Unveiling polyphenol-protein interactions: a comprehensive computational analysis

Polyphenolsclassified as secondary plant metabolitesare ubiquitously present in a wide range of food sourcesincluding vegetablesfruitsgrainsand various beverages [1]. Neverthelessthe scientific literature often grapples with the precise definition and chemical structure of polyphenolsleading to some ambiguity in their characterization [2]. Strictly speakingpolyphenols consist of one or more aromatic rings adorned with hydroxyl group(s). Despite this shared characteristicthey encompass a wide array of molecules with diverse chemical structures. Growing evidence underscores the significance of polyphenols for human healthattributing to them antioxidantanti-inflammatoryand anticarcinogenic propertiesas well as with protective effects against metabolic disorders and chronic diseases [3].

Numerous benefits associated with polyphenols concerning human health have historically been ascribed to their antioxidant properties [45]. Howevercontemporary perspectives have shifted away from this hypothesisas compelling evidence now supports the idea that polyphenols can exert their effects through specific interactions with protein targetsirrespective of their redox properties [67]. These interactionsin turnenable polyphenols to modulate signalling and metabolic pathways implicated in various diseases [89]. Moreoverrecent findings provide evidence that polyphenols can interact with protein targets within microorganismsendowing polyphenols with antimicrobial properties. Notablytheir ability to inhibit crucial viral [10,11,12]bacterial [13]or fungal [14] enzymes highlights their potential in combating microbial threats. The multifaceted interactions with both human and microbial proteins position polyphenols as promising bioactive compounds with diverse therapeutic implications.

In this studyour objective was to leverage the wealth of information within the Protein Data Bank (PDB) database [15] to discern the intricacies of polyphenolic interactions within protein binding sites. By doing sowe aimed to identify a characteristic set of interactions that each class of polyphenols establishes with proteins. This endeavour tried to shed light on the well-documented promiscuity of polyphenolsa phenomenon widely acknowledged but whose precise molecular mechanisms remain largely unexplored.

Building upon our prior investigationswherein extensive molecular dynamics (MD) simulations were conducted on three distinct protein systems binding polyphenols extracted from rosemary (carnosic acidcarnosolrosmanoland rosmarinic acid)we made a notable observation regarding the pivotal role played by water molecules in stabilizing their binding [16] (Fig. 1a). It became evident that the investigated polyphenols formed a significant number of hydrogen bonds with water moleculesand our findings underscored the significance of water-mediated interactions in the intricate interplay between polyphenols and proteins. We hypothesized that the inherent tendency of polyphenols to engage in interactions between their numerous hydroxyl groups and conserved water molecules within protein binding sites of diverse configurations could offer a plausible explanation for their promiscuous binding behavior. This observation gains additional support from the prevalence of water-mediated H-bond interactions between polyphenols and amino-acid residuesas evidenced by numerous high-resolution structures in the PDB. A case in point represents the recently published high-resolution structure of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) main proteasewhere flavonoid myricetin binds to the protease binding site through several water-mediated H-bond bridges (Fig. 1b) [12]. Similar water-mediated interactions are present in additional structures such as resveratrol-3-O-glucuronide bound to transthyretin [17]rosmarinic acid bound to myotoxin II [18] or catechol bound to urease [19].

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Polyphenols form water-mediated H-bonds in static as well as dynamic structures. a A prototypical snapshot from an MD simulation where rosmarinic acid (ROAcarbons denoted as dark-green sticks) binds to the factor X enzyme [16]. We observe several water-mediated H-bond bridges (red lines) within the active binding site that stabilize ROA binding. b The crystal structure (PDB ID: 7B3E) of flavonoid myricetin (MYCcarbons denoted as light-green sticks) covalently bound to the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) main protease. MYC forms several water-mediated H-bond bridges (red lines) within the binding site. MYC also forms direct H-bondswhich are shown as purple lines

The utility of high-resolution protein structures is emphasized in this contextas structures solved at lower resolutions often lack structural waters and may underestimate the internal hydrogen bond networks of proteins [20]. Given the relative scarcity of high-resolution structures involving polyphenols bound to proteins in the PDBin silico approaches are essential for locating bridging water molecules. An example of such an approach represents MADE (and ProBiS H2O) applicationswhich have been developed to identify conserved water molecules in macromolecular systems [2122]. The MADE workflow scans available experimental PDB data to identify binding sites structurally similar to the binding site of the query protein. These identified similar binding sites are then superimposedfacilitating a transfer of water molecules within such sites to the query protein. The resulting water location data is clustered to identify discrete spaces exhibiting a high conservation of water moleculesproviding a powerful visualization tool in the context of the studied protein system. MADE thus represents a rapid method that harnesses existing experimental data to place conserved water molecules within protein binding sites.

Moreoveras already emphasized earlierthe inclusion of explicit water molecules in MD simulations is imperative when investigating water-mediated polyphenolic interactions. This approach proves essential in discerning potential bridging water molecules that play a pivotal role in polyphenolic binding [1318].

