Protein crystallography for non-crystallographers, or how to get the best (but not more) from published macromolecular structures

Structure Refinement at Atomic Resolution

X-Ray diffraction data at atomic resolution, i.e., beyond 1.2 Å, provide the most detailed and reliable information we have about the structure of macromolecules, which is especially important for validating new discoveries and resolving subtle issues of molecular mechanisms. Refinement at atomic resolution allows reliable interpretation of static disorder and solvent structure, as well as modeling of anisotropic atomic vibrations and even of H atoms. Stereochemical restraints can be relaxed or removed, providing unbiased information about macromolecular stereochemistry, which in turn can be used to define improved conformation-dependent libraries, and the surplus of data allows estimation of least-squares uncertainties in the derived parameters. At ultrahigh resolution it is possible to study charge density distribution by multipolar refinement of electrons in non-spherical orbitals.

Ligand-Based Pharmacophore Screening Strategy: a Pragmatic Approach for Targeting HER Proteins

Targeting ErbB family of receptors is an important therapeutic option, because of its essential role in the broad spectrum of human cancers, including non-small cell lung cancer (NSCLC). Therefore, in the present work, considerable effort has been made to develop an inhibitor against HER family proteins, by combining the use of pharmacophore modelling, docking scoring functions, and ADME property analysis. Initially, a five-point pharmacophore model was developed using known HER family inhibitors. The generated model was then used as a query to screen a total of 468,880 compounds of three databases namely ZINC, ASINEX, and DrugBank. Subsequently, docking analysis was carried out to obtain hit molecules that could inhibit the HER receptors. Further, analysis of GLIDE scores and ADME properties resulted in one hit namely BAS01025917 with higher glide scores, increased CNS involvement, and good pharmaceutically relevant properties than reference ligand, afatinib. Furthermore, the inhibitory activity of the lead compounds was validated by performing molecular dynamic simulations. Of note, BAS01025917 was found to possess scaffolds with a broad spectrum of antitumor activity. We believe that this novel hit molecule can be further exploited for the development of a pan-HER inhibitor with low toxicity and greater potential.

Overview of refinement procedures within REFMAC5: utilizing data from different sources

Refinement is a process that involves bringing into agreement the structural model, available prior knowledge and experimental data. To achieve this, the refinement procedure optimizes a posterior conditional probability distribution of model parameters, including atomic coordinates, atomic displacement parameters (B factors), scale factors, parameters of the solvent model and twin fractions in the case of twinned crystals, given observed data such as observed amplitudes or intensities of structure factors. A library of chemical restraints is typically used to ensure consistency between the model and the prior knowledge of stereochemistry. If the observation-to-parameter ratio is small, for example when diffraction data only extend to low resolution, the Bayesian framework implemented in REFMAC5 uses external restraints to inject additional information extracted from structures of homologous proteins, prior knowledge about secondary-structure formation and even data obtained using different experimental methods, for example NMR. The refinement procedure also generates the `best' weighted electron-density maps, which are useful for further model (re)building. Here, the refinement of macromolecular structures using REFMAC5 and related tools distributed as part of the CCP4 suite is discussed.

Anticipating innovations in structural biology

In this review, we describe how the interplay among science, technology and community interests contributed to the evolution of four structural biology data resources. We present the method by which data deposited by scientists are prepared for worldwide distribution, and argue that data archiving in a trusted repository must be an integral part of any scientific investigation.

Validation of Molecular Simulation: An Overview of Issues

Computer simulation of molecular systems enables structure-energy-function relationships of molecular processes to be described at the sub-atomic, atomic, supra-atomic, or supra-molecular level. To interpret results of such simulations appropriately, the quality of the calculated properties must be evaluated. This depends on the way the simulations are performed and on the way they are validated by comparison to values Q^exp of experimentally observable quantities Q. One must consider 1) the accuracy of Q^exp , 2) the accuracy of the function Q(r^N ) used to calculate a Q-value based on a molecular configuration r^N of N particles, 3) the sensitivity of the function Q(r^N ) to the configuration r^N , 4) the relative time scales of the simulation and experiment, 5) the degree to which the calculated and experimental properties are equivalent, and 6) the degree to which the system simulated matches the experimental conditions. Experimental data is limited in scope and generally corresponds to averages over both time and space. A critical analysis of the various factors influencing the apparent degree of (dis)agreement between simulations and experiment is presented and illustrated using examples from the literature. What can be done to enhance the validation of molecular simulation is also discussed.

Replica-Exchange and Standard State Binding Free Energies with Grand Canonical Monte Carlo

The ability of grand canonical Monte Carlo (GCMC) to create and annihilate molecules in a given region greatly aids the identification of water sites and water binding free energies in protein cavities. However, acceptance rates without the application of biased moves can be low, resulting in large variations in the observed water occupancies. Here, we show that replica-exchange of the chemical potential significantly reduces the variance of the GCMC data. This improvement comes at a negligible increase in computational expense when simulations comprise of runs at different chemical potentials. Replica-exchange GCMC is also found to substantially increase the precision of water binding free energies as calculated with grand canonical integration, which has allowed us to address a missing standard state correction.

Enhancing Structure Prediction and Design of Soluble and Membrane Proteins with Explicit Solvent-Protein Interactions

Solvent molecules interact intimately with proteins and can profoundly regulate their structure and function. However, accurately and efficiently modeling protein solvation effects at the molecular level has been challenging. Here, we present a method that improves the atomic-level modeling of soluble and membrane protein structures and binding by efficiently predicting de novo protein-solvent molecule interactions. The method predicted with unprecedented accuracy buried water molecule positions, solvated protein conformations, and challenging mutational effects on protein binding. When applied to homology modeling, solvent-bound membrane protein structures, pockets, and cavities were recapitulated with near-atomic precision even from distant homologs. Blindly refined atomic-level structures of evolutionary distant G protein-coupled receptors imply strikingly different functional roles of buried solvent between receptor classes. The method should prove useful for refining low-resolution protein structures, accurately modeling drug-binding sites in structurally uncharacterized receptors, and designing solvent-mediated protein catalysis, recognition, ligand binding, and membrane protein signaling.

