pith. sign in

arxiv: 2606.21370 · v1 · pith:HCHI5SMZnew · submitted 2026-06-19 · ⚛️ physics.bio-ph

Computationally guided modifications of CviUPO to improve catalytic activity

Hanna-Friederike Poggemann , Tim Dirks , Timo Jacob , Christoph Jung This is my paper

Pith reviewed 2026-06-26 12:48 UTC · model grok-4.3

classification ⚛️ physics.bio-ph
keywords unspecific peroxygenasesCviUPOQM/MM simulationsenergy barriersenzyme mutationsheme anchoringcatalytic activityperoxygenase reactivity
0
0 comments X

The pith

Replacing the heme-anchoring cysteine with histidine in CviUPO lowers the energy barriers for catalysis in QM/MM simulations.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper uses computational modeling to test targeted amino acid changes in the unspecific peroxygenase CviUPO with the aim of raising catalytic activity while limiting peroxide-induced inactivation. Two mutations near the active site were examined through Quantum Mechanics/Molecular Mechanics Nudged Elastic Band calculations that track the height of the barriers leading to the activated complex. Substituting glutamic acid with the shorter aspartic acid raises the barrier and is therefore expected to reduce activity. Substituting the cysteine that anchors the heme with histidine lowers the barriers substantially and is presented as the more promising change.

Core claim

QM/MM NEB simulations show that the cysteine-to-histidine mutation at the heme anchor decreases the energy barriers significantly while the glutamic-to-aspartic acid mutation increases them; the histidine change may also shift the enzyme toward peroxidase behavior, an outcome the calculations cannot rule out.

What carries the argument

Quantum Mechanics/Molecular Mechanics Nudged Elastic Band (NEB) simulations that compute changes in activation energy barriers after single-residue mutations close to the active center.

If this is right

  • The cysteine-to-histidine mutation is predicted to improve catalytic performance provided the enzyme retains peroxygenase reactivity.
  • The glutamic-to-aspartic acid mutation is predicted to impair activity by raising the reaction barrier.
  • Spin states and the degree of active-pocket hydration are identified as factors that control barrier height in the catalytic cycle.
  • Efficient engineering of UPOs requires iterative combination of simulation predictions with experimental tests of substrate specificity and stability.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same computational protocol could be applied to other UPO variants to identify stabilizing mutations at the heme anchor without exhaustive library screening.
  • If the histidine mutant retains peroxygenase function, it would expand the range of conditions under which these enzymes can operate before inactivation occurs.
  • Testing whether the mutation alters the preferred spin state during catalysis would directly address one of the paper's highlighted mechanistic factors.
  • The results point to heme-ligating residues as a general hotspot worth prioritizing in future rounds of UPO redesign.

Load-bearing premise

The simulations correctly describe the catalytic mechanism and the histidine mutation leaves the enzyme's peroxygenase character intact rather than converting it to a peroxidase.

What would settle it

Direct measurement of catalytic turnover rate and product profile for the histidine-mutant CviUPO acting on a saturated hydrocarbon substrate, compared with the wild-type enzyme under identical peroxide conditions.

Figures

Figures reproduced from arXiv: 2606.21370 by Christoph Jung, Hanna-Friederike Poggemann, Tim Dirks, Timo Jacob.

