Protein NMR assignment by isotope pattern recognition

TY - JOUR

T1 - Protein NMR assignment by isotope pattern recognition

AU - Rasulov, Uluk

AU - Wang, Harrison K.

AU - Viennet, Thibault

AU - Droemer, Maxim A.

AU - Matosin, Srđan

AU - Schindler, Sebastian

AU - Sun, Zhen Yu J.

AU - Mureddu, Luca

AU - Vuister, Geerten W.

AU - Robson, Scott A.

AU - Arthanari, Haribabu

AU - Kuprov, Ilya

PY - 2024/9/6

Y1 - 2024/9/6

N2 - The current standard method for amino acid signal identification in protein NMR spectra is sequential assignment using triple-resonance experiments. Good software and elaborate heuristics exist, but the process remains laboriously manual. Machine learning does help, but its training databases need millions of samples that cover all relevant physics and every kind of instrumental artifact. In this communication, we offer a solution to this problem. We propose polyadic decompositions to store millions of simulated three-dimensional NMR spectra, on-the-fly generation of artifacts during training, a probabilistic way to incorporate prior and posterior information, and integration with the industry standard CcpNmr software framework. The resulting neural nets take [1H,13C] slices of mixed pyruvate–labeled HNCA spectra (different CA signal shapes for different residue types) and return an amino acid probability table. In combination with primary sequence information, backbones of common proteins (GB1, MBP, and INMT) are rapidly assigned from just the HNCA spectrum.

AB - The current standard method for amino acid signal identification in protein NMR spectra is sequential assignment using triple-resonance experiments. Good software and elaborate heuristics exist, but the process remains laboriously manual. Machine learning does help, but its training databases need millions of samples that cover all relevant physics and every kind of instrumental artifact. In this communication, we offer a solution to this problem. We propose polyadic decompositions to store millions of simulated three-dimensional NMR spectra, on-the-fly generation of artifacts during training, a probabilistic way to incorporate prior and posterior information, and integration with the industry standard CcpNmr software framework. The resulting neural nets take [1H,13C] slices of mixed pyruvate–labeled HNCA spectra (different CA signal shapes for different residue types) and return an amino acid probability table. In combination with primary sequence information, backbones of common proteins (GB1, MBP, and INMT) are rapidly assigned from just the HNCA spectrum.

UR - http://www.scopus.com/inward/record.url?scp=85203318074&partnerID=8YFLogxK

U2 - 10.1126/sciadv.ado0403

DO - 10.1126/sciadv.ado0403

M3 - Journal article

C2 - 39231223

AN - SCOPUS:85203318074

SN - 2375-2548

VL - 10

JO - Science Advances

JF - Science Advances

IS - 36

M1 - eado0403

ER -