survey the dynamic behavior of proteins and epitope mapping. Matrix-Assisted Laser Desorption Ionization-Time of Flight Mass Spectrometry (MALDI-TOF MS) is a quantifying tool to assay for HDX in the protein of interest. We combined HDX-MALDI-TOF MS and molecular docking/MD simulation to identify accessible amino acids and analyze their contribution into the structural changes of profilin-1 (PFN-1). The molecular docking/MD simulations are computational tools for enabling the analysis of the type of amino acids that may be involved via HDX identified under the lowest binding energy condition. Glycine to valine amino acid (G117V) substitution mutation is linked to amyotrophic lateral sclerosis (ALS). This mutation is found to be in the actin-binding site of PFN-1 and prevents the dimerization/polymerization of actin and invokes a pathologic toxicity that leads to ALS. In this study, we sought to understand the PFN-1 protein dynamic behavior using purified wild type and mutant PFN-1 proteins. The data obtained from HDX-MALDI-TOF MS for PFN-1^WT and PFN-1^G117V at various time intervals, from seconds to hours, revealed multiple peaks corresponding to molecular weights from monomers to multimers. PFN-1/Benzaldehyde complexes identified 20 accessible amino acids to HDX that participate in the docking simulation in the surface of WT and mutant PFN-1. Consistent results from HDX-MALDI-TOF MS and docking simulation predict candidate amino acid(s) involved in the dimerization/polymerization of PFN^G117V. This information may shed critical light on the structural and conformational changes with details of amino acid epitopes for mutant PFN-1s' dimerization, oligomerization, and aggregation.

Rational modulator design by exploitation of protein-protein complex structures

The horizon of drug discovery is currently expanding to target and modulate protein-protein interactions (PPIs) in globular proteins and intrinsically disordered proteins that are involved in various diseases. To either interrupt or stabilize PPIs, the 3D structure of target protein-protein (or protein-peptide) complexes can be exploited to rationally design PPI modulators (inhibitors or stabilizers) through structure-based molecular design. In this review, we present an overview of experimental and computational methods that can be used to determine 3D structures of protein-protein complexes. Several approaches including rational and in silico methods that can be applied to design peptides, peptidomimetics and small compounds by utilization of determined 3D protein-protein/peptide complexes are summarized and illustrated.

Assessment of ab initio models of protein complexes by molecular dynamics

Determining how proteins interact to form stable complexes is of crucial importance, for example in the development of novel therapeutics. Computational methods to determine the thermodynamically stable conformation of complexes from the structure of the binding partners, such as RosettaDock, might potentially emerge to become a promising alternative to traditional structure determination methods. However, while models virtually identical to the correct experimental structure can in some cases be generated, the main difficulty remains to discriminate correct or approximately correct models from decoys. This is due to the ruggedness of the free-energy landscape, the approximations intrinsic in the scoring functions, and the intrinsic flexibility of proteins. Here we show that molecular dynamics simulations performed starting from a number top-scoring models can not only discriminate decoys and identify the correct structure, but may also provide information on an initial map of the free energy landscape that elucidates the binding mechanism.

Determining how proteins fold and form complexes is of crucial importance, for example in the development of novel therapeutics. Experimental determination of structures is costly and lengthy. Computational methods to determine the thermodynamically stable conformation of complexes from the structure of the binding partners are available and constantly improving. Such methods generate a large number of diverse conformations and rank them for their likelihood to be correct. Even a model very similar to the correct structure is rarely the top-scoring one, but, as in the examples presented here, only within the top ~10–100 (the exact number depends on the complexity of the structure, and could be much higher). Here we show through atomistic simulation that good models are kinetically stable and bad models most often are not. More surprisingly, we also see that some bad models spontaneously find the correct (i.e., experimentally determined) conformation. This is remarkable, and could become an additional tool to contribute to structure determination of protein complexes. Such a result can also be expected, because evolution sculpted the free energy landscape in a way that the biologically active state is not only the one of lowest free energy (i.e., the most likely state) but also robustly reachable and kinetically stable (i.e., at the bottom of a funnel on the free energy landscape).

