Protein structure prediction constrained by solution X-ray scattering data and structural homology identification

SAXS Ensemble Refinement of ESCRT-III CHMP3 Conformational Transitions

Histotoxic clostridia produce collagenases responsible for extensive tissue destruction in gas gangrene. The C-terminal collagen-binding domain (CBD) of these enzymes is the minimal segment required to bind to collagen fibril. Collagen binding efficiency of CBD is more pronounced in the presence of Ca(2+). We have shown that CBD can be functional to anchor growth factors in local tissue. A (1)H-(15)N HSQC NMR titration study with three different tropocollagen analogues ((POG)(10))(3), ((GPOG)(7)PRG)(3), and (GPRG(POG)(7)C-carbamidomethyl)(3), mapped a saddle-like binding cleft on CBD. NMR titrations with three nitroxide spin-labeled analogues of collagenous peptide, (PROXYL-G(POG)(7)PRG)(3), (PROXYL-G(POG)(7))(3), and (GPRG(POG)(7)C-PROXYL)(3) (where PROXYL represents 2,2,5,5-tetramethyl-l-pyrrolidinyloxy), unambiguously demonstrated unidirectional binding of CBD to the tropocollagen analogues. Small angle x-ray scattering data revealed that CBD binds closer to a terminus for each of the five different tropocollagen analogues, which in conjunction with NMR titration studies, implies a binding mode where CBD binds to the C terminus of the triple helix.

A new approach to assisted protein-structure prediction has been proposed, which is based on running multiplexed replica exchange molecular dynamics simulations with the coarse-grained UNRES force field with restraints derived from knowledge-based models and distance distribution from small angle X-ray scattering (SAXS) measurements. The latter restraints are incorporated into the target function as a maximum-likelihood term that guides the shape of the simulated structures towards that defined by SAXS. The approach was first verified with the 1KOY protein, for which the distance distribution was calculated from the experimental structure, and subsequently used to predict the structures of 11 data-assisted targets in the CASP12 experiment. Major improvement of the GDT_TS was obtained for 2 targets, minor improvement for other 2 while, for 6 target GDT_TS deteriorated compared with that calculated for predictions without the SAXS data, partly because of assuming a wrong multimeric state (for Ts866) or because the crystal conformation was more compact than the solution conformation (for Ts942). Particularly good results were obtained for Ts909, in which use of SAXS data resulted in the selection of a correctly packed trimer and, subsequently, increased the GDT_TS of monomer prediction. It was found that running simulations with correct oligomeric state is essential for the success in SAXS-data-assisted prediction.

Several bacterial solute transport mechanisms involve members of the periplasmic binding protein (PBP) superfamily that bind and deliver ligand to integral membrane transport proteins in the ATP-binding cassette, tripartite tricarboxylate transporter, or tripartite ATP-independent (TRAP) families. PBPs involved in ATP-binding cassette transport systems have been well characterized, but only a few PBPs involved in TRAP transport have been studied. We have measured the thermal stability, determined the oligomerization state by small angle x-ray scattering, and solved the x-ray crystal structure to 1.9 A resolution of a TRAP-PBP (open reading frame tm0322) from the hyperthermophilic bacterium Thermotoga maritima (TM0322). The overall fold of TM0322 is similar to other TRAP transport related PBPs, although the structural similarity of backbone atoms (2.5-3.1 A root mean square deviation) is unusually low for PBPs within the same group. Individual monomers within the tetrameric asymmetric unit of TM0322 exhibit high root mean square deviation (0.9 A) to each other as a consequence of conformational heterogeneity in their binding pockets. The gel filtration elution profile and the small angle x-ray scattering analysis indicate that TM0322 assembles as dimers in solution that in turn assemble into a dimer of dimers in the crystallographic asymmetric unit. Tetramerization has been previously observed in another TRAP-PBP (the Rhodobacter sphaeroides alpha-keto acid-binding protein) where quaternary structure formation is postulated to be an important requisite for the transmembrane transport process.

