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Typical result of AquaSAXS on Urate Oxidase. The deposited structure 3L8W was used as model. The PQR file was generated with PDB2PQR (34), using CHARMM parameters. The solvent map was generated with AquaSol. 65 points per edge, equally spaced by 2.2 Å define the cubic grid (using a higher resolution map did not significantly improve the fit). The solute was immersed in an ion atmosphere of 0.1 M NaCl, and the solute region was defined by its solvent-accessible surface (with a probe radius of 1.4 Å). One of the profile displayed here (in blue) was output by AquaSAXS after fitting, along with the fitting parameters: C1 = 1.021 and C2 = 1.022. The goodness of fit is: χ = 1.69. The computation took less than 5 minutes. 9436 atoms were considered. The profiles fitted using FoXS (green) and CRYSOL (orange) are shown for comparison. Their respective values for the goodness-of-fit is 2.46 and 1.53. FoXS used C1 = 1.09 and C2 = 2.9 as fitting parameters, while CRYSOL used = 0.025, Ra = 1.560 Å and Vol = 179 493 Å3 (which corresponds to a volume 20% more important than the volume actually deduced from the average radius Ra). Additional parameters for CRYSOL were the use of up to the 30th order of spherical harmonics, and 18th order for the Fibonacci grid. In every case, the bulk density was set at 0.334 e.Å−3.The figure in inset displays the goodness-of-fit computed by AquaSAXS for the range of C1 and C2 scanned by the program. Only values of between the minimum and 6 are shown, for clarity.
Source publication
Small Angle X-ray Scattering (SAXS) techniques are becoming more and more useful for structural biologists and biochemists, thanks to better access to dedicated synchrotron beamlines, better detectors and the relative easiness of sample preparation. The ability to compute the theoretical SAXS profile of a given structural model, and to compare this...
Similar publications
Small- and wide-angle X-ray scattering (SWAXS) has evolved into a powerful tool to study biological macromolecules in solution. The interpretation of SWAXS curves requires their accurate predictions from structural models. Such predictions are complicated by scattering contributions from the hydration layer and by effects from thermal fluctuations....
Citations
... These algorithms most often include fitting parameters to adjust for differences in solvation or solution conditions, or for experimental errors. Several more advanced and computationally expensive algorithms exist for modeling solvent terms explicitly, including using molecular dynamics (MD) simulations, to fit high resolution WAXS data accurately with minimal or no free parameters (20)(21)(22)(23). ...
... Others have sped up the calculation by using a spherical harmonics expansion to approximate the scattering profile rather than the Debye equation (16). Another method is the "cube" method (29) that several algorithms employ which calculates an explicit 3D Fourier transform from atomic coordinates using a cubic lattice, and then performs spherical averaging numerically in reciprocal space using a sufficient number of particle orientations to generate the 1D scattering profile (18,(20)(21)(22)(23). As noted in other works, while the Debye equation scales linearly with the number of q values, other methods may scale with the square or cube of the maximum q, or with the number of harmonics (16,21,25,30,31). ...
... A typical value for ( is that of pure water at room temperature, 0.334 e -/Å 3 , but this value can be affected by temperature or other components in the aqueous solution including salts and buffer molecules that can alter the average bulk density value. Some approaches model the excluded volume term as a simple flat value inside the envelope defined by the solute (18,(20)(21)(22)(23). Other methods use molecular dynamics simulations to model the solute in explicit solvent, and separately model the bulk solvent, taking the difference of the two calculations as the contrast (21,22,34). ...
... These algorithms most often include fitting parameters to adjust for differences in solvation or solution conditions, or for experiment errors. Several more advanced and computationally expensive algorithms exist for modeling solvent terms explicitly, including utilizing molecular dynamics (MD) simulations, to fit high resolution WAXS data accurately with minimal free parameters (20)(21)(22)(23). ...
... Others have sped up the calculation by using a spherical harmonics expansion to approximate the scattering profile rather than the Debye equation (16). Another method is the "cube" method (28) that several algorithms employ which calculates an explicit 3D Fourier transform from atomic coordinates using a cubic lattice, and then performs spherical averaging numerically in reciprocal space using a sufficient number of particle orientations to generate the 1D scattering profile (18,(20)(21)(22)(23). ...
