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.

International Clinical Research Center St Anne's University Hospital Brno Brno Czechia

Loschmidt Laboratories Department of Experimental Biology and Research Centre for Toxic Compounds in the Environment RECETOX Faculty of Science Masaryk University Brno Czechia

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Abraham M. J., Murtola T., Schulz R., Páll S., Smith J. C., Hess B., et al. (2015). GROMACS: High performance molecular simulations through multi-level parallelism from laptops to supercomputers. SoftwareX 1–2, 19–25. 10.1016/j.softx.2015.06.001 DOI

Berendsen H. J. C., Postma J. P. M., van Gunsteren W. F., DiNola A., Haak J. R. (1984). Molecular dynamics with coupling to an external bath. J. Chem. Phys. 81, 3684–3690. 10.1063/1.448118 DOI

Bonomi M., Barducci A., Parrinello M. (2009). Reconstructing the equilibrium boltzmann distribution from well-tempered metadynamics. J. Comput. Chem. 30, 1615–1621. 10.1002/jcc.21305 PubMed DOI

Bonomi M., Branduardi D., Gervasio F. L., Parrinello M. (2008). The unfolded ensemble and folding mechanism of the C-terminal GB1 β-hairpin. J. Am. Chem. Soc. 130, 13938–13944. 10.1021/ja803652f PubMed DOI

Bosma T., Pikkemaat M. G., Kingma J., Dijk J., Janssen D. B. (2003). Steady-state and pre-steady-state kinetic analysis of halopropane conversion by a rhodococcus haloalkane dehalogenase. Biochemistry 42, 8047–8053. 10.1021/bi026907m PubMed DOI

Branduardi D., Gervasio F. L., Parrinello M. (2007). From A to B in free energy space. J. Chem. Phys. 126:054103. 10.1063/1.2432340 PubMed DOI

Bruce N. J., Ganotra G. K., Kokh D. B., Sadiq S. K., Wade R. C. (2018). New approaches for computing ligand-receptor binding kinetics. Curr. Opin. Struct. Biol. 49, 1–10. 10.1016/j.sbi.2017.10.001 PubMed DOI

Bussi G., Donadio D., Parrinello M. (2007). Canonical sampling through velocity rescaling. J. Chem. Phys. 126:014101. 10.1063/1.2408420 PubMed DOI

Case D. A., Babin V., Berryman J. T., Betz R. M., Cai Q., Cerutti S., Cheatham T. E. III, et al. (2014). AMBER 14 (version 14). San Francisco, CA: University of California.

CaverDock (2018). Available online at: https://loschmidt.chemi.muni.cz/caverdock/

Childers M. C., Daggett V. (2018). Validating molecular dynamics simulations against experimental observables in light of underlying conformational ensembles. J. Phys. Chem. B 122, 6673–6689. 10.1021/acs.jpcb.8b02144 PubMed DOI PMC

Chiu S. H., Xie L. (2016). Toward high-throughput predictive modeling of protein binding/unbinding kinetics. J. Chem. Inform. Model. 56, 1164–1174. 10.1021/acs.jcim.5b00632 PubMed DOI PMC

Chovancova E., Pavelka A., Benes P., Strnad O., Brezovsky J., Kozlikova B., et al. (2012). CAVER 3.0: a tool for the analysis of transport pathways in dynamic protein structures. Edited by Andreas Prlic. PLoS Comput. Biol. 8:e1002708 10.1371/journal.pcbi.1002708 PubMed DOI PMC

Darden T., York D., Pedersen L. (1993). Particle mesh ewald: an N·log(N) method for ewald sums in large systems. J. Chem. Phys. 98, 10089–10092. 10.1063/1.464397 DOI

Dickson A., Tiwary P., Vashisth H. (2017). Kinetics of ligand binding through advanced computational approaches: a review. Curr. Top. Med. Chem. 17, 2626–2641. 10.2174/1568026617666170414142908 PubMed DOI