The focus of this work is directed towards globular proteins characterized by well-defined binding cavitiesnotably enzymes and receptorswhere strong binding constants and specific interactions between polyphenols and proteins can be anticipated [23,24,25]. This stands in contrast to conformationally open proteins with multiple binding sitesexemplified by proline-rich salivary proteinswhere small polyphenols can be expected to exhibit a weak binding.

In the initial phase of our studywe meticulously examined polyphenol-protein interactions across the entire PDBcategorizing polyphenoles into distinct classes for a systematic analysis. Exploration of the entire PDB revealed a wide array of polyphenolic structureseach engaging in noncovalent interactions with proteins. Across crystal structures representing different classes of polyphenolswe observed common interaction patternsindicating a consistent behavior across various structural motifs.

Howeversubsequent extensive MD simulations of polyphenol-protein complexes uncover dynamic binding patternsemphasizing the influx of water molecules into the binding sites and exposing the limitations of static crystal structures. Notablywater-mediated interactions emerge as pivotal in polyphenol-protein bindingcontributing to the variable binding patterns observed in MD simulations. Moreovercomparing high- and low-resolution crystal structures as initial points of MD simulations demonstrates their robustness. Furthermorewe explored the influence of glycosylation on polyphenol bindingshedding light on its role in modulating interactions with proteins.

The work presented here marks the initial phase of a comprehensive project aimed at constructing a database delineating polyphenol-protein interaction profilesutilizing known structures deposited in the PDB. An establishment of such a database holds immense value for future endeavors in target identification and drug design. Specificallyit provides a practical means of validating pose predictions derived from classical- and inverse-docking procedures. If a given docking pose of a polyphenol aligns with the interactions identified in this study for a particular polyphenolic class (e.g.stilbenes)there is an enhanced confidence in the accuracy of such a pose.

Moreoverby harnessing existing polyphenol-protein interaction datawe aspire to formulate in the future a knowledge-based scoring function tailored to polyphenolic structures. This scoring function would systematically capture specific interactions within a queried protein binding siteconsidering distances between specific atom types. The scoring mechanism would be informed by existing polyphenol-protein interactions observed in crystal structures deposited in the PDB. Such an approach promises to enhance the precision and reliability of scoring in the context of polyphenol-protein interactionsthereby contributing to the advancement of rational natural-drug design strategies.

Mining publicly available databases

To identify protein structures within the PDB that bind polyphenolswe initiated the process by retrieving a comprehensive list of polyphenols from the Phenol-Explorer online database [26,27,28]which encompasses approximately 500 polyphenols and around 380 metabolites identified in biofluids following the consumption of polyphenol-rich sources. To compile a thorough catalog of polyphenol-protein interactionswe utilized the list of corresponding Simplified Molecular Input Line Entry System (SMILES) strings for polyphenols (including their metabolites) from the PhenolExplorer to query the entire PDB.

Employing OpenBabel [29] each polyphenol SMILES string was systematically compared to the entirety of ligands deposited in the PDB (PDB database obtained on September 1st 2023)generating Tanimoto-expressed similarities based on FP2 fingerprints. We then extracted each PDB structure containing a ligand that exhibited a Tanimoto coefficient of 0.90 or higher. Subsequentlybased on its molecular structureeach polyphenol was manually classified into one of the 12 distinct classesas depicted in Fig. 2.

Following the procedure outlined for identifying ligands with Tanimoto coefficients of \(\ge\) 0.90a manual curation step was implemented. Structures deemed excessively simpleincluding phenol and benzoic acidwere removed. On the other handcertain structures that did not fit the strict definition of polyphenols were retained. Notablycinnamic acid was retained due to its widespread occurrencerecognized significance in plant-based medicineand due to its role as a parent compound for other essential hydroxycinnamic derivatives such as caffeic acid [3031]. Similarlyspecific monoterpenes like thymol or carvacrol were also retained for identical reasons. Compounds featuring aromatic ether moieties in lieu of hydroxy groups were also preserved.

The final database comprises of 939 entries from the PDB first biological assemblies (Supporting Information Table S1). Notablyeach alternative conformationwhen presentis considered a separate entryresulting in a total of 1431 structures. Within these protein structures193 unique polyphenolic ligands have been identified. We emphasize that polyphenols containing covalent interactions with the protein targets were also retained in our studyas covalently bound polyphenols still maintain noncovalent interactionswhich typically facilitate initial recognition and binding events [32].

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Our classification of polyphenols known to bind to proteins sourced from the PDBwith each class represented by a prototypical compound

Interaction identification and analysis

Interaction analysis of variable types using PLIP

Each polyphenol-ligand complex from the PDB underwent analyses using the automated version of the Protein-Ligand Interaction Profiler (PLIP) algorithm [3334]. PLIP employs a meticulous approach to identify major noncovalent interactions at the single-atom level between small molecules and proteins. The algorithm detects seven interaction types: hydrogen bondshydrophobic contacts\(\uppi\)-stacking\(\uppi\)-cation interactionssalt bridgeswater bridgesand metal complexes. It is worth noting that PLIP also identifies halogen bonds; howeverthey are not expected to be present in polyphenols and are not discussed in this paper.