... A typical value for ( is that of pure water at room temperature, 0.334 e -/Å 3 , but this value can be affected by temperature or other components in the aqueous solution including salts and buffer molecules that can alter the average bulk density value. Some approaches model the excluded volume term as a simple flat value inside the envelope defined by the solute (18,(20)(21)(22)(23). Other methods use molecular dynamics simulations to model the solute in explicit solvent, and separately model the bulk solvent, taking the difference of the two calculations as the contrast (21,22,31). ...
Solution scattering techniques, such as small and wide-angle X-ray scattering (SWAXS), provide valuable insights into the structure and dynamics of biological macromolecules in solution. In this study, we present an approach to accurately predict solution X-ray scattering profiles at wide angles from atomic models by generating high-resolution electron density maps. Our method accounts for the excluded volume of bulk solvent by calculating unique adjusted atomic volumes directly from the atomic coordinates. This approach eliminates the need for a free fitting parameter commonly used in existing algorithms, resulting in improved accuracy of the calculated SWAXS profile. An implicit model of the hydration shell is generated which uses the form factor of water. Two parameters, namely the bulk solvent density and the mean hydration shell contrast, are adjusted to best fit the data. Results using eight publicly available SWAXS profiles show high quality fits to the data. In each case, the optimized parameter values show small adjustments demonstrating that the default values are close to the true solution. Disabling parameter optimization results in a significant improvement of the calculated scattering profiles compared to the leading software. The algorithm is computationally efficient, showing more than tenfold reduction in execution time compared to the leading software. The algorithm is encoded in a command line script called denss.pdb2mrc.py and is available open source as part of the DENSS v1.7.0 software package ( https://github.com/tdgrant1/denss ). In addition to improving the ability to compare atomic models to experimental SWAXS data, these developments pave the way for increasing the accuracy of modeling algorithms utilizing SWAXS data while decreasing the risk of overfitting.
Statement of Significance
Accurate calculation of small and wide-angle scattering (SWAXS) profiles from atomic models is useful for studying the solution state and conformational dynamics of biological macromolecules in solution. Here we present a new approach to calculating SWAXS profiles from atomic models using high resolution real space density maps. This approach includes novel calculations of solvent contributions that remove a significant fitting parameter. The algorithm is tested on multiple high quality experimental SWAXS datasets, showing improved accuracy compared to leading software. The algorithm is computationally efficient and robust to overfitting, paving the way for increasing the accuracy and resolution of modeling algorithms utilizing experimental SWAXS data.
... Since the publication of the first programs to calculate small-angle X-ray scattering (SAXS; Svergun et al., 1995) and small-angle neutron scattering (SANS; Svergun et al., 1998) profiles from atomic coordinates there has been an ongoing acceleration in the rate of biomolecular SAS publications and citations (Trewhella, 2022). Since these first programs, there have been numerous further developments and new approaches to SAS profile prediction (see, for example, Grishaev et al., 2010;Poitevin et al., 2011;Chen & Hub, 2014;Schneidman-Duhovny et al., 2016;Grudinin et al., 2017;Hub, 2018), including an extension to ensembles for dynamic and multistate systems (see, for example, Bernadó et al., 2007;Schneidman-Duhovny et al., 2016;Cordeiro et al., 2017). Each developer has chosen a preferred set of experimental data against which to test their approach as there is no standard set of data to evaluate the differences among the different approaches or to test new approaches in a standard way. ...
