When are two hydrogen bonds better than one? Accurate first-principles models explain the balance of hydrogen bond donors and acceptors found in proteins

Vennelakanti, Vyshnavi; Qi, Helena W.; Mehmood, Rimsha; Kulik, Heather J.

doi:10.1039/d0sc05084a

“…5 and Supporting Information Tables S11-S12). In contrast with prior studies, we include additional protein residues within 4.0 Å of 12-epi-fischerindole U, such as Asn74 and Val81, in our QM regions to detect any HB interactions that could have otherwise gone undetected [39][40][42][43] with geometric criteria or classical interaction analysis from MD.…”

Section: Resultsmentioning

confidence: 99%

“…39 This approach was necessary 39 because free molecular dynamics with approximate force fields can provide inaccurate descriptions of hydrogen bonding interactions and otherwise fail to adequately sample short, non-covalent distances. [40][41][42][43] Our simulations provided insight on previously unknown protein-substrate interactions that give rise to experimentally-measured substrate positions, which in turn impact reaction selectivity in SyrB2. In this work, we apply the same computational protocol to recently discovered carrier-protein independent non-heme iron halogenases WelO5 11,[44][45] and BesD 14,46 for which substrate-bound crystal structures have been obtained.…”

Section: Introductionmentioning

confidence: 91%

Revealing Substrate Positioning Dynamics in Non-heme Fe(II)/αKG-dependent Halogenases Through Spectroscopically Guided Simulation

Mehmood

¹

,

Vennelakanti

²

,

Kulik

³

2021

Preprint

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ABTRACT: Non-heme iron halogenases, such as SyrB2, WelO5, and BesD, halogenate unactivated carbon atoms of diverse substrates at ambient conditions with exquisite selectivity seldom matched by non-biological catalysts. Although crystallography, spectroscopy, and kinetic measurements provide foundational knowledge of enzyme structure and function, critical gaps remain in our understanding of how the protein environment dynamically reorganizes to a catalytically active state capable of halogenation. Using experimentally-guided molecular dynamics (MD) simulations augmented with multi-scale (i.e., QM/MM) simulations of substratebound complexes of BesD and WelO5, we investigate substrate/active-site dynamics that enable selective halogenation. Our simulations reveal that active-site configurational isomerization is necessary in WelO5 to attain substrate/active-site geometry consistent with its observed chemoand regioselectivity. Conversely, a slight reorientation of the substrate from its crystal-structure position is sufficient to enable regioselective chlorination in BesD without the need to invoke active-site isomerization. We observe how substrate-protein interactions evolve during experimentally-motivated MD of halogenases. We relate the nature of these interactions to the distinct substrates. For BesD, we resolve the uncertainty around the mechanistic relevance of Asn219 based on a prior mutagenesis study. By quantifying the presence of thermodynamically competitive C-H bonds on substrates of SyrB2, BesD, and WelO5, we confirm the need for the protein environment to strategically position the substrate to impart regioselectivity to halogenases. Our simulations reveal that the optimum substrate/active-site geometry also outweighs interactions between active site ligands and the protein environment in facilitating the required chemoselectivity in halogenases.

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“…Hydrogen bonding interactions are essential in variety of physicochemical processes, such as, enzymatic catalysis, [1][2][3][4][5] protein-protein interactions, 6 nucleobase interactions in RNA and DNA, 7 solidliquid interfaces, 8,9 polymerizations, 10 molecular recognition 11 etc.. Hydrogen bonding interactions which are considered as electrostatic in nature, can also have a certain extent of covalency in the bonding characteristics. 12,13 Consequently, the energy spectrum of hydrogen bonds (HBs) lies in the broad range ∼1-40 kcal/mol.…”

Section: Introductionmentioning

confidence: 99%

Intramolecular proton transfer reaction dynamics using machine learned ab initio potential energy surfaces

Raghunathan

¹

2021

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Hydrogen bonding interactions central to various physicochemical processes are investigated in the present study using ab initio-based machine learning potential energy surfaces. Abnormally strong intramolecular O-H...O hydrogen bonds occurring in beta-diketone enols of malonaldehyde, and its derivatives with substituents ranging from various electron-withdrawing to electron-donating functional groups are studied. Machine learning force fields were constructed by using a kernel-based force learning model employing ab initio molecular dynamics reference data. These models were used for molecular dynamics simulations at finite temperature, and dynamical properties were determined by computing their proton transfer free energy surfaces. Chemical systems studied here show progression towards forming barrierless proton transfer events at an accuracy of highly correlated electronic structure methods. Markov state models of the conformational states indicate shorter intramolecular hydrogen bonds exhibiting higher proton transfer rates. We demonstrate how functional group substitution can modulate the strength of intramolecular hydrogen bonds by studying their thermodynamic and kinetic properties.

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“…Hydrogen bonding interactions are essential in a variety of physicochemical processes, such as, enzymatic catalysis, [1][2][3][4][5] protein-protein interactions, 6 nucleobase interactions in RNA and DNA, 7 solidliquid interfaces, 8,9 polymerizations, 10 molecular recognition 11 , etc.. Hydrogen bonding interactions, which are considered as electrostatic in nature, can also have a certain extent of covalency in the bonding characteristics. 12,13 Consequently, the energy spectrum of hydrogen bonds (HBs) lies in the broad range ∼1-40 kcal/mol.…”

Section: Introductionmentioning

confidence: 99%

Intramolecular proton transfer reaction dynamics using machine learned ab initio potential energy surfaces

Raghunathan

¹

2021

Preprint

0

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Hydrogen bonding interactions central to various physicochemical processes are investigated in the present study using ab initio-based machine learning potential energy surfaces. Abnormally strong intramolecular O-H...O hydrogen bonds occurring in beta-diketone enols of malonaldehyde, and its derivatives with substituents ranging from various electron-withdrawing to electron-donating functional groups are studied. Machine learning force fields were constructed by using a kernel-based force learning model employing ab initio molecular dynamics reference data. These models were used for molecular dynamics simulations at finite temperature, and dynamical properties were determined by computing their proton transfer free energy surfaces. Chemical systems studied here show progression towards forming barrierless proton transfer events at an accuracy of highly correlated electronic structure methods. Markov state models of the conformational states indicate shorter intramolecular hydrogen bonds exhibiting higher proton transfer rates. We demonstrate how functional group substitution can modulate the strength of intramolecular hydrogen bonds by studying their thermodynamic and kinetic properties.

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When are two hydrogen bonds better than one? Accurate first-principles models explain the balance of hydrogen bond donors and acceptors found in proteins

Abstract: Correlated wavefunction theory predicts and high-resolution crystal structure analysis confirms the important, stabilizing effect of simultaneous hydrogen bond donor and acceptor interactions in proteins.

Cited by 29 publications

References 125 publications

Revealing Substrate Positioning Dynamics in Non-heme Fe(II)/αKG-dependent Halogenases Through Spectroscopically Guided Simulation

Revealing Substrate Positioning Dynamics in Non-heme Fe(II)/αKG-dependent Halogenases Through Spectroscopically Guided Simulation

Intramolecular proton transfer reaction dynamics using machine learned ab initio potential energy surfaces

Intramolecular proton transfer reaction dynamics using machine learned ab initio potential energy surfaces

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