Before identifying interactionsthe input structure undergoes hydrogenationand the ligand is extracted along with its binding site. Subsequentlythe algorithm characterizes ligand atoms and functional groups by discerning hydrophobic regions and identifying acceptor/donor functional groups crucial for hydrogen bonds. MoreoverPLIP identifies aromatic rings and charge centersessential for the formation of \(\uppi\)-stacking\(\uppi\)-cation interactionsor salt bridges.

The interaction profile of each polyphenol-protein complex will be discussed based on the classification of the polyphenol itself (e.g.flavonoidstilbeneetc.)presented in Fig. 2. This approach streamlines the identification of possible characteristic interactions that each polyphenolic class can establish with proteins.

AdditionallyPLIP was utilized to generate time-dependent interaction contact maps through an in-house Python script that executed the PLIP analysis on each frame of the MD simulations.

Atom type classification and radial distribution analysis of polyphenol-protein interactions

To facilitate the future development of a polyphenol-specific scoring functioneach heavy atom and polar hydrogen of the polyphenol and protein was also categorized into specific typesreflecting their topology and hybridization states (e.g.C.3-sp³-hybridized carbonO.2-sp²-hybridized oxygenetc.). For each polyphenolic atomwe tallied the number of ligand and receptor atom types in contact with each other (e.g.the hydroxyl O.3 of the polyphenol and the N.am of the Gln sidechain). Close intermolecular interactions between specific atom types that occur more frequently than expected in a random distribution are likely to be energetically favorable andthereforecontribute positively to the binding affinity within the scoring function [35]. The key advantage of such knowledge-based potentialsto be developed in the futurelies in their ability to circumvent the need to balance multiple opposing contributions to the bindingsuch as desolvation and entropyas these factors are treated implicitly.

The atom types used for this purpose are defined in Supporting Information Table S2 and are based on Tripos SYBYL mol2 atom typeswith additional discrimination based on the location of the atom within the binding site (e.g.protein backbonesidechainor cofactor/ion/water) [36]. The interactions were evaluated using pair distribution function \(g_{i,j}(r)\) with normalization based on the total number of pair observations and the volume of shells corresponding to each bin of the histogram:

where \(p_{i,j}\) is the occurrence of atom type pairs i and j within each histogram binN is the total occurrence of protein-polyphenol atom pairs within a 7.5 Å distancer is the average distance value for each histogram binand b is the width of the histogram interval. The term \(g_{ab}(r)\) represents the normalized radial distribution between all polyphenol-protein atom pairsindependent of their type.

Normalizing with the shell volumes \(4 \pi r^2b\) and with the radial distribution of all atom types \(g_{ab}(r)\) eliminates the “non-interacting” background distribution from the protein-polyphenol systemsfacilitating a faster convergence to zero at large distances [36,37,38]. The selected maximal distance of 7.5 Å aligns with the default cutoff of PLIP [3334].

Molecular dynamics

To comprehensively understand the impact of polyphenol bindingit is essential to consider the conformational changes experienced by the protein targets. To attain an atomistic view of how polyphenols interact with proteins and influence their structural dynamicswe conducted MD simulations on a carefully selected subset of protein PDB structures bound to polyphenols.

We specifically observed how the binding patterns evolved during MD simulationsproviding insights into the stability of polyphenolic poses within the protein binding site. This approach is particularly crucial for identifying water-mediated H-bond bridgesgiven their well-known transient nature [39,40,41].

The protein-ligand complexes were prepared for subsequent MD simulations using the Chemistry at Harvard Macromolecular Mechanics graphical user interface (CHARMM-GUI) [42]utilizing structures obtained from the PDB. Prior to commencing MD simulationsthe complexes underwent solvation in rectangular TIP3P water boxes (with a 15 Å padding using periodic boundary conditions) including 0.15 M NaCl. To maintain system electro neutralitythe appropriate number of Na⁺ or Cl⁻ ions was added. Protonation states of ionizable amino-acid residues followed standard conventions in Chemistry at Harvard Macromolecular Mechanics (CHARMM)where Asp/Glu residues are negatively chargedArg/Lys residues are positively chargedand His residues are singly protonated at the N1\(\updelta\) atom. The CHARMM36 forcefield parameters were employed for proteins [4344]augmented by the CHARMM36-WYF set to enhance the description of \(\uppi\)-cation interactions [45]. Forcefield parameters for all polyphenols were developed using the automated ParamChem web server [46]. While acknowledging the limitations of automated methods in parameter determination for drug-like small moleculesour decision was informed by the low reported nonbonded penalties for most sterically accessible ligand atoms and by the relatively low penalties for bonded interactions corresponding to flexible moietiesthus providing confidence in the suitability of developed parameters for our study.

The coordinate files of polyphenolsproteins and water molecules were combinedand 50 steps of steepest descent and 50 steps of adopted basis Newton–Raphson energy minimization were carried out to remove any potential steric clashes that may occuras well as to optimize the atomic coordinates of the complexes. The complex was then equilibrated using NAMD [4748] at 310.15 K using the HOOVER thermostat and an integration timestep of 1 fs during a brief MD simulation. The NVT ensemble’s (constant number of particlesvolume and temperature) equilibration molecular dynamics took 0.125 ns to complete. This was followed by two independent production runs (the main and replica simulations) of 1 \(\upmu\mathrm{s}\)again performed using NAMD. Production runs were carried out in the NPT ensemblewith the timestep of 2 fs and the HOOVER thermostat and barostat set to 310.15 K and 1 barrespectively. Van der Waals interactions were cut off between 10 and 12 Å using the force switch method (VFSWIt). The electrostatic potential used the force shifting method (FSHIft) with a cutoff of 12 Å. The particle mesh Ewald summation [49] was applied to address long-range electrostatic interactions. Bonds involving hydrogen atoms were constrained using the SHAKE algorithm.

