In later rounds of refinement, the model was refined with amplitude based twin refinement.
This model was refined by real-space refinement (phenix.real_space_refine) in Phenix (Adams et al., 2010), with stereochemical and secondary structure constraints applied.
Each model was refined through iterative cycles of refinement with PHENIX and manual model rebuilding using COOT.
The model was refined with Refmac5 [75] using restrained maximum-likelihood refinement and TLS refinement [79].
The model was refined by REFMAC5 in CCP4 or the Refinement module in PHENIX.
The model was refined in stages by CNS 1.1; the refinement parameters are included in Table 1.
The model was refined using gradient minimization with weight optimization and maximum-likelihood targets, TLS refinement, and individual atomic B-factor refinement.
The final model was refined in real space and displayed good geometry (Table S2).
The model was refined using REFMAC5 (Murshudov et al. 1997) and CNS (Brünger et al. 1998).
The cooling tower numerical model was refined and validated with the experimental data.
The model was refined through interviews with participating landholders and other key stakeholders and, finally, parameterised using expert-elicited information.
