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Solving structures of protein complexes by molecular replacement with Phaser.

McCoy AJ - Acta Crystallogr. D Biol. Crystallogr. (2006)

Bottom Line: Maximum-likelihood MR functions enable complex asymmetric units to be built up from individual components with a ;tree search with pruning' approach.These include cases where there are a large number of copies of the same component in the asymmetric unit or where the components of the asymmetric unit have greatly varying B factors.Two case studies are presented to illustrate how Phaser can be used to best advantage in the standard ;automated MR' mode and two case studies are used to show how to modify the automated search strategy for problematic cases.

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Affiliation: University of Cambridge, Department of Haematology, Cambridge Institute for Medical Research, Wellcome Trust/MRC Building, Hills Road, Cambridge CB2 2XY, England. ajm201@cam.ac.uk

ABSTRACT
Molecular replacement (MR) generally becomes more difficult as the number of components in the asymmetric unit requiring separate MR models (i.e. the dimensionality of the search) increases. When the proportion of the total scattering contributed by each search component is small, the signal in the search for each component in isolation is weak or non-existent. Maximum-likelihood MR functions enable complex asymmetric units to be built up from individual components with a ;tree search with pruning' approach. This method, as implemented in the automated search procedure of the program Phaser, has been very successful in solving many previously intractable MR problems. However, there are a number of cases in which the automated search procedure of Phaser is suboptimal or encounters difficulties. These include cases where there are a large number of copies of the same component in the asymmetric unit or where the components of the asymmetric unit have greatly varying B factors. Two case studies are presented to illustrate how Phaser can be used to best advantage in the standard ;automated MR' mode and two case studies are used to show how to modify the automated search strategy for problematic cases.

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Tree search with pruning MR search strategy for a crystal with four search components in the asymmetric unit. Row 1 represents the results of the search for the first component, where seven of eight solutions meet the selection criteria. Row 2 represents the results from the search for the second component. The search is performed using the seven possible placements for the first component as the background for seven separate searches for the second component. 13 of the 22 results of the seven searches that do not meet the selection criteria are pruned from the search tree. At the end of this step, two of the four components have been placed in nine potential solutions. Row 3 represents the results from the search for the third component. As the percentage of the total scattering being modelled increases so does the signal-to-noise ratio of the search and there is better discrimination of the best solution in this step, where 17 of 23 branches are pruned. Row 4 represents the results of searching for the fourth and final component. The correct solution, which includes placements for all four components, stands out well above the noise. The history of this solution can be traced through the search tree (shown in black)
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fig3: Tree search with pruning MR search strategy for a crystal with four search components in the asymmetric unit. Row 1 represents the results of the search for the first component, where seven of eight solutions meet the selection criteria. Row 2 represents the results from the search for the second component. The search is performed using the seven possible placements for the first component as the background for seven separate searches for the second component. 13 of the 22 results of the seven searches that do not meet the selection criteria are pruned from the search tree. At the end of this step, two of the four components have been placed in nine potential solutions. Row 3 represents the results from the search for the third component. As the percentage of the total scattering being modelled increases so does the signal-to-noise ratio of the search and there is better discrimination of the best solution in this step, where 17 of 23 branches are pruned. Row 4 represents the results of searching for the fourth and final component. The correct solution, which includes placements for all four components, stands out well above the noise. The history of this solution can be traced through the search tree (shown in black)

Mentions: Maximum-likelihood rotation and translation functions can include partial structure information. Partial structure information increases the signal-to-noise ratio of the search for the second and subsequent components of the asymmetric unit and enables a ‘tree search with pruning’ search strategy (flow diagram shown in Fig. 3 ▶). In this strategy, all potential placements for the first component are used as the ‘background’ for the search for the second component, branching the search at each of these first component placements. Placing the second molecule correctly increases the signal of the correct placement (of the two components together) and so the correct (combined) placement will be high in the list of potential placements. The lowest placements can thus be pruned away without losing the correct placement. This process is repeated for as many components as are present. Ideally, at the end of the search strategy there will be a single branch (solution) with high signal-to-noise ratio containing placements for all the components. By default, Phaser prunes away solutions that have a log-likelihood gain that is less than 75% of the value of the difference between the highest log-likelihood gain and the mean log-likelihood gain (other selection criteria, using Z scores or saving a defined number of solutions, are also possible).


Solving structures of protein complexes by molecular replacement with Phaser.

