Computational methods for constructing protein structure models from 3D electron microscopy maps

Esquivel‐Rodríguez, Juan

doi:10.1016/j.jsb.2013.06.008

Cited by 38 publications

(37 citation statements)

References 106 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…When possible, cryo-EM data are therefore combined with high-resolution atomic models of subunits for a proper structural understanding of the data. Typically, the first step in the modeling process is placing the subunits in the density as rigid bodies, after which the models can be refined using some flexible fitting procedure (Esquivel-Rodriguez and Kihara, 2013).…”

Section: Introductionmentioning

confidence: 99%

Defining the limits and reliability of rigid-body fitting in cryo-EM maps using multi-scale image pyramids

Zundert

Bonvin

2016

Journal of Structural Biology

View full text Add to dashboard Cite

. Defining the limits and reliability of rigid-body fitting in cryo-EM maps using multi-scale image pyramids. J. Struct. Biol., 195, 252-258 (2016).https://doi.org/10. 1016/j.jsb.2016.06.011 Defining the limits and reliability of rigid-body fitting in cryo-EM maps using multi-scale image pyramids Keywords1Cross correlation; PowerFit; Ribosome; Fisher z-transformation; Core-weighted; Modeling. AbstractCryo-electron microscopy provides fascinating structural insight into large macromolecular machines at increasing detail. Despite significant advances in the field, the resolution of the resulting three-dimensional images is still typically insufficient for de novo model building.To bridge the resolution gap and give an atomic interpretation to the data, high-resolution models are typically placed into the density as rigid bodies. Unfortunately, this is often done manually using graphics software, a subjective method that can lead to over-interpretation of the data. A more objective approach is to perform an exhaustive cross-correlation-based search to fit subunits into the density. Here we show, using five experimental ribosome maps ranging in resolution from 5.5 to 6.9Å, that cross-correlation-based fitting is capable of successfully fitting subunits correctly in the density for over 90% of the cases. Importantly, we provide indicators for the reliability and ambiguity of a fit, using the Fisher ztransformation and its associated confidence intervals, giving a formal approach to identify over-interpreted regions in the density. In addition, we quantify the resolution requirement for a successful fit as a function of the subunit size. For larger subunits the resolution of the data can be down-filtered to 20Å while still retaining an unambiguous fit. We leverage this information through the use of multi-scale image pyramids to accelerate the search up to 30-fold on CPUs and 40-fold on GPUs at a negligible loss in success rate. We implemented this approach in our rigid-body fitting software PowerFit, which can be freely downloaded from https://github.com/haddocking/powerfit.

show abstract

Section: Introductionmentioning

confidence: 99%

Defining the limits and reliability of rigid-body fitting in cryo-EM maps using multi-scale image pyramids

Zundert

Bonvin

2016

Journal of Structural Biology

View full text Add to dashboard Cite

show abstract

“…A number of large molecular complexes, such as ribosome and viruses, have been resolved to near atomic resolutions (2-5 Å ) (Anger et al, 2013;Jiang et al, 2008;Zhang et al, 2013). Many more have reached medium resolutions (5-10 Å ) (Esquivel-Rodríguez and Kihara, 2013;Lawson et al, 2011). At medium resolutions, molecular features are less resolved, and it is challenging to derive atomic structures from such density maps.…”

Section: Introductionmentioning

confidence: 99%

“…The locations of secondary structures are critical in modeling atomic structure from density maps and as overall shape descriptors in identifying similar structures (Esquivel-Rodríguez and Kihara, 2013). Although it is not possible to distinguish the amino acid at medium resolutions, secondary structures such as a helices (red lines in Figure 1B) and b sheets (blue density voxels in Figure 1B) can be identified Del Palu et al, 2006;Jiang et al, 2001;Kong and Ma, 2003;Rusu and Wriggers, 2012;Si et al, 2012;Yu and Bajaj, 2008).…”

Section: Introductionmentioning

confidence: 99%

Tracing Beta Strands Using StrandTwister from Cryo-EM Density Maps at Medium Resolutions

2014

Structure

View full text Add to dashboard Cite

Major secondary structure elements such as α helices and β sheets can be computationally detected from cryoelectron microscopy (cryo-EM) density maps with medium resolutions of 5-10 Å. However, a critical piece of information for modeling atomic structures is missing, because there are no tools to detect β strands from cryo-EM maps at medium resolutions. We propose a method, StrandTwister, to detect the traces of β strands through the analysis of twist, an intrinsic nature of a β sheet. StrandTwister has been tested using 100 β sheets simulated at 10 Å resolution and 39 β sheets computationally detected from cryo-EM density maps at 4.4-7.4 Å resolutions. Although experimentally derived cryo-EM maps contain errors, StrandTwister's best detections over 39 cases were able to detect 81.87% of the β strands, with an overall 1.66 Å two-way distance between the detected and observed β traces. StrandTwister appears to detect the traces of β strands on major β sheets quite accurately, particularly at the central area of a β sheet.

show abstract

“…While such high resolution cryo-EM structures have been achieved in a handful of cases, the vast majority of cryo-EM reconstructions achieve lower resolution, making interpretation much more challenging [7] [8] [9]. At these resolutions, the pitch of helices, separation of individual strands in β -sheets and even some bulky sidechains may be visible in the density map but often lack the detail for traditional protein structure modeling [10].…”

Section: Introductionmentioning

confidence: 99%

De Novo modeling in cryo-EM density maps with Pathwalking

Chen

Baldwin

Ludtke

et al. 2016

Journal of Structural Biology

View full text Add to dashboard Cite

As electron cryo-microscopy(cryo-EM) can now frequently achieve near atomic resolution, accurate interpretation of these density maps in terms of atomistic detail has become paramount in deciphering macromolecular structure and function. However, there are few software tools for modeling protein structure from cryo-EM density maps in this resolution range. Here, we present an extension of our original Pathwalking protocol, which can automatically trace a protein backbone directly from a near-atomic resolution (3 – 6 Å) density map. The original Pathwalking approach utilized a Traveling Salesman Problem solver for backbone tracing, but manual adjustment was still required during modeling. In the new version, human intervention is minimized and we provide a more robust approach for backbone modeling. This includes iterative secondary structure identification, termini detection and the ability to model multiple subunits without prior segmentation. Overall, the new Pathwalking procedure provides a more complete and robust tool for annotating protein structure function in near-atomic resolution density maps.

show abstract

Computational methods for constructing protein structure models from 3D electron microscopy maps

Cited by 38 publications

References 106 publications

Defining the limits and reliability of rigid-body fitting in cryo-EM maps using multi-scale image pyramids

Defining the limits and reliability of rigid-body fitting in cryo-EM maps using multi-scale image pyramids

Tracing Beta Strands Using StrandTwister from Cryo-EM Density Maps at Medium Resolutions

De Novo modeling in cryo-EM density maps with Pathwalking

Contact Info

Product

Resources

About