Figure 1
Figure 1. Figure 1: Graphical abstract arXiv:2606.21370v1 [physics.bio-ph] 19 Jun 2026 [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Catalytic cycle of CviUPO following the heterolysis-homolysis mechanis. For simplicity, the cycle does not start with a water molecule as a distant ligand in the initial state but with the H2O2 already approaching the iron center. The second step is the formation of Compound 0, where the glutamic acid Glu162 is involved in abstracting one hydrogen from the H2O2 . Subsequently, this hydrogen recombines with… view at source ↗
Figure 3
Figure 3. Figure 3: The plot shows the temporal progression of the average water molecule density in the active pocket for each of the enzymes, the native CviUPO (orange), the CviUPO with Asp modification (blue) and the CviUPO with His modification (green), respectively. The dashed lines correspond to the respective time averaged values. For the analysis 120 ns MD simulations were performed – the first 20 were excluded from t… view at source ↗
Figure 4
Figure 4. Figure 4: Free energy diagram of native CviUPO. The x-axis shows the state of the iron co-substrate complex in which the system is located, while the y-axis shows the free energy G in (a) and ∆G in (b), respectively. In (b), the values of each curve are referenced to the initial state. Each spin state is represented by its own color, the LS is shown in orange, the IS in blue, and the HS in green. The models shown in… view at source ↗
Figure 5
Figure 5. Figure 5: The plots show the free energy diagram of Asp-modified CviUPO in (a) and His-modified CviUPO in (b). Each curve is referenced to the initial state. Each spin state is represented by its own color, the LS is shown in orange, the IS in blue, and the HS in green. The corresponding inlets display the 3D-representations of the active center in the different states. Four important steps along the formation of Co… view at source ↗
Figure 6
Figure 6. Figure 6: Reaction pathway from the initial state of the catalytic cycle via Compound 0 (Cpd 0 in the plot) towards Compound I (Cpd 1 in the plot) for native CviUPO (a), CviUPO with aspartic acid modification (b), and CviUPO with histidine modification (c), derived by NEB calculations. The values of each curve are referenced to its initial state. Each spin state is represented by its own color, the LS is shown in or… view at source ↗
Figure 7
Figure 7. Figure 7: Free energy diagram of native CviUPO (a) and His CviUPO (b) with ETBE in the active pocket. Each spin state is represented by its own color, the LS is shown in orange, the IS in blue, and the HS in green. The x-axis shows the state of the iron co-substrate complex in which the system is located and the ∆G is plotted on the y-axis. Each curve is referenced to its initial state, the non-normalized values can… view at source ↗
read the original abstract

Unspecific peroxygenases (UPOs) are promising biocatalysts that selectively oxyfunctionalize saturated hydrocarbons using only hydrogen peroxide as a co-substrate. Peroxide-induced enzyme inactivation makes targeted enzyme engineering essential to mitigate this effect and also enhance catalytic performance. To meet this need, systematic approaches are used, including extensive database studies for rational enzyme design, as well as computational enzyme engineering. In this study, we followed the latter strategy and explored the possibility for computationally-guided modification of UPOs. Specifically, our focus was on uncovering the influence of active site amino acids on the catalytic activity of the enzyme CviUPO. Two mutations were introduced close to the active center, and the changes in the energy barriers leading to the activated complex were investigated in detail by Quantum Mechanics/Molecular Mechanics Nudged Elastic Band simulations. Our studies revealed that a change of the glutamic acid, assisting the catalytic cycle, by the shorter aspartic acid, leads to an increased reaction barrier, probably decreasing the catalytic activity of the enzyme. Exchanging the heme-anchoring cysteine group by a histidine exhibited promising behavior as the energy barriers decreased significantly. However, it is possible that the histidine modification also alters the reaction behavior of the peroxygenase, turning it into a peroxidase, an aspect that so far could not be confirmed beyond doubt. Simulations alone cannot conclusively determine whether substrate specificity and reactivity are maintained in the modifications tested. Nevertheless, our results highlight the importance of spin states and active pocket hydration for the catalytic reaction and demonstrate why a synergistic approach of theoretical predictions and experimental verifications is required for efficient enzyme engineering.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 1 minor

Summary. The manuscript uses QM/MM NEB simulations to examine the effects of two active-site mutations in CviUPO on the barriers to the activated complex. Replacement of the assisting glutamic acid by aspartic acid is reported to raise the barrier, while replacement of the heme-ligating cysteine by histidine is reported to lower the barriers substantially; the authors note that the histidine change may convert the enzyme to peroxidase behavior but state that this could not be confirmed.

