Improving IDP theoretical chemical shift accuracy and
efficiency through a combined MD/ADMA/DFT and machine learning
approach

J 2022

Improving IDP theoretical chemical shift accuracy and efficiency through a combined MD/ADMA/DFT and machine learning approach

BAKKER, Michael J., Arnošt MLÁDEK, Hugo SEMRÁD, Vojtěch ZAPLETAL, Jana PAVLÍKOVÁ PŘECECHTĚLOVÁ et. al.

Základní údaje

Originální název

Improving IDP theoretical chemical shift accuracy and efficiency through a combined MD/ADMA/DFT and machine learning approach

Autoři

BAKKER, Michael J., Arnošt MLÁDEK, Hugo SEMRÁD (203 Česká republika, domácí), Vojtěch ZAPLETAL a Jana PAVLÍKOVÁ PŘECECHTĚLOVÁ (garant)

Vydání

Physical Chemistry Chemical Physics, Royal Society of Chemistry, 2022, 1463-9076

Další údaje

Jazyk

angličtina

Typ výsledku

Článek v odborném periodiku

Obor

10403 Physical chemistry

Stát vydavatele

Velká Británie a Severní Irsko

Utajení

není předmětem státního či obchodního tajemství

Odkazy

URL

Impakt faktor

Impact factor: 3.300

Kód RIV

RIV/00216224:14310/22:00127220

Organizační jednotka

Přírodovědecká fakulta

DOI

http://dx.doi.org/10.1039/D2CP01638A

UT WoS

000885111900001

Klíčová slova česky

NMR spectroscopy; chemical shift; disordered protein; cluster analysis; optimization in normal modes; molecular dynamics; protein fragmentation; adjustable density matrix assembler; density functional theory; multi-scale modelling

Klíčová slova anglicky

Štítky

rivok

Příznaky

Mezinárodní význam, Recenzováno

Změněno: 1. 12. 2022 15:07, Mgr. Hugo Semrád, Ph.D.

Anotace

V originále

This work extends the multi-scale computational scheme for the quantum mechanics (QM) calculations of Nuclear Magnetic Resonance (NMR) chemical shifts (CSs) in proteins that lack a well-defined 3D structure. The scheme couples the sampling of an intrinsically disordered protein (IDP) by classical molecular dynamics (MD) with protein fragmentation using the adjustable density matrix assembler (ADMA) and density functional theory (DFT) calculations. In contrast to our early investigation on IDPs (Pavlíková Přecechtělová et al., J. Chem. Theory Comput., 2019, 15, 5642–5658) and the state-of-the art NMR calculations for structured proteins, a partial re-optimization was implemented on the raw MD geometries in vibrational normal mode coordinates to enhance the accuracy of the MD/ADMA/DFT computational scheme. In addition, machine-learning based cluster analysis was performed on the scheme to explore its potential in producing protein structure ensembles (CLUSTER ensembles) that yield accurate CSs at a reduced computational cost. The performance of the cluster-based calculations is validated against results obtained with conventional structural ensembles consisting of MD snapshots extracted from the MD trajectory at regular time intervals (REGULAR ensembles). CS calculations performed with the refined MD/ADMA/DFT framework employed the 6-311++G(d,p) basis set that outperformed IGLO-III calculations with the same density functional approximation (B3LYP) and both explicit and implicit solvation. The partial geometry optimization did not universally improve the agreement of computed CSs with the experiment but substantially decreased errors associated with the ensemble averaging. A CLUSTER ensemble with 50 structures yielded ensemble averages close to those obtained with a REGULAR ensemble consisting of 500 MD frames. The cluster based calculations thus required only a fraction of the computational time.

Návaznosti

LM2018140, projekt VaV

Název: e-Infrastruktura CZ (Akronym: e-INFRA CZ)

Investor: Ministerstvo školství, mládeže a tělovýchovy ČR, e-Infrastruktura CZ

Citovat

BAKKER, Michael J., Arnošt MLÁDEK, Hugo SEMRÁD, Vojtěch ZAPLETAL a Jana PAVLÍKOVÁ PŘECECHTĚLOVÁ. Improving IDP theoretical chemical shift accuracy and efficiency through a combined MD/ADMA/DFT and machine learning approach. Physical Chemistry Chemical Physics. Royal Society of Chemistry, 2022, roč. 24, č. 45, s. 27678-27692. ISSN 1463-9076. Dostupné z: https://dx.doi.org/10.1039/D2CP01638A.