Through an expansive international effort that involved data collection on 12 small-angle X-ray scattering (SAXS) and four small-angle neutron scattering (SANS) instruments, 171 SAXS and 76 SANS measurements for five proteins (ribonuclease A, lysozyme, xylanase, urate oxidase and xylose isomerase) were acquired. From these data, the solvent-subtracted protein scattering profiles were shown to be reproducible, with the caveat that an additive constant adjustment was required to account for small errors in solvent subtraction. Further, the major features of the obtained consensus SAXS data over theq measurement range 0–1 Å⁻¹ are consistent with theoretical prediction. The inherently lower statistical precision for SANS limited the reliably measuredq-range to <0.5 Å⁻¹, but within the limits of experimental uncertainties the major features of the consensus SANS data were also consistent with prediction for all five proteins measured in H2O and in D2O. Thus, a foundation set of consensus SAS profiles has been obtained for benchmarking scattering-profile prediction from atomic coordinates. Additionally, two sets of SAXS data measured at different facilities to q > 2.2 Å⁻¹ showed good mutual agreement, affirming that this region has interpretable features for structural modelling. SAS measurements with inline size-exclusion chromatography (SEC) proved to be generally superior for eliminating sample heterogeneity, but with unavoidable sample dilution during column elution, while batch SAS data collected at higher concentrations and for longer times provided superior statistical precision. Careful merging of data measured using inline SEC and batch modes, or low- and high-concentration data from batch measurements, was successful in eliminating small amounts of aggregate or interparticle interference from the scattering while providing improved statistical precision overall for the benchmarking data set.
... The electron density within this layer is non-homogeneous, which can significantly influence on the profile of the SASPAR curves. The conclusion about the presence of a hydration layer near a protein surface is also not new, and many authors introduce this effect into the intensity calculations in some way or another (see, e.g., [8,[52][53][54][55]). Our contribution is that we directly confirm the existence of this hydrated layer using the fact that the SASPAR and SASCUBE scattering curves clearly diverge from one another. ...
a smirnav 2@mail.ru Corresponding author: A.V. Smirnov, smirnav 2@mail.ru PACS 61.05.cf, 71.15. Pd, 87.15.B-ABSTRACT The SASPAR program for calculation of SAXS of proteins in solution uses trajectories of molecular dynamics (MD) and an explicit solvent model. The program allows one to take into account real interactions of solvent molecules both between each other and with the protein molecule. The previously developed SAS-CUBE program (the "cube method") is also used, it assumes that the protein structures in crystal and in solution coincide, and the water surrounding the proteins is considered as a homogeneous continuum. Using these programs, SAXS curves were calculated for 18 proteins of different molecular weights and then compared with one another and with the corresponding experimental scattering curves. "Vacuum" SAXS curves (i.e., without taking into account the surrounding water) were also calculated for each protein for two approaches: a) based on the coordinates of protein atoms in crystal and b) based on the coordinates of protein atoms for each MD frame with further averaging of the intensities from all the frames. 1) It was shown that for the 14 single-domain proteins considered, the "vacuum" scattering curves calculated by two methods coincide well for almost each protein. Hence, the structure of the studied proteins in a solution is similar to their structure in a crystal and, therefore, the presence of the surrounding water molecules does not alter the protein structure itself significantly. The SASPAR-and SASCUBE-curves coincide well only in two cases (i.e., water is only slightly structured near the protein surface), but in the other cases these curves are markedly different, which indicates the structuredness of the water near the protein surface, although to a different extent. 2) It was shown that for the 4 multi-domain proteins considered, their "vacuum" scattering curves, calculated with the two methods indicated above, differ noticeably, which is an evidence that their crystalline and "water" structures are different. It was also shown that the most of the calculated curves coincide well with the experimental ones. KEYWORDS small-angle X-ray solution scattering, molecular dynamics, protein structure in solution, water structure. ACKNOWLEDGEMENTS The authors are grateful to Dr. Alexander Grishaev for providing the experimental SAXS scattering curves of the GB3 protein and to the Master's student K. S. Mazokha for the assistance with research. FOR CITATION Smirnov A.V., Semenov A.M., Porozov Yu.B., Fedorov B.A. Assessment of structural changes in proteins and surrounding water molecules in solution according to SAXS and MD data. Nanosystems: Phys. Chem. Math., 2022, 13 (3), 274-284.