Root-mean-square deviations (RMSD) were calculated with the MDAnalysis Python library [5051]and directas well as water mediated H-bonds were analyzed implementing the recently developed Bridge2 software [5253]. Each simulated system was also carefully visually inspected in order to confirm the accuracy of the predictions.

Hydrogen-bond network analysis via Bridge2

All graph calculations were performed using Bridge/Bridge2a graph-based algorithm with a user-friendly graphical interface that efficiently computes both direct and water-mediated H-bond interaction networks [5253]. H-bonds were identified using geometric criteriawhich included either the donor-acceptor distance (from PDB structures) orif hydrogen atom coordinates were available from MD simulationsa combination of distance and H-bond angle criteria. The H-bond angles are computed using an optimized implementation of the Einstein summation conventionapplied to the position vectors of the donoracceptorand hydrogen atoms.

In H-bond graphs provided by Bridge/Bridge2nodes represent protein groups involved in H-bondswhile edges denote either direct or water-mediated H-bonds. Bridge/Bridge2 is particularly efficient in calculating water-mediated bridges between protein groups. To identify potential H-bond donors and acceptorsthe program uses a k-d tree approachwhich scales as \(n \times \log (n)\)where \(n\) is the number of spatial data pointsproviding substantial efficiency compared to the naive method of calculating all pairwise distanceswhich scales as \(n^2\).

We applied a distance criterion of \(\le\) 3.5 Å between donor and acceptor atoms. For H-bonds identified from atomic-level MD simulations containing hydrogen atom coordinateswe also applied an additional H-bond angle cutoff of \(\le\) 60°. Water-mediated bridges involving up to three water molecules were included in the H-bond graph analysis.

Water density clustering using MADE (ProBiS H2O)

Water density clustering was performed using MADE software (ProBiS H2O). The MADE (Macromolecular Density and Structure Analysis) software basically supersedes previous ProBiS H2O (MD) approach. Implemented as a user-friendly PyMOL pluginis a tool for identifying water/heteroatom conserved locations in proteins using experimental structural dataAlphaFold models or MD trajectories. The approach first performs structure alignment superimposed onto a querywhere suitable protein chains are identified based on used alignment and superposition algorithm (e.g.PyMOL’s align and superTM-alignDeepAlignProBiSGANGSTA+). Then3D clustering follows using 3D-DBSCAN to locate dense regions where specific species (e.g.metal ionswater moleculesetc.) occur across the examined structures or trajectories. High conservation clusters signify biologically relevant sites. The last step is the prediction/identification of studied species conserved positions across MD trajectories or structural clusters with visualization in PyMOL. The approach is robust and can successfully generalize beyond waters and ions to other molecular species and can be used for water network analysisdynamic binding eventsand protein binding site elaboration.

For further structure validationwe examined the epicatechin-3-gallate bound to glutamate dehydrogenase protein conformation with the highest occupancy (main trajectory 0-600 ns; also found in a replica from 226 ns to the end). We collected 10 ns equidistant MD snapshots (took care to designate the chain on the protein and ligandse.g.chain Pand set the HETEROATOM flag for corresponding atoms in the snapshot PDBs) and performed alignment using PyMOL’s align sequence-aware method on 61 snapshots alltogether. Thenall TIP3P waters were collected and subjected to 3D-DBSCAN using \(\varepsilon = 0.9\) parameter and exploring all possible clusters. TIP3P water clusters with 16 or more molecules and conservation above 0.26 were visualized for the reader [2254].

Diversity and characteristics of polyphenol-protein complexes in the extracted database

The extracted database encompasses proteins from all six biological kingdoms: animalsplantsfungiprotistabacteriaand archaeaand also includes viral proteins (Fig. 3a). Bacterial proteins constitute the largest portionrepresenting 48% of the databasefollowed by human proteins at 22%. Non-human animals constitute approximately 8% to the database. The plant kingdom is represented by 13%fungi by 6%viruses by 2%protists by 1%and archaea by less than 1%.

The predominance of human and bacterial proteins highlights that the majority of research efforts related to polyphenols have been concentrated on their benefits in human healthas receptor/enzyme modulators or as antibacterial agents. The plant kingdom category contains a variety of enzymes involved in the synthesis of polyphenolsexemplified by the structure of chalcone synthases complexed with naringenin (PDB ID 7VF0). This enzyme catalyzes the condensation of one molecule of p-coumaroyl-CoA with three molecules of malonyl-CoAforming naringenin chalconethe precursor of all flavonoids [55].