Significance. If the peroxygenase mechanism is preserved and the barrier changes are robust, the work would illustrate how proximal residues and spin-state/hydration effects modulate UPO catalysis and would support the value of QM/MM NEB for guiding peroxygenase engineering. The explicit caveat about possible mechanism switching and the call for experimental verification are appropriate.

major comments (2)
  1. [Abstract and histidine-mutation results] Abstract and the histidine-mutation results section: the NEB paths for the Cys-to-His mutant follow only the original peroxygenase cycle; no barriers or reaction coordinates are supplied for the competing O–O cleavage or Compound-I formation channels that would be required to rule out a switch to peroxidase reactivity, even though the abstract itself flags this possibility.
  2. [Results and Methods (NEB)] Results and Methods sections on the NEB calculations: the manuscript states directional changes in barriers (“increased,” “decreased significantly”) but supplies neither the numerical barrier heights, their standard errors, nor convergence or spin-state sampling details, so the magnitude and statistical reliability of the reported effects cannot be evaluated.
minor comments (1)
  1. [Abstract] The abstract would benefit from a single sentence stating the computed barrier changes (in kcal/mol) if those values appear in the main text or SI.

Simulated Author's Rebuttal

2 responses · 1 unresolved

We thank the referee for their constructive comments. We address each major point below, indicating planned revisions where appropriate.

read point-by-point responses

  1. Referee: [Abstract and histidine-mutation results] Abstract and the histidine-mutation results section: the NEB paths for the Cys-to-His mutant follow only the original peroxygenase cycle; no barriers or reaction coordinates are supplied for the competing O–O cleavage or Compound-I formation channels that would be required to rule out a switch to peroxidase reactivity, even though the abstract itself flags this possibility.

    Authors: We acknowledge that the NEB calculations addressed only the peroxygenase reaction coordinate. The abstract already states that a switch to peroxidase behavior could not be confirmed. We will revise the abstract and results to state explicitly that the reported barrier lowering applies solely to the peroxygenase pathway and that alternative channels were not computed. This limitation follows from the study scope; the computational evidence still indicates a lowered barrier along the original cycle, supporting the call for experimental verification. revision: partial

  2. Referee: [Results and Methods (NEB)] Results and Methods sections on the NEB calculations: the manuscript states directional changes in barriers (“increased,” “decreased significantly”) but supplies neither the numerical barrier heights, their standard errors, nor convergence or spin-state sampling details, so the magnitude and statistical reliability of the reported effects cannot be evaluated.

    Authors: We agree that numerical barrier values and methodological details would improve evaluability. In the revised manuscript we will report the specific NEB barrier heights, spin-state sampling protocol, and convergence criteria employed in the QM/MM calculations. revision: yes

standing simulated objections not resolved
  • Providing barriers or reaction coordinates for the competing O–O cleavage and Compound-I formation channels in the Cys-to-His mutant, which would require new QM/MM NEB simulations beyond the original study.

Circularity Check

0 steps flagged

No circularity: results from independent QM/MM simulations

full rationale

The paper reports direct QM/MM NEB calculations of energy barriers for CviUPO wild-type and two point mutants (Glu-to-Asp, Cys-to-His). No parameters are fitted to experimental outcomes, no self-citations underpin the central barrier comparisons, and the derivation chain consists of standard electronic-structure plus classical MD steps whose outputs are not definitionally equivalent to their inputs. The abstract itself flags the open question of peroxygenase vs. peroxidase behavior, confirming the result is not smuggled in by assumption.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claims rest on the domain assumption that QM/MM NEB accurately models the rate-limiting steps and that the chosen mutations do not induce unmodeled changes in mechanism or substrate access.

axioms (1)
  • domain assumption QM/MM NEB simulations with the chosen level of theory and spin states correctly identify the lowest-energy reaction paths and barrier heights for the catalytic cycle.
    Invoked when interpreting the Glu-to-Asp and Cys-to-His barrier changes as direct predictors of catalytic activity.

pith-pipeline@v0.9.1-grok · 5827 in / 1147 out tokens · 26501 ms · 2026-06-26T12:48:44.125118+00:00 · methodology

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Reference graph

Works this paper leans on

53 extracted references · 51 canonical work pages

  1. [1]