Chemical shift-based methods in NMR structure determination

Chemical shifts are highly sensitive probes harnessed by NMR spectroscopists and structural biologists as conformational parameters to characterize a range of biological molecules. Traditionally, assignment of chemical shifts has been a labor-intensive process requiring numerous samples and a suite of multidimensional experiments. Over the past two decades, the development of complementary computational approaches has bolstered the analysis, interpretation and utilization of chemical shifts for elucidation of high resolution protein and nucleic acid structures. Here, we review the development and application of chemical shift-based methods for structure determination with a focus on ab initio fragment assembly, comparative modeling, oligomeric systems, and automated assignment methods. Throughout our discussion, we point out practical uses, as well as advantages and caveats, of using chemical shifts in structure modeling. We additionally highlight (i) hybrid methods that employ chemical shifts with other types of NMR restraints (residual dipolar couplings, paramagnetic relaxation enhancements and pseudocontact shifts) that allow for improved accuracy and resolution of generated 3D structures, (ii) the utilization of chemical shifts to model the structures of sparsely populated excited states, and (iii) modeling of sidechain conformations. Finally, we briefly discuss the advantages of contemporary methods that employ sparse NMR data recorded using site-specific isotope labeling schemes for chemical shift-driven structure determination of larger molecules. With this review, we aim to emphasize the accessibility and versatility of chemical shifts for structure determination of challenging biological systems, and to point out emerging areas of development that lead us towards the next generation of tools.

Revealing the architecture of protein complexes by an orthogonal approach combining HDXMS, CXMS, and disulfide trapping

Many cellular functions necessitate structural assemblies of two or more associated proteins. The structural characterization of protein complexes using standard methods, such as X-ray crystallography, is challenging. Herein, we describe an orthogonal approach using hydrogen-deuterium-exchange mass spectrometry (HDXMS), cross-linking mass spectrometry (CXMS), and disulfide trapping to map interactions within protein complexes. HDXMS measures changes in solvent accessibility and hydrogen bonding upon complex formation; a decrease in HDX rate could account for newly formed intermolecular or intramolecular interactions. To distinguish between inter- and intramolecular interactions, we use a CXMS method to determine the position of direct interface regions by trapping intermolecular residues in close proximity to various cross-linkers (e.g., disuccinimidyl adipate (DSA)) of different lengths and reactive groups. Both MS-based experiments are performed on high-resolution mass spectrometers (e.g., an Orbitrap Elite hybrid mass spectrometer). The physiological relevance of the interactions identified through HDXMS and CXMS is investigated by transiently co-expressing cysteine mutant pairs, one mutant on each protein at the discovered interfaces, in an appropriate cell line, such as HEK293. Disulfide-trapped protein complexes are formed within cells spontaneously or are facilitated by addition of oxidation reagents such as H₂O₂ or diamide. Western blotting analysis, in the presence and absence of reducing reagents, is used to determine whether the disulfide bonds are formed in the proposed complex interface in physiologically relevant milieus. The procedure described here requires 1-2 months. We demonstrate this approach using the β2-adrenergic receptor-β-arrestin1 complex as the model system.

Fragment-based quantum mechanical calculation of protein-protein binding affinities

The electrostatically embedded generalized molecular fractionation with conjugate caps (EE-GMFCC) method has been successfully utilized for efficient linear-scaling quantum mechanical (QM) calculation of protein energies. In this work, we applied the EE-GMFCC method for calculation of binding affinity of Endonuclease colicin-immunity protein complex. The binding free energy changes between the wild-type and mutants of the complex calculated by EE-GMFCC are in good agreement with experimental results. The correlation coefficient (R) between the predicted binding energy changes and experimental values is 0.906 at the B3LYP/6-31G*-D level, based on the snapshot whose binding affinity is closest to the average result from the molecular mechanics/Poisson-Boltzmann surface area (MM/PBSA) calculation. The inclusion of the QM effects is important for accurate prediction of protein-protein binding affinities. Moreover, the self-consistent calculation of PB solvation energy is required for accurate calculations of protein-protein binding free energies. This study demonstrates that the EE-GMFCC method is capable of providing reliable prediction of relative binding affinities for protein-protein complexes. © 2018 Wiley Periodicals, Inc.

Recent developments in solution nuclear magnetic resonance (NMR)-based molecular biology

Visualizing post-translational modifications, conformations, and interaction surfaces of protein structures at atomic resolution underpins the development of novel therapeutics to combat disease. As computational resources expand, in silico calculations coupled with experimentally derived structures and functional assays have led to an explosion in structure-based drug design (SBDD) with several compounds in clinical trials. It is increasingly clear that "hidden" transition-state structures along activation trajectories can be harnessed to develop novel classes of allosteric inhibitors. The goal of this mini-review is to empower the clinical researcher with a general knowledge of the strengths and weaknesses of nuclear magnetic resonance (NMR) spectroscopy in molecular medicine. Although NMR can determine protein structures at atomic resolution, its unrivaled strength lies in sensing subtle changes in a nuclei's chemical environment as a result of intrinsic conformational dynamics, solution conditions, and binding interactions. These can be recorded at atomic resolution, without explicit structure determination, and then incorporated with static structures or molecular dynamics simulations to produce a complete biological picture.