The most frequently represented ligand in the database is p-coumaric acidan isomer of hydroxycinnamic acid (Fig. 3b). This is followed by simpler structures such as salicylic acid and 4-hydroxybenzoic acid. More complex compounds like stilbene resveratrol and flavonoid quercetin are also prevalent. Hydroxycinnamic acid derivative ferulic acid followsshowcasing a diverse representation of polyphenolsranging from low molecular mass structures to largermore complex molecules.

The classification of ligands reveals that the majority belongs to the phenolic acids classfollowed by flavonoids and hydroxycinnamic acid derivativeshydroxybenzenesstilbenesand coumarins (Fig. 3c). Phenolic acids mostly include simple structures with a carboxylic group directly bound to a phenol ringencompassing salicylic acid and gallate. Flavonoids cover various subclassifications such as flavanolsflavonsanthocyanidinsand isoflavonoids. Hydroxycinnamic acid derivatives feature typical structures like ferulic and caffeic acidtheir esters (e.g.rosmarinic acid)and reduced derivatives like coniferaldehyde. Hydroxybenzenes describe simple structuresincluding all benzenediols and benzenetriolsalong with typical essential oil constituents like eugenol and thymol. Other classessuch as stilbenes and lignansmaintain stricter definitions. 27 complexes containing polyphenols are classified under the “others” category due to the absence of an appropriate classificationwith ellagic and mandelic acids forming notable examples.

The database is predominantly composed of enzymeswith oxidoreductasestransferasesand hydrolases forming the most represented classes (Fig. 3d). A relatively high number of structures also consists of photoreceptorsmore specifically photoactive yellow proteinwhich contains p-coumaric acid as a chromophore [56].

Classifying crystal structures with a resolution of 2.0 Å or better as high resolutionapproximately two-thirds of them meet this criterion (Fig. 3e). The importance of high resolution becomes evident when distinguishing ordered water molecules involved in ligand binding from free water molecules not engaged in binding interactions. High-resolution structures can facilitate the precise identification of individual ordered water molecules [5758]. In contrastlow-resolution structures may lack the detailed information necessary to discern fine hydrogen bonds and interactions between water molecules and surrounding atoms. The influence of resolution on the number of identified waters is well-established [2059].

On averagewe found that structures with a resolution lower or equal to 2.0 Å exhibit approximately 1.9 water molecules in proximity to polyphenolic ligandswhile strctures with resolution larger than 2.5 Å have only 0.6 water molecule on average around the ligand. The average number of detected bridging waters across all structures is 1.7. This underscores the significance of resolution in elucidating the water-mediated interactions crucial for understanding polyphenol-protein complexeswhich we further explore in this work.

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The main properties of the constructed protein-polyphenol database. a Classification of polyphenol-protein complexes into biological kingdoms. b The most represented polyphenols within the database. c Number of polyphenols in each class. d Classification of proteins containing polyphenolic ligands. e Distribution of polyphenol-protein complexes based on the resolution of the solved crystal structures. In all casesalternative conformations are not counted separately

Atom type preferences of polyphenols in binding to proteins and metals

We analyzed 272 protein-polyphenol atom pairs within a cutoff distance of 7.5 Åutilizing SYBYL mol2 types (Supporting Information Table S2). Among these pairs94 occurred more than 1000 times (Supporting Information Table S3). The most prevalent pairsconstituting 43% of all casesinvolved aromatic ligand carbons (C.ar) and various protein atomsincluding sp3-hybridized side chain carbons (C.3\(_{\hbox {sc}}\))aromatic side chain carbons (C.2\(_{\hbox {sc}}\))and backbone atoms (C.3\(_{\hbox {bk}}\)N.am\(_{\hbox {bk}}\)O.2\(_{\hbox {bk}}\)and C.2\(_{\hbox {bk}}\)). Howeverit is crucial to note that the abundance of these atom types is at the source of the high occurrence of these pairspotentially reflecting general atom type prevalence rather than specific interactions.

The sp³-hybridized hydroxy groups (O.3) of polyphenols play a crucial role in their binding to proteins (Fig. 1). Strong H-bond interactions were observed between polyphenolic O.3 and side chain oxygens (O.3\(_{\hbox {sc}}\)) of SerThrand Tyr residuesdenoted by a distinct peak in the radial distribution function (RDF) at approximately 2.8 Å (Fig. 4a) [60]. AdditionallyH-bond and polar interactions were identified between polyphenolic O.3 and side chain carboxylate oxygens (O.co2) of Asp and Glu residuesas evidenced by the two peaks in the RDF at around 2.8 Å and 4.5 Årespectively (Fig. 4b). Somewhat similar interaction motifs were detected between polyphenolic O.3 and side chain nitrogens (N.ar\(_{\hbox {sc}}\) and N.3\(_{\hbox {sc}}\)) of AsnGlnand Arg residues (Fig. 4c and d). Moreoverpolyphenolic catechols formed water bridges with water oxygens at around 2.8 Åagreeing with the expectations of H-bond interactions [61] (Fig. 4e). Consistent interaction profiles were obtained when reversing the atom types between protein atoms and ligand atomsaffirming the reciprocity of these interactions (Fig. S1).

