McNicholas S, Croll T, Burnley T, Palmer CM, Hoh SW, Jenkins HT, Dodson E, Cowtan K, Agirre J. Automating tasks in protein structure determination with the
clipper python module.
Protein Sci 2018;
27:207-216. [PMID:
28901669 PMCID:
PMC5734304 DOI:
10.1002/pro.3299]
[Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2017] [Revised: 09/08/2017] [Accepted: 09/11/2017] [Indexed: 11/06/2022]
Abstract
Scripting programming languages provide the fastest means of prototyping complex functionality. Those with a syntax and grammar resembling human language also greatly enhance the maintainability of the produced source code. Furthermore, the combination of a powerful, machine-independent scripting language with binary libraries tailored for each computer architecture allows programs to break free from the tight boundaries of efficiency traditionally associated with scripts. In the present work, we describe how an efficient C++ crystallographic library such as Clipper can be wrapped, adapted and generalized for use in both crystallographic and electron cryo-microscopy applications, scripted with the Python language. We shall also place an emphasis on best practices in automation, illustrating how this can be achieved with this new Python module.
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