Computational Study of Protein-Ligand Unbinding for Enzyme
Engineering

J 2019

Computational Study of Protein-Ligand Unbinding for Enzyme Engineering

MARQUES, Sérgio Manuel, David BEDNÁŘ and Jiří DAMBORSKÝ

Basic information

Original name

Computational Study of Protein-Ligand Unbinding for Enzyme Engineering

Authors

MARQUES, Sérgio Manuel (620 Portugal, belonging to the institution), David BEDNÁŘ (203 Czech Republic, belonging to the institution) and Jiří DAMBORSKÝ (203 Czech Republic, guarantor, belonging to the institution)

Edition

FRONTIERS IN CHEMISTRY, LAUSANNE, FRONTIERS MEDIA SA, 2019, 2296-2646

Other information

Language

English

Type of outcome

Článek v odborném periodiku

Field of Study

10401 Organic chemistry

Country of publisher

Switzerland

Confidentiality degree

není předmětem státního či obchodního tajemství

References:

URL

Impact factor

Impact factor: 3.693

RIV identification code

RIV/00216224:14310/19:00113203

Organization unit

Faculty of Science

DOI

http://dx.doi.org/10.3389/fchem.2018.00650

UT WoS

000455109700001

Keywords in English

unbinding kinetics; protein engineering; molecular dynamics; metadynamics; adaptive sampling; CaverDock

Abstract

V originále

The computational prediction of unbinding rate constants is presently an emerging topic in drug design. However, the importance of predicting kinetic rates is not restricted to pharmaceutical applications. Many biotechnologically relevant enzymes have their efficiency limited by the binding of the substrates or the release of products. While aiming at improving the ability of our model enzyme haloalkane dehalogenase DhaA to degrade the persistent anthropogenic pollutant 1,2,3-trichloropropane (TCP), the DhaA31 mutant was discovered. This variant had a 32-fold improvement of the catalytic rate toward TCP, but the catalysis became rate-limited by the release of the 2,3-dichloropropan-1-ol (DCP) product from its buried active site. Here we present a computational study to estimate the unbinding rates of the products from DhaA and DhaA31. The metadynamics and adaptive sampling methods were used to predict the relative order of kinetic rates in the different systems, while the absolute values depended significantly on the conditions used (method, force field, and water model). Free energy calculations provided the energetic landscape of the unbinding process. A detailed analysis of the structural and energetic bottlenecks allowed the identification of the residues playing a key role during the release of DCP from DhaA31 via the main access tunnel. Some of these hot-spots could also be identified by the fast CaverDock tool for predicting the transport of ligands through tunnels. Targeting those hot-spots by mutagenesis should improve the unbinding rates of the DCP product and the overall catalytic efficiency with TCP.

Links

LM2015047, research and development project

Name: Česká národní infrastruktura pro biologická data (Acronym: ELIXIR-CZ)

Investor: Ministry of Education, Youth and Sports of the CR, Czech National Infrastructure for Biological Data

LM2015055, research and development project

Name: Centrum pro systémovou biologii (Acronym: C4SYS)

Investor: Ministry of Education, Youth and Sports of the CR

LM2015085, research and development project

Name: CERIT Scientific Cloud (Acronym: CERIT-SC)

Investor: Ministry of Education, Youth and Sports of the CR, CERIT Scientific Cloud

Citovat

MARQUES, Sérgio Manuel, David BEDNÁŘ and Jiří DAMBORSKÝ. Computational Study of Protein-Ligand Unbinding for Enzyme Engineering. FRONTIERS IN CHEMISTRY. LAUSANNE: FRONTIERS MEDIA SA, 2019, vol. 6, JAN 2019, p. 1-15. ISSN 2296-2646. Available from: https://dx.doi.org/10.3389/fchem.2018.00650.

@article{1632601,
   author = {Marques, Sérgio Manuel and Bednář, David and Damborský, Jiří},
   article_location = {LAUSANNE},
   article_number = {JAN 2019},
   doi = {http://dx.doi.org/10.3389/fchem.2018.00650},
   keywords = {unbinding kinetics; protein engineering; molecular dynamics; metadynamics; adaptive sampling; CaverDock},
   language = {eng},
   issn = {2296-2646},
   journal = {FRONTIERS IN CHEMISTRY},
   title = {Computational Study of Protein-Ligand Unbinding for Enzyme Engineering},
   url = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6331733/},
   volume = {6},
   year = {2019}
}

TY  - JOUR
ID  - 1632601
AU  - Marques, Sérgio Manuel - Bednář, David - Damborský, Jiří
PY  - 2019
TI  - Computational Study of Protein-Ligand Unbinding for Enzyme Engineering
JF  - FRONTIERS IN CHEMISTRY
VL  - 6
IS  - JAN 2019
SP  - 1-15
EP  - 1-15
PB  - FRONTIERS MEDIA SA
SN  - 22962646
KW  - unbinding kinetics
KW  - protein engineering
KW  - molecular dynamics
KW  - metadynamics
KW  - adaptive sampling
KW  - CaverDock
UR  - https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6331733/
L2  - https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6331733/
N2  - The computational prediction of unbinding rate constants is presently an emerging topic in drug design. However, the importance of predicting kinetic rates is not restricted to pharmaceutical applications. Many biotechnologically relevant enzymes have their efficiency limited by the binding of the substrates or the release of products. While aiming at improving the ability of our model enzyme haloalkane dehalogenase DhaA to degrade the persistent anthropogenic pollutant 1,2,3-trichloropropane (TCP), the DhaA31 mutant was discovered. This variant had a 32-fold improvement of the catalytic rate toward TCP, but the catalysis became rate-limited by the release of the 2,3-dichloropropan-1-ol (DCP) product from its buried active site. Here we present a computational study to estimate the unbinding rates of the products from DhaA and DhaA31. The metadynamics and adaptive sampling methods were used to predict the relative order of kinetic rates in the different systems, while the absolute values depended significantly on the conditions used (method, force field, and water model). Free energy calculations provided the energetic landscape of the unbinding process. A detailed analysis of the structural and energetic bottlenecks allowed the identification of the residues playing a key role during the release of DCP from DhaA31 via the main access tunnel. Some of these hot-spots could also be identified by the fast CaverDock tool for predicting the transport of ligands through tunnels. Targeting those hot-spots by mutagenesis should improve the unbinding rates of the DCP product and the overall catalytic efficiency with TCP.
ER  -

MARQUES, Sérgio Manuel, David BEDNÁŘ and Jiří DAMBORSKÝ. Computational Study of Protein-Ligand Unbinding for Enzyme Engineering. \textit{FRONTIERS IN CHEMISTRY}. LAUSANNE: FRONTIERS MEDIA SA, 2019, vol.~6, JAN 2019, p.~1-15. ISSN~2296-2646. Available from: https://dx.doi.org/10.3389/fchem.2018.00650.

Detailed Information on Publication Record