@article{2233433,
   author = {Bakker, Michael J. and Mládek, Arnošt and Semrád, Hugo and Zapletal, Vojtěch and Pavlíková Přecechtělová, Jana},
   article_number = {45},
   doi = {http://dx.doi.org/10.1039/D2CP01638A},
   keywords = {NMR spectroscopy; chemical shift; disordered protein; cluster analysis; optimization in normal modes; molecular dynamics; protein fragmentation; adjustable density matrix assembler; density functional theory; multi-scale modelling},
   language = {eng},
   issn = {1463-9076},
   journal = {Physical Chemistry Chemical Physics},
   title = {Improving IDP theoretical chemical shift accuracy and efficiency through a combined MD/ADMA/DFT and machine learning approach},
   url = {https://pubs.rsc.org/en/content/articlelanding/2022/CP/D2CP01638A},
   volume = {24},
   year = {2022}
}

TY  - JOUR
ID  - 2233433
AU  - Bakker, Michael J. - Mládek, Arnošt - Semrád, Hugo - Zapletal, Vojtěch - Pavlíková Přecechtělová, Jana
PY  - 2022
TI  - Improving IDP theoretical chemical shift accuracy and efficiency through a combined MD/ADMA/DFT and machine learning approach
JF  - Physical Chemistry Chemical Physics
VL  - 24
IS  - 45
SP  - 27678-27692
EP  - 27678-27692
PB  - Royal Society of Chemistry
SN  - 14639076
KW  - NMR spectroscopy
KW  - chemical shift
KW  - disordered protein
KW  - cluster analysis
KW  - optimization in normal modes
KW  - molecular dynamics
KW  - protein fragmentation
KW  - adjustable density matrix assembler
KW  - density functional theory
KW  - multi-scale modelling
UR  - https://pubs.rsc.org/en/content/articlelanding/2022/CP/D2CP01638A
N2  - This work extends the multi-scale computational scheme for the quantum mechanics (QM) calculations of Nuclear Magnetic Resonance (NMR) chemical shifts (CSs) in proteins that lack a well-defined 3D structure. The scheme couples the sampling of an intrinsically disordered protein (IDP) by classical molecular dynamics (MD) with protein fragmentation using the adjustable density matrix assembler (ADMA) and density functional theory (DFT) calculations. In contrast to our early investigation on IDPs (Pavlíková Přecechtělová et al., J. Chem. Theory Comput., 2019, 15, 5642–5658) and the state-of-the art NMR calculations for structured proteins, a partial re-optimization was implemented on the raw MD geometries in vibrational normal mode coordinates to enhance the accuracy of the MD/ADMA/DFT computational scheme. In addition, machine-learning based cluster analysis was performed on the scheme to explore its potential in producing protein structure ensembles (CLUSTER ensembles) that yield accurate CSs at a reduced computational cost. The performance of the cluster-based calculations is validated against results obtained with conventional structural ensembles consisting of MD snapshots extracted from the MD trajectory at regular time intervals (REGULAR ensembles). CS calculations performed with the refined MD/ADMA/DFT framework employed the 6-311++G(d,p) basis set that outperformed IGLO-III calculations with the same density functional approximation (B3LYP) and both explicit and implicit solvation. The partial geometry optimization did not universally improve the agreement of computed CSs with the experiment but substantially decreased errors associated with the ensemble averaging. A CLUSTER ensemble with 50 structures yielded ensemble averages close to those obtained with a REGULAR ensemble consisting of 500 MD frames. The cluster based calculations thus required only a fraction of the computational time.
ER  -

BAKKER, Michael J., Arnošt MLÁDEK, Hugo SEMRÁD, Vojtěch ZAPLETAL a Jana PAVLÍKOVÁ PŘECECHTĚLOVÁ. Improving IDP theoretical chemical shift accuracy and efficiency through a combined MD/ADMA/DFT and machine learning approach. \textit{Physical Chemistry Chemical Physics}. Royal Society of Chemistry, 2022, roč.~24, č.~45, s.~27678-27692. ISSN~1463-9076. Dostupné z: https://dx.doi.org/10.1039/D2CP01638A.

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