Analysis of the quality of crystallographic data and the limitations of structural models

Arkhipova et al. caution that the limitations of structural models be taken into account when interpreting crystallographic data.

Crystal structures provide visual models of biological macromolecules, which are widely used to interpret data from functional studies and generate new mechanistic hypotheses. Because the quality of the collected x-ray diffraction data directly affects the reliability of the structural model, it is essential that the limitations of the models are carefully taken into account when making interpretations. Here we use the available crystal structures of members of the glutamate transporter family to illustrate the importance of inspecting the data that underlie the structural models. Crystal structures of glutamate transporters in multiple different conformations have been solved, but most structures were determined at relatively low resolution, with deposited models based on crystallographic data of moderate quality. We use these examples to demonstrate the extent to which mechanistic interpretations can be made safely.

Estimating Electron Density Support for Individual Atoms and Molecular Fragments in X-ray Structures

Macromolecular structures resolved by X-ray crystallography are essential for life science research. While some methods exist to automatically quantify the quality of the electron density fit, none of them is without flaws. Especially the question of how well individual parts like atoms, small fragments, or molecules are supported by electron density is difficult to quantify. While taking experimental uncertainties correctly into account, they do not offer an answer on how reliable an individual atom position is. A rapid quantification of this atomic position reliability would be highly valuable in structure-based molecular design. To overcome this limitation, we introduce the electron density score EDIA for individual atoms and molecular fragments. EDIA assesses rapidly, automatically, and intuitively the fit of individual as well as multiple atoms (EDIA_m) into electron density accompanied by an integrated error analysis. The computation is based on the standard 2fo - fc electron density map in combination with the model of the molecular structure. For evaluating partial structures, EDIA_m shows significant advantages compared to the real-space R correlation coefficient (RSCC) and the real-space difference density Z score (RSZD) from the molecular modeler's point of view. Thus, EDIA abolishes the time-consuming step of visually inspecting the electron density during structure selection and curation. It supports daily modeling tasks of medicinal and computational chemists and enables a fully automated assembly of large-scale, high-quality structure data sets. Furthermore, EDIA scores can be applied for model validation and method development in computer-aided molecular design. In contrast to measuring the deviation from the structure model by root-mean-squared deviation, EDIA scores allow comparison to the underlying experimental data taking its uncertainty into account.

Conformations of the HIV-1 protease: A crystal structure data set analysis

The HIV protease is an important drug target for HIV/AIDS therapy, and its structure and function have been extensively investigated. This enzyme performs an essential role in viral maturation by processing specific cleavage sites in the Gag and Gag-Pol precursor polyproteins so as to release their mature forms. This 99 amino acid aspartic protease works as a homodimer, with the active site localized in a central cavity capped by two flexible flap regions. The dimer presents closed or open conformations, which are involved in the substrate binding and release. Here the results of the analysis of a HIV-1 protease data set containing 552 dimer structures are reported. Different dimensionality reduction methods have been used in order to get information from this multidimensional database. Most of the structures in the data set belong to two conformational clusters. An interesting observation that comes from the analysis of these data is that some protease sequences are localized preferentially in specific areas of the conformational landscape of this protein.

Conformational energy range of ligands in protein crystal structures: The difficult quest for accurate understanding

In this review, we address a fundamental question: What is the range of conformational energies seen in ligands in protein-ligand crystal structures? This value is important biophysically, for better understanding the protein-ligand binding process; and practically, for providing a parameter to be used in many computational drug design methods such as docking and pharmacophore searches. We synthesize a selection of previously reported conflicting results from computational studies of this issue and conclude that high ligand conformational energies really are present in some crystal structures. The main source of disagreement between different analyses appears to be due to divergent treatments of electrostatics and solvation. At the same time, however, for many ligands, a high conformational energy is in error, due to either crystal structure inaccuracies or incorrect determination of the reference state. Aside from simple chemistry mistakes, we argue that crystal structure error may mainly be because of the heuristic weighting of ligand stereochemical restraints relative to the fit of the structure to the electron density. This problem cannot be fixed with improvements to electron density fitting or with simple ligand geometry checks, though better metrics are needed for evaluating ligand and binding site chemistry in addition to geometry during structure refinement. The ultimate solution for accurately determining ligand conformational energies lies in ultrahigh-resolution crystal structures that can be refined without restraints.

Structural study of the Fox-1 RRM protein hydration reveals a role for key water molecules in RRM-RNA recognition

The Fox-1 RNA recognition motif (RRM) domain is an important member of the RRM protein family. We report a 1.8 Å X-ray structure of the free Fox-1 containing six distinct monomers. We use this and the nuclear magnetic resonance (NMR) structure of the Fox-1 protein/RNA complex for molecular dynamics (MD) analyses of the structured hydration. The individual monomers of the X-ray structure show diverse hydration patterns, however, MD excellently reproduces the most occupied hydration sites. Simulations of the protein/RNA complex show hydration consistent with the isolated protein complemented by hydration sites specific to the protein/RNA interface. MD predicts intricate hydration sites with water-binding times extending up to hundreds of nanoseconds. We characterize two of them using NMR spectroscopy, RNA binding with switchSENSE and free-energy calculations of mutant proteins. Both hydration sites are experimentally confirmed and their abolishment reduces the binding free-energy. A quantitative agreement between theory and experiment is achieved for the S155A substitution but not for the S122A mutant. The S155 hydration site is evolutionarily conserved within the RRM domains. In conclusion, MD is an effective tool for predicting and interpreting the hydration patterns of protein/RNA complexes. Hydration is not easily detectable in NMR experiments but can affect stability of protein/RNA complexes.

