We have recently demonstrated that incorporation of small-angle X-ray scattering (SAXS)-based filtering in our heavily used docking server ClusPro improves docking results. However, the filtering step is time consuming, since ≈10 conformations have to be sequentially processed. At the same time, we have demonstrated the possibility of ultra-fast systematic energy evaluation for all rigid body orientations of two proteins, by sampling using Fast Manifold Fourier Transform (FMFT), if energies are represented as a combination of convolution-like expressions. Here we present a novel FMFT-based algorithm FMFT-SAXS for massive SAXS computation on multiple conformations of a protein complex. This algorithm exploits the convolutional form of SAXS calculation function. FMFT-SAXS allows computation of SAXS profiles for millions of conformations in a matter of minutes, providing an opportunity to explore the whole conformational space of two interacting proteins. We demonstrate the application of the new FMFT-SAXS approach to significantly speed up SAXS filtering step in our current docking protocol (1 to 2 orders of magnitude faster, running in several minutes on a modern 16-core CPU) without loss of accuracy. This is demonstrated on the benchmark set as well as on the experimental data. The new approach is available as a part of ClusPro server (https://beta.cluspro.org) and as an open source C library (https://bitbucket.org/abc-group/libfmftsaxs).
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http://dx.doi.org/10.1016/j.jmb.2018.03.010 | DOI Listing |
While novel deep learning and statistics-based techniques predict accurate structural models for proteins and non-coding RNA, describing their macromolecular conformations in solution is still challenging. Small-angle X-ray scattering (SAXS) in solution is an efficient technique to validate structural predictions by comparing the experimental SAXS profile with those calculated from predicted structures. There are two main challenges in comparing SAXS profiles to RNA structures: the structures often lack cations necessary for stability and charge neutralization, and a single structure inadequately represents the conformational plasticity of RNA.
View Article and Find Full Text PDFLipid nanoparticles (LNPs) are the most advanced delivery system currently available for RNA therapeutics. Their development has accelerated since the success of Patisiran, the first siRNA-LNP therapeutic, and the mRNA vaccines that emerged during the COVID-19 pandemic. Designing LNPs with specific targeting, high potency, and minimal side effects is crucial for their successful clinical use.
View Article and Find Full Text PDFACS Nano
January 2025
Department of Chemistry, University of Minnesota, Minneapolis, Minnesota 55455, United States.
Med Phys
January 2025
Breast Imaging Department, Red Cross Hospital Munich, Munich, Germany.
Background: A significant proportion of false positive recalls of mammography-screened women is due to benign breast cysts and simple fibroadenomas. These lesions appear mammographically as smooth-shaped dense masses and require the recalling of women for a breast ultrasound to obtain complementary imaging information. They can be identified safely by ultrasound with no need for further assessment or treatment.
View Article and Find Full Text PDFMacromolecules
December 2024
Dainton Building, Department of Chemistry, University of Sheffield, Brook Hill, Sheffield, South Yorkshire S3 7HF, U.K.
We report the reversible addition-fragmentation chain transfer (RAFT) dispersion polymerization of 2-hydroxyethyl methacrylate (HEMA) in -dodecane using a poly(lauryl methacrylate) (PLMA) precursor at 90 °C. This formulation is an example of polymerization-induced self-assembly (PISA), which leads to the formation of a colloidal dispersion of spherical PLMA-PHEMA nanoparticles at 10-20% w/w solids. PISA syntheses involving polar monomers in non-polar media have been previously reported but this particular system offers some unexpected and interesting challenges in terms of both synthesis and characterization.
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