Doerr S., Harvey M. J., Noé F., De Fabritiis G. (2016). HTMD: high-throughput molecular dynamics for molecular discovery. J. Chem. Theory Comput. 12, 1845–1852. 10.1021/acs.jctc.6b00049 PubMed DOI

Dvorak P., Bidmanova S., Damborsky J., Prokop Z. (2014). Immobilized synthetic pathway for biodegradation of toxic recalcitrant pollutant 1,2,3-trichloropropane. Environ. Sci. Tech. 48, 6859–6866. 10.1021/es500396r PubMed DOI

Feenstra K. A., Hess B., Berendsen H. J. C. (1999). Improving efficiency of large time-scale molecular dynamics simulations of hydrogen-rich systems. J. Comput. Chem. 20, 786–798. 10.1002/(SICI)1096-987X(199906)20:8<786::AID-JCC5>3.0.CO;2-B PubMed DOI

Ferruz N., De Fabritiis G. (2016). Binding kinetics in drug discovery. Molecul. Inform. 35, 216–226. 10.1002/minf.201501018 PubMed DOI

Filipovič J., Vávra O., Plhák J., Bednár D., Marques S. M., Brezovský J., et al. (2018). CaverDock: A Novel Method for the Fast Analysis of Ligand Transport. Available online at: https://arxiv.org/abs/1809.03453 PubMed

Harvey M. J., De Fabritiis G. (2009). An implementation of the smooth particle mesh ewald method on GPU hardware. J. Chem. Theory Comput. 5, 2371–2377. 10.1021/ct900275y PubMed DOI

Harvey M. J., Giupponi G., Fabritiis G. D. (2009). ACEMD: accelerating biomolecular dynamics in the microsecond time scale. J. Chem. Theory Comput. 5, 1632–1639. 10.1021/ct9000685 PubMed DOI

Hess B., Bekker H., Berendsen H., Fraaije J. G. E. M. (1997). LINCS: a linear constraint solver for molecular simulations. J. Computat. Chem. 18, 1463–1472. 10.1002/(SICI)1096-987X(199709)18:12<1463::AID-JCC4>3.0.CO;2-H DOI

Hopkins C. W., Le Grand S., Walker R. C., Roitberg A. E. (2015). Long-time-step molecular dynamics through hydrogen mass repartitioning. J. Chem. Theory Comput. 11, 1864–1874. 10.1021/ct5010406 PubMed DOI

Hornak V., Abel R., Okur A., Strockbine B., Roitberg A., Simmerling C. (2006). Comparison of multiple amber force fields and development of improved protein backbone parameters. Proteins 65, 712–725. 10.1002/prot.21123 PubMed DOI PMC

Humphrey W., Dalke A., Schulten K. (1996). VMD: visual molecular dynamics. J. Mol. Graphics 14, 33–38. 10.1016/0263-7855(96)00018-5 PubMed DOI

Izadi S., Onufriev A. V. (2016). Accuracy limit of rigid 3-point water models. J. Chem. Phys. 145:074501. 10.1063/1.4960175 PubMed DOI PMC

Jorgensen W. L., Chandrasekhar J., Madura J. D., Impey R. W., Klein M L. (1983). Comparison of simple potential functions for simulating liquid water. J. Chem. Phys. 79, 926–935. 10.1063/1.445869 DOI

Joung I. S., Cheatham T. E. (2008). Determination of alkali and halide monovalent ion parameters for use in explicitly solvated biomolecular simulations. J. Phys. Chem. B 112, 9020–9041. 10.1021/jp8001614 PubMed DOI PMC

Joung I. S., Cheatham T. E. (2009). Molecular dynamics simulations of the dynamic and energetic properties of alkali and halide ions using water-model-specific ion parameters. J. Phys. Chem. B 113, 13279–13290. 10.1021/jp902584c PubMed DOI PMC

Kaushik S., Marques S. M., Khirsariya P., Paruch K., Libichova L., Brezovsky J., et al. . (2018). Impact of the access tunnel engineering on catalysis is strictly ligand-specific. FEBS J. 285, 1456–1476. 10.1111/febs.14418 PubMed DOI