A distinctive three-peak profile is evident in instances where the side chain of arginine (N.pl³) interacts with the carboxylic acid group (O.co2) of polyphenolic compoundsexemplified by hydroxycinnamic and rosmarinic acids (Fig. 4f). The peakssituated at around 2.73.5and 4.8 Ålikely signify different types of interactions: a peak at 2.7 Å could correspond to strong hydrogen bondsthe peak at 3.5 Å to weak hydrogen bondsand the peak at 4.8 Å to salt bridgesdemonstrating the multifaceted nature of interactions involved in polyphenol-protein binding [62].

The aromatic rings of polyphenols exhibit interactions with the aromatic side chains of HisPheTrpand Tyr residues (Fig. 1)involving \(\uppi\)-stacking\(\uppi\)-cationand hydrophobic interactions (Fig. 4g). The distance distribution between the aromatic carbons of the polyphenols (C.ar) and the side chains (C.ar\(_{\hbox {sc}}\)) displayed a broad peak ranging from around 3.0 to 6.5 Åindicating a spectrum of possible distances for \(\uppi\)-stacking interactions. Weaker and less specific interactions were also observed between aromatic carbons and sp³ or sp² carbonswhich can correspond to hydrophobic interactions (Fig. S1)contributing to the overall diversity and adaptability of polyphenol-protein interactions.

Certain protein backbone atomsspecifically the amide oxygen (O.2\(_{\hbox {bk}}\)) or nitrogen (N.am\(_{\hbox {bk}}\))displayed a notable preference for binding to specific polyphenolic atomssuch as O.3 or carboxylic oxygens (O.co2)at approximately 2.7 Å. The observed proximity of these atoms suggests the formation of strong H-bonds between them (Fig. 4hSupporting Information Fig. S1).

The most frequent interaction between metals and polyphenols primarily involved the aromatic carbons (C.ar) of the ligandsalthough this was largely influenced by the abundance of these ligand atoms. The RDF between metals and C.ar displayed three peaks: a prominent one at around 2.7 Å and two smaller ones at around 4.5 and 5.5 Årespectively. These peaks suggest the attraction of metal ions to the \(\uppi\)-systems of the benzene rings and the shift of \(\uppi\)-carbons towards metal ionsparticularly due to the binding of catecholic OH groups to metals (Fig. 4i). A more representative interaction between metals and polyphenols focused on the hydroxyl oxygens (O.3) of the ligandsoccurring in 575 cases. The distance distribution between metals and O.3 exhibited a sharp peak at around 2.4 Åfollowed by a rapid drop to zero (Fig. 4j)indicating a strong preference for metal-O.3 coordination. This preference highlights the significance of hydroxyl oxygens in mediating metal-ligand interactions within polyphenol-protein complexes.

All pair-pair RDF profiles that occur more than 1000 times are displayed in Supporting Information Fig. S1.

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Radial pair distribution functions (RDFs) illustrating the spatial relationships between selected pairs of protein and polyphenolic atoms. The RDFs depict the distance distributions between: a O.3\(_{\hbox {sc}}\) and O.3b O.co2\(_{\hbox {sc}}\) and O.3c N.ar\(_{\hbox {sc}}\) and O3d N.pl3\(_{\hbox {sc}}\) and O.3e O.3\(_{\hbox {w}}\) and O3f N.pl3\(_{\hbox {sc}}\) and O.co2g C.ar\(_{\hbox {sc}}\) and Carh O.2\(_{\hbox {bk}}\) and O.3(i) metal ions (M) and C.arand (j) metal ions (M) and O.3 atom types. In all casesthe left-hand atom-type corresponds to a protein atomwhile the right-hand atom corresponds to a polyphenolic atom. Each pair is present more than 1000 timesexcept for the M - O.3 pairwhich is present 575 times. RDFs of other atom pairs that are present more than 1000 times are displayed in Fig. S1

Interaction analysis across polyphenol classes

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Representative noncovalent interactions commonly formed by standard polyphenolic compounds

The non-covalent interactions between polyphenols and proteins primarily involve hydrogen bondshydrophobic interactionsmetal coordinationπ-stackingπ-cation interactionsand salt bridges (Fig. 5Tables S4-S15). Hydrogen bonds are typically formed between the aminoamideand hydroxyl groups of amino acid residues and the hydroxyl groups of polyphenols. The phenolic hydroxyl group acts as both a hydrogen bond donor (via the H-atom) and acceptor (via the O-atom). Keto moietiesfound in certain flavonoids (e.g.flavononesflavanones)chalconescoumarinscoumestanscurcuminoidsand naphthoquinonesalso contribute as H-bond acceptors. Naturallyglycosyl moieties in glycosylated polyphenols are common contributors of hydrogen bonds.

Direct H-bonds commonly form between these groups and amino acid side chains or backbone atomswith average donor-acceptor distances ranging from approximately 3.1–3.4 Å and donor-hydrogen-acceptor angles around 135–145°. The average distances and standard deviations of direct H-bonds exhibit slight variations among different polyphenol classeswhich suggests a consistent distribution and a pivotal role in complex stability (Fig. 6). Tyrosine and serine residues frequently participate in hydrogen bonding with polyphenols.