Molecular modeling of biomolecules by paramagnetic NMR and computational hybrid methods

The 3D atomic structures of biomolecules and their complexes are key to our understanding of biomolecular function, recognition, and mechanism. However, it is often difficult to obtain structures, particularly for systems that are complex, dynamic, disordered, or exist in environments like cell membranes. In such cases sparse data from a variety of paramagnetic NMR experiments offers one possible source of structural information. These restraints can be incorporated in computer modeling algorithms that can accurately translate the sparse experimental data into full 3D atomic structures. In this review, we discuss various types of paramagnetic NMR/computational hybrid modeling techniques that can be applied to successful modeling of not only the atomic structure of proteins but also their interacting partners. This article is part of a Special Issue entitled: Biophysics in Canada, edited by Lewis Kay, John Baenziger, Albert Berghuis and Peter Tieleman.

Impaired coordination of nucleophile and increased hydrophobicity in the +1 subsite shift levansucrase activity towards transfructosylation

Bacterial levansucrases produce β(2,6)-linked levan-type polysaccharides using sucrose or sucrose analogs as donor/acceptor substrates. However, the dominant reaction of Bacillus megaterium levansucrase (Bm-LS) is hydrolysis. Single domain levansucrases from Gram-positive bacteria display a wide substrate-binding pocket with open access to water, challenging engineering for transfructosylation-efficient enzymes. We pursued a shift in reaction specificity by either modifying the water distribution in the active site or the coordination of the catalytic acid/base (E352) and the nucleophile (D95), thus affecting the fructosyl-transfer rate and allowing acceptors other than water to occupy the active site. Two serine (173/422) and two water-binding tyrosine (421/439) residues located in the first shell of the catalytic pocket were modified. Library variants of S173, Y421 and S422, which coordinate the position of D95 and E352, show increased transfructosylation (30–200%) and modified product spectra. Substitutions at position 422 have a higher impact on sucrose affinity, while changes at position 173 and 421 have a strong effect on the overall catalytic rate. As most retaining glycoside hydrolases (GHs) Bm-LS catalyzes hydrolysis and transglycosylation via a double displacement reaction involving two-transition states (TS1 and TS2). Hydrogen bonds of D95 with the side chains of S173 and S422 contribute a total of 2.4 kcal mol⁻¹ to TS1 stabilization, while hydrogen bonds between invariant Y421, E352 and the glucosyl C-2 hydroxyl-group of sucrose contribute 2.15 kcal mol⁻¹ stabilization. Changes at Y439 render predominantly hydrolytic variants synthesizing shorter oligosaccharides.

The In Situ Tryptophan Analogue Probes the Conformational Dynamics in Asparaginase Isozymes

Dynamic water solvation is crucial to protein conformational reorganization and hence to protein structure and functionality. We report here the characterization of water dynamics on the L-asparaginase structural homology isozymes L-asparaginases I (AnsA) and II (AnsB), which are shown via fluorescence spectroscopy and dynamics in combination with molecular dynamics simulation to have distinct catalytic activity. By use of the tryptophan (Trp) analog probe 2,7-diaza-tryptophan ((2,7-aza)Trp), which exhibits unique water-catalyzed proton-transfer properties, AnsA and AnsB are shown to have drastically different local water environments surrounding the single Trp. In AnsA, (2,7-aza)Trp exhibits prominent green N(7)-H emission resulting from water-catalyzed excited-state proton transfer. In stark contrast, the N(7)-H emission is virtually absent in AnsB, which supports a water-accessible and a water-scant environment in the proximity of Trp for AnsA and AnsB, respectively. In addition, careful analysis of the emission spectra and corresponding relaxation dynamics, together with the results of molecular dynamics simulations, led us to propose two structural states associated with the rearrangement of the hydrogen-bond network in the vicinity of Trp for the two Ans. The water molecules revealed in the proximity of the Trp residue have semiquantitative correlation with the observed emission spectral variations of (2,7-aza)Trp between AnsA and AnsB. Titration of aspartate, a competitive inhibitor of Ans, revealed an increase in N(7)-H emission intensity in AnsA but no obvious spectral changes in AnsB. The changes in the emission profiles reflect the modulation of structural states by locally confined environment and trapped-water collective motions.

Direct protein crystallization on ultrathin membranes for diffraction measurements at X-ray free-electron lasers

A new era of protein crystallography started when X-ray free-electron lasers (XFELs) came into operation, as these provide an intense source of X-rays that facilitates data collection in the `diffract-before-destroy' regime. In typical experiments, crystals sequentially delivered to the beam are exposed to X-rays and destroyed. Therefore, the novel approach of serial crystallography requires thousands of nearly identical samples. Currently applied sample-delivery methods, in particular liquid jets or drop-on-demand systems, suffer from significant sample consumption of the precious crystalline material. Direct protein microcrystal growth by the vapour diffusion technique inside arrays of nanolitre-sized wells is a method specifically tailored to crystallography at XFELs. The wells, with X-ray transparent Si3N4windows as bottoms, are fabricated in silicon chips. Their reduced dimensions can significantly decrease protein specimen consumption. Arrays provide crystalline samples positioned in an ordered way without the need to handle fragile crystals. The nucleation process inside these microfabricated cavities was optimized to provide high membrane coverage and a quasi-random crystal distribution. Tight sealing of the chips and protection of the crystals from dehydration were achieved, as confirmed by diffraction experiments at a protein crystallography beamline. Finally, the test samples were shown to be suitable for time-resolved measurements at an XFEL at femtosecond resolution.