    C., Baldenius, K

    Wu, S., Snajdrova, R., Moore, J. C., Baldenius, K. & Bornscheuer, U. T. Biocatalysis: Enzymatic synthesis for industrial applications.Angewandte Chemie Int. Ed.60, 88–119, DOI: 10.1002/ANIE.202006648 (2021)

    work page doi:10.1002/anie.202006648 2021

  2. [2]

    & Ullrich, R

    Hofrichter, M. & Ullrich, R. Oxidations catalyzed by fungal peroxygenases.Curr. Opin. Chem. Biol.19, 116–125, DOI: 10.1016/j.cbpa.2014.01.015 (2014)

    work page doi:10.1016/j.cbpa.2014.01.015 2014

  3. [3]

    & Hofrichter, M

    Karich, A., Kluge, M., Ullrich, R. & Hofrichter, M. Benzene oxygenation and oxidation by the peroxygenase of Agrocybe aegerita.AMB Express3, 5, DOI: 10.1186/2191-0855-3-5 (2013)

    work page doi:10.1186/2191-0855-3-5 2013

  4. [4]

    & Hofrichter, M

    Karich, A., Scheibner, K., Ullrich, R. & Hofrichter, M. Exploring the catalase activity of unspecific peroxygenases and the mechanism of peroxide-dependent heme destruction.J. Mol. Catal. B: Enzym.134, 238–246, DOI: 10.1016/j.molcatb. 2016.10.014 (2016)

    work page doi:10.1016/j.molcatb 2016

  5. [5]

    O., Bormann, S., Hollmann, F., Bloh, J

    Burek, B. O., Bormann, S., Hollmann, F., Bloh, J. Z. & Holtmann, D. Hydrogen peroxide driven biocatalysis.Green Chem. 21, 3232–3249, DOI: 10.1039/C9GC00633H (2019)

    work page doi:10.1039/c9gc00633h 2019

  6. [6]

    Hrycay, E. G. & Bandiera, S. M. The monooxygenase, peroxidase, and peroxygenase properties of cytochrome P450.Arch. Biochem. Biophys.522, 71–89, DOI: 10.1016/j.abb.2012.01.003 (2012)

    work page doi:10.1016/j.abb.2012.01.003 2012

  7. [7]

    Hofrichter, M.et al.Peroxide-Mediated Oxygenation of Organic Compounds by Fungal Peroxygenases.Antioxidants11, 163, DOI: 10.3390/antiox11010163 (2022)

    work page doi:10.3390/antiox11010163 2022

  8. [8]

    Antioxidants11, 891, DOI: 10.3390/antiox11050891 (2022)

    Linde, D.et al.Structural Characterization of Two Short Unspecific Peroxygenases: Two Different Dimeric Arrangements. Antioxidants11, 891, DOI: 10.3390/antiox11050891 (2022)

    work page doi:10.3390/antiox11050891 2022

  9. [9]

    ACS Chem

    Ramirez-Escudero, M.et al.Structural Insights into the Substrate Promiscuity of a Laboratory-Evolved Peroxygenase. ACS Chem. Biol.13, 3259–3268, DOI: 10.1021/acschembio.8b00500 (2018)

    work page doi:10.1021/acschembio.8b00500 2018

  10. [10]

    & Poelarends, G

    Sigmund, M.-C. & Poelarends, G. J. Current state and future perspectives of engineered and artificial peroxygenases for the oxyfunctionalization of organic molecules.Nat. Catal.3, 690–702, DOI: 10.1038/s41929-020-00507-8 (2020)

    work page doi:10.1038/s41929-020-00507-8 2020

  11. [11]

    Costa, G. J. & Liang, R. Understanding the Multifaceted Mechanism of Compound I Formation in Unspecific Peroxygenases through Multiscale Simulations.The J. Phys. Chem. B127, 8809–8824, DOI: 10.1021/acs.jpcb.3c04589 (2023). 12/15 12.Groves, J. T. Using push to get pull.Nat. Chem.6, 89–91, DOI: 10.1038/nchem.1855 (2014)

    work page doi:10.1021/acs.jpcb.3c04589 2023

  12. [12]