Water-mediated H-bonds are prevalent across all classesespecially in compounds with a larger number of hydroxyl groupslike stilbenes and flavonoids (Fig. 7Tables S5 and S8)enhancing their interaction networks within protein binding sites. The average distances of water bridges are generally similar across most polyphenol classesalthough they are notably lower in coumarins (Fig. 6Table S9)which is likely an artifact of low statistical sampling rather than specific structural properties.

Polyphenolsparticularly those with gallol or catechol groupsare known for their strong metal-chelating propertieseffectively coordinating metal ions such as iron and copper within metalloprotein binding sites [63]. Metal coordination generally involves the binding of polyphenol oxygen atoms (from hydroxyl or carboxyl groups) to metal ions. Phenolic acids and hydroxybenzenes frequently coordinate with metalsespecially ironforming complexes with average ligand-metal distances of around 2.1 Å (Fig. 6Tables S4 and S7). Flavonoids also participate in metal coordinationbinding ions such as Mn\(^{2+}\)Ni\(^{2+}\)Zn\(^{2+}\)Mg\(^{2+}\)or Fe\(^{2+}\)with average distances of 2.2 Å and coordination numbers typically ranging from four to sixoften adopting octahedral or trigonal bipyramidal geometries.

Stilbenes and coumarins generally do not engage in metal coordination within the observed PDB structures. The variability in metal coordination observed across different polyphenol classes may be attributed to differences in sampling size rather than inherent variations in metal-binding potential.

All classes of polyphenols form hydrophobic interactions with proteinsprimarily through their aromatic rings andin some casesaliphatic linkers. Notablycinnamic acid derivativesstilbeneslignanschalconesand curcuminoids possess significant aliphatic linkers connecting their aromatic ringswhich enhance hydrophobic interactions with proteins (Tables S3S8S11S13S14). The average distance for these interactions is around 3.6\(-\)3.8 Åonce again indicating an overall stable interaction pattern across all classes (Fig. 6). Leucine and phenylalanine are the most commonly involved amino acid residuesinteracting with polyphenols in a consistent manner across classes.

The aromatic systems of polyphenols are capable of forming geometrically varied π-stacking interactions with the side chains of phenylalaninetyrosinetryptophanand histidine. These interactions typically involve either face-to-face (parallel) or edge-to-face (T-shaped) configurations. Among the more represented polyphenol classes-flavonoidshydroxybenzenesand stilbenes-both T-type and P-type π-stacking interactions are observed in roughly equal proportions (Table S5S7S8).

In contrastphenolic acids and cinnamic acid derivatives generally adopt T-shaped stackingwith slightly larger distances of 4.7\(-\)5.1 Å and angles near 77–79° (Fig. 6Tables S4 and S6). The increased propensity for T-stacking configurations observed for phenolic acids and hydroxycinnamic acid derivatives is likely due to the electron-withdrawing effects of their carboxyl groupswhich alter the electronic properties of the aromatic ring. T-type stacking configurations generally have longer distances compared to P-typecontributing to the variation seen in average distances across these interactions (Fig. 6). The observed variations in average distances and standard deviations appear to be influenced by the inherent structural characteristics of each polyphenol class and reflect differences in their ability to participate in π-stacking.

\(\uppi\)-cation interactions involve the attraction between the electron-rich aromatic rings of polyphenols and positively charged side chains of lysinearginineand histidine. Flavonoids and stilbenes display relatively more \(\uppi\)-cation interactions compared to other classesprimarily with lysine and arginine residuesfacilitated by their multiple aromatic rings and electron-rich systems (Fig. 7Tables S5 and S8).

Salt bridges are formed only by polyphenols containing a carboxylate group capable of ionic interactions with positively charged residues lysine and arginine. Phenolic acids and cinnamic acid derivatives frequently form salt bridges due to their carboxylic acid moietieswith average heavy atom distances around 3.9 Å (Fig. 6Tables S4 and S6). Hydroxybenzenes possessing a carboxyl groupsuch as hydroxyphenylacetic acidsalso engage in salt bridge interactions. In contrastflavonoidsstilbenesand coumarins generally lack charged groups in their aglycone forms and thus rarely form salt bridges unless modified or conjugated with additional acidic groups.

For detailed discussions and notable examples of polyphenol-protein complexes with high representation in the PDBplease refer to the Supporting Information Section S1 and corresponding Fig. S2–S7. There we also provide the analysis of less represented classes (coumestanslignansnaphthoquinonescurcuminoidschalconesand nonclassified polyphenols) in Section S2 and Fig. S8. These compounds engage in similar non-covalent interactions; howevercaution is advised due to low data availabilitywhen drawing definitive conclusions.

