Phasertng: directed acyclic graphs for crystallographic phasing.

Phaser Phasertng SAD phasing directed acyclic graphs machine learning maximum likelihood molecular replacement

Journal

Acta crystallographica. Section D, Structural biology
ISSN: 2059-7983
Titre abrégé: Acta Crystallogr D Struct Biol
Pays: United States
ID NLM: 101676043

Informations de publication

Date de publication:
01 Jan 2021
Historique:
received: 16 01 2020
accepted: 06 11 2020
entrez: 6 1 2021
pubmed: 7 1 2021
medline: 19 8 2021
Statut: ppublish

Résumé

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.

Identifiants

pubmed: 33404520
pii: S2059798320014746
doi: 10.1107/S2059798320014746
pmc: PMC7787104
doi:

Substances chimiques

Proteins 0

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

1-10

Subventions

Organisme : NIGMS NIH HHS
ID : P01 GM063210
Pays : United States
Organisme : Wellcome Trust
ID : 209407/Z/17/Z
Pays : United Kingdom
Organisme : Wellcome Trust
Pays : United Kingdom
Organisme : NIH HHS
ID : P01GM063210
Pays : United States

Informations de copyright

open access.

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Auteurs

Airlie J McCoy (AJ)

Department of Haematology, Cambridge Institute for Medical Research, University of Cambridge, Hills Road, Cambridge CB2 0XY, United Kingdom.

Duncan H Stockwell (DH)

Department of Haematology, Cambridge Institute for Medical Research, University of Cambridge, Hills Road, Cambridge CB2 0XY, United Kingdom.

Massimo D Sammito (MD)

Department of Haematology, Cambridge Institute for Medical Research, University of Cambridge, Hills Road, Cambridge CB2 0XY, United Kingdom.

Robert D Oeffner (RD)

Department of Haematology, Cambridge Institute for Medical Research, University of Cambridge, Hills Road, Cambridge CB2 0XY, United Kingdom.

Kaushik S Hatti (KS)

Department of Haematology, Cambridge Institute for Medical Research, University of Cambridge, Hills Road, Cambridge CB2 0XY, United Kingdom.

Tristan I Croll (TI)

Department of Haematology, Cambridge Institute for Medical Research, University of Cambridge, Hills Road, Cambridge CB2 0XY, United Kingdom.

Randy J Read (RJ)

Department of Haematology, Cambridge Institute for Medical Research, University of Cambridge, Hills Road, Cambridge CB2 0XY, United Kingdom.

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