Intermolecular correlations are necessary to explain diffuse scattering from protein crystals

Conformational changes drive protein function, including catalysis, allostery, and signaling. X-ray diffuse scattering from protein crystals has frequently been cited as a probe of these correlated motions, with significant potential to advance our understanding of biological dynamics. However, recent work challenged this prevailing view, suggesting instead that diffuse scattering primarily originates from rigid body motions and could therefore be applied to improve structure determination. To investigate the nature of the disorder giving rise to diffuse scattering, and thus the potential applications of this signal, a diverse repertoire of disorder models was assessed for its ability to reproduce the diffuse signal reconstructed from three protein crystals. This comparison revealed that multiple models of intramolecular conformational dynamics, including ensemble models inferred from the Bragg data, could not explain the signal. Models of rigid body or short-range liquid-like motions, in which dynamics are confined to the biological unit, showed modest agreement with the diffuse maps, but were unable to reproduce experimental features indicative of long-range correlations. Extending a model of liquid-like motions to include disorder across neighboring proteins in the crystal significantly improved agreement with all three systems and highlighted the contribution of intermolecular correlations to the observed signal. These findings anticipate a need to account for intermolecular disorder in order to advance the interpretation of diffuse scattering to either extract biological motions or aid structural inference.Comment: 12 pages, 5 figures (not including Supplementary Information

Heterogeneous reconstruction of deformable atomic models in Cryo-EM

Cryogenic electron microscopy (cryo-EM) provides a unique opportunity to study the structural heterogeneity of biomolecules. Being able to explain this heterogeneity with atomic models would help our understanding of their functional mechanisms but the size and ruggedness of the structural space (the space of atomic 3D cartesian coordinates) presents an immense challenge. Here, we describe a heterogeneous reconstruction method based on an atomistic representation whose deformation is reduced to a handful of collective motions through normal mode analysis. Our implementation uses an autoencoder. The encoder jointly estimates the amplitude of motion along the normal modes and the 2D shift between the center of the image and the center of the molecule . The physics-based decoder aggregates a representation of the heterogeneity readily interpretable at the atomic level. We illustrate our method on 3 synthetic datasets corresponding to different distributions along a simulated trajectory of adenylate kinase transitioning from its open to its closed structures. We show for each distribution that our approach is able to recapitulate the intermediate atomic models with atomic-level accuracy.Comment: 8 pages, 1 figur

Challenges in solving structures from radiation-damaged tomograms of protein nanocrystals assessed by simulation

Structure-determination methods are needed to resolve the atomic details that underlie protein function. X-ray crystallography has provided most of our knowledge of protein structure, but is constrained by the need for large, well ordered crystals and the loss of phase information. The rapidly developing methods of serial femtosecond crystallography, micro-electron diffraction and single-particle reconstruction circumvent the first of these limitations by enabling data collection from nanocrystals or purified proteins. However, the first two methods also suffer from the phase problem, while many proteins fall below the molecular-weight threshold required for single-particle reconstruction. Cryo-electron tomography of protein nanocrystals has the potential to overcome these obstacles of mainstream structure-determination methods. Here, a data-processing scheme is presented that combines routines from X-ray crystallography and new algorithms that have been developed to solve structures from tomograms of nanocrystals. This pipeline handles image-processing challenges specific to tomographic sampling of periodic specimens and is validated using simulated crystals. The tolerance of this workflow to the effects of radiation damage is also assessed. The simulations indicate a trade-off between a wider tilt range to facilitate merging data from multiple tomograms and a smaller tilt increment to improve phase accuracy. Since phase errors, but not merging errors, can be overcome with additional data sets, these results recommend distributing the dose over a wide angular range rather than using a finer sampling interval to solve the protein structure

Challenges in solving structures from radiation-damaged tomograms of protein nanocrystals assessed by simulation

Structure determination methods are needed to resolve the atomic details that underlie protein function. X-ray crystallography has provided most of our knowledge of protein structure but is constrained by the need for large, well-ordered crystals and the loss of phase information. The rapidly developing methods of serial femtosecond crystallography, micro-electron diffraction, and single-particle reconstruction circumvent the first of these limitations by enabling data collection from nanocrystals or purified proteins. However, the first two methods also suffer from the phase problem, while many proteins fall below the molecular weight threshold required by single-particle reconstruction. Cryo-electron tomography of protein nanocrystals has the potential to overcome these obstacles of mainstream structure determination methods. Here we present a data processing scheme that combines routines from X-ray crystallography and new algorithms we developed to solve structures from tomograms of nanocrystals. This pipeline handles image processing challenges specific to tomographic sampling of periodic specimens and is validated using simulated crystals. We also assess the tolerance of this workflow to the effects of radiation damage. Our simulations indicate a trade-off between a wider tilt-range to facilitate merging data from multiple tomograms and a smaller tilt increment to improve phase accuracy. Since phase errors but not merging errors can be overcome with additional datasets, these results recommend distributing the dose over a wide angular range rather than using a finer sampling interval to solve the protein structure