    T., Dawson, J

    Green, M. T., Dawson, J. H. & Gray, H. B. Oxoiron(IV) in Chloroperoxidase Compound II Is Basic: Implications for P450 Chemistry.Science304, 1653–1656, DOI: 10.1126/science.1096897 (2004)

    work page doi:10.1126/science.1096897 2004

  13. [13]

    H.et al.Iron(IV)hydroxide pKa and the Role of Thiolate Ligation in C–H Bond Activation by Cytochrome P450

    Yosca, T. H.et al.Iron(IV)hydroxide pKa and the Role of Thiolate Ligation in C–H Bond Activation by Cytochrome P450. Science342, 825–829, DOI: 10.1126/science.1244373 (2013)

    work page doi:10.1126/science.1244373 2013

  14. [14]

    & Groves, J

    Wang, X., Peter, S., Kinne, M., Hofrichter, M. & Groves, J. T. Detection and Kinetic Characterization of a Highly Reactive Heme–Thiolate Peroxygenase Compound I.J. Am. Chem. Soc.134, 12897–12900, DOI: 10.1021/ja3049223 (2012)

    work page doi:10.1021/ja3049223 2012

  15. [15]

    & Groves, J

    Wang, X., Peter, S., Ullrich, R., Hofrichter, M. & Groves, J. T. Driving Force for Oxygen-Atom Transfer by Heme-Thiolate Enzymes.Angewandte Chemie Int. Ed.52, 9238–9241, DOI: 10.1002/anie.201302137 (2013)

    work page doi:10.1002/anie.201302137 2013

  16. [16]

    Henkelman, G., Uberuaga, B. P. & Jónsson, H. A climbing image nudged elastic band method for finding saddle points and minimum energy paths.The J. Chem. Phys.113, 9901–9904, DOI: 10.1063/1.1329672 (2000)

    work page doi:10.1063/1.1329672 2000

  17. [17]

    Improved tangent estimate in the nudged elastic band method for finding minimum energy paths and saddle points

    Henkelman, G. & Jónsson, H. Improved tangent estimate in the nudged elastic band method for finding minimum energy paths and saddle points.The J. Chem. Phys.113, 9978–9985, DOI: 10.1063/1.1323224 (2000)

    work page doi:10.1063/1.1323224 2000

  18. [18]

    & Henkelman, G

    Sheppard, D. & Henkelman, G. Paths to which the nudged elastic band converges.J. Comput. Chem.32, 1769–1771, DOI: 10.1002/jcc.21748 (2011)

    work page doi:10.1002/jcc.21748 2011

  19. [19]

    & Mazumdar, S

    Taher, M., Dutta Dubey, K. & Mazumdar, S. Computationally guided bioengineering of the active site, substrate access pathway, and water channels of thermostable cytochrome P450, CYP175A1, for catalyzing the alkane hydroxylation reaction.Chem. Sci.14, 14316–14326, DOI: 10.1039/D3SC02857G (2023)

    work page doi:10.1039/d3sc02857g 2023

  20. [20]

    K.et al.Fast product release requires active-site water dynamics in carbonic anhydrase.Nat

    Kim, J. K.et al.Fast product release requires active-site water dynamics in carbonic anhydrase.Nat. Commun.16, 4404, DOI: 10.1038/s41467-025-59645-x (2025)

    work page doi:10.1038/s41467-025-59645-x 2025

  21. [21]

    Spin-State Energetics of Heme-Related Models from DFT and Coupled Cluster Calculations.J

    Rado´n, M. Spin-State Energetics of Heme-Related Models from DFT and Coupled Cluster Calculations.J. Chem. Theory Comput.10, 2306–2321, DOI: 10.1021/ct500103h (2014)

    work page doi:10.1021/ct500103h 2014

  22. [22]

    Schöneboom, J. C. & Thiel, W. The Resting State of P450cam: A QM/MM Study.The J. Phys. Chem. B108, 7468–7478, DOI: 10.1021/jp049596t (2004)

    work page doi:10.1021/jp049596t 2004

  23. [23]

