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Figure 10 Multiple ARIMA model rebuild times.

Figure 8 ARIMA model rebuild times.

And historical data length has no prominent influence on ARIMA model rebuild times.

We can see that the ARIMA model rebuild times decreases with the increase of error threshold.

We can draw the conclusion that error threshold is inversely proportional to ARIMA model rebuild times.

Figure 10 demonstrates the influence of error threshold and historical data length on ARIMA model rebuild times in an overall view.

Coot was used for visualization model rebuilding and PDB_REDO was used for validation and model improvement67,68.

In Table 2 at low PER levels, since rule-based model rebuilds the FA entrance sequence according to the defined rules rather than optimization, mean SSAR difference is high relative to high PER levels.

The model rebuilding and refinement were carried out with COOT [38] and refinement using REFMAC [39].

Manual model rebuilding between refinement cycles was performed using Coot [40].

This combined solution was subjected to multiple cycles of refinement in Refmac5 to 2.7 Å resolution [28], followed by model rebuilding in the program O [29].