Massive X-ray screening reveals two allosteric drug binding sites of SARS-CoV-2 main protease

The coronavirus disease (COVID-19) caused by SARS-CoV-2 is creating tremendous health problems and economical challenges for mankind. To date, no effective drug is available to directly treat the disease and prevent virus spreading. In a search for a drug against COVID-19, we have performed a massive X-ray crystallographic screen of repurposing drug libraries containing 5953 individual compounds against the SARS-CoV-2 main protease (Mpro), which is a potent drug target as it is essential for the virus replication. In contrast to commonly applied X-ray fragment screening experiments with molecules of low complexity, our screen tested already approved drugs and drugs in clinical trials. From the three-dimensional protein structures, we identified 37 compounds binding to Mpro. In subsequent cell-based viral reduction assays, one peptidomimetic and five non-peptidic compounds showed antiviral activity at non-toxic concentrations. Interestingly, two compounds bind outside the active site to the native dimer interface in close proximity to the S1 binding pocket. Another compound binds in a cleft between the catalytic and dimerization domain of Mpro. Neither binding site is related to the enzymatic active site and both represent attractive targets for drug development against SARS-CoV-2. This X-ray screening approach thus has the potential to help deliver an approved drug on an accelerated time-scale for this and future pandemics

X-ray screening identifies active site and allosteric inhibitors of SARS-CoV-2 main protease

The coronavirus disease (COVID-19) caused by SARS-CoV-2 is creating tremendous human suffering. To date, no effective drug is available to directly treat the disease. In a search for a drug against COVID-19, we have performed a high-throughput X-ray crystallographic screen of two repurposing drug libraries against the SARS-CoV-2 main protease (M^(pro)), which is essential for viral replication. In contrast to commonly applied X-ray fragment screening experiments with molecules of low complexity, our screen tested already approved drugs and drugs in clinical trials. From the three-dimensional protein structures, we identified 37 compounds that bind to M^(pro). In subsequent cell-based viral reduction assays, one peptidomimetic and six non-peptidic compounds showed antiviral activity at non-toxic concentrations. We identified two allosteric binding sites representing attractive targets for drug development against SARS-CoV-2

Montage tilt-series

Montage tilt-series from the thin edge of a HeLa cell. The tilt angles associated the the tilt-series are in the associated .rawtlt file, as some angles were omitted during data processing.<strong>Tilt Series Date:</strong> 2020-12-23</p> <strong>Data Taken By:</strong> Ariana Peck</p> <strong>Species / Specimen:</strong> HeLa cells</p> <strong>Strain:</strong> ATCC</p> <strong>Tilt Series Settings:</strong> Single Axis, tilt range: (-58.0°, 54.0°), step: grouped dose-symmetric°, constant angular increment, dosage: 60.0 eV/Ų, defocus: -5.0 μm, magnification: 33000x. </p> <strong>Microscope:</strong> Caltech Titan Krios</p> <strong>Acquisition Software:</strong> Serial EM</p> <strong>Upload Method:</strong> webload</p> <strong>Processing Software Used:</strong> custom, see https://github.com/apeck12/montage</p> <strong>Collaborators and Roles:</strong> sample prepared and data collected by Stephen Carter; data processed by Ariana Peck</p> <strong>Purification / Growth Conditions / Treatment:</strong> plated at a density of 2e5 cells/mL and incubated for 12 hours in standard media</p> <strong>Sample Preparation:</strong> R3.5/1 London finder Quantifoil grid, 20 nm gold fiducials</p>Files available via S3 at https://renc.osn.xsede.org/ini210004tommorrell/tomography_archive/apn2020-12-23-1</p>stack.mrc, Tilt Series (Pixel Size 0.53 nm) custom montage tilt-series, 7.9 GB <a role="button" class="ui compact mini button" href="https://renc.osn.xsede.org/ini210004tommorrell/tomography_archive/apn2020-12-23-1/rawdata/stack.mrc" > <i class="download icon"></i> Download </a></p> stack.rawtlt, , 183.0 B <a role="button" class="ui compact mini button" href="https://renc.osn.xsede.org/ini210004tommorrell/tomography_archive/apn2020-12-23-1/file_28525/stack.rawtlt" > <i class="download icon"></i> Download </a></p&gt