Model-free extraction of spin label position distributions from pseudocontact shift data

Not a point, but a cloud: advanced PCS data analysis using 3D probability density reconstruction provides more information.

A significant problem with paramagnetic tags attached to proteins and nucleic acids is their conformational mobility. Each tag is statistically distributed within a volume between 5 and 10 Angstroms across; structural biology conclusions from NMR and EPR work are necessarily diluted by this uncertainty. The problem is solved in electron spin resonance, but remains open in the other major branch of paramagnetic resonance – pseudocontact shift (PCS) NMR spectroscopy, where structural biologists have so far been reluctantly using the point paramagnetic centre approximation. Here we describe a new method for extracting probability densities of lanthanide tags from PCS data. The method relies on Tikhonov-regularised 3D reconstruction and opens a new window into biomolecular structure and dynamics because it explores a very different range of conditions from those accessible to double electron resonance work on paramagnetic tags: a room-temperature solution rather than a glass at cryogenic temperatures. The method is illustrated using four different Tm³⁺ DOTA-M8 tagged mutants of human carbonic anhydrase II; the results are in good agreement with rotamer library and DEER data. The wealth of high-quality pseudocontact shift data accumulated by the biological magnetic resonance community over the last 30 years, and so far only processed using point models, could now become a major source of useful information on conformational distributions of paramagnetic tags in biomolecules.

Principles and Overview of Sampling Methods for Modeling Macromolecular Structure and Dynamics

Investigation of macromolecular structure and dynamics is fundamental to understanding how macromolecules carry out their functions in the cell. Significant advances have been made toward this end in silico, with a growing number of computational methods proposed yearly to study and simulate various aspects of macromolecular structure and dynamics. This review aims to provide an overview of recent advances, focusing primarily on methods proposed for exploring the structure space of macromolecules in isolation and in assemblies for the purpose of characterizing equilibrium structure and dynamics. In addition to surveying recent applications that showcase current capabilities of computational methods, this review highlights state-of-the-art algorithmic techniques proposed to overcome challenges posed in silico by the disparate spatial and time scales accessed by dynamic macromolecules. This review is not meant to be exhaustive, as such an endeavor is impossible, but rather aims to balance breadth and depth of strategies for modeling macromolecular structure and dynamics for a broad audience of novices and experts.

Correlation of chemical shifts predicted by molecular dynamics simulations for partially disordered proteins

There has been a longstanding interest in being able to accurately predict NMR chemical shifts from structural data. Recent studies have focused on using molecular dynamics (MD) simulation data as input for improved prediction. Here we examine the accuracy of chemical shift prediction for intein systems, which have regions of intrinsic disorder. We find that using MD simulation data as input for chemical shift prediction does not consistently improve prediction accuracy over use of a static X-ray crystal structure. This appears to result from the complex conformational ensemble of the disordered protein segments. We show that using accelerated molecular dynamics (aMD) simulations improves chemical shift prediction, suggesting that methods which better sample the conformational ensemble like aMD are more appropriate tools for use in chemical shift prediction for proteins with disordered regions. Moreover, our study suggests that data accurately reflecting protein dynamics must be used as input for chemical shift prediction in order to correctly predict chemical shifts in systems with disorder.

NMR approaches for structural analysis of multidomain proteins and complexes in solution

NMR spectroscopy is a key method for studying the structure and dynamics of (large) multidomain proteins and complexes in solution. It plays a unique role in integrated structural biology approaches as especially information about conformational dynamics can be readily obtained at residue resolution. Here, we review NMR techniques for such studies focusing on state-of-the-art tools and practical aspects. An efficient approach for determining the quaternary structure of multidomain complexes starts from the structures of individual domains or subunits. The arrangement of the domains/subunits within the complex is then defined based on NMR measurements that provide information about the domain interfaces combined with (long-range) distance and orientational restraints. Aspects discussed include sample preparation, specific isotope labeling and spin labeling; determination of binding interfaces and domain/subunit arrangements from chemical shift perturbations (CSP), nuclear Overhauser effects (NOEs), isotope editing/filtering, cross-saturation, and differential line broadening; and based on paramagnetic relaxation enhancements (PRE) using covalent and soluble spin labels. Finally, the utility of complementary methods such as small-angle X-ray or neutron scattering (SAXS, SANS), electron paramagnetic resonance (EPR) or fluorescence spectroscopy techniques is discussed. The applications of NMR techniques are illustrated with studies of challenging (high molecular weight) protein complexes.