Kokh D. B., Amaral M., Bomke J., Grädler U., Musil D., Buchstaller H. P., et al. . (2018). Estimation of drug-target residence times by τ-random acceleration molecular dynamics simulations. J. Chem. Theory Comput. 14, 3859–3869. 10.1021/acs.jctc.8b00230 PubMed DOI

Koudelakova T., Bidmanova S., Dvorak P., Pavelka A., Chaloupkova R., Prokop Z., et al. . (2013). Haloalkane dehalogenases: biotechnological applications. Biotech. J. 8, 32–45. 10.1002/biot.201100486 PubMed DOI

Kurumbang N. P., Dvorak P., Bendl J., Brezovsky J., Prokop Z., Damborsky J. (2014). Computer-assisted engineering of the synthetic pathway for biodegradation of a toxic persistent pollutant. ACS Synth. Biol. 3, 172–181. 10.1021/sb400147n PubMed DOI

Kutý M., Damborský J., Prokop M., Koča J. (1998). A molecular modeling study of the catalytic mechanism of haloalkane dehalogenase. 2. quantum chemical study of complete reaction mechanism. J. Chem. Inform. Comp. Sci. 38, 736–741. 10.1021/ci970290b DOI

Limongelli V., Bonomi M., Parrinello M. (2013). Funnel metadynamics as accurate binding free-energy method. Proc. Natl. Acad. Sci. U.S.A. 110, 6358–6363. 10.1073/pnas.1303186110 PubMed DOI PMC

Lu H., Tonge P. J. (2010). Drug-target residence time: critical information for lead optimization. Curr. Opin. Chem. Biol. 14, 467–474. 10.1016/j.cbpa.2010.06.176 PubMed DOI PMC

Maier J. A., Martinez C., Kasavajhala K., Wickstrom L., Hauser K. E., Simmerling C. (2015). Ff14SB: improving the accuracy of protein side chain and backbone parameters from Ff99SB. J. Chem. Theory Comput. 11, 3696–3713. 10.1021/acs.jctc.5b00255 PubMed DOI PMC

Marques S. M., Dunajova Z., Prokop Z., Chaloupkova R., Brezovsky J., Damborsky J. (2017). Catalytic cycle of haloalkane dehalogenases toward unnatural substrates explored by computational modeling. J. Chem. Inform. Model. 57, 1970–1989. 10.1021/acs.jcim.7b00070 PubMed DOI

Miller B. R., McGee T. D., Swails J. M., Homeyer N., Gohlke H., Roitberg A. E. (2012). MMPBSA.Py: an efficient program for end-state free energy calculations. J. Chem. Theory Comput. 8, 3314–3321. 10.1021/ct300418h PubMed DOI

Morris G. M., Huey R., Lindstrom W., Sanner M. F., Belew R. K., Goodsell D. S., et al. . (2009). AutoDock4 and AutoDockTools4: automated docking with selective receptor flexibility. J. Comput. Chem. 30, 2785–2791. 10.1002/jcc.21256 PubMed DOI PMC

Naritomi Y., Fuchigami S. (2011). Slow dynamics in protein fluctuations revealed by time-structure based independent component analysis: the case of domain motions. J. Chem. Phys. 134:065101. 10.1063/1.3554380 PubMed DOI

Nguyen H., Maier J., Huang H., Perrone V., Simmerling C. (2014). Folding simulations for proteins with diverse topologies are accessible in days with a physics-based force field and implicit solvent. J. Am. Chem. Soc. 136, 13959–13962. 10.1021/ja5032776 PubMed DOI PMC

Pavlova M., Klvana M., Prokop Z., Chaloupkova R., Banas P., Otyepka M., et al. . (2009). Redesigning dehalogenase access tunnels as a strategy for degrading an anthropogenic substrate. Nat. Chem. Biol. 5, 727–733. 10.1038/nchembio.205 PubMed DOI