... In the last three decades, a wide range of methods have been developed for predicting SAS intensities from atomistic structures, reflecting the increasing importance of SAS experiments for biomolecular research and the need for a physically founded interpretation of the data (Svergun et al., 1995;Merzel and Smith, 2002a,b;Tjioe and Heller, 2007;Bardhan et al., 2009;Ravikumar et al., 2013;Yang et al., 2009;Schneidman-Duhovny et al., 2010Stovgaard et al., 2010;Poitevin et al., 2011;Liu et al., 2012;Nguyen et al., 2014;Putnam et al., 2015;Grudinin et al., 2017;Oroguchi et al., 2009;Oroguchi and Ikeguchi, 2012;Grishaev et al., 2010;Virtanen et al., 2011;Park et al., 2009;Köfinger and Hummer, 2013;Chen and Hub, 2014;Knight and Hub, 2015). The methods mainly differ by • the method for modeling the hydration layer, involving explicit all-atom models, increased atomic form factors for solvent-exposed solute atoms, layers of uniform electron density, implicit models with internal structure, or several others; ...
Small-angle X-ray or neutron scattering (SAXS/SANS/SAS) is widely used to obtain structural information on biomolecules or soft-matter complexes in solution. Deriving a molecular interpretation of the scattering signals requires methods for predicting SAS patterns from a given atomistic structural model. Such SAS predictions are non-trivial because the patterns are influenced by the hydration layer of the solute, the excluded solvent, and by thermal fluctuations. Many computationally efficient methods use simplified, implicit models for the hydration layer and excluded solvent, leading to some uncertainties and to free parameters that require fitting against experimental data. SAS predictions based on explicit-solvent molecular dynamics (MD) simulations overcome such limitations at the price of an increased computational cost. To rationalize the need for explicit-solvent methods, we first review the approximations underlying implicit-solvent methods. Next, we describe the theory behind explicit-solvent SAS predictions that are easily accessible via the WAXSiS web server. We present the workflow for computing SAS pattern from a given molecular dynamics trajectory with a freely available via a modified version of the GROMACS simulations software, coined GROMACS-SWAXS, which implements the WAXSiS method. Practical considerations for running routine explicit-solvent SAS predictions are discussed.
... AquaSAXS is a new and better program than AquaSol, which uses a dipolar solvent model. AquaSAXS is working based on a hydration map around the macromolecule, as a solvent density map on a 3D grid [173]. MDpocket is free, and an open-source tool is used for detection of protein pocket cavity for tracking small molecule binding sites and gas migration pathways on molecular dynamics (MDs) trajectories or other conformational ensembles [174]. ...
In the current era of modern drug design & development via computer-aided drug design, the potential role of computational software tools is widely enlarged in use. Computer-based drug design is revolutionary in the new drug discovery process because these processes are fast, time, and cost-saving with more efficient pharmacological activity. Computer-Based drug design is mainly applied for the drug-design and gets many successes in new drug research. There is plenty of software available in drug design; however; still, many issues are rising during its use. To clarify these issues, an attempt has been provided here in this article about the information about worldwide used 189 computation tools along with citation of software tools, download links, computer operative system and application of tools for available software such as Molecular modeling, docking, proteins conformation, pharmacophore mapping, ADMET, Docking pose visualization, force field calculation, homology modeling, 3D structure generator, Computational Crystallography, protein Database, and calculation software. This vital information enlightens all the software right from old to a recent one. Review article important for choice and application of wide-reaching used Drug Design software.
... Small angle X-ray scattering (SAXS) SAXS provides global size and shape information for proteins in solution (close to the physiological conditions), including the radius of gyration, volume, and molecular mass [86]. Given a structure model, theoretical SAXS profiles are computed using FoXS [87][88][89], AquaSAXS [90], WAXSiS [91], or CRYSOL [92]. The theoretical SAXS profiles are iteratively matched to the experimental SAXS profile to produce an ensemble of optimized structures [89]. ...
Chromogranin A (CgA), which is an intrinsically disordered protein that belongs to the granin family, was first discovered in the bovine adrenal medulla, and later identified in various organs. Under certain physiological conditions, CgA is cleaved into functionally diverse peptides, such as vasostatin-1, pancreastatin, and catestatin. In this review, we first describe the historical and systematic challenges for elucidating the molecular structures of CgA and its derived peptides and give a perspective of utilizing emerging techniques through integrative approaches. Subsequently, we review specific biological processes associated with CgA and its derived peptides in the neuroendocrine, immune, and digestive systems. Finally, we discuss biomedical applications of CgA as a biomarker, suggesting future directions toward translational and precision medicine.
