Improving the convergence rate of a hybrid input–output phasing algorithm by varying the reflection data weight. Issue 1 (15th November 2017)

• Record Type: Journal Article
• Authors: He, Hongxing; Su, Wu-Pei
• Is Part Of: Acta crystallographica. Volume 74:Issue 1(2018:Jan.)
• Page Start: 36
• Page End: 43
• Publication Date: 2017-11-15
• Subjects: iterative projection algorithm -- hybrid input–output -- data weighting -- ab initio phasing -- protein crystallography; Crystallography -- Periodicals; Condensed matter -- Periodicals; 548
• DOI: 10.1107/S205327331701436X
• Languages: English
• ISSNs: 2053-2733

Abstract

Abstract : It is demonstrated that, by inputting the reflection data in an incremental fashion starting with low‐ and medium‐resolution reflections, the convergence rate of the hybrid input–output ab initio phasing algorithm can be significantly increased. Abstract : In an iterative projection algorithm proposed for ab initio phasing, the error metrics typically exhibit little improvement until a sharp decrease takes place as the iteration converges to the correct high‐resolution structure. Related to that is the small convergence probability for certain structures. As a remedy, a variable weighting scheme on the diffraction data is proposed. It focuses on phasing low‐ and medium‐resolution data first. The weighting shifts to incorporate more high‐resolution reflections when the iteration proceeds. It is found that the precipitous drop in error metrics is replaced by a less dramatic drop at an earlier stage of the iteration. It seems that once a good configuration is formed at medium resolution, convergence towards the correct high‐resolution structure is almost guaranteed. The original problem of phasing all diffraction data at once is reduced to a much more manageable one due to the dramatically smaller number of reflections involved. As a result, the success rate is significantly enhanced and the speed of convergence is raised. This is illustrated by applying the new algorithm to several structures, some of which are very difficult to solve without data weighting.