Biomedical subjects

Sjors H W Scheres

Publications and source records attributed to Sjors H W Scheres.

6 recordsLinked to original sources

PubMed2005

Fast maximum-likelihood refinement of electron microscopy images.

MOTIVATION: Maximum-likelihood (ML) image refinement is a promising candidate to improve attainable resolution limits in 3D-EM. However, its large CPU requirements may prohibit application to 3D-structure optimization. RESULTS: We speeded up ML image refinement by reducing its search space over the alignment parameters. Application of this reduced-search approach to a cryo-EM dataset yielded practically identical results as the original approach, but in approximately one day instead of one week of CPU. AVAILABILITY: This work has been implemented in the public domain package Xmipp. Documentation and download instructions may be found at: http://www.cnb.uam.es/~bioinfo

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PubMed2005

Maximum-likelihood multi-reference refinement for electron microscopy images.

A maximum-likelihood approach to multi-reference image refinement is presented. In contrast to conventional cross-correlation refinement, the new approach includes a formal description of the noise, implying that it is especially suited to cases with low signal-to-noise ratios. Application of this approach to a cryo-electron microscopy dataset revealed two major classes for projections of simian virus 40 large T-antigen in complex with an asymmetric DNA-probe, containing the origin of simian virus 40 replication. Strongly bent projections of dodecamers showed density that may be attributed to the complexed double-stranded DNA, while almost straight projections revealed a twist in the relative orientation of the hexameric subunits. This new level of detail for large T-antigen projections was not detected using conventional techniques. For a negative stain dataset, maximum-likelihood refinement yielded results that were practically identical to those obtained using conventional multi-reference refinement. Results obtained using simulated data suggest that the efficiency of the maximum-likelihood approach may be further enhanced by explicitly incorporating the microscope contrast transfer function in the image formation model.

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PubMed2005

SPI-EM: towards a tool for predicting CATH superfamilies in 3D-EM maps.

In this paper the theoretical framework used to build a superfamily probability in electron microscopy (SPI-EM) is presented. SPI-EM is a new tool for determining the homologous superfamily to which a protein domain belongs looking at its three-dimensional electron microscopy map. The homologous superfamily is assigned according to the domain-architecture database CATH. Our method follows a probabilistic approach applied to the results of fitting protein domains into maps of proteins and the computation of local cross-correlation coefficient measures. The method has been tested and its usefulness proven with isolated domains at a resolution of 8 A and 12 A. Results obtained with simulated and experimental data at 10 A suggest that it is also feasible to detect the correct superfamily of the domains when dealing with electron microscopy maps containing multi-domain proteins. The inherent difficulties and limitations that multi-domain proteins impose are discussed. Our procedure is complementary to other techniques existing in the field to detect structural elements in electron microscopy maps like alpha-helices and beta-sheets. Based on the proposed methodology, a database of relevant distributions is being built to serve the community.

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PubMed2005

Classification of single-projection reconstructions for cryo-electron microscopy data of icosahedral viruses.

We present a novel strategy for classification of heterogeneous electron microscopy data of icosahedral virus particles. The effectiveness of the procedure, which is based on classification of single-projection reconstructions (SPRs), is first investigated using simulated data. Of several reconstruction approaches examined, best results were obtained with algebraic reconstruction techniques (ART) when providing prior information about the reconstruction in the form of a starting volume. The results presented indicate that SPR-classification is sufficiently sensitive to classify assemblies with differences of only a few percent of the total mass. The usefulness of this procedure is illustrated by application to a heterogeneous cryo-electron microscopy dataset of adenovirus mutant dl313, lacking minor coat protein IX. These data were successfully divided into two distinct classes, in agreement with gel analysis and immuno-electron microscopy results. The classes yielded a wildtype-like reconstruction and a reconstruction representing the polypeptide IX-deficient dl313 virion. As the largest difference between these volumes is found at the location previously assigned to the external portion of minor coat protein polypeptide IIIa, questions arise concerning the current adenovirus model.

Adenoviruses, Human↗

PubMed2004

The potentials of conditional optimization in phasing and model building of protein crystal structures.

Model building is a pivotal step in protein-structure determination, because with an atomic model available the vast amount of geometrical prior knowledge may be applied to the structure-determination process. Here, conditional optimization, a method that does not require interpretation of the electron-density map, is described. Instead, this method refines loose atoms for which all chemical interpretations are considered simultaneously using an N-particle formalism. This method bears the potential of introducing the geometrical data much earlier in the structure-determination process, i.e. well before an interpretable electron-density map has been obtained. Here, results from two tests are presented: automated model building of three proteins with diffraction data extending to 2.4-3.0 A resolution and ab initio phasing of a small four-helical bundle with diffraction data to 2.0 A resolution. Models built automatically by the widely used programs ARP/wARP and RESOLVE and those from conditional optimization per se, without discrete modelling steps, had comparable phase quality and completeness, except in loop regions, which are poorly modelled by the current force field in conditional optimization. Optimization of multiple random starting models by conditional optimization yielded models revealing the four helices of the four-helical bundle.

Bacterial Outer Membrane Proteins↗

PubMed2003

Development of a force field for conditional optimization of protein structures.

Conditional optimization allows the incorporation of extensive geometrical information in protein structure refinement, without the requirement of an explicit chemical assignment of the individual atoms. Here, a mean-force potential for the conditional optimization of protein structures is presented that expresses knowledge of common protein conformations in terms of interatomic distances, torsion angles and numbers of neighbouring atoms. information is included for protein fragments up to several residues long in alpha-helical, beta-strand and loop conformations, comprising the main chain and side chains up to the gamma position in three distinct rotamers. Using this parameter set, conditional optimization of three small protein structures against 2.0 A observed diffraction data shows a large radius of convergence, validating the presented force field and illustrating the feasibility of the approach. The generally applicable force field allows the development of novel phase-improvement procedures using the conditional optimization technique.

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