A new MR-SAD algorithm for the automatic building of protein models from low-resolution X-ray data and a poor starting model

作者：Skubak Pavol^*; Arac Demet; Bowler Matthew W; Correia Ana R; Hoelz Andre; Larsen Sine; Leonard Gordon A; McCarthy Andrew A; McSweeney Sean; Mueller Dieckmann Christoph; Otten Harm; Salzman Gabriel; Pannu Navraj S^*

来源：IUCrJ, 2018, 5(2): 166-171.

DOI：10.1107/S2052252517017961

• low resolution
• X-ray crystallography
• single-wavelength anomalous diffraction
• multivariate statistics
• model bias
• structure determination
• membrane proteins
• refinement
• multi-protein complexes

Determining macromolecular structures from X-ray data with resolution worse than 3 angstrom remains a challenge. Even if a related starting model is available, its incompleteness or its bias together with a low observation-to-parameter ratio can render the process unsuccessful or very time-consuming. Yet, many biologically important macromolecules, especially large macromolecular assemblies, membrane proteins and receptors, tend to provide crystals that diffract to low resolution. A new algorithm to tackle this problem is presented that uses a multivariate function to simultaneously exploit information from both an initial partial model and low-resolution single-wavelength anomalous diffraction data. The new approach has been used for six challenging structure determinations,including the crystal structures of membrane proteins and macromolecular complexes that have evaded experts using other methods, and large structures from a 3.0 angstrom resolution F-1-ATPase data set and a 4.5 angstrom resolution SecYEG-SecA complex data set. All of the models were automatically built by the method to Rfree values of between 28.9 and 39.9% and were free from the initial model bias.

• 出版日期2018-3