Refinement of severely incomplete structures with maximum likelihood in<i>BUSTER–TNT</i>

TLDR

BUSTER–TNT is a maximum‑likelihood macromolecular refinement package. BUSTER–TNT integrates BUSTER, which assembles the model, scales amplitudes, and computes likelihood, with TNT, which applies stereochemistry, NCS restraints, and adjusts coordinates, B factors, and occupancies, modeling missing atoms as low‑resolution probability distributions in real space and treating the structure‑factor distribution as a two‑dimensional Gaussian in reciprocal space whose spread incorporates errors from all components. When the atomic model is highly incomplete, BUSTER–TNT’s treatment of missing structure and its consistent statistical model improve scale factor accuracy, reduce refinement bias, enhance error modeling, and enable selective density modification of unbuilt regions.

Abstract

BUSTER–TNT is a maximum-likelihood macromolecular refinement package. BUSTER assembles the structural model, scales observed and calculated structure-factor amplitudes and computes the model likelihood, whilst TNT handles the stereochemistry and NCS restraints/constraints and shifts the atomic coordinates, B factors and occupancies. In real space, in addition to the traditional atomic and bulk-solvent models, BUSTER models the parts of the structure for which an atomic model is not yet available (`missing structure') as low-resolution probability distributions for the random positions of the missing atoms. In reciprocal space, the BUSTER structure-factor distribution in the complex plane is a two-dimensional Gaussian centred around the structure factor calculated from the atomic, bulk-solvent and missing-structure models. The errors associated with these three structural components are added to compute the overall spread of the Gaussian. When the atomic model is very incomplete, modelling of the missing structure and the consistency of the BUSTER statistical model help structure building and completion because (i) the accuracy of the overall scale factors is increased, (ii) the bias affecting atomic model refinement is reduced by accounting for some of the scattering from the missing structure, (iii) the addition of a spatial definition to the source of incompleteness improves on traditional Luzzati and σA-based error models and (iv) the program can perform selective density modification in the regions of unbuilt structure alone.