ALMOST: an all atom molecular simulation toolkit for protein structure determination

Almost (all atom molecular simulation toolkit) is an open source computational package for structure determination and analysis of complex molecular systems including proteins, and nucleic acids. Almost has been designed with two primary goals: to provide tools for molecular structure determination using various types of experimental measurements as conformational restraints, and to provide methods for the analysis and assessment of structural and dynamical properties of complex molecular systems. The methods incorporated in Almost include the determination of structural and dynamical features of proteins using distance restraints derived from nuclear Overhauser effect measurements, orientational restraints obtained from residual dipolar couplings and the structural restraints from chemical shifts. Here, we present the first public release of Almost, highlight the key aspects of its computational design and discuss the main features currently implemented. Almost is available for the most common Unix-based operating systems, including Linux and Mac OS X. Almost is distributed free of charge under the GNU Public License, and is available both as a source code and as a binary executable from the project web site at http://www.open-almost.org. Interested users can follow and contribute to the further development of Almost on http://sourceforge.net/projects/almost.

Using chemical shift perturbation to characterise ligand binding

Chemical shift perturbation (CSP, chemical shift mapping or complexation-induced changes in chemical shift, CIS) follows changes in the chemical shifts of a protein when a ligand is added, and uses these to determine the location of the binding site, the affinity of the ligand, and/or possibly the structure of the complex. A key factor in determining the appearance of spectra during a titration is the exchange rate between free and bound, or more specifically the off-rate koff. When koff is greater than the chemical shift difference between free and bound, which typically equates to an affinity Kd weaker than about 3μM, then exchange is fast on the chemical shift timescale. Under these circumstances, the observed shift is the population-weighted average of free and bound, which allows Kd to be determined from measurement of peak positions, provided the measurements are made appropriately. (1)H shifts are influenced to a large extent by through-space interactions, whereas (13)Cα and (13)Cβ shifts are influenced more by through-bond effects. (15)N and (13)C' shifts are influenced both by through-bond and by through-space (hydrogen bonding) interactions. For determining the location of a bound ligand on the basis of shift change, the most appropriate method is therefore usually to measure (15)N HSQC spectra, calculate the geometrical distance moved by the peak, weighting (15)N shifts by a factor of about 0.14 compared to (1)H shifts, and select those residues for which the weighted shift change is larger than the standard deviation of the shift for all residues. Other methods are discussed, in particular the measurement of (13)CH3 signals. Slow to intermediate exchange rates lead to line broadening, and make Kd values very difficult to obtain. There is no good way to distinguish changes in chemical shift due to direct binding of the ligand from changes in chemical shift due to allosteric change. Ligand binding at multiple sites can often be characterised, by simultaneous fitting of many measured shift changes, or more simply by adding substoichiometric amounts of ligand. The chemical shift changes can be used as restraints for docking ligand onto protein. By use of quantitative calculations of ligand-induced chemical shift changes, it is becoming possible to determine not just the position but also the orientation of ligands.

NightShift: NMR shift inference by general hybrid model training--a framework for NMR chemical shift prediction

Background

NMR chemical shift prediction plays an important role in various applications in computational biology. Among others, structure determination, structure optimization, and the scoring of docking results can profit from efficient and accurate chemical shift estimation from a three-dimensional model.

A variety of NMR chemical shift prediction approaches have been presented in the past, but nearly all of these rely on laborious manual data set preparation and the training itself is not automatized, making retraining the model, e.g., if new data is made available, or testing new models a time-consuming manual chore.

Results

In this work, we present the framework NightShift (NMR Shift Inference by General Hybrid Model Training), which enables automated data set generation as well as model training and evaluation of protein NMR chemical shift prediction.

In addition to this main result – the NightShift framework itself – we describe the resulting, automatically generated, data set and, as a proof-of-concept, a random forest model called Spinster that was built using the pipeline.

Conclusion

By demonstrating that the performance of the automatically generated predictors is at least en par with the state of the art, we conclude that automated data set and predictor generation is well-suited for the design of NMR chemical shift estimators.

The framework can be downloaded from https://bitbucket.org/akdehof/nightshift. It requires the open source Biochemical Algorithms Library (BALL), and is available under the conditions of the GNU Lesser General Public License (LGPL). We additionally offer a browser-based user interface to our NightShift instance employing the Galaxy framework via https://ballaxy.bioinf.uni-sb.de/.

