-
Je něco špatně v tomto záznamu ?
Improving IDP theoretical chemical shift accuracy and efficiency through a combined MD/ADMA/DFT and machine learning approach
MJ. Bakker, A. Mládek, H. Semrád, V. Zapletal, J. Pavlíková Přecechtělová
Jazyk angličtina Země Anglie, Velká Británie
Typ dokumentu časopisecké články
Odkazy
PubMed
36373847
DOI
10.1039/d2cp01638a
Knihovny.cz E-zdroje
- MeSH
- kvantová teorie MeSH
- simulace molekulární dynamiky MeSH
- strojové učení MeSH
- vnitřně neuspořádané proteiny * chemie MeSH
- Publikační typ
- časopisecké články MeSH
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.
- 000
- 00000naa a2200000 a 4500
- 001
- bmc22032604
- 003
- CZ-PrNML
- 005
- 20230131150905.0
- 007
- ta
- 008
- 230120s2022 enk f 000 0|eng||
- 009
- AR
- 024 7_
- $a 10.1039/d2cp01638a $2 doi
- 035 __
- $a (PubMed)36373847
- 040 __
- $a ABA008 $b cze $d ABA008 $e AACR2
- 041 0_
- $a eng
- 044 __
- $a enk
- 100 1_
- $a Bakker, Michael J $u Faculty of Pharmacy in Hradec Králové, Charles University, Akademika Heyrovského 1203/8, 500 05 Hradec Králové, Czech Republic. precechj@faf.cuni.cz $1 https://orcid.org/0000000245235570
- 245 10
- $a Improving IDP theoretical chemical shift accuracy and efficiency through a combined MD/ADMA/DFT and machine learning approach / $c MJ. Bakker, A. Mládek, H. Semrád, V. Zapletal, J. Pavlíková Přecechtělová
- 520 9_
- $a 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.
- 650 12
- $a vnitřně neuspořádané proteiny $x chemie $7 D064267
- 650 _2
- $a strojové učení $7 D000069550
- 650 _2
- $a simulace molekulární dynamiky $7 D056004
- 650 _2
- $a kvantová teorie $7 D011789
- 655 _2
- $a časopisecké články $7 D016428
- 700 1_
- $a Mládek, Arnošt $u Faculty of Pharmacy in Hradec Králové, Charles University, Akademika Heyrovského 1203/8, 500 05 Hradec Králové, Czech Republic. precechj@faf.cuni.cz
- 700 1_
- $a Semrád, Hugo $u Faculty of Pharmacy in Hradec Králové, Charles University, Akademika Heyrovského 1203/8, 500 05 Hradec Králové, Czech Republic. precechj@faf.cuni.cz $u Department of Chemistry, Faculty of Science, Masaryk University, Kotlářská 267/2, 611 37 Brno, Czech Republic
- 700 1_
- $a Zapletal, Vojtěch $u Faculty of Pharmacy in Hradec Králové, Charles University, Akademika Heyrovského 1203/8, 500 05 Hradec Králové, Czech Republic. precechj@faf.cuni.cz
- 700 1_
- $a Pavlíková Přecechtělová, Jana $u Faculty of Pharmacy in Hradec Králové, Charles University, Akademika Heyrovského 1203/8, 500 05 Hradec Králové, Czech Republic. precechj@faf.cuni.cz $1 https://orcid.org/0000000308442666
- 773 0_
- $w MED00008271 $t Physical chemistry chemical physics : PCCP $x 1463-9084 $g Roč. 24, č. 45 (2022), s. 27678-27692
- 856 41
- $u https://pubmed.ncbi.nlm.nih.gov/36373847 $y Pubmed
- 910 __
- $a ABA008 $b sig $c sign $y p $z 0
- 990 __
- $a 20230120 $b ABA008
- 991 __
- $a 20230131150901 $b ABA008
- 999 __
- $a ok $b bmc $g 1891394 $s 1183939
- BAS __
- $a 3
- BAS __
- $a PreBMC-MEDLINE
- BMC __
- $a 2022 $b 24 $c 45 $d 27678-27692 $e 20221123 $i 1463-9084 $m PCCP. Physical chemistry chemical physics $n Phys Chem Chem Phys $x MED00008271
- LZP __
- $a Pubmed-20230120