The AUDANA algorithm for automated protein 3D structure determination from NMR NOE data

By Woonghee Lee, C. Petit, Gabriel Cornilescu, Jaime L. Stark, John L. Markley. 2016

40 citations recorded by the source corpus.

We introduce AUDANA (Automated Database-Assisted NOE Assignment), an algorithm for determining three-dimensional structures of proteins from NMR data that automates the assignment of 3D-NOE spectra, generates distance constraints, and conducts iterative high temperature molecular dynamics and simulated annealing. The protein sequence, chemical shift assignments, and NOE spectra are the only required inputs. Distance constraints generated automatically from ambiguously assigned NOE peaks are validated during the structure calculation against information from an enlarged version of the freely available PACSY database that incorporates information on protein structures deposited in the Protein Data Bank (PDB). This approach yields robust sets of distance constraints and 3D structures. We evaluated the performance of AUDANA with input data for 14 proteins ranging in size from 6 to 25 kDa that had 27–98 % sequence identity to proteins in the database. In all cases, the automatically calculated 3D structures passed stringent validation tests. Structures were determined with and without database support. In 9/14 cases, database support improved the agreement with manually determined structures in the PDB and in 11/14 cases, database support lowered the r.m.s.d. of the family of 20 structural models.

Subfields: Materials Chemistry, Molecular Biology

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Citations in the corpus

Predicting multiple conformations via sequence clustering and AlphaFold2 (2023, positive): “Generated distance constraints from AUDANA were carefully validated using the PONDEROSA Analyzer interfaced with the PONDEROSA Connec-tor tool in the POKY and PyMOL 78 software.”
PINE-SPARKY.2 for automated NMR-based protein structure research (2017, methodology): “The Integrative NMR package (Lee et al., 2016a) offered a partial solution by integrating NMRFAMSPARKY (Lee et al., 2015) with APES for peak picking (Shin et al. 2008), PINE for automated assignment (Bahrami et al. 2009), ARECA (Dashti et al. 2016) for validation of peak assignments; TALOS-N for shi”
I-PINE web server: an integrative probabilistic NMR assignment system for proteins (2019, methodology): “In addition, the availability of 3D coordinates improves the efficiency of AUDANA (Lee et al. 2016), the database assisted NOE assignment algorithm that interfaces with NMRFAM-SPARKY and I-PINE.”