Most computational protein structure prediction methods are designed for either template based or template-free (ab initio) structure prediction. The approaches that integrate the prediction capabilities of both template-based modeling and template-free modeling are needed to synergistically combine the two kinds of methods to improve protein structure prediction. In this work, we develop a new method to integrate several protein structure prediction methods including our template-based MULTICOM server, our ab initio contact-based protein structure prediction method CONFOLD, our multi-template-based model generation tool MTMG, and locally installed external Rosetta, I-TASSER and RaptorX protein structure prediction tools to improve protein structure prediction of a fullspectrum difficulty ranging from easy, to medium and to hard. Our method participated in the 11(th) community-wide Critical Assessment of Techniques for Protein Structure Prediction (CASP11) in 2014 as MULTICOM-NOVEL server. It was ranked among top 10 methods for protein tertiary structure prediction according to the official CASP11 assessment, which demonstrates that integrating complementary modeling methods is useful for advancing protein structure prediction.

To solve protein structure prediction (PSP) problems computationally, a plethora of template-based methods exist. However, there are very few ab initio models for PSP. Template-based modeling relies on the existing structures and therefore is not effective for non-homologous sequence-based structure prediction. Thus, ab initio modeling is indispensable in such cases, even though it is a challenging optimization problem. To cope, we utilize an effective energy function (called 3DIGARS) and an advanced search algorithm (called KGA) based ab initio PSP, called 3DIGARS-PSP. To address critical search, the proposed genetic algorithm deploys two effective operators: angle rotation and segment translation. Further, propensities of torsion angle and secondary structure distribution have been utilized to guide the conformation search. Crucial features, such as sequence-specific accessibility, hydrophobic-hydrophilic properties and torsion angles of protein residues are mined to formulate an optimized energy function, which is then combined with the advanced sampling algorithm to explore critical conformational space. Consequently, 3DIGARS-PSP performed well compared to the state-of-the-art method for a set of low TMscore models from CASP data.

An ab initio protein structure prediction system called ABLE is described. It is based on the fragment assembly method, which consists of two steps: dividing a target sequence into overlapping subsequences (fragments) of short length and assigning a local structure to each fragment; and generating models by assembling the local structures and selecting the models with low potential energy. One of the most important problems in conventional fragment assembly methods is the difficulty of selecting native-like structures by energy minimization only. ABLE thus employs a structural clustering method to select the native-like models from among the generated models. By applying the unit-vector root mean square distance (URMS) as a measure of structure similarity, we achieve more robust, effective structural clustering. When no enough clusters of good quality are obtained, ABLE runs the energy minimization procedure again by incorporating structural restraint conditions obtained from the consensus substructures in the previously generated models. This approach is based on our observation that there is a high probability that the consensus substructures of the generated models have native-like structures. Another feature of ABLE is that in assigning local structures to fragments, it assigns mainchain dihedral angles (phi, psi) to the central residue of each fragment according to a probability distribution map built from candidate sequences similar to each fragment. This enables the system to generate appropriate local structures that may not already exist in a protein structure database. We applied our system to 25 small proteins and obtain near-native folds for more than half of them. We also demonstrate the performance of our structural clustering method, which can be applied to other protein structure prediction systems.

