PosterPDF Available

Analysis of substrate exchange between bulk solvent and buried enzyme active site via multiple molecular tunnels using high-throughput simulations

Poster

Analysis of substrate exchange between bulk solvent and buried enzyme active site via multiple molecular tunnels using high-throughput simulations

Analysis of substrate exchange between bulk solvent and buried enzyme active site via
multiple molecular tunnels using high-throughput simulations
Dheeraj Kumar Sarkar,1,2 Jan Brezovsky1,2
1 Laboratory of Biomolecular Interactions and Transport, Institute of Molecular Biology and Biotechnology, Faculty of Biology, Adam
Mickiewicz University, Poznan, Poland, 2 International Institute of Molecular and Cell Biology, Warsaw, Poland
Introduction
1. Methods
3. Robustness of scheme 4
2. Metastable states and transport kinetics
4. Conclusion
A majority of enzymes facilitates its activity by exchange of
molecules by molecular transport paths known as tunnels [1].
Haloalkane dehalogenase LinB is one such enzyme.
Utilization of these tunnels by small molecules is of growing
interest in the field of structure-based drug design and protein
engineering [2].
Recently, a potent mutant of LinB was designed with improved
rates of its substrate and product transport namely, LinB86,
having four major well-characterized tunnels (Figure 1) P1a,
P1b, P2 and P3 [3].
We evaluated four schemes for selecting initial seed positions
ranging from randomly placed substrate to more
knowledge-based positioning to start adaptive simulations [4],
focusing on the robustness of individual schemes in terms of
tunnel exploration and converged kinetic models.
P1a
P1b
P3
P2
Figure 1: Haloalkane dehalogenase LinB86 with
four characterized paths and substrate DBE
(bromine atoms in red)
1,2-dibromoethane(DBE)
Questions










 






How efficient and robust is the investigation process?
Are the relevant transport paths explored?
Are the ligand (un)binding rates meaningful?
Buried active
site
Transport
tunnels
Substrate
Random to knowledge-based ligand positioning
Scheme 1 Scheme 2 Scheme 3 Scheme 4
Ligand in bulk Ligand in
active site
Ligand in
active site
and bulk
Ligand in
favourable
regions of
tunnels
Designed schemes for selecting initial positions of ligand
Scheme 4
Tunnel
clusters
Profiling of
cheapest
tunnels
Ligand
positioning in
cheapest tunnels
Tunnel identification
from MD trajectories
Markov state models (MSMs)
P3
P1a, P1b
P3
P3
Bound state,
P1a, P1b P3
P2
Scheme 1 Scheme 2 Scheme 3 Scheme 4
Bound state,
P1a, P1b
Bound state,
P1a, P1b
Unbound
state Unbound
state Unbound
state
Unbound
state
“P2” entry
P3
Bound state,
P1a, P1b
Unbound
state
P2
Bound state,
P1a, P1b, P3
Uncharacterized
paths
Unbound
state
Bound state,
P1a, P1b
P3
“P2” entry
Unbound
state
“P2” entry
Uncharacterized
paths
Unbound
state
Bound state,
P1a, P1b
P3
“P2” entry Unbound
state
Uncharacterized
paths
Bound state,
P1a, P1b Bound state,
P1a, P1b
“P2” entry
Bound state,
P1a, P1b
P2
P3
Unbound
state
Unbound
state Unbound
state
A.
B.
C.
Figure 2: Metastable states obtained from three replicated runs A, B and C.
kon/koff: association and dissociation rates; kd
eq: equilibrium dissociation constant; all with respect to unbound and bound states.
Table 1 Kinetic parameters obtained from three replicate adaptive sampling of studied schemes
10 epochs (15 μs) 20 epochs (30 μs)
Bound state
P1a, P1b
Unbound
state
P2
P3
Unbound
state
Bound state
P1a, P1b
P2
P3
Bound state
P1a, P1b Unbound
state
P2
P3
Unbound
state
Bound state
P1a, P1b
P2
P3
Bound state
P1a, P1b Unbound
state
P2
P3 Unbound
state
Bound state
P1a, P1b
P2
P3
A
.
B.
C.
Figure 3: Metastable states obtained from 10 & 20 epochs out of 30 epochs of scheme 4 obtained from
three replicated runs A, B and C.
Different schemes have been able to reveal the details of substrate transport in LinB86 enzyme by exploring (un)binding states.
Scheme 4 used for initial seed generation performed best in terms of all tunnels being explored for substrate transport, while exhibiting
reasonable convergence in calculated kinetic parameters and showing good agreement with experimental results.
Infusion of more knowledge into initial seeds could render computational analyses of transport mechanisms in enzymes more efficient
and accurate, having a clear potential to translate into faster rational protein design and drug development efforts.
References
[1] Pravda et al. 2014, BMC Bioinform., 15, 379; Monzon et al. 2017, PLoS Comput Biol. 13, e1005398.
[2] Marques et al. 2017 Med. Res. Rev., 37, 1095–1139; Kokkonen et al. 2019, Biotechnol. Adv. 37, 107386.
[3] Brezovsky et al. 2016, ACS Catal., 6, 7597-7610.
[4] Doerr et al. 2016, J. Chem. Theory Comput., 12, 1845-1852.
Acknowledgement
This research was supported by POWR.03.02.00-00-I006/17 project; and National Science Centre, Poland (grant no. 2017/26/E/NZ1/00548).
Calculations were performed at Poznan Supercomputing and Networking Center.
Table 2 Kinetic parameters derived from
scheme 4 with reduced sampling
Kinetics Scheme 1 Scheme 2 Scheme 3 Scheme 4 Experiment
kon(1/M 1/s x 109)3.4 ± 1.8 3.7 ± 1.0 2.6 ± 1.2 2.6 ± 1.5 N/A
koff(1/s x 107)4.2 ± 2.6 1.0 ± 0.2 1.2 ± 0.4 1.1 ± 0.5 N/A
kd
eq(M x 10-3)16.8 ± 17.8 3.0 ± 1.5 4.9 ± 1.2 4.7 ± 1.3 17
Kinetics 10 epochs 20 epochs
kon(1/M 1/s x 109)2.5 ± 2.6 1.8 ± 2.0
koff(1/s x 107)1.7 ± 1.6 2.0 ± 2.7
kd
eq(M x 10-3)8.4 ± 4.7 9.2 ± 3.6
3 replicates of adaptive sampling simulations
(30 parallel simulations x 30 epochs => 45 μs)
Observations
All MSMs could reach bound states except for Scheme 1.
Auxiliary P2 and P3 tunnels were systematically resolved
by scheme 4 only.
The deviation of predicted kinetic parameters were lower
for schemes with increased knowledge-based positioning
of ligands, i.e., schemes 2-4.
The predicted and experimentally determined equillibrium
dissociation constants reached good agreement, all in the
order of low mM.
ResearchGate has not been able to resolve any citations for this publication.
  • Pravda
Pravda et al. 2014, BMC Bioinform., 15, 379; Monzon et al. 2017, PLoS Comput Biol. 13, e1005398.
  • Marques
Marques et al. 2017 Med. Res. Rev., 37, 1095-1139; Kokkonen et al. 2019, Biotechnol. Adv. 37, 107386.
  • Brezovsky
Brezovsky et al. 2016, ACS Catal., 6, 7597-7610.
  • Doerr
Doerr et al. 2016, J. Chem. Theory Comput., 12, 1845-1852.