Pinto G., Vávra O., Filipovi,č J., Bednar D., Damborsky J. (2018). Fast Screening of Binding and Unbinding of Inhibitors Using Novel Software Tool CaverDock. Under Publication, September. PubMed PMC

Reuveni S., Urbakh M., Klafter J. (2014). Role of substrate unbinding in michaelis–menten enzymatic reactions. Proc. Natl. Acad. Sci.U.S.A. 111, 4391–4396. 10.1073/pnas.1318122111 PubMed DOI PMC

Rydzewski J., Nowak W. (2017). Ligand diffusion in proteins via enhanced sampling in molecular dynamics. Physics Life Rev. 22–23, 58–74. 10.1016/j.plrev.2017.03.003 PubMed DOI

Salvalaglio M., Tiwary P., Parrinello M. (2014). Assessing the reliability of the dynamics reconstructed from metadynamics. J. Chem. Theory Comput. 10, 1420–1425. 10.1021/ct500040r PubMed DOI

Samin G., Janssen D. B. (2012). Transformation and Biodegradation of 1,2,3-Trichloropropane (TCP). Environ. Sci. Pollut. Res. Int. 19, 3067–3078. 10.1007/s11356-012-0859-3 PubMed DOI PMC

Sousa da Silva A. W., Vranken W. F. (2012). ACPYPE - antechamber python parser interfacE. BMC Res. 5:367. 10.1186/1756-0500-5-367 PubMed DOI PMC

Swails J. (2010). ParmEd. Available online at: https://github.com/ParmEd/ParmEd.

The PyMO L Molecular Graphics System (2014). (version 1.7.4). Schrödinger, LLC.

Tiwary P., Limongelli V., Salvalaglio M., Parrinello M. (2015). Kinetics of protein-ligand unbinding: predicting pathways, rates, and rate-limiting steps. Proc. Natl. Acad. Sci. U.S.A. 112, E386–E391. 10.1073/pnas.1424461112 PubMed DOI PMC

Tiwary P., Parrinello M. (2013). From metadynamics to dynamics. Phys. Rev. Lett. 111:230602. 10.1103/PhysRevLett.111.230602 PubMed DOI

Tiwary P., Parrinello M. (2015). A time-independent free energy estimator for metadynamics. J. Phys. Chem. B 119, 736–742. 10.1021/jp504920s PubMed DOI

Tribello G. A., Bonomi M., Branduardi D., Camilloni C., Bussi D. (2014). PLUMED 2: new feathers for an old bird. Comput. Phys. Commun. 185, 604–613. 10.1016/j.cpc.2013.09.018 DOI

Vávra O. J., Filipovič J, Plhák J., Bednár D., Marques S. M., Brezovský J., et al. (2018). CaverDock: Ligand Transport Analysis Based on Molecular Docking. Under Publication.

Verlet L. (1967). Computer ‘Experiments' on classical fluids. I. thermodynamical properties of lennard-jones molecules. Phys. Rev. 159, 98–103.

Verschueren K. H., Seljée F., Rozeboom H. J., Kalk K. H., Dijkstra B. W. (1993). Crystallographic analysis of the catalytic mechanism of haloalkane dehalogenase. Nature 363, 693–698. 10.1038/363693a0 PubMed DOI

Wang L. H., Tsai A. L., Hsu P. Y. (2001). Substrate binding is the rate-limiting step in thromboxane synthase catalysis. J. Biol. Chem. 276, 14737–14743. 10.1074/jbc.M009177200 PubMed DOI

Yao L., Li Y., Wu Y., Liu A., Yan H. (2005). Product release is rate-limiting in the activation of the prodrug 5-fluorocytosine by yeast cytosine deaminase. Biochemistry 44, 5940–5947. 10.1021/bi050095n PubMed DOI

In silico investigation of cholesterol-lowering drugs to find potential inhibitors of dehydrosqualene synthase in Staphylococcus aureus

Mechanism-Based Strategy for Optimizing HaloTag Protein Labeling

Caver Web 1.0: identification of tunnels and channels in proteins and analysis of ligand transport