Montage tilt-series

Montage tilt-series from the thin edge of a HeLa cell. Some angles were omitted during data processing; the retained angles are listed in the associated .rawtlt file.<strong>Tilt Series Date:</strong> 2020-12-23</p> <strong>Data Taken By:</strong> Ariana Peck</p> <strong>Species / Specimen:</strong> HeLa cells</p> <strong>Strain:</strong> ATCC</p> <strong>Tilt Series Settings:</strong> Single Axis, tilt range: (-42.0°, 42.0°), step: grouped dose symmetric°, constant angular increment, dosage: 60.0 eV/Ų, defocus: -7.5 μm, magnification: 33000x. </p> <strong>Microscope:</strong> Caltech Titan Krios</p> <strong>Acquisition Software:</strong> Serial EM</p> <strong>Upload Method:</strong> webload</p> <strong>Processing Software Used:</strong> custom, see https://github.com/apeck12/montage</p> <strong>Collaborators and Roles:</strong> sample prepared and data collected by Stephen Carter; data processed by Ariana Peck</p> <strong>Purification / Growth Conditions / Treatment:</strong> plated at a density of 2e5 cells/mL and incubated for 12 hours in standard mediaplated at a density of 2e5 cells/mL and incubated for 12 hours in standard media</p> <strong>Sample Preparation:</strong> R3.5/1 London finder Quantifoil grid, 20 nm gold fiducials</p>Files available via S3 at https://renc.osn.xsede.org/ini210004tommorrell/tomography_archive/apn2020-12-23-2</p>stack.mrc, Tilt Series (Pixel Size 0.53 nm) custom montage tilt-series, 6.0 GB <a role="button" class="ui compact mini button" href="https://renc.osn.xsede.org/ini210004tommorrell/tomography_archive/apn2020-12-23-2/rawdata/stack.mrc" > <i class="download icon"></i> Download </a></p> stack.rawtlt, , 138.0 B <a role="button" class="ui compact mini button" href="https://renc.osn.xsede.org/ini210004tommorrell/tomography_archive/apn2020-12-23-2/file_28524/stack.rawtlt" > <i class="download icon"></i> Download </a></p&gt

Montage tilt-series

Montage tilt-series from the thin edge of a HeLa cell. Tilt angles are listed in the .rawtlt file since some were discarded during data processing.<strong>Tilt Series Date:</strong> 2020-12-23</p> <strong>Data Taken By:</strong> Ariana Peck</p> <strong>Species / Specimen:</strong> HeLa cells</p> <strong>Strain:</strong> ATCC</p> <strong>Tilt Series Settings:</strong> Single Axis, tilt range: (-46.0°, 60.0°), step: grouped dose-symmetric°, constant angular increment, dosage: 100.0 eV/Ų, defocus: 9.3 μm, magnification: 33000x. </p> <strong>Microscope:</strong> Caltech Titan Krios</p> <strong>Acquisition Software:</strong> Serial EM</p> <strong>Upload Method:</strong> webload</p> <strong>Processing Software Used:</strong> custom, see https://github.com/apeck12/montage</p> <strong>Collaborators and Roles:</strong> sample prepared and data collected by Stephen Carter; data processed by Ariana Peck</p> <strong>Purification / Growth Conditions / Treatment:</strong> plated at a density of 2e5 cells/mL and incubated for 12 hours in standard media</p> <strong>Sample Preparation:</strong> R3.5/1 London finder Quantifoil grid, 20 nm gold fiducials</p>Files available via S3 at https://renc.osn.xsede.org/ini210004tommorrell/tomography_archive/apn2020-12-23-3</p>stack.mrc, Tilt Series (Pixel Size 0.53 nm) custom montage tilt-series, 7.8 GB <a role="button" class="ui compact mini button" href="https://renc.osn.xsede.org/ini210004tommorrell/tomography_archive/apn2020-12-23-3/rawdata/stack.mrc" > <i class="download icon"></i> Download </a></p> stack.rawtlt, , 176.0 B <a role="button" class="ui compact mini button" href="https://renc.osn.xsede.org/ini210004tommorrell/tomography_archive/apn2020-12-23-3/file_28523/stack.rawtlt" > <i class="download icon"></i> Download </a></p&gt