    & Shaik, S

    Chen, H., Hirao, H., Derat, E., Schlichting, I. & Shaik, S. Quantum Mechanical/Molecular Mechanical Study on the Mechanisms of Compound I Formation in the Catalytic Cycle of Chloroperoxidase: An Overview on Heme Enzymes.The J. Phys. Chem. B112, 9490–9500, DOI: 10.1021/jp803010f (2008)

    work page doi:10.1021/jp803010f 2008

  24. [24]

    Teune, M.et al.Insights into a water-mediated catalytic triad architecture in CE20 carbohydrate esterases.Nat. Commun. 16, 7034, DOI: 10.1038/s41467-025-62387-5 (2025)

    work page doi:10.1038/s41467-025-62387-5 2025

  25. [25]

    & Zeinalipour-Yazdi, C

    Elahi, N. & Zeinalipour-Yazdi, C. D. A DFT assessment of the activation barrier for concerted proton transfer in cyclic water clusters (H2O)n where n = 3–8.Comput. Theor. Chem.1244, 115061, DOI: 10.1016/j.comptc.2024.115061 (2025)

    work page doi:10.1016/j.comptc.2024.115061 2024

  26. [26]

    Jacob, C. R. & Reiher, M. Spin in density-functional theory.Int. J. Quantum Chem.112, 3661–3684, DOI: 10.1002/qua. 24309 (2012)

    work page doi:10.1002/qua 2012

  27. [27]

    & Han, K

    Li, D., Wang, Y . & Han, K. Recent density functional theory model calculations of drug metabolism by cytochrome P450. Coord. Chem. Rev.256, 1137–1150, DOI: 10.1016/j.ccr.2012.01.016 (2012)

    work page doi:10.1016/j.ccr.2012.01.016 2012

  28. [28]

    Rev.110, 949–1017, DOI: 10.1021/cr900121s (2010)

    Shaik, S.et al.P450 Enzymes: Their Structure, Reactivity, and Selectivity—Modeled by QM/MM Calculations.Chem. Rev.110, 949–1017, DOI: 10.1021/cr900121s (2010)

    work page doi:10.1021/cr900121s 2010

  29. [29]

    A., Heel, T., Buller, A

    McIntosh, J. A., Heel, T., Buller, A. R., Chio, L. & Arnold, F. H. Structural Adaptability Facilitates Histidine Heme Ligation in a Cytochrome P450.J. Am. Chem. Soc.137, 13861–13865, DOI: 10.1021/jacs.5b07107 (2015). 31.Unspecific peroxygenase from Collariella virescens: 7zcl, DOI: 10.2210/pdb7zcl/pdb (2022)

    work page doi:10.1021/jacs.5b07107 2015

  30. [30]

    C.et al.H++: a server for estimating p Ka s and adding missing hydrogens to macromolecules.Nucleic Acids Res.33, W368–W371, DOI: 10.1093/nar/gki464 (2005)

    Gordon, J. C.et al.H++: a server for estimating p Ka s and adding missing hydrogens to macromolecules.Nucleic Acids Res.33, W368–W371, DOI: 10.1093/nar/gki464 (2005)

    work page doi:10.1093/nar/gki464 2005

  31. [31]

    & Onufriev, A

    Myers, J., Grothaus, G., Narayanan, S. & Onufriev, A. A simple clustering algorithm can be accurate enough for use in calculations of pKs in macromolecules.Proteins: Struct. Funct. Bioinforma.63, 928–938, DOI: 10.1002/prot.20922 (2006)

    work page doi:10.1002/prot.20922 2006

  32. [32]

    & Onufriev, A

    Anandakrishnan, R., Aguilar, B. & Onufriev, A. V . H++ 3.0: automating pK prediction and the preparation of biomolecular structures for atomistic molecular modeling and simulations.Nucleic Acids Res.40, W537–W541, DOI: 10.1093/nar/gks375 (2012). 13/15 35.GROMACS 2022.2 Source code, DOI: 10.5281/zenodo.6637571 (2022)

    work page doi:10.1093/nar/gks375 2012

  33. [33]