McCoy AJ - Acta Crystallogr. D Biol. Crystallogr. (2006)

Tree search with pruning MR search strategy for a crystal with four search components in the asymmetric unit. Row 1 represents the results of the search for the first component, where seven of eight solutions meet the selection criteria. Row 2 represents the results from the search for the second component. The search is performed using the seven possible placements for the first component as the background for seven separate searches for the second component. 13 of the 22 results of the seven searches that do not meet the selection criteria are pruned from the search tree. At the end of this step, two of the four components have been placed in nine potential solutions. Row 3 represents the results from the search for the third component. As the percentage of the total scattering being modelled increases so does the signal-to-noise ratio of the search and there is better discrimination of the best solution in this step, where 17 of 23 branches are pruned. Row 4 represents the results of searching for the fourth and final component. The correct solution, which includes placements for all four components, stands out well above the noise. The history of this solution can be traced through the search tree (shown in black)
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2483468&req=5

fig3: Tree search with pruning MR search strategy for a crystal with four search components in the asymmetric unit. Row 1 represents the results of the search for the first component, where seven of eight solutions meet the selection criteria. Row 2 represents the results from the search for the second component. The search is performed using the seven possible placements for the first component as the background for seven separate searches for the second component. 13 of the 22 results of the seven searches that do not meet the selection criteria are pruned from the search tree. At the end of this step, two of the four components have been placed in nine potential solutions. Row 3 represents the results from the search for the third component. As the percentage of the total scattering being modelled increases so does the signal-to-noise ratio of the search and there is better discrimination of the best solution in this step, where 17 of 23 branches are pruned. Row 4 represents the results of searching for the fourth and final component. The correct solution, which includes placements for all four components, stands out well above the noise. The history of this solution can be traced through the search tree (shown in black)
Mentions: Maximum-likelihood rotation and translation functions can include partial structure information. Partial structure information increases the signal-to-noise ratio of the search for the second and subsequent components of the asymmetric unit and enables a ‘tree search with pruning’ search strategy (flow diagram shown in Fig. 3 ▶). In this strategy, all potential placements for the first component are used as the ‘background’ for the search for the second component, branching the search at each of these first component placements. Placing the second molecule correctly increases the signal of the correct placement (of the two components together) and so the correct (combined) placement will be high in the list of potential placements. The lowest placements can thus be pruned away without losing the correct placement. This process is repeated for as many components as are present. Ideally, at the end of the search strategy there will be a single branch (solution) with high signal-to-noise ratio containing placements for all the components. By default, Phaser prunes away solutions that have a log-likelihood gain that is less than 75% of the value of the difference between the highest log-likelihood gain and the mean log-likelihood gain (other selection criteria, using Z scores or saving a defined number of solutions, are also possible).

Bottom Line: Maximum-likelihood MR functions enable complex asymmetric units to be built up from individual components with a ;tree search with pruning' approach.These include cases where there are a large number of copies of the same component in the asymmetric unit or where the components of the asymmetric unit have greatly varying B factors.Two case studies are presented to illustrate how Phaser can be used to best advantage in the standard ;automated MR' mode and two case studies are used to show how to modify the automated search strategy for problematic cases.

View Article: PubMed Central - HTML - PubMed

Affiliation: University of Cambridge, Department of Haematology, Cambridge Institute for Medical Research, Wellcome Trust/MRC Building, Hills Road, Cambridge CB2 2XY, England. ajm201@cam.ac.uk

ABSTRACT
Molecular replacement (MR) generally becomes more difficult as the number of components in the asymmetric unit requiring separate MR models (i.e. the dimensionality of the search) increases. When the proportion of the total scattering contributed by each search component is small, the signal in the search for each component in isolation is weak or non-existent. Maximum-likelihood MR functions enable complex asymmetric units to be built up from individual components with a ;tree search with pruning' approach. This method, as implemented in the automated search procedure of the program Phaser, has been very successful in solving many previously intractable MR problems. However, there are a number of cases in which the automated search procedure of Phaser is suboptimal or encounters difficulties. These include cases where there are a large number of copies of the same component in the asymmetric unit or where the components of the asymmetric unit have greatly varying B factors. Two case studies are presented to illustrate how Phaser can be used to best advantage in the standard ;automated MR' mode and two case studies are used to show how to modify the automated search strategy for problematic cases.

Show MeSH