Price for Opening the Transient Specificity Pocket in Human Aldose Reductase upon Ligand Binding: Structural, Thermodynamic, Kinetic, and Computational Analysis

Insights into the thermodynamic and kinetic signature of the transient opening of a protein-binding pocket resulting from accommodation of suitable substituents attached to a given parent ligand scaffold are presented. As a target, we selected human aldose reductase, an enzyme involved in the development of late-stage diabetic complications. To recognize a large scope of substrate molecules, this reductase opens a transient specificity pocket. The pocket-opening step was studied by X-ray crystallography, microcalorimetry, and surface plasmon resonance using a narrow series of 2-carbamoyl-phenoxy-acetic acid derivatives. Molecular dynamics simulations suggest that pocket opening occurs only once an appropriate substituent is attached to the parent scaffold. Transient pocket opening of the uncomplexed protein is hardly recorded. Hydration-site analysis suggests that up to five water molecules entering the opened pocket cannot stabilize this state. Sole substitution with a benzyl group stabilizes the opened state, and the energetic barrier for opening is estimated to be ∼5 kJ/mol. Additional decoration of the pocket-opening benzyl substituent with a nitro group results in a huge enthalpy-driven potency increase; on the other hand, an isosteric carboxylic acid group reduces the potency 1000-fold, and binding occurs without pocket opening. We suggest a ligand induced-fit mechanism for the pocket-opening step, which, however, does not represent the rate-determining step in binding kinetics.

Mg2+ ions: do they bind to nucleobase nitrogens?

Given the many roles proposed for Mg2+ in nucleic acids, it is essential to accurately determine their binding modes. Here, we surveyed the PDB to classify Mg2+ inner-sphere binding patterns to nucleobase imine N1/N3/N7 atoms. Among those, purine N7 atoms are considered to be the best nucleobase binding sites for divalent metals. Further, Mg2+ coordination to N7 has been implied in several ribozyme catalytic mechanisms. We report that Mg2+ assigned near imine nitrogens derive mostly from poor interpretations of electron density patterns and are most often misidentified Na+, K+, NH4+ ions, water molecules or spurious density peaks. Consequently, apart from few documented exceptions, Mg2+ ions do not bind to N7 atoms. Without much of a surprise, Mn2+, Zn2+ and Cd2+, which have a higher affinity for nitrogens, may contact N7 atoms when present in crystallization buffers. In this respect, we describe for the first time a potential Zn2+ ribosomal binding site involving two purine N7 atoms. Further, we provide a set of guidelines to help in the assignment of Mg2+ in crystallographic, cryo-EM, NMR and model building practices and discuss implications of our findings related to ion substitution experiments.

Stereochemistry and Validation of Macromolecular Structures

Macromolecular structure is governed by the strict rules of stereochemistry. Several approaches to the validation of the correctness of the interpretation of crystallographic and NMR data that underlie the models deposited in the PDB are utilized in practice. The stereochemical rules applicable to macromolecular structures are discussed in this chapter. Practical, computer-based methods and tools of verification of how well the models adhere to those established structural principles to assure their quality are summarized.

Deriving Structural Information from Experimentally Measured Data on Biomolecules

During the past half century, the number and accuracy of experimental techniques that can deliver values of observables for biomolecular systems have been steadily increasing. The conversion of a measured value Q^exp of an observable quantity Q into structural information is, however, a task beset with theoretical and practical problems: 1) insufficient or inaccurate values of Q^exp , 2) inaccuracies in the function Q(r→) used to relate the quantity Q to structure r→ , 3) how to account for the averaging inherent in the measurement of Q^exp , 4) how to handle the possible multiple-valuedness of the inverse r→(Q) of the function Q(r→) , to mention a few. These apply to a variety of observable quantities Q and measurement techniques such as X-ray and neutron diffraction, small-angle and wide-angle X-ray scattering, free-electron laser imaging, cryo-electron microscopy, nuclear magnetic resonance, electron paramagnetic resonance, infrared and Raman spectroscopy, circular dichroism, Förster resonance energy transfer, atomic force microscopy and ion-mobility mass spectrometry. The process of deriving structural information from measured data is reviewed with an eye to non-experts and newcomers in the field using examples from the literature of the effect of the various choices and approximations involved in the process. A list of choices to be avoided is provided.

The Intersection of Structural and Chemical Biology - An Essential Synergy

The continual improvement in our ability to generate high resolution structural models of biological molecules has stimulated and supported innovative chemical biology projects that target increasingly challenging ligand interaction sites. In this review we outline some of the recent developments in chemical biology and rational ligand design and show selected examples that illustrate the synergy between these research areas.

Identifying and Visualizing Macromolecular Flexibility in Structural Biology

Structural biology comprises a variety of tools to obtain atomic resolution data for the investigation of macromolecules. Conventional structural methodologies including crystallography, NMR and electron microscopy often do not provide sufficient details concerning flexibility and dynamics, even though these aspects are critical for the physiological functions of the systems under investigation. However, the increasing complexity of the molecules studied by structural biology (including large macromolecular assemblies, integral membrane proteins, intrinsically disordered systems, and folding intermediates) continuously demands in-depth analyses of the roles of flexibility and conformational specificity involved in interactions with ligands and inhibitors. The intrinsic difficulties in capturing often subtle but critical molecular motions in biological systems have restrained the investigation of flexible molecules into a small niche of structural biology. Introduction of massive technological developments over the recent years, which include time-resolved studies, solution X-ray scattering, and new detectors for cryo-electron microscopy, have pushed the limits of structural investigation of flexible systems far beyond traditional approaches of NMR analysis. By integrating these modern methods with powerful biophysical and computational approaches such as generation of ensembles of molecular models and selective particle picking in electron microscopy, more feasible investigations of dynamic systems are now possible. Using some prominent examples from recent literature, we review how current structural biology methods can contribute useful data to accurately visualize flexibility in macromolecular structures and understand its important roles in regulation of biological processes.

Natural Inhibitors of Snake Venom Metalloendopeptidases: History and Current Challenges

The research on natural snake venom metalloendopeptidase inhibitors (SVMPIs) began in the 18th century with the pioneering work of Fontana on the resistance that vipers exhibited to their own venom. During the past 40 years, SVMPIs have been isolated mainly from the sera of resistant animals, and characterized to different extents. They are acidic oligomeric glycoproteins that remain biologically active over a wide range of pH and temperature values. Based on primary structure determination, mammalian plasmatic SVMPIs are classified as members of the immunoglobulin (Ig) supergene protein family, while the one isolated from muscle belongs to the ficolin/opsonin P35 family. On the other hand, SVMPIs from snake plasma have been placed in the cystatin superfamily. These natural antitoxins constitute the first line of defense against snake venoms, inhibiting the catalytic activities of snake venom metalloendopeptidases through the establishment of high-affinity, non-covalent interactions. This review presents a historical account of the field of natural resistance, summarizing its main discoveries and current challenges, which are mostly related to the limitations that preclude three-dimensional structural determinations of these inhibitors using “gold-standard” methods; perspectives on how to circumvent such limitations are presented. Potential applications of these SVMPIs in medicine are also highlighted.