    J.et al.GROMACS: High performance molecular simulations through multi-level parallelism from laptops to supercomputers.SoftwareX1-2, 19–25, DOI: 10.1016/j.softx.2015.06.001 (2015)

    Abraham, M. J.et al.GROMACS: High performance molecular simulations through multi-level parallelism from laptops to supercomputers.SoftwareX1-2, 19–25, DOI: 10.1016/j.softx.2015.06.001 (2015)

    work page doi:10.1016/j.softx.2015.06.001 2015

  34. [34]

    Berendsen, H. J. C., van der Spoel, D. & van Drunen, R. GROMACS: A message-passing parallel molecular dynamics implementation.Comput. Phys. Commun.91, 43–56, DOI: 10.1016/0010-4655(95)00042-E (1995)

    work page doi:10.1016/0010-4655(95)00042-e 1995

  35. [35]

    & Lindahl, E

    Hess, B., Kutzner, C., van der Spoel, D. & Lindahl, E. GROMACS 4: Algorithms for Highly Efficient, Load-Balanced, and Scalable Molecular Simulation.J. Chem. Theory Comput.4, 435–447, DOI: 10.1021/ct700301q (2008)

    work page doi:10.1021/ct700301q 2008

  36. [36]

    & van der Spoel, D

    Lindahl, E., Hess, B. & van der Spoel, D. GROMACS 3.0: a package for molecular simulation and trajectory analysis. Mol. modeling annual7, 306–317, DOI: 10.1007/s008940100045 (2001)

    work page doi:10.1007/s008940100045 2001

  37. [37]

    J., Kutzner, C., Hess, B

    Páll, S., Abraham, M. J., Kutzner, C., Hess, B. & Lindahl, E. Tackling Exascale Software Challenges in Molecular Dynamics Simulations with GROMACS. In Markidis, S. & Laure, E. (eds.)Solving Software Challenges for Exascale, 3–27, DOI: 10.1007/978-3-319-15976-8_1 (Springer International Publishing, Cham, 2015)

    work page doi:10.1007/978-3-319-15976-8_1 2015

  38. [38]

    Páll, S.et al.Heterogeneous parallelization and acceleration of molecular dynamics simulations in GROMACS.The J. Chem. Phys.153, 134110, DOI: 10.1063/5.0018516 (2020)

    work page doi:10.1063/5.0018516 2020

  39. [39]

    Bioinformatics29, 845–854, DOI: 10.1093/bioinformatics/btt055 (2013)

    Pronk, S.et al.GROMACS 4.5: a high-throughput and highly parallel open source molecular simulation toolkit. Bioinformatics29, 845–854, DOI: 10.1093/bioinformatics/btt055 (2013)

    work page doi:10.1093/bioinformatics/btt055 2013

  40. [40]

    Van Der Spoel, D.et al.GROMACS: Fast, flexible, and free.J. Comput. Chem.26, 1701–1718, DOI: 10.1002/jcc.20291 (2005)

    work page doi:10.1002/jcc.20291 2005

  41. [41]

    Soteras Gutiérrez, I.et al.Parametrization of halogen bonds in the CHARMM general force field: Improved treatment of ligand–protein interactions.Bioorganic & Medicinal Chem.24, 4812–4825, DOI: 10.1016/j.bmc.2016.06.034 (2016)

    work page doi:10.1016/j.bmc.2016.06.034 2016

  42. [42]

    Vanommeslaeghe, K.et al.CHARMM general force field: A force field for drug-like molecules compatible with the CHARMM all-atom additive biological force fields.J. Comput. Chem.31, 671–690, DOI: 10.1002/jcc.21367 (2010)

    work page doi:10.1002/jcc.21367 2010

  43. [43]

    & MacKerell, A

    Vanommeslaeghe, K. & MacKerell, A. D. J. Automation of the CHARMM General Force Field (CGenFF) I: Bond Perception and Atom Typing.J. Chem. Inf. Model.52, 3144–3154, DOI: 10.1021/ci300363c (2012). 47.Vanommeslaeghe, K., Raman, E. P. & MacKerell, A. D. J. Automation of the CHARMM General Force Field (CGenFF) II: Assignment of Bonded Parameters and Partial A...