Protein structure prediction using global optimization by basin-hopping with NMR shift restraints

Computational methods that utilize chemical shifts to produce protein structures at atomic resolution have recently been introduced. In the current work, we exploit chemical shifts by combining the basin-hopping approach to global optimization with chemical shift restraints using a penalty function. For three peptides, we demonstrate that this approach allows us to find near-native structures from fully extended structures within 10,000 basin-hopping steps. The effect of adding chemical shift restraints is that the α and β secondary structure elements form within 1000 basin-hopping steps, after which the orientation of the secondary structure elements, which produces the tertiary contacts, is driven by the underlying protein force field. We further show that our chemical shift-restraint BH approach also works for incomplete chemical shift assignments, where the information from only one chemical shift type is considered. For the proper implementation of chemical shift restraints in the basin-hopping approach, we determined the optimal weight of the chemical shift penalty energy with respect to the CHARMM force field in conjunction with the FACTS solvation model employed in this study. In order to speed up the local energy minimization procedure, we developed a function, which continuously decreases the width of the chemical shift penalty function as the minimization progresses. We conclude that the basin-hopping approach with chemical shift restraints is a promising method for protein structure prediction.

Accurate structure and dynamics of the metal-site of paramagnetic metalloproteins from NMR parameters using natural bond orbitals

A natural bond orbital (NBO) analysis of unpaired electron spin density in metalloproteins is presented, which allows a fast and robust calculation of paramagnetic NMR parameters. Approximately 90% of the unpaired electron spin density occupies metal–ligand NBOs, allowing the majority of the density to be modeled by only a few NBOs that reflect the chemical bonding environment. We show that the paramagnetic relaxation rate of protons can be calculated accurately using only the metal–ligand NBOs and that these rates are in good agreement with corresponding rates measured experimentally. This holds, in particular, for protons of ligand residues where the point-dipole approximation breaks down. To describe the paramagnetic relaxation of heavy nuclei, also the electron spin density in the local orbitals must be taken into account. Geometric distance restraints for ¹⁵N can be derived from the paramagnetic relaxation enhancement and the Fermi contact shift when local NBOs are included in the analysis. Thus, the NBO approach allows us to include experimental paramagnetic NMR parameters of ¹⁵N nuclei as restraints in a structure optimization protocol. We performed a molecular dynamics simulation and structure determination of oxidized rubredoxin using the experimentally obtained paramagnetic NMR parameters of ¹⁵N. The corresponding structures obtained are in good agreement with the crystal structure of rubredoxin. Thus, the NBO approach allows an accurate description of the geometric structure and the dynamics of metalloproteins, when NMR parameters are available of nuclei in the immediate vicinity of the metal-site.

Determination of secondary structure populations in disordered states of proteins using nuclear magnetic resonance chemical shifts

One of the major open challenges in structural biology is to achieve effective descriptions of disordered states of proteins. This problem is difficult because these states are conformationally highly heterogeneous and cannot be represented as single structures, and therefore it is necessary to characterize their conformational properties in terms of probability distributions. Here we show that it is possible to obtain highly quantitative information about particularly important types of probability distributions, the populations of secondary structure elements (α-helix, β-strand, random coil, and polyproline II), by using the information provided by backbone chemical shifts. The application of this approach to mammalian prions indicates that for these proteins a key role in molecular recognition is played by disordered regions characterized by highly conserved polyproline II populations. We also determine the secondary structure populations of a range of other disordered proteins that are medically relevant, including p53, α-synuclein, and the Aβ peptide, as well as an oligomeric form of αB-crystallin. Because chemical shifts are the nuclear magnetic resonance parameters that can be measured under the widest variety of conditions, our approach can be used to obtain detailed information about secondary structure populations for a vast range of different protein states.

Structure of the ultra-high-affinity colicin E2 DNase--Im2 complex

How proteins achieve high-affinity binding to a specific protein partner while simultaneously excluding all others is a major biological problem that has important implications for protein design. We report the crystal structure of the ultra-high-affinity protein-protein complex between the endonuclease domain of colicin E2 and its cognate immunity (Im) protein, Im2 (K(d)∼10(-)(15) M), which, by comparison to previous structural and biophysical data, provides unprecedented insight into how high affinity and selectivity are achieved in this model family of protein complexes. Our study pinpoints the role of structured water molecules in conjoining hotspot residues that govern stability with residues that control selectivity. A key finding is that a single residue, which in a noncognate context massively destabilizes the complex through frustration, does not participate in specificity directly but rather acts as an organizing center for a multitude of specificity interactions across the interface, many of which are water mediated.

