<i>TEMPy</i>2: a Python library with improved 3D electron microscopy density-fitting and validation workflows

Cragnolini, Tristan; Sahota, Harpal; Joseph, Agnel Praveen; Sweeney, Aaron; Malhotra, Sony; Vasishtan, Daven; Topf, Maya

doi:10.1107/s2059798320014928

Cited by 36 publications

(49 citation statements)

References 31 publications

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“…3 (top panel). For each LASV L protein structure, the SMOC scores per residue were calculated by TEMPy within CCP-EM 49, 62 providing the respective resolution as described in Supplementary Table 1 and plotted revealing the quality of the local fit (bottom panels). The domain structure of LASV L protein as shown in the top panel has been added as background to each SMOC plot to highlight which domains are missing/present in each structure.…”

Section: Supplementary Tables and Figure Captionsmentioning

confidence: 99%

Conformational changes in Lassa virus L protein associated with promoter binding and RNA synthesis activity

Kouba

Vogel

Thorkelsson

et al. 2021

Preprint

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Lassa virus, which causes annual outbreaks in West Africa with increasing case numbers in recent years, is recognized by the WHO R&D blueprint as a significant threat for public health with high epidemic potential and no effective countermeasures. The viral large (L) protein, which contains the RNA-dependent RNA polymerase, is a key player for transcription of viral mRNA and genome replication. Here we present nine cryo-EM structures of Lassa virus L protein in the apo-, promoter-bound pre-initiation and active RNA synthesis states. We characterize distinct binding pockets for the conserved genomic 3’ and 5’ promoter RNAs and show how full-promoter binding induces a distinct pre-initiation conformation. In the apo- and elongation states, the endonuclease is inhibited by the binding of two distinct L protein peptides in the active site, respectively, whereas in the pre-initiation state, the endonuclease is uninhibited. In the stalled, early elongation state, a template- product duplex is bound in the active site cavity together with an incoming non-hydrolysable nucleotide. In this configuration, the full C-terminal region of the L protein, including the putative cap-binding domain, is highly ordered. The structural data are complemented by in vitro and cell-based studies testing a broad range of L protein mutants to probe functional relevance. These data advance our mechanistic understanding of how this flexible and multifunctional molecular machine is activated and will underpin antiviral drug development targeting the arenavirus L protein.

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Section: Supplementary Tables and Figure Captionsmentioning

confidence: 99%

Conformational changes in Lassa virus L protein associated with promoter binding and RNA synthesis activity

Kouba

Vogel

Thorkelsson

et al. 2021

Preprint

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“…MolProbity is the most widely used geometry validation tool, and includes analyses of clashes, rotamers and the Ramachandran plot (Chen et al, 2010). Map-model quality is assessed using real-space local correlations (Cragnolini et al, 2021), which have commonly been used in crystallography (Tickle, 2012). In reciprocal-space refinement, the R factor can be calculated as in crystallography, but the map-model Fourier shell correlation (FSC) is preferred as it does not depend on resolution-dependent scaling and takes phases into account explicitly.…”

Section: Introductionmentioning

confidence: 99%

Cryo-EM single-particle structure refinement and map calculation usingServalcat

Yamashita¹,

Palmer²,

Burnley³

et al. 2021

Acta Crystallogr D Struct Biol

193

131

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In 2020, cryo-EM single-particle analysis achieved true atomic resolution thanks to technological developments in hardware and software. The number of high-resolution reconstructions continues to grow, increasing the importance of the accurate determination of atomic coordinates. Here, a new Python package and program called Servalcat is presented that is designed to facilitate atomic model refinement. Servalcat implements a refinement pipeline using the program REFMAC5 from the CCP4 package. After the refinement, Servalcat calculates a weighted F o − F c difference map, which is derived from Bayesian statistics. This map helps manual and automatic model building in real space, as is common practice in crystallography. The F o − F c map helps in the visualization of weak features including hydrogen densities. Although hydrogen densities are weak, they are stronger than in the electron-density maps produced by X-ray crystallography, and some H atoms are even visible at ∼1.8 Å resolution. Servalcat also facilitates atomic model refinement under symmetry constraints. If point-group symmetry has been applied to the map during reconstruction, the asymmetric unit model is refined with the appropriate symmetry constraints.

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“…Also, it is a necessary operation in the difference map calculation and likelihood-based map averaging, where the input maps are required to be pre-aligned. There are several tools available for superposing maps such as Chimera's Fit-in-map (Pettersen et al, 2004), TEMPy2 (Cragnolini et al, 2021) etc. EMDA map overlay is based on the maximisation of the likelihood function given by equation 5 using a quasi-Newton method.…”

Section: Examples Of Use Of Map Overlay 421 Emda Map Overlaymentioning

confidence: 99%

EMDA: A Python package for Electron Microscopy Data Analysis

Warshamanage

Yamashita

Murshudov

2021

Preprint

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An open-source Python library EMDA for cryo-EM map and model manipulation is presented with a specific focus on validation. The architecture of the library and its programmable and command-line interfaces are presented. The use of several functionalities in the library is presented through several examples. The utility of local correlation as a metric for identifying model errors and unmodeled regions in maps, and how it is used as a metric of map-model validation is demonstrated. The mapping of local correlation to individual atoms, and its use to draw insights on local signal variations are discussed. EMDA`s likelihood-based map overlay is demonstrated by carrying out a superposition of two domains in two related structures. The overlay is carried out globally to bring both maps into the same coordinate frame and then locally to estimate the relative movement of domains. Finally, the map magnification refinement in EMDA is presented with an example to highlight the importance of correcting map magnification in structural comparison studies.

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TEMPy2: a Python library with improved 3D electron microscopy density-fitting and validation workflows

Cited by 36 publications

References 31 publications

Conformational changes in Lassa virus L protein associated with promoter binding and RNA synthesis activity

Conformational changes in Lassa virus L protein associated with promoter binding and RNA synthesis activity

Cryo-EM single-particle structure refinement and map calculation usingServalcat

EMDA: A Python package for Electron Microscopy Data Analysis

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