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All models were scored for energy and sterical correctness using the ANOLEA [45], VERIFY_3D and ERRAT (http://nihserver.mbi.ucla.edu) online servers.
Initial models were scored for energy content and sterical correctness and the best model further optimized using GROMACS molecular dynamics simulations [44] was used.
The obtained molecular models were scored again and grouped thereafter into clusters using an algorithm described by Daura et al., [73], based on a 7.5 Å cut-off.
Finally, the models were scored according to the number of cross-links violating the 30 Å cutoff distance.
These models were scored by Rosetta, and the top models were relaxed iteratively (see Figure 2 and Figures S3 and S4 of the Supporting Information).
All models were scored according to how well they fit with the sentinel data, adopting a likelihood-based approach by using the Akaike information criterion.
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The fragments are edited and superimposed using common residues, full atomic models are scored using RAGTOP's knowledge-based potential, and geometries of top scoring models is optimized.
Models are scored based on the entire training set and not only on the drawn sub-sample used for their construction.
Next, the all-atom models are scored by our in-house implementation of DFIRE-AA [35].
The predicted models are scored with a statistical potential and an all-atom force field.
Transcription of the annotated gene models was scored using a previously described method [24] and the results are shown in Figure S1.
Related(20)
predictions were scored
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models were analyzed
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models were sent
models were interspersed
models were cut
models were discontinued
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