... It is therefore essential to model this hydration shell efficiently when calculating the scattering curve of a biomolecule from its structure. Various computational tools have been developed for this purpose using different hydration shell modeling, for example, CRYSOL [157], FOXS [158], WAXSiS [159], Pepsi-SAXS [160], AquaSAXS [161], or AXES [162], among others. ...
The study of complex and dynamic biomolecular assemblies is a key challenge in structural biology and requires the use of multiple methodologies providing complementary spatial and temporal information. NMR spectroscopy is a powerful technique that allows high-resolution structure determination of biomolecules as well as investigating their dynamic properties in solution. However, for high molecular weight systems, such as biomolecular complexes or multi-domain proteins, it is often only possible to obtain sparse NMR data, posing significant challenges to structure determination. Combining NMR data with information obtained from other solution techniques is therefore an attractive approach. The combination of NMR with small angle X-ray and/or neutron scattering (SAXS/SANS) has been shown to be particularly fruitful. These scattering approaches provide low resolution information of biomolecules in solution and reflect ensemble-averaged contributions of dynamic conformations for scattering molecules up to Megadalton molecular weight. Here, we review recent developments in the combination of NMR and SAS experiments. We briefly outline the different types of information that provided by these two approaches. We then discuss computational methods that have been developed to integrate NMR and SAS data, particularly considering the presence of dynamic structural ensembles and flexibility of the investigated biomolecules. Finally, recent examples of the successful combination of NMR and SAS are presented to illustrate the utility of their combination.
... CRYSOL and the FoXS package, which was developed by Schneidman-Duhovny et al., 7 adjust an implicit "shell" of scattering ("implicit" meaning they do not model individual solvent molecules). Other packages treat the shell explicitly using either molecular dynamics (AquaSAXS) 8 or a geometric filling approach (the SCT suite). 9 Allowing for a shell that can have gaps and fill cavities in the protein model gives a more reliable fit to the data. ...
Small angle X-ray scattering (SAXS) is an important tool for investigating the structure of proteins in solution. We present a novel ab-initio method representing polypeptide chains as discrete curves used to derive a meaningful three-dimensional model from \textbf{only} the primary sequence and SAXS data. High resolution structures were used to generate probability density functions for each common secondary structural element found in proteins, which are used to place realistic restraints on the model curve's geometry. This is coupled with a novel explicit hydration shell model in order derive physically meaningful 3D models by optimizing against experimental SAXS data. The efficacy of this model is verified on an established benchmark protein set, then it is used to predict the Lysozyme structure using only its primary sequence and SAXS data. The method is used to generate a biologically plausible model of the coiled-coil component of the human synaptonemal complex central element protein.
... Because SAXS data often provide low-resolution information that warrants a uniform density approximation for 3D models, we binarized the voxelized objects before auto-encoder training. Owing to the uniform density approximation of the reconstructed models, the SAXS data comparison were limited up to q = 0.2 Å À1 (Grant, 2018;Poitevin et al., 2011). The auto-encoder model is based on the VGG network (Simonyan and Zisserman, 2015), composed of convolutional, pooling, and full-connected layers (see Figure 1, Table S1 and the Transparent Methods). ...
Small-angle X-ray scattering (SAXS) method is widely used in investigating protein structures in solution, but high-quality 3D model reconstructions are challenging. We present a new algorithm based on a deep learning method for model reconstruction from SAXS data. An auto-encoder for protein 3D models was trained to compress 3D shape information into vectors of a 200-dimensional latent space, and the vectors are optimized using genetic algorithms to build 3D models that are consistent with the scattering data. The program has been tested with experimental SAXS data, demonstrating the capacity and robustness of accurate model reconstruction. Furthermore, the model size information can be optimized using this algorithm, enhancing the automation in model reconstruction directly from SAXS data. The program was implemented using Python with the TensorFlow framework, with source code and webserver available from http://liulab.csrc.ac.cn/decodeSAXS.