Application of advanced X-ray methods in life sciences

BACKGROUND

Synchrotron radiation (SR) sources provide diverse X-ray methods for the investigation of structure-function relationships in biological macromolecules.

SCOPE OF REVIEW

Recent developments in SR sources and in the X-ray tools they offer for life sciences are reviewed. Specifically, advances in macromolecular crystallography, small angle X-ray solution scattering, X-ray absorption and fluorescence spectroscopy, and imaging are discussed with examples.

MAJOR CONCLUSIONS

SR sources offer a range of X-ray techniques that can be used in a complementary fashion in studies of biological systems at a wide range of resolutions from atomic to cellular scale. Emerging applications of X-ray techniques include the characterization of disordered proteins, noncrystalline and nonequilibrium systems, elemental imaging of tissues, cells and organs, and detection of time-resolved changes in molecular structures.

GENERAL SIGNIFICANCE

X-ray techniques are in the center of hybrid approaches that are used to gain insight into complex problems relating to biomolecular mechanisms, disease and possible therapeutic solutions. This article is part of a Special Issue entitled "Science for Life". Guest Editors: Dr. Austen Angell, Dr. Salvatore Magazù and Dr. Federica Migliardo.

On the reliability of peptide nonplanarity seen in ultra-high resolution crystal structures

Ultra-high resolution protein crystal structures have been considered as relatively reliable sources for defining details of protein geometry, such as the extent to which the peptide unit deviates from planarity. Chellapa and Rose (Proteins 2015; 83:1687) recently called this into question, reporting that for a dozen representative protein structures determined at ∼ 1 Å resolution, the diffraction data could be equally well fit with models restrained to have highly planar peptides, i.e. having a standard deviation of the ω torsion angles of only ∼ 1° instead of the typically observed value of ∼ 6°. Here, we document both conceptual and practical shortcomings of that study and show that the more tightly restrained models are demonstrably incorrect and do not fit the diffraction data equally well. We emphasize the importance of inspecting electron density maps when investigating the agreement between a model and its experimental data. Overall, this report reinforces that modern standard refinement protocols have been well-conceived and that ultra-high resolution protein crystal structures, when evaluated carefully and used with an awareness of their levels of coordinate uncertainty, are powerful sources of information for providing reliable information about the details of protein geometry.

XModeScore: a novel method for accurate protonation/tautomer-state determination using quantum-mechanically driven macromolecular X-ray crystallographic refinement

Gaining an understanding of the protein-ligand complex structure along with the proper protonation and explicit solvent effects can be important in obtaining meaningful results in structure-guided drug discovery and structure-based drug discovery. Unfortunately, protonation and tautomerism are difficult to establish with conventional methods because of difficulties in the experimental detection of H atoms owing to the well known limitations of X-ray crystallography. In the present work, it is demonstrated that semiempirical, quantum-mechanics-based macromolecular crystallographic refinement is sensitive to the choice of a protonation-state/tautomer form of ligands and residues, and can therefore be used to explore potential states. A novel scoring method, called XModeScore, is described which enumerates the possible protomeric/tautomeric modes, refines each mode against X-ray diffraction data with the semiempirical quantum-mechanics (PM6) Hamiltonian and scores each mode using a combination of energetic strain (or ligand strain) and rigorous statistical analysis of the difference electron-density distribution. It is shown that using XModeScore it is possible to consistently distinguish the correct bound protomeric/tautomeric modes based on routine X-ray data, even at lower resolutions of around 3 Å. These X-ray results are compared with the results obtained from much more expensive and laborious neutron diffraction studies for three different examples: tautomerism in the acetazolamide ligand of human carbonic anhydrase II (PDB entries 3hs4 and 4k0s), tautomerism in the 8HX ligand of urate oxidase (PDB entries 4n9s and 4n9m) and the protonation states of the catalytic aspartic acid found within the active site of an aspartic protease (PDB entry 2jjj). In each case, XModeScore applied to the X-ray diffraction data is able to determine the correct protonation state as defined by the neutron diffraction data. The impact of QM-based refinement versus conventional refinement on XModeScore is also discussed.

The young person's guide to the PDB

The Protein Data Bank (PDB), created in 1971 when merely seven protein crystal structures were known, today holds over 120, 000 experimentally-determined three-dimensional models of macromolecules, including gigantic structures comprised of hundreds of thousands of atoms, such as ribosomes and viruses. Most of the deposits come from X-ray crystallography experiments, with important contributions also made by NMR spectroscopy and, recently, by the fast growing Cryo-Electron Microscopy. Although the determination of a macromolecular crystal structure is now facilitated by advanced experimental tools and by sophisticated software, it is still a highly complicated research process requiring specialized training, skill, experience and a bit of luck. Understanding the plethora of structural information provided by the PDB requires that its users (consumers) have at least a rudimentary initiation. This is the purpose of this educational overview.

Beyond Membrane Protein Structure: Drug Discovery, Dynamics and Difficulties

Most of the previous content of this book has focused on obtaining the structures of membrane proteins. In this chapter we explore how those structures can be further used in two key ways. The first is their use in structure based drug design (SBDD) and the second is how they can be used to extend our understanding of their functional activity via the use of molecular dynamics. Both aspects now heavily rely on computations. This area is vast, and alas, too large to consider in depth in a single book chapter. Thus where appropriate we have referred the reader to recent reviews for deeper assessment of the field. We discuss progress via the use of examples from two main drug target areas; G-protein coupled receptors (GPCRs) and ion channels. We end with a discussion of some of the main challenges in the area.