Experimental data offers empowering constraints for structure prediction. These constraints can be used to filter equivalently scored models or more powerfully within optimization functions toward prediction. In CASP12, Small Angle X-ray Scattering (SAXS) and Cross-Linking Mass Spectrometry (CLMS) data, measured on an exemplary set of novel fold targets, were provided to the CASP community of protein structure predictors. As solution-based techniques, SAXS and CLMS can efficiently measure states of the full-length sequence in its native solution conformation and assembly. However, this experimental data did not substantially improve prediction accuracy judged by fits to crystallographic models. One issue, beyond intrinsic limitations of the algorithms, was a disconnect between crystal structures and solution-based measurements. Our analyses show that many targets had substantial percentages of disordered regions (up to 40%) or were multimeric or both. Thus, solution measurements of flexibility and assembly support variations that may confound prediction algorithms trained on crystallographic data and expecting globular fully-folded monomeric proteins. Here, we consider the CLMS and SAXS data collected, the information in these solution measurements, and the challenges in incorporating them into computational prediction. As improvement opportunities were only partly realized in CASP12, we provide guidance on how data from the full-length biological unit and the solution state can better aid prediction of the folded monomer or subunit. We furthermore describe strategic integrations of solution measurements with computational prediction programs with the aim of substantially improving foundational knowledge and the accuracy of computational algorithms for biologically-relevant structure predictions for proteins in solution.

Molecular dynamics (MD) simulations are extensively used in the study of the structures and functions of proteins. Ab initio protein structure prediction is one of the most important subjects in computational biology, and many trials have been performed using MD simulation so far. Since the results of MD simulations largely depend on the force field, reliable force field parameters are indispensable for the success of MD simulation. In this work, we have modified atom charges in a standard force field on the basis of water-phase quantum chemical calculations. The modified force field turned out appropriate for ab initio protein structure prediction by the MD simulation with the generalized Born method. Detailed analysis was performed in terms of the conformational stability of amino acid residues, the stability of secondary structure of proteins, and the accuracy for prediction of protein tertiary structure, comparing the modified force field with a standard one. The energy balance between alpha-helix and beta-sheet structures was significantly improved by the modification of charge parameters.

Structural changes in non-isothermal crystallization process of melt-cooled polyoxymethylene[II] evolution of lamellar stacking structure derived from SAXS and WAXS data analysis

To date, the sizes of the nanoscale ionic aggregates present in ionomers as determined by scanning transmission electron microscopy (STEM) experiments are inconsistent with small-angle X-ray scattering (SAXS) data interpreted by the Yarusso−Cooper model. To address this discrepancy, we have investigated a pair of model nanoparticles (11- and 55-atom Au clusters) with both STEM and SAXS. To mimic ionic aggregates, these nanoparticles were supported by amorphous PS films of predetermined thickness. While the size of the STEM probe was inconsequential to the resolution and measurement of the particles, the optimal specimen thickness was determined to be less than 50 nm. SAXS was performed on dilute solutions of the nanoparticles and fit using a monodisperse, noninteracting hard-sphere form factor model. For Au 11, STEM finds a diameter of 1.3 ± 0.14 nm and SAXS finds a diameter of 1.4 nm. Similarly, both STEM and SAXS determine a diameter of 1.7 nm for Au 55. For a combined system of Au 11 and Au 55, STEM data suggests a bimodal distribution of particle sizes consistent with those of the individual particles while SAXS data indicate hard spheres with diameter of 1.6 nm. Analysis of this model system has allowed us to optimize several experimental conditions of potential importance in reconciling STEM and SAXS data from ionomers.

The main objective of this study is to showcase the use of synergistic utilization of model‐independent and model‐dependent approaches for analyzing small angle X‐ray scattering (SAXS) data to determine the shape and size of different nanostructures viz. spherical, nanorod, and core‐shell. Herein, different gold nanostructures viz. spherical, nanorod, and core‐shell are synthesized and analyzed using SAXS. First, the SAXS data are analyzed using Guinier law, PDDF (pair–distance distribution function) plots, double logarithmic plots, and electron density curves to get primary information about the size and shape of nanostructures. This information is utilized for choosing an appropriate model and initial input parameters for fitting the SAXS data. The observations obtained from SAXS studies are compared with transmission electron microscopy and UV–vis spectroscopy. It is believed that this study will help in understanding the analysis of SAXS data using a systematic and synergistic approach of utilizing the two methods. It is believed that the study will further add to the understanding of the technique as an alternative approach for transmission electron microscopy.