Nuclease colicins and their immunity proteins

It is more than 80 years since Gratia first described 'a remarkable antagonism between two strains of Escherichia coli'. Shown subsequently to be due to the action of proteins (or peptides) produced by one bacterium to kill closely related species with which it might be cohabiting, such bacteriocins have since been shown to be commonplace in the internecine warfare between bacteria. Bacteriocins have been studied primarily from the twin perspectives of how they shape microbial communities and how they penetrate bacteria to kill them. Here, we review the modes of action of a family of bacteriocins that cleave nucleic acid substrates in E. coli, known collectively as nuclease colicins, and the specific immunity (inhibitor) proteins that colicin-producing organisms make in order to avoid committing suicide. In a process akin to targeting in mitochondria, nuclease colicins engage in a variety of cellular associations in order to translocate their cytotoxic domains through the cell envelope to the cytoplasm. As well as informing on the process itself, the study of nuclease colicin import has also illuminated functional aspects of the host proteins they parasitize. We also review recent studies where nuclease colicins and their immunity proteins have been used as model systems for addressing fundamental problems in protein folding and protein-protein interactions, areas of biophysics that are intimately linked to the role of colicins in bacterial competition and to the import process itself.

Rapid measurement of residual dipolar couplings for fast fold elucidation of proteins

It has been demonstrated that protein folds can be determined using appropriate computational protocols with NMR chemical shifts as the sole source of experimental restraints. While such approaches are very promising they still suffer from low convergence resulting in long computation times to achieve accurate results. Here we present a suite of time- and sensitivity optimized NMR experiments for rapid measurement of up to six RDCs per residue. Including such an RDC data set, measured in less than 24 h on a single aligned protein sample, greatly improves convergence of the Rosetta-NMR protocol, allowing for overnight fold calculation of small proteins. We demonstrate the performance of our fast fold calculation approach for ubiquitin as a test case, and for two RNA-binding domains of the plant protein HYL1. Structure calculations based on simulated RDC data highlight the importance of an accurate and precise set of several complementary RDCs as additional input restraints for high-quality de novo structure determination.

Structure-based prediction of methyl chemical shifts in proteins

Protein methyl groups have recently been the subject of much attention in NMR spectroscopy because of the opportunities that they provide to obtain information about the structure and dynamics of proteins and protein complexes. With the advent of selective labeling schemes, methyl groups are particularly interesting in the context of chemical shift based protein structure determination, an approach that to date has exploited primarily the mapping between protein structures and backbone chemical shifts. In order to extend the scope of chemical shifts for structure determination, we present here the CH3Shift method of performing structure-based predictions of methyl chemical shifts. The terms considered in the predictions take account of ring current, magnetic anisotropy, electric field, rotameric type, and dihedral angle effects, which are considered in conjunction with polynomial functions of interatomic distances. We show that the CH3Shift method achieves an accuracy in the predictions that ranges from 0.133 to 0.198 ppm for (1)H chemical shifts for Ala, Thr, Val, Leu and Ile methyl groups. We illustrate the use of the method by assessing the accuracy of side-chain structures in structural ensembles representing the dynamics of proteins.

Quantitative use of chemical shifts for the modeling of protein complexes

Despite recent advances in the modeling of protein-protein complexes by docking, additional information is often required to identify the best solutions. For this purpose, NMR data deliver valuable restraints that can be used in the sampling and/or the scoring stage, like in the data-driven docking approach HADDOCK that can make use of NMR chemical shift perturbation (CSP) data to define the binding site on each protein and drive the docking. We show here that a quantitative use of chemical shifts (CS) in the scoring stage can help to resolve ambiguities. A quantitative CS-RMSD score based on (1) H(α) ,(13) C(α) , and (15) N chemical shifts ranks the best solutions always at the top, as demonstrated on a small benchmark of four complexes. It is implemented in a new docking protocol, CS-HADDOCK, which combines CSP data as ambiguous interaction restraints in the sampling stage with the CS-RMSD score in the final scoring stage. This combination of qualitative and quantitative use of chemical shifts increases the reliability of data-driven docking for the structure determination of complexes from limited NMR data.