Crystallographic Data and Model Quality

This article gives a consistent classification of sources of random and systematic errors in crystallographic data, and their influence on the averaged dataset obtained from a diffraction experiment. It discusses the relation between precision and accuracy and the crystallographic indicators used to estimate them, as well as topics like completeness and high-resolution cutoff. These concepts are applied in the context of presenting good practices for data processing with a widely used package, XDS. Recommendations are given for how to minimize the impact of several typical problems, like ice rings and shaded areas. Then, procedures for optimizing the processing parameters are explained. Finally, a simple graphical expression of some basic relations between data error and model error is suggested.

Current approaches to studying membrane organization

The local structure and composition of the outer membrane of an animal cell are important factors in the control of many membrane processes and mechanisms. These include signaling, sorting, and exo- and endocytic processes that are occurring all the time in a living cell. Paradoxically, not only are the local structure and composition of the membrane matters of much debate and discussion, the mechanisms that govern its genesis remain highly controversial. Here, we discuss a swathe of new technological advances that may be applied to understand the local structure and composition of the membrane of a living cell from the molecular scale to the scale of the whole membrane.

Homology Modeling Procedures for Cytoskeletal Proteins of Tetrahymena and Other Ciliated Protists

In recent years there has been an explosive increase in the number of annotated protein sequences available through genome sequencing, as well as an accumulation of published protein structural data based on crystallographic and NMR methods. When taken together with the development of computational methods for the prediction of protein structural and functional properties through homology modeling, an opportunity exists for prediction of properties of cytoskeletal proteins in a suitable model organism, such as Tetrahymena thermophila and its ciliated protist relatives. In particular, the recently sequenced genome of T. thermophila, long a model for cytoskeletal studies, provides a good starting point for undertaking such homology modeling studies. Homology modeling can produce functional predictions, for example regarding potential molecular interactions, that are of great interest to the drug industry and Tetrahymena is an attractive model system in which to follow up computational predictions with experimental analyses. We provide here procedures that can be followed to gain entry into this promising avenue of analysis.

Assessing and maximizing data quality in macromolecular crystallography

The quality of macromolecular crystal structures depends, in part, on the quality and quantity of the data used to produce them. Here, we review recent shifts in our understanding of how to use data quality indicators to select a high resolution cutoff that leads to the best model, and of the potential to greatly increase data quality through the merging of multiple measurements from multiple passes of single crystals or from multiple crystals. Key factors supporting this shift are the introduction of more robust correlation coefficient based indicators of the precision of merged data sets as well as the recognition of the substantial useful information present in extensive amounts of data once considered too weak to be of value.

X-ray crystallography over the past decade for novel drug discovery - where are we heading next?

INTRODUCTION

Macromolecular X-ray crystallography has been the primary methodology for determining the three-dimensional structures of proteins, nucleic acids and viruses. Structural information has paved the way for structure-guided drug discovery and laid the foundations for structural bioinformatics. However, X-ray crystallography still has a few fundamental limitations, some of which may be overcome and complemented using emerging methods and technologies in other areas of structural biology.

AREAS COVERED

This review describes how structural knowledge gained from X-ray crystallography has been used to advance other biophysical methods for structure determination (and vice versa). This article also covers current practices for integrating data generated by other biochemical and biophysical methods with those obtained from X-ray crystallography. Finally, the authors articulate their vision about how a combination of structural and biochemical/biophysical methods may improve our understanding of biological processes and interactions.

EXPERT OPINION

X-ray crystallography has been, and will continue to serve as, the central source of experimental structural biology data used in the discovery of new drugs. However, other structural biology techniques are useful not only to overcome the major limitation of X-ray crystallography, but also to provide complementary structural data that is useful in drug discovery. The use of recent advancements in biochemical, spectroscopy and bioinformatics methods may revolutionize drug discovery, albeit only when these data are combined and analyzed with effective data management systems. Accurate and complete data management is crucial for developing experimental procedures that are robust and reproducible.

100 Years later: Celebrating the contributions of x-ray crystallography to allergy and clinical immunology

Current knowledge of molecules involved in immunology and allergic disease results from the significant contributions of x-ray crystallography, a discipline that just celebrated its 100th anniversary. The histories of allergens and x-ray crystallography are intimately intertwined. The first enzyme structure to be determined was lysozyme, also known as the chicken food allergen Gal d 4. Crystallography determines the exact 3-dimensional positions of atoms in molecules. Structures of molecular complexes in the disciplines of immunology and allergy have revealed the atoms involved in molecular interactions and mechanisms of disease. These complexes include peptides presented by MHC class II molecules, cytokines bound to their receptors, allergen-antibody complexes, and innate immune receptors with their ligands. The information derived from crystallographic studies provides insights into the function of molecules. Allergen function is one of the determinants of environmental exposure, which is essential for IgE sensitization. Proteolytic activity of allergens or their capacity to bind LPSs can also contribute to allergenicity. The atomic positions define the molecular surface that is accessible to antibodies. In turn, this surface determines antibody specificity and cross-reactivity, which are important factors for the selection of allergen panels used for molecular diagnosis and the interpretation of clinical symptoms. This review celebrates the contributions of x-ray crystallography to clinical immunology and allergy, focusing on new molecular perspectives that influence the diagnosis and treatment of allergic diseases.

A posteriori determination of the useful data range for small-angle scattering experiments on dilute monodisperse systems

Small-angle X-ray and neutron scattering (SAXS and SANS) experiments on solutions provide rapidly decaying scattering curves, often with a poor signal-to-noise ratio, especially at higher angles. On modern instruments, the noise is partially compensated for by oversampling, thanks to the fact that the angular increment in the data is small compared with that needed to describe adequately the local behaviour and features of the scattering curve. Given a (noisy) experimental data set, an important question arises as to which part of the data still contains useful information and should be taken into account for the interpretation and model building. Here, it is demonstrated that, for monodisperse systems, the useful experimental data range is defined by the number of meaningful Shannon channels that can be determined from the data set. An algorithm to determine this number and thus the data range is developed, and it is tested on a number of simulated data sets with various noise levels and with different degrees of oversampling, corresponding to typical SAXS/SANS experiments. The method is implemented in a computer program and examples of its application to analyse the experimental data recorded under various conditions are presented. The program can be employed to discard experimental data containing no useful information in automated pipelines, in modelling procedures, and for data deposition or publication. The software is freely accessible to academic users.