Nanoparticle suspensions (also called nanofluids) are often polydisperse and tend to settle with time. Settling kinetics in these systems are known to be complex and hence challenging to understand. In this work, polydisperse spherical alumina (Al2O3) nanoparticles in the size range of ~10-100nm were dispersed in water and examined for aggregation and settling behaviour near its isoelectric point (IEP). A series of settling experiments were conducted and the results were analysed by photography and by Small Angle X-ray Scattering (SAXS). The settling curve obtained from standard bed height measurement experiments indicated two different types of behaviour, both of which were also seen in the SAXS data. But the SAXS data were remarkably able to pick out the rapid settling regime as a result of the high temporal resolution (10s) used. By monitoring the SAXS intensity, it was further possible to record the particle aggregation process for the first time. Optical microscopy images were produced on drying and dried droplets extracted from the suspension at various times. Dried deposits showed the rapid decrease in the number of very large particles with time which qualitatively validates the SAXS prediction, and therefore its suitability as a tool to study unstable polydisperse colloids. Keywords: Nanoparticles, nanofluids, polydisperse, aggregation, settling, alumina, microscopy, SAXS

Amide hydrogen-deuterium exchange (HDX) has long been used to determine regional flexibility and binding sites in proteins; however, the data are too sparse for full structural characterization. Experiments that measure HDX rates, such as HDX-NMR, have far higher throughput compared to structure determination via X-ray crystallography, cryo-EM, or a full suite of NMR experiments. Data from HDX-NMR experiments encode information on the protein structure, making HDX a prime candidate to be supplemented by computational algorithms for protein structure prediction. We have developed a methodology to incorporate HDX-NMR data into ab initio protein structure prediction using the Rosetta software framework to predict structures based on experimental agreement. To demonstrate the efficacy of our algorithm, we examined 38 proteins with HDX-NMR data available, comparing the predicted model with and without the incorporation of HDX data into scoring. The root-mean-square deviation (rmsd, a measure of the average atomic distance between superimposed models) of the predicted model improved by 1.42 Å on average after incorporating the HDX-NMR data into scoring. The average rmsd improvement for the proteins where the selected model rmsd changed after incorporating HDX data was 3.63 Å, including one improvement of more than 11 Å and seven proteins improving by greater than 4 Å, with 12/15 proteins improving overall. Additionally, for independent verification, two proteins that were not part of the original benchmark were scored including HDX data, with a dramatic improvement of the selected model rmsd of nearly 9 Å for one of the proteins. Moreover, we have developed a confidence metric allowing us to successfully identify near-native models in the absence of a native structure. Improvement in model selection with a strong confidence measure demonstrates that protein structure prediction with HDX-NMR is a powerful tool which can be performed with minimal additional computational strain and expense.

Small angle X-ray scattering (SAXS) is an experimental technique used for structural characterization of macromolecules in solution. Here, we introduce BCL::SAXS--an algorithm designed to replicate SAXS profiles from rigid protein models at different levels of detail. We first show our derivation of BCL::SAXS and compare our results with the experimental scattering profile of hen egg white lysozyme. Using this protein we show how to generate SAXS profiles representing: (1) complete models, (2) models with approximated side chain coordinates, and (3) models with approximated side chain and loop region coordinates. We evaluated the ability of SAXS profiles to identify a correct protein topology from a non-redundant benchmark set of proteins. We find that complete SAXS profiles can be used to identify the correct protein by receiver operating characteristic (ROC) analysis with an area under the curve (AUC) > 99%. We show how our approximation of loop coordinates between secondary structure elements improves protein recognition by SAχS for protein models without loop regions and side chains. Agreement with SAXS data is a necessary but not sufficient condition for structure determination. We conclude that experimental SAXS data can be used as a filter to exclude protein models with large structural differences from the native.