An official website of the United States government
Abstract Small angle X‐ray scattering (SAXS) measures comprehensive distance information on a protein's structure, which can constrain and guide computational structure prediction algorithms. Here, we evaluate structure predictions of 11 monomeric and oligomeric proteins for which SAXS data were collected and provided to predictors in the 13th round of the Critical Assessment of protein Structure Prediction (CASP13). The category for SAXS‐assisted predictions made gains in certain areas for CASP13 compared to CASP12. Improvements included higher quality data with size exclusion chromatography‐SAXS (SEC‐SAXS) and better selection of targets and communication of results by CASP organizers. In several cases, we can track improvements in model accuracy with use of SAXS data. For hard multimeric targets where regular folding algorithms were unsuccessful, SAXS data helped predictors to build models better resembling the global shape of the target. For most models, however, no significant improvement in model accuracy at the domain level was registered from use of SAXS data, when rigorously comparing SAXS‐assisted models to the best regular server predictions. To promote future progress in this category, we identify successes, challenges, and opportunities for improved strategies in prediction, assessment, and communication of SAXS data to predictors. An important observation is that, for many targets, SAXS data were inconsistent with crystal structures, suggesting that these proteins adopt different conformation(s) in solution. This CASP13 result, if representative of PDB structures and future CASP targets, may have substantive implications for the structure training databases used for machine learning, CASP, and use of prediction models for biology.
more »
« less
Resolving Structure of ssDNA in Solution by Fusing Molecular Simulations and Scattering Experiments with Machine Learning
Abstract Single‐stranded DNA (ssDNA) plays a pivotal role in both nanotechnology and various biological processes. Many processes and applications can be better understood with enhanced structural characterization of ssDNA; however, the dynamic nature of the molecule makes accurate characterization with atomistic resolution extremely difficult. This study uses a method that integrates experimental small‐angle X‐ray scatter (SAXS) data and molecular modeling data using a genetic algorithm (GA) to predict an all‐atom conformational ensemble of ssDNA. The results of this study also validate the performance of various AMBER force fields and implicit solvent models for ssDNA. Overall, the results are able to determine the most accurate atomistic representation of poly‐Thymine (polyT) in solution to date that closely matches the experimental SAXS observations enabling a better understanding of the behavior of ssDNA in solution.
more »
« less
- Award ID(s):
- 2203979
- PAR ID:
- 10480139
- Publisher / Repository:
- Wiley Blackwell (John Wiley & Sons)
- Date Published:
- Journal Name:
- Advanced Theory and Simulations
- Volume:
- 6
- Issue:
- 12
- ISSN:
- 2513-0390
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Neurotransmitters are crucial for the proper functioning of neural systems, with dopamine playing a pivotal role in cognition, emotions, and motor control. Dysregulated dopamine levels are linked to various disorders, underscoring the need for accurate detection in research and diagnostics. Single-stranded DNA (ssDNA) aptamers are promising bioreceptors for dopamine detection due to their selectivity, improved stability, and synthesis feasibility. However, discrepancies in dopamine specificity have presented challenges. Here, we surface-functionalized a nano-plasmonic biosensing platform with a dopamine-specific ssDNA aptamer for selective detection. The biosensor, featuring narrowband hybrid plasmonic resonances, achieves high specificity through functionalization with aptamers and passivation processes. Sensitivity and selectivity for dopamine detection are demonstrated across a wide range of concentrations, including in diverse biological samples like protein solutions, cerebrospinal fluid, and whole blood. These results highlight the potential of plasmonic “aptasensors” for developing rapid and accurate diagnostic tools for disease monitoring, medical diagnostics, and targeted therapies.more » « less
-
Abstract Polymer‐protein hybrids can be deployed to improve protein solubility and stability in denaturing environments. While previous work used robotics and active machine learning to inform new designs, further biophysical information is required to ascertain structure–function behavior. Here, we show the value of tandem small‐angle x‐ray scattering (SAXS) and quartz crystal microbalance with dissipation (QCMD) experiments to reveal detailed polymer‐protein interactions with horseradish peroxidase (HRP) as a test case. Of particular interest was the process of polymer‐protein complex formation under thermal stress whereby SAXS monitors formation in solution while QCMD follows these dynamics at an interface. The radius of gyration (Rg) of the protein as measured by SAXS does not change significantly in the presence of polymer under denaturing conditions, but thickness and dissipation changes were observed in QCMD data. SAXS data with and without thermal stress were utilized to create bead models of the potential complexes and denatured enzyme, and each model fit provided insight into the degree of interactions. Additionally, QCMD data demonstrated that HRP deforms by spreading upon surface adsorption at low concentration as shown by longer adsorption times and smaller frequency shifts. In contrast, thermally stressed and highly inactive HRP had faster adsorption kinetics. The combination of SAXS and QCMD serves as a framework for biophysical characterization of interactions between proteins and polymers which could be useful in designing polymer‐protein hybrids.more » « less
-
Abstract Single‐atom and subnanocluster catalysts (SSCs) represent a highly promising class of low‐cost materials with high catalytic activity and high atom‐utilization efficiency. However, SSCs are susceptible to undergo restructuring during the reactions. Exploring the active sites of catalysts through in situ characterization techniques plays a critical role in studying reaction mechanism and guiding the design of optimum catalysts. In situ X‐ray absorption spectroscopy/small‐angle X‐ray scattering (XAS/SAXS) is promising and widely used for monitoring electronic structure, atomic configuration, and size changes of SSCs during real‐time working conditions. Unfortunately, there is no detailed summary of XAS/SAXS characterization results of SSCs. The recent advances in applying in situ XAS/SAXS to SSCs are thoroughly summarized in this review, including the atomic structure and oxidation state variations under open circuit and realistic reaction conditions. Furthermore, the reversible transformation of single‐atom catalysts (SACs) to subnanoclusters/nanoparticles and the application of in situ XAS/SAXS in subnanoclusters are discussed. Finally, the outlooks in modulating the SSCs and developing operando XAS/SAXS for SSCs are highlighted.more » « less
-
ABSTRACT Block copolymers play a vital role in materials science due to their diverse self‐assembly behavior. Traditionally, exploring the block copolymer self‐assembly and associated structure–property relationships involve iterative synthesis, characterization, and theory, which is labor‐intensive both experimentally and computationally. Here, we introduce a versatile, high‐throughput workflow toward materials discovery that integrates controlled polymerization and automated chromatographic separation with a novel physics‐informed machine‐learning algorithm for the rapid analysis of small‐angle X‐ray scattering data. Leveraging the expansive and high‐quality experimental data sets generated by fractionating polymers using automated chromatography, this machine‐learning method effectively reduces data dimensionality by extracting chemical‐independent features from SAXS data. This new approach allows for the rapid and accurate prediction of morphologies without repetitive and time‐consuming manual analysis, achieving out‐of‐sample predictive accuracy of around 95% for both novel and existing materials in the training data set. By focusing on a subset of samples with large predictive uncertainty, only a small fraction of the samples needs to be inspected to further improve accuracy. Collectively, the synergistic combination of controlled synthesis, automated chromatography, and data‐driven analysis creates a powerful workflow that markedly expedites the discovery of structure–property relationships in advanced soft materials.more » « less