High-resolution crystal structures of protein helices reconciled with three-centered hydrogen bonds and multipole electrostatics

Theoretical and experimental evidence for non-linear hydrogen bonds in protein helices is ubiquitous. In particular, amide three-centered hydrogen bonds are common features of helices in high-resolution crystal structures of proteins. These high-resolution structures (1.0 to 1.5 Å nominal crystallographic resolution) position backbone atoms without significant bias from modeling constraints and identify Φ = -62°, ψ = -43 as the consensus backbone torsional angles of protein helices. These torsional angles preserve the atomic positions of α-β carbons of the classic Pauling α-helix while allowing the amide carbonyls to form bifurcated hydrogen bonds as first suggested by Némethy et al. in 1967. Molecular dynamics simulations of a capped 12-residue oligoalanine in water with AMOEBA (Atomic Multipole Optimized Energetics for Biomolecular Applications), a second-generation force field that includes multipole electrostatics and polarizability, reproduces the experimentally observed high-resolution helical conformation and correctly reorients the amide-bond carbonyls into bifurcated hydrogen bonds. This simple modification of backbone torsional angles reconciles experimental and theoretical views to provide a unified view of amide three-centered hydrogen bonds as crucial components of protein helices. The reason why they have been overlooked by structural biologists depends on the small crankshaft-like changes in orientation of the amide bond that allows maintenance of the overall helical parameters (helix pitch (p) and residues per turn (n)). The Pauling 3.6₁₃ α-helix fits the high-resolution experimental data with the minor exception of the amide-carbonyl electron density, but the previously associated backbone torsional angles (Φ, Ψ) needed slight modification to be reconciled with three-atom centered H-bonds and multipole electrostatics. Thus, a new standard helix, the 3.6_13/10-, Némethy- or N-helix, is proposed. Due to the use of constraints from monopole force fields and assumed secondary structures used in low-resolution refinement of electron density of proteins, such structures in the PDB often show linear hydrogen bonding.

Evaluating transition state structures of vanadium-phosphatase protein complexes using shape analysis

Shape analysis of coordination complexes is well-suited to evaluate the subtle distortions in the trigonal bipyramidal (TBPY-5) geometry of vanadium coordinated in the active site of phosphatases and characterized by X-ray crystallography. Recent studies using the tau (τ) analysis support the assertion that vanadium is best described as a trigonal bipyramid, because this geometry is the ideal transition state geometry of the phosphate ester substrate hydrolysis (C.C. McLauchlan, B.J. Peters, G.R. Willsky, D.C. Crans, Coord. Chem. Rev. http://dx.doi.org/10.1016/j.ccr.2014.12.012 ; D.C. Crans, M.L. Tarlton, C.C. McLauchlan, Eur. J. Inorg. Chem. 2014, 4450-4468). Here we use continuous shape measures (CShM) analysis to investigate the structural space of the five-coordinate vanadium-phosphatase complexes associated with mechanistic transformations between the tetrahedral geometry and the five-coordinate high energy TBPY-5 geometry was discussed focusing on the protein tyrosine phosphatase 1B (PTP1B) enzyme. No evidence for square pyramidal geometries was observed in any vanadium-protein complexes. The shape analysis positioned the metal ion and the ligands in the active site reflecting the mechanism of the cleavage of the organic phosphate in a phosphatase. We identified the umbrella distortions to be directly on the reaction path between tetrahedral phosphate and the TBPY-5-types of high-energy species. The umbrella distortions of the trigonal bipyramid are therefore identified as being the most relevant types of transition state structures for the phosphoryl group transfer reactions for phosphatases and this may be related to the possibility that vanadium is an inhibitor for enzymes that support both exploded and five-coordinate transition states.

A Newcomer's Guide to Peptide Crystallography

Here we provide a guide for adapting the tools developed for protein X-ray crystallography to study the structures and supramolecular assembly of peptides. Peptide crystallography involves selecting a suitable peptide, crystallizing the peptide, collecting X-ray diffraction data, processing the diffraction data, determining the crystallographic phases and generating an electron density map, building and refining models, and depositing the crystallographic structure in the Protein Data Bank (PDB). Advances in technology make this process easy for a newcomer to adopt. This paper describes techniques for determining the X-ray crystallographic structures of peptides: incorporation of amino acids containing heavy atoms for crystallographic phase determination, commercially available kits to crystallize peptides, modern techniques for X-ray crystallographic data collection, and free user-friendly software for data processing and producing a crystallographic structure.

Validation of a computational docking methodology to identify the non-covalent binding site of ligands to DNA

Despite the biomedical consequences of carcinogen-DNA interactions and the potential of DNA as a drug target in medicinal chemistry, only a small number of studies have validated or used docking methods for the prediction of the physical binding of small molecules to DNA. Knowledge of the DNA-physically-bound ligand geometry can lead to the elucidation of the molecular-level mechanism of drugs as well as predicting the subsequent chemical interactions that lead to DNA damage from carcinogens. We sought to validate AutoDock 4.2, a docking method that includes a physics-based free energy function and a Lamarckian Genetic Algorithm, for the prediction of ligand geometries upon physical binding to DNA. We performed simulations by systematically changing the length of the search process for a comprehensive set of 32 ligand-DNA molecular systems with different physico-chemical properties, and we used a free-energy-based convergence criterion to terminate our simulations. For 11 out of 28 molecular systems for which convergence was achieved, the lowest binding free energy geometries were within 2 Å of the experimentally determined geometry. Considering all predicted sites with free energy changes within 20% of the lowest binding free energy site, we found a site within 2 Å of the experimentally determined geometry for 24 out of the 28 systems. However, the predicted hydrogen bonding interactions were different for most molecular systems compared to the same interactions in the experimentally determined geometry. We discuss reasons for the successes and failures, implications, and the importance of ensuring an adequate search in docking calculations. Overall, we concluded that AutoDock 4.2 can be used to predict the non-covalent binding geometry of a small molecule to DNA with some limitations.