    work page doi:10.1021/ci300363c 2012

  44. [44]

    & MacKerell Jr., A

    Yu, W., He, X., Vanommeslaeghe, K. & MacKerell Jr., A. D. Extension of the CHARMM general force field to sulfonyl-containing compounds and its utility in biomolecular simulations.J. Comput. Chem.33, 2451–2468, DOI: 10.1002/jcc.23067 (2012)

    work page doi:10.1002/jcc.23067 2012

  45. [45]

    From proteins to perturbed hamiltonians: A suite of tutorials for the GROMACS-2018 molecular simulation package.Living J

    Lemkul, J. From proteins to perturbed hamiltonians: A suite of tutorials for the GROMACS-2018 molecular simulation package.Living J. Comput. Mol. Sci.1, DOI: 10.33011/livecoms.1.1.5068 (2019). 50.Schrödinger, LLC & DeLano, W. The PyMOL molecular graphics system, version 1.8 (2015)

    work page doi:10.33011/livecoms.1.1.5068 2018

  46. [46]

    D., de Oliveira, C

    Durrant, J. D., de Oliveira, C. A. F. & McCammon, J. A. POVME: An Algorithm for Measuring Binding-Pocket V olumes. J. molecular graphics & modelling29, 773–776 (2011)

    2011

  47. [47]

    D., V otapka, L., Sørensen, J

    Durrant, J. D., V otapka, L., Sørensen, J. & Amaro, R. E. POVME 2.0: An Enhanced Tool for Determining Pocket Shape and V olume Characteristics.J. Chem. Theory Comput.10, 5047–5056, DOI: 10.1021/ct500381c (2014)

    work page doi:10.1021/ct500381c 2014

  48. [48]

    Model Simul Mater Sci Eng 18:015012

    Stukowski, A. Visualization and analysis of atomistic simulation data with OVITO–the Open Visualization Tool.Model. Simul. Mater. Sci. Eng.18, 015012, DOI: 10.1088/0965-0393/18/1/015012 (2009)

    work page doi:10.1088/0965-0393/18/1/015012 2009

  49. [49]

    & Olson, A

    Trott, O. & Olson, A. J. AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading.J. Comput. Chem.31, 455–461, DOI: 10.1002/jcc.21334 (2010)

    work page doi:10.1002/jcc.21334 2010

  50. [50]

    Eberhardt, J., Santos-Martins, D., Tillack, A. F. & Forli, S. AutoDock Vina 1.2.0: New Docking Methods, Expanded Force Field, and Python Bindings.J. Chem. Inf. Model.61, 3891–3898, DOI: 10.1021/acs.jcim.1c00203 (2021)

    work page doi:10.1021/acs.jcim.1c00203 2021

  51. [51]

    Senn, H. M. & Thiel, W. QM/MM methods for biological systems.At. Approaches Mod. Biol.268, 173–290, DOI: 10.1007/128_2006_084 (2006). 57.Neese, F. The ORCA program system.WIREs Comput. Mol. Sci.2, 73–78, DOI: 10.1002/wcms.81 (2012)

    work page doi:10.1007/128_2006_084 2006

  52. [52]

    Software Update: The ORCA Program System—Version 6.0.WIREs Comput

    Neese, F. Software Update: The ORCA Program System—Version 6.0.WIREs Comput. Mol. Sci.15, e70019, DOI: 10.1002/wcms.70019 (2025). 14/15

    work page doi:10.1002/wcms.70019 2025

  53. [53]

    & Wennmohs, F.ORCA 6.0 Manual

    Neese, F. & Wennmohs, F.ORCA 6.0 Manual. FACCTs GmbH, Mülheim a. d. Ruhr, Germany, 1st edn. (2025). Available at https://www.faccts.de/docs/orca/6.0/manual/index.html. Acknowledgments (not compulsory) C.J. and T.J. gratefully acknowledge funding through the DFG (CRC 1316-2). The authors acknowledge support by the state of Baden-Württemberg through bwHPC a...

    2025