CaltechAUTHORS
Published November 11, 2022 | Submitted
Discussion Paper Open

NUPACK: Analysis and Design of Nucleic Acid Structures, Devices, and Systems

Fornace, Mark E. ORCID icon
Huang, Jining ORCID icon
Newman, Cody T.
Porubsky, Nicholas J. ORCID icon
Pierce, Marshall B.
Pierce, Niles A. ORCID icon

Abstract

NUPACK is a growing software suite for the analysis and design of nucleic acid structures, devices, and systems serving the needs of researchers in the fields of nucleic acid nanotechnology, molecular programming, synthetic biology, and across the life sciences. NUPACK algorithms are unique in treating complex and test tube ensembles containing arbitrary numbers of interacting strand species, providing crucial tools for capturing concentration effects essential to analyzing and designing the intermolecular interactions that are a hallmark of these fields. The all-new NUPACK web app (nupack.org) has been re-architected for the cloud, leveraging a cluster that scales dynamically in response to user demand to enable rapid job submission and result inspection even at times of peak user demand. The web app exploits the all-new NUPACK 4 scientific code base as its backend, offering enhanced physical models (coaxial and dangle stacking subensembles), dramatic speedups (20-120x for test tube analysis), and increased scalability for large complexes. NUPACK 4 algorithms can also be run locally using the all-new NUPACK Python module.

Additional Information

The content is available under CC BY NC ND 4.0 License. We thank all the NUPACK users that have helped out as alpha and beta testers over the years, as well as the many NUPACK users that have emailed support@nupack.org to request features or report bugs. This work was supported by the National Science Foundation (Software Elements NSF-OAC-1835414, INSPIRE NSF-CHE-1643606, Molecular Programming Project NSF-CCF-1317694, XSEDE NSF-ACI-1548562), by the National Institutes of Health (National Research Service Award T32 GM007616), by a Schmidt Academy Scholarship, by the Caltech cloud credits program from Amazon Web Services, and by the Programmable Molecular Technology Center (PMTC) within the Beckman Institute at Caltech. The authors declare the following competing financial interest(s): a patent.

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