The near-symmetry of proteins

The majority of protein oligomers form clusters which are nearly symmetric. Understanding of that imperfection, its origins, and perhaps also its advantages requires the conversion of the currently used vague qualitative descriptive language of the near-symmetry into an accurate quantitative measure that will allow to answer questions such as: "What is the degree of symmetry deviation of the protein?," "how do these deviations compare within a family of proteins?," and so on. We developed quantitative methods to answer this type of questions, which are capable of analyzing the whole protein, its backbone or selected portions of it, down to comparison of symmetry-related specific amino-acids, and which are capable of visualizing the various levels of symmetry deviations in the form of symmetry maps. We have applied these methods on an extensive list of homomers and heteromers and found that apparently all proteins never reach perfect symmetry. Strikingly, even homomeric protein clusters are never ideally symmetric. We also found that the main burden of symmetry distortion is on the amino-acids near the symmetry axis; that it is mainly the more hydrophilic amino-acids that take place in symmetry-distortive interactions; and more. The remarkable ability of heteromers to preserve near-symmetry, despite the different sequences, was also shown and analyzed. The comprehensive literature on the suggested advantages symmetric oligomerizations raises a yet-unsolved key question: If symmetry is so advantageous, why do proteins stop shy of perfect symmetry? Some tentative answers to be tested in further studies are suggested in a concluding outlook.

You are lost without a map: Navigating the sea of protein structures

X-ray crystal structures propel biochemistry research like no other experimental method, since they answer many questions directly and inspire new hypotheses. Unfortunately, many users of crystallographic models mistake them for actual experimental data. Crystallographic models are interpretations, several steps removed from the experimental measurements, making it difficult for nonspecialists to assess the quality of the underlying data. Crystallographers mainly rely on "global" measures of data and model quality to build models. Robust validation procedures based on global measures now largely ensure that structures in the Protein Data Bank (PDB) are largely correct. However, global measures do not allow users of crystallographic models to judge the reliability of "local" features in a region of interest. Refinement of a model to fit into an electron density map requires interpretation of the data to produce a single "best" overall model. This process requires inclusion of most probable conformations in areas of poor density. Users who misunderstand this can be misled, especially in regions of the structure that are mobile, including active sites, surface residues, and especially ligands. This article aims to equip users of macromolecular models with tools to critically assess local model quality. Structure users should always check the agreement of the electron density map and the derived model in all areas of interest, even if the global statistics are good. We provide illustrated examples of interpreted electron density as a guide for those unaccustomed to viewing electron density.

Size does matter! Label-free detection of small molecule-protein interaction

This review is focused on methods for detecting small molecules and, in particular, the characterisation of their interaction with natural proteins (e.g. receptors, ion channels). Because there are intrinsic advantages to using label-free methods over labelled methods (e.g. fluorescence, radioactivity), this review only covers label-free techniques. We briefly discuss available techniques and their advantages and disadvantages, especially as related to investigating the interaction between small molecules and proteins. The reviewed techniques include well-known and widely used standard analytical methods (e.g. HPLC-MS, NMR, calorimetry, and X-ray diffraction), newer and more specialised analytical methods (e.g. biosensors), biological systems (e.g. cell lines and animal models), and in-silico approaches.

Avoidable errors in deposited macromolecular structures: an impediment to efficient data mining

Whereas the vast majority of the more than 85 000 crystal structures of macromolecules currently deposited in the Protein Data Bank are of high quality, some suffer from a variety of imperfections. Although this fact has been pointed out in the past, it is still worth periodic updates so that the metadata obtained by global analysis of the available crystal structures, as well as the utilization of the individual structures for tasks such as drug design, should be based on only the most reliable data. Here, selected abnormal deposited structures have been analysed based on the Bayesian reasoning that the correctness of a model must be judged against both the primary evidence as well as prior knowledge. These structures, as well as information gained from the corresponding publications (if available), have emphasized some of the most prevalent types of common problems. The errors are often perfect illustrations of the nature of human cognition, which is frequently influenced by preconceptions that may lead to fanciful results in the absence of proper validation. Common errors can be traced to negligence and a lack of rigorous verification of the models against electron density, creation of non-parsimonious models, generation of improbable numbers, application of incorrect symmetry, illogical presentation of the results, or violation of the rules of chemistry and physics. Paying more attention to such problems, not only in the final validation stages but during the structure-determination process as well, is necessary not only in order to maintain the highest possible quality of the structural repositories and databases but most of all to provide a solid basis for subsequent studies, including large-scale data-mining projects. For many scientists PDB deposition is a rather infrequent event, so the need for proper training and supervision is emphasized, as well as the need for constant alertness of reason and critical judgment as absolutely necessary safeguarding measures against such problems. Ways of identifying more problematic structures are suggested so that their users may be properly alerted to their possible shortcomings.

Structure determination, refinement, and validation

It can be a tremendous advantage to have the X-ray crystal structure of a protein that is targeted for drug discovery. Due to recent advances in methods, software and hardware, crystallographic structure determination no longer requires a specialist in the method, but rather it has become a technique that can be readily applied to many research problems. The high-throughput approaches developed and used by structural genomics projects can be adapted and used to aid drug discovery efforts. It should be emphasized, however, that one cannot blindly accept the results of automated approaches and that it is essential to carefully validate the model, which is the interpretation of the observed electron density. It is important to be sure that it correctly describes the structure.The investigator that wishes to make use of the extensive database of protein structures in the Protein Data Bank (PDB) also needs to be know how to evaluate structural models, understand how they are related to the experimental data and be able to utilize computer graphics programs to look at the electron density distribution together with the model.