TY - JOUR
T1 - Phasertng
T2 - Directed acyclic graphs for crystallographic phasing
AU - McCoy, Airlie J.
AU - Stockwell, Duncan H.
AU - Sammito, Massimo D.
AU - Oeffner, Robert D.
AU - Hatti, Kaushik S.
AU - Croll, Tristan I.
AU - Read, Randy J.
N1 - Funding Information:
The following funding is acknowledged: Wellcome Trust Principal Research Fellowship (grant No. 209407/Z/17/Z to RJR); NIH (grant No. P01GM063210 to RJR). MDS gratefully acknowledges fellowship support from the European Union’s Horizon 2020 research and innovation program under the Marie Skłodowska-Curie grant (number 790122). KSH was supported by funding from CCP4.
Publisher Copyright:
© 2021.
Copyright:
Copyright 2021 Elsevier B.V., All rights reserved.
PY - 2021/1/1
Y1 - 2021/1/1
N2 - Crystallographic phasing strategies increasingly require the exploration and ranking of many hypotheses about the number, types and positions of atoms, molecules and/or molecular fragments in the unit cell, each with only a small chance of being correct. Accelerating this move has been improvements in phasing methods, which are now able to extract phase information from the placement of very small fragments of structure, from weak experimental phasing signal or from combinations of molecular replacement and experimental phasing information. Describing phasing in terms of a directed acyclic graph allows graph-management software to track and manage the path to structure solution. The crystallographic software supporting the graph data structure must be strictly modular so that nodes in the graph are efficiently generated by the encapsulated functionality. To this end, the development of new software, Phasertng, which uses directed acyclic graphs natively for input/output, has been initiated. In Phasertng, the codebase of Phaser has been rebuilt, with an emphasis on modularity, on scripting, on speed and on continuing algorithm development. As a first application of phasertng, its advantages are demonstrated in the context of phasertng.xtricorder, a tool to analyse and triage merged data in preparation for molecular replacement or experimental phasing. The description of the phasing strategy with directed acyclic graphs is a generalization that extends beyond the functionality of Phasertng, as it can incorporate results from bioinformatics and other crystallographic tools, and will facilitate multifaceted search strategies, dynamic ranking of alternative search pathways and the exploitation of machine learning to further improve phasing strategies.
AB - Crystallographic phasing strategies increasingly require the exploration and ranking of many hypotheses about the number, types and positions of atoms, molecules and/or molecular fragments in the unit cell, each with only a small chance of being correct. Accelerating this move has been improvements in phasing methods, which are now able to extract phase information from the placement of very small fragments of structure, from weak experimental phasing signal or from combinations of molecular replacement and experimental phasing information. Describing phasing in terms of a directed acyclic graph allows graph-management software to track and manage the path to structure solution. The crystallographic software supporting the graph data structure must be strictly modular so that nodes in the graph are efficiently generated by the encapsulated functionality. To this end, the development of new software, Phasertng, which uses directed acyclic graphs natively for input/output, has been initiated. In Phasertng, the codebase of Phaser has been rebuilt, with an emphasis on modularity, on scripting, on speed and on continuing algorithm development. As a first application of phasertng, its advantages are demonstrated in the context of phasertng.xtricorder, a tool to analyse and triage merged data in preparation for molecular replacement or experimental phasing. The description of the phasing strategy with directed acyclic graphs is a generalization that extends beyond the functionality of Phasertng, as it can incorporate results from bioinformatics and other crystallographic tools, and will facilitate multifaceted search strategies, dynamic ranking of alternative search pathways and the exploitation of machine learning to further improve phasing strategies.
KW - directed acyclic graphs
KW - machine learning
KW - maximum likelihood
KW - molecular replacement
KW - Phaser
KW - Phasertng
KW - SAD phasing
UR - http://www.scopus.com/inward/record.url?scp=85099113229&partnerID=8YFLogxK
U2 - 10.1107/S2059798320014746
DO - 10.1107/S2059798320014746
M3 - Article
C2 - 33404520
AN - SCOPUS:85099113229
SN - 2059-7983
VL - 77
SP - 1
EP - 10
JO - Acta Crystallographica Section D: Structural Biology
JF - Acta Crystallographica Section D: Structural Biology
IS - Part 1
ER -