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Overview of the average interaction distances and their standard deviations based on polyphenol classes. PA phenolic acidsF flavonoidsHAD hydroxycinnamic acid derivativesHB hydroxybenzenesS stilbenesC coumarins

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Relative frequency distributions of non-covalent interactions identified with PLIPcategorized by polyphenol classes with high representation in the PDB

Assessing the impact of crystallographic resolution and resolved waters on hydrogen bond networks

MD simulations of epicatechin-3-gallate binding to glutamate dehydrogenase solved at a low resolution: the role of water molecules and flexible binding modes

We employed MD simulations to explore the potential existence of water-mediated H-bonds in a low-resolution structure of the open conformation of bovine glutamate dehydrogenase (GDHPDB ID 6DHLresolution of 3.624 Å) bound to the flavonoid epicatechin-3-gallate (PDB ID: XEG) [64]. GDH represents an enzyme that catalyzes the oxidative deamination of glutamate to 2-oxoglutarate using NAD(P)\(^{+}\) as a cofactor. It plays a crucial role in amino-acid metabolism and cellular energy production. AdditionallyGDH is implicated in the regulation of insulin secretion by pancreatic beta-cellsand mutations in GDH can lead to hyperinsulinism/hyperammonemia syndromea rare genetic disorder affecting glucose and ammonia levels in the blood [64].

This structure was selected as it represents one of the lowest-resolution protein-polyphenol complex in the PDB. Our objective was to assess whether MD simulations could elucidate potential water-mediated H-bond network within the protein binding sitesince the experimental crystal structure does not contain resolved water molecules.

In the initial crystal structureepicatechin-3-gallate binds to the same allosteric site as the regulator ADPwith the interaction primarily driven by polar contacts (Fig. S9). Due to the absence of experimentally resolved water molecules and the likely dynamic nature of the binding modewe used MD simulations to gain further insights into the interaction landscape of this complexspecifically focusing on the role of water molecules and the flexibility of the binding modes.

RMSD analysis of the main trajectory revealed two significant conformational transitions (Fig. S9a). Initiallythe protein remained in a stable conformation for up to 0.5 µsafter which it transitioned to a new conformation around 0.75 µs and remained stable until the end of the simulation. Qualitativelysimilar behavior was observed in the replica trajectory (Fig. S9b). By analysing the structural changes in the helical regions of XEG bound to GDHwe revealed two distinct conformations with notable transitions and moderate helical displacements while preserving the integrity of the binding site as elaborated in SI - Section S3and Fig. S10.

To further characterize the distinct conformational states sampled during the simulationswe conducted hierarchical clustering based on RMSD matrices derived from 1,000 snapshots of both the main trajectory and the replica. In the main trajectorythe clustering analysis identified three conformational ensembles: the first spanning from 0 to 598 snapshotsthe second from 599 to 745 snapshotsand the third from 746 to 1,000 snapshots (Fig. S9a). The replica trajectory showed two conformational states: the first spanning from 0 to 226 snapshotsand the second continuing until the end of the simulation (Fig. S9b). These observations indicate that the system samples distinct conformational ensembleswith the replica trajectory eventually settling into the same ensemble observed in the main trajectory.

In addition to RMSD and clustering analyseswe performed a time-dependent analysis of protein-ligand interactions to investigate the evolution of key interactions during the simulation. Interaction fingerprints plotted over time demonstrated distinct interaction profiles that correlated with the observed conformational changes. In the main trajectoryan initial interaction pattern was observed up to approximately frame 500. This was followed by a transition that was completed around frame 750correlating with the shift to a new conformational ensemble (Fig. 8a). In the replica trajectorythe initial stabilization phase involved consistent interactions with Asp119 and Tyr382after which the system adopted a conformational ensemble similar to the beginning of the main trajectory (Fig. 8b)characterized by interactions involving Val120Pro121Phe122Asn388Lys488and Val492.

Utilizing Bridge2 [5253]we examined the water-mediated hydrogen bond formation between XEG and the protein residues in the initial and subsequent time-frames based on the above-described clustering analysis throughout the MD simulation. Our analysis revealed the entry of numerous water molecules into the binding siteparticipating in dynamic H-bond networks involved in ligand bindingwith observable changes over the course of the simulation.

Based on Bridge2 calculationswe observe in the initial crystal structure that the ligand XEG establishes direct H-bonding interactions with the side-chain atoms of Arg387ASer393AArg396AArg459BLys488Band Arg491B (Fig. S9c-e). Additionallytwo H-bonds are formed with backbone atoms of Cys115B and Val120B. During main MD simulations the three cluster-based water networks (snapshots at 0-598599-745and 746-1,000 ps) are overall similar at high occupancy (more than 75%)with slight differences in the residues involved and the complexity of the water-mediated interactions. A similar water network was also identified throughout the entire simulation using the MADE approach as TIP3P waters persisted as conserved clusters at key bridging locations between XEG and also Asp119Glu487His85Arg86Lys387Asn388His209 and Ser393 (Section S4 and Fig. S11).

The water-mediated hydrogen bond networks across the simulations (Fig. 8c–k and Fig. S9f,g) reveal a dynamic and adaptable systemwith a core set of residues-Asp119 and RArg86 from chain B and His209 from chain A-consistently participating in interactions. The initial cluster (frames 0-598(Fig. 8c–e) contains the fewest residueswhile the second cluster (599-745(Fig. 8f–h) adds His391 and Ile203 from chain Aexpanding the network. The third cluster (746-1000(Fig. 8i–k) demonstrates significant rearrangementslosing interactions with F122 (chain B) and Ser393Lys387Asn388 (chain A) but gaining new ones with Val120Arg491 (chain B) and His391Ser204Gln205 (chain A). Replica simulations show further evolutionwith the first replica network (0-226Fig. S9