Includes stereochemical, steric, nomenclature, and packing quality checks. Electron Density Server - a collection of reports on models from the PDB for which experimental crystallographic data has been deposited with a focus on the fit of the model to the data. Uses to analyze the statistics of non-bonded interactions between different atom types. Metaserver for quality assessment of protein structures optimized for theoretical models.
The refinement procedure used by the Feig group consumed 75, core hours 12 days on cores on the multicore Intel Xenon CPU machines of the day, just to refine a single 3D model for a single protein target This means that MDS refinement protocols, such as those developed by the Feig group, would be less practical to be used routinely for large scale fully automated structure prediction pipelines.
Thus, faster, more practical and, ideally, as accurate and consistent, fully automated refinement servers are required.
While many automated servers exists, few servers make use of the power of MDS and few adequately evaluate the models and present users with accurate reports of the likely improvement in errors. User feedback on when and, more specifically, where a 3D model has been improved is important to get right and it is often neglected.
We have a good track record in building 3D models 13 — 15 and estimating the likely errors they may have, recently termed Accuracy Self Estimates or ASE 16 — We have had some success at using quality assessment guided multiple template based modelling to improve upon errors in single template models 19 , but this approach requires sequence-structure alignments and template data. The ReFOLD server is our first successful attempt at developing a method for directly fixing likely errors in any user supplied 3D models, using global quality assessment guided refinement.
The ReFOLD server also equips users with the ability to identify specific domains or regions in a protein that are likely to be correctly refined, via its accurate per-residue error estimates. Input models were refined and evaluated over a number of successive stages. This iterative filtering process led to the generation of hundreds of alternative refined models, which were then ranked by quality. The first protocol simply used a rapid iterative strategy for refinement of starting models, with 20 refinement cycles iterations of i3Drefine 7.
Although the authors recommend not to run i3Drefine for more than 10 iterations, an optimal number of iterations is not specified, and often models will improve further beyond 10 iterations. Simply, all i3Drefine requires is a starting model, in PDB format, and a given number iterations as input parameters. Simulations were conducted at k under neutralized pH conditions with 1 bar of atmospheric pressure to resemble normal cellular conditions. Weak harmonic restraints with a spring constant of 0.
Only non-bonded interactions were calculated with bonded interactions excluded; the exclusion parameter was set to exclude up to four pairs of bonded atoms.
All hydrogen bonding was rigidified, using the rigidBonds functions, allowing the time step to be increased to 2 fs. PME was used to calculate full electrostatics and the temperature in the system was controlled through Langevin dynamics 24 , which balances random noise with friction to push atoms to the target temperature K. Periodic boundary conditions were used to achieve maintenance of conditions such as pressure, density and water box and also enable the use of PME, making the simulation more biologically realistic.
The first stage of the MD simulation was steps of minimization, to lower the potential energy of the system reducing bad initial contacts, high force and temperature regions.
This process zeros velocity, so that the reinitvels function returns the system to the desired temperature K. For CASP12, the second protocol was run using multiple short trajectories in place of a single long trajectory; four parallel simulations were run for 2 ns, giving a cumulative simulation time of 8 ns per target.
The third protocol was a combination of the first two approaches, where the top ranked model from the second protocol was further refined using 20 iterations of i3Drefine.
Finally, each refined model was evaluated and compared with the original starting model in terms of local and global model quality scores. Static and dynamic graphical outputs were generated using the raw QA scores in order to display the top refined models and estimated improvements in a user friendly manner.
Users may optionally provide a name for their protein sequence and their email address. The ReFOLD server results page provides users with an accurate estimate of the likely percentage improvement in their global quality score based on the top refined model Figure 1A. In addition, the server is unique in providing output for multiple alternative refined models in a way that allows users to quickly visualize the key residue locations, which are likely to have been improved upon compared to their original model.
The results page provides users with a series of per-residue error plots, which demonstrate the reduction in local errors in the refined models compared with the uploaded original Figure 1B. This is important, as global refinement of a full chain model may not always occur, whereas local regions, or individual domains, may often be much improved. Presenting results to users in this way also gives them the choice to easily compare alternative refined models, allowing them to focus their attention to key interacting residues or specific domains.
No plugins are required and, conveniently, interactive results may also be viewed on mobile devices. The full table of scores for every alternative refined model is displayed below the top hit truncated here to fit page.
Clicking on the images on the main results page allows results to be visualized in more detail and downloaded. B Histogram of the local or per-residue ModFOLD6 errors for the top refined model green bars compared with the original model. Plots for each alternative refined model may be downloaded. ReFOLD gave us a significant performance boost in the main tertiary structure prediction category, where it enabled us to further improve the quality of some of the very best initial server models.
As a result of our high performance, we were invited to speak at the meeting in Gaeta about our template based modelling TBM strategy. Arguably, this benchmark represents a more realistic user test case, where each of the starting models have been selected in a fully automated manner and have been generated for full length protein chains. The MolProbity score denotes the expected resolution with respect to experimental structures, therefore models with lower MolProbity scores are more physically realistic.
Middle panels, superposition of the top selected server model cyan , refined model magenta and native structure green. It is clear that the success of refinement is related to the quality of the starting model, when targets are subdivided into domains Supplementary Tables S3—S5.
Dividing targets into domains allows us to pinpoint where the method performance is strongest. In addition, for many of the starting server models, the developers also attempted refinement, which clearly produces a problem of diminishing returns for further refinement. Nevertheless, considering full chain models across regular targets, on average the automatically selected initial models are successfully improved upon by the ReFOLD pipeline.
The time taken to refine a model is dependent on the sequence length. Smaller models were quicker to refine e. The other components of the method, including the quality assessment, are run in parallel and will usually take no more than a few extra hours. The user friendly, dynamic results pages let users visualise potential improvements for over alternative refined models, at both a global and local level. Providing users with visual comparisons of estimated local improvement allows them to quickly identify those models, which are likely to have been improved upon in a specific region of interest.
In addition, the server provides users with a compressed archive all of the generated refined models, which they may rank using their own alternative quality assessment protocols.
Malaysian Government to A. Funding for open access charge: University of Reading; Malaysian Government. Nugent T.
Evaluation of predictions in the CASP10 model refinement category. Google Scholar. Kryshtafovych A. Protein structure prediction and model quality assessment. It was also ranked the best for function prediction in CASP9. The server is in active development with the goal to provide the most accurate protein structure and function predictions using state-of-the-art algorithms.
The server is only for non-commercial use. More explanation on how to add restraints. Exclude homologous templates Type a cutoff e. Keep my results public uncheck this box if you want to keep your job private, and a key will be assigned for you to access the results.
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