Structural NMR of protein oligomers using hybrid methods

Solving structures of native oligomeric protein complexes using traditional high-resolution NMR techniques remains challenging. However, increased utilization of computational platforms, and integration of information from less traditional NMR techniques with data from other complementary biophysical methods, promises to extend the boundary of NMR-applicable targets. This article reviews several of the techniques capable of providing less traditional and complementary structural information. In particular, the use of orientational constraints coming from residual dipolar couplings and residual chemical shift anisotropy offsets are shown to simplify the construction of models for oligomeric complexes, especially in cases of weak homo-dimers. Combining this orientational information with interaction site information supplied by computation, chemical shift perturbation, paramagnetic surface perturbation, cross-saturation and mass spectrometry allows high resolution models of the complexes to be constructed with relative ease. Non-NMR techniques, such as mass spectrometry, EPR and small angle X-ray scattering, are also expected to play increasingly important roles by offering alternative methods of probing the overall shape of the complex. Computational platforms capable of integrating information from multiple sources in the modeling process are also discussed in the article. And finally a new, detailed example on the determination of a chemokine tetramer structure will be used to illustrate how a non-traditional approach to oligomeric structure determination works in practice.

Strengths and weaknesses of data-driven docking in critical assessment of prediction of interactions

The recent CAPRI rounds have introduced new docking challenges in the form of protein-RNA complexes, multiple alternative interfaces, and an unprecedented number of targets for which homology modeling was required. We present here the performance of HADDOCK and its web server in the CAPRI experiment and discuss the strengths and weaknesses of data-driven docking. HADDOCK was successful for 6 out of 9 complexes (6 out of 11 targets) and accurately predicted the individual interfaces for two more complexes. The HADDOCK server, which is the first allowing the simultaneous docking of generic multi-body complexes, was successful in 4 out of 7 complexes for which it participated. In the scoring experiment, we predicted the highest number of targets of any group. The main weakness of data-driven docking revealed from these last CAPRI results is its vulnerability for incorrect experimental data related to the interface or the stoichiometry of the complex. At the same time, the use of experimental and/or predicted information is also the strength of our approach as evidenced for those targets for which accurate experimental information was available (e.g., the 10 three-stars predictions for T40!). Even when the models show a wrong orientation, the individual interfaces are generally well predicted with an average coverage of 60% ± 26% over all targets. This makes data-driven docking particularly valuable in a biological context to guide experimental studies like, for example, targeted mutagenesis.

Trends in template/fragment-free protein structure prediction

Predicting the structure of a protein from its amino acid sequence is a long-standing unsolved problem in computational biology. Its solution would be of both fundamental and practical importance as the gap between the number of known sequences and the number of experimentally solved structures widens rapidly. Currently, the most successful approaches are based on fragment/template reassembly. Lacking progress in template-free structure prediction calls for novel ideas and approaches. This article reviews trends in the development of physical and specific knowledge-based energy functions as well as sampling techniques for fragment-free structure prediction. Recent physical- and knowledge-based studies demonstrated that it is possible to sample and predict highly accurate protein structures without borrowing native fragments from known protein structures. These emerging approaches with fully flexible sampling have the potential to move the field forward.

Using NMR chemical shifts as structural restraints in molecular dynamics simulations of proteins

We introduce a procedure to determine the structures of proteins by incorporating NMR chemical shifts as structural restraints in molecular dynamics simulations. In this approach, the chemical shifts are expressed as differentiable functions of the atomic coordinates and used to compute forces to generate trajectories that lead to the reduction of the differences between experimental and calculated chemical shifts. We show that this strategy enables the folding of a set of proteins with representative topologies starting from partially denatured initial conformations without the use of additional experimental information. This method also enables the straightforward combination of chemical shifts with other standard NMR restraints, including those derived from NOE, J-coupling, and residual dipolar coupling measurements. We illustrate this aspect by calculating the structure of a transiently populated excited state conformation from chemical shift and residual dipolar coupling data measured by relaxation dispersion NMR experiments.

Quantitative approaches to defining normal and aberrant protein homeostasis

Protein homeostasis refers to the ability of cells to generate and regulate the levels of their constituent proteins in terms of conformations, interactions, concentrations and cellular localisation. We discuss here an approach in which physico-chemical properties of proteins and their environments are used to understand the underlying principles governing this process, which is crucial in all living systems. By adopting the strategy of characterising the origins of specific diseases to inform us about normal biology, we are bringing together methods and concepts from chemistry, physics, engineering, genetics and medicine. In particular, we are using a combination of in vitro, in silico and in vivo approaches to study protein homeostasis through the analysis of the effects that result from its perturbation in a select group of specific proteins, from either amino acid mutations, or changes in concentration and solubility, or interactions with other molecules. By developing a coherent and quantitative description of such phenomena, we are finding that it is possible to shed new light on how the physical and chemical properties of the cellular components can provide an understanding of the normal and aberrant behaviour of living systems. Through such an approach it is possible to provide new insights into the origin and consequences of the failure to maintain homeostasis that is associated with neurodegenerative diseases, in particular, and the phenomenon of ageing, in general, and hence provide a framework for the rational design of therapeutic approaches.

Predicting continuous local structure and the effect of its substitution for secondary structure in fragment-free protein structure prediction

Local structures predicted from protein sequences are used extensively in every aspect of modeling and prediction of protein structure and function. For more than 50 years, they have been predicted at a low-resolution coarse-grained level (e.g., three-state secondary structure). Here, we combine a two-state classifier with real-value predictor to predict local structure in continuous representation by backbone torsion angles. The accuracy of the angles predicted by this approach is close to that derived from NMR chemical shifts. Their substitution for predicted secondary structure as restraints for ab initio structure prediction doubles the success rate. This result demonstrates the potential of predicted local structure for fragment-free tertiary-structure prediction. It further implies potentially significant benefits from using predicted real-valued torsion angles as a replacement for or supplement to the secondary-structure prediction tools used almost exclusively in many computational methods ranging from sequence alignment to function prediction.

DNA structures from phosphate chemical shifts

For B-DNA, the strong linear correlation observed by nuclear magnetic resonance (NMR) between the (31)P chemical shifts (deltaP) and three recurrent internucleotide distances demonstrates the tight coupling between phosphate motions and helicoidal parameters. It allows to translate deltaP into distance restraints directly exploitable in structural refinement. It even provides a new method for refining DNA oligomers with restraints exclusively inferred from deltaP. Combined with molecular dynamics in explicit solvent, these restraints lead to a structural and dynamical view of the DNA as detailed as that obtained with conventional and more extensive restraints. Tests with the Jun-Fos oligomer show that this deltaP-based strategy can provide a simple and straightforward method to capture DNA properties in solution, from routine NMR experiments on unlabeled samples.

4D prediction of protein (1)H chemical shifts

A 4D approach for protein (1)H chemical shift prediction was explored. The 4th dimension is the molecular flexibility, mapped using molecular dynamics simulations. The chemical shifts were predicted with a principal component model based on atom coordinates from a database of 40 protein structures. When compared to the corresponding non-dynamic (3D) model, the 4th dimension improved prediction by 6-7%. The prediction method achieved RMS errors of 0.29 and 0.50 ppm for Halpha and HN shifts, respectively. However, for individual proteins the RMS errors were 0.17-0.34 and 0.34-0.65 ppm for the Halpha and HN shifts, respectively. X-ray structures gave better predictions than the corresponding NMR structures, indicating that chemical shifts contain invaluable information about local structures. The (1)H chemical shift prediction tool 4DSPOT is available from http://www.uku.fi/kemia/4dspot .

Simultaneous prediction of protein folding and docking at high resolution

Interleaved dimers and higher order symmetric oligomers are ubiquitous in biology but present a challenge to de novo structure prediction methodology: The structure adopted by a monomer can be stabilized largely by interactions with other monomers and hence not the lowest energy state of a single chain. Building on the Rosetta framework, we present a general method to simultaneously model the folding and docking of multiple-chain interleaved homo-oligomers. For more than a third of the cases in a benchmark set of interleaved homo-oligomers, the method generates near-native models of large alpha-helical bundles, interlocking beta sandwiches, and interleaved alpha/beta motifs with an accuracy high enough for molecular replacement based phasing. With the incorporation of NMR chemical shift information, accurate models can be obtained consistently for symmetric complexes with as many as 192 total amino acids; a blind prediction was within 1 A rmsd of the traditionally determined NMR structure, and fit independently collected RDC data equally well. Together, these results show that the Rosetta "fold-and-dock" protocol can produce models of homo-oligomeric complexes with near-atomic-level accuracy and should be useful for crystallographic phasing and the rapid determination of the structures of multimers with limited NMR information.

Determination of protein structures in the solid state from NMR chemical shifts

Solid-state NMR spectroscopy does not require proteins to form crystalline or soluble samples and can thus be applied under a variety of conditions, including precipitates, gels, and microcrystals. It has recently been shown that NMR chemical shifts can be used to determine the structures of the native states of proteins in solution. By considering the cases of two proteins, GB1 and SH3, we provide an initial demonstration here that this type of approach can be extended to the use of solid-state NMR chemical shifts to obtain protein structures in the solid state without the need for measuring interatomic distances.