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The eight C-alpha graph differences between 2K39 and 6V5D do not occur at four tested heavy-atom cutoffs.

We tested whether eight C-alpha majority-graph differences between ubiquitin coordinate ensembles 2K39 and 6V5D persist under C-beta, all-heavy-atom, and side-chain-heavy-atom contact rules. We used all 116 models of 2K39 (revision 1.3) and all 176 models of 6V5D (revision 1.2), chain A author residues 1–70, and residue separation >2. At the original 8 Å C-alpha rule, the graphs differed at eight pairs. At C-beta <8 Å, one of those differences remained (8–68), depending on the inclusive half-count majority rule. None of the eight differed under either all-heavy-atom or side-chain-heavy-atom rules at any of four tested cutoffs (3.5, 4.0, 4.5, and 5.0 Å). Those graphs still differed at other pairs. The analysis reports results for supplied snapshots and discrete rules; it does not identify a physically correct contact definition, establish a more accurate ensemble, or separate atom-set effects from the different cutoffs. The ensembles are related, and their models and residue pairs are dependent. No independent-sample confidence interval or p-value is justified.

ubiquitin · protein structure · contact graph · NMR ensemble · C-alpha · side-chain contacts · distance cutoff

Admitted 2 citing publications

References

5
  1. Deposited-model and cutoff sensitivity of ubiquitin Cα contact maps sci:sha256:48903fcc23599e9ca1df58d55fee49705b5ce8c4dd81ebe1cf50a5d6abb4ea7d

    [Legacy relation: reproduces] The new code reproduces all 4,556 primary C-alpha contact counts and 13,668 minimum, median, and maximum distance summaries for 2K39 and 6V5D. The scope is chain A, residues 1–70, separation >2, strict 8 Å distance, and an inclusive half-count majority. The prior CSV and matching source hashes enable this scoped check and identify the eight pairs for the new atom-definition audit. This check excludes other entries, the full prior cutoff grid, alignment results, and experiment fits.

  2. doi:10.1126/science.1157092
  3. doi:10.1007/s10858-019-00288-8
  4. doi:10.1093/bioinformatics%2Fbtn069
  5. doi:10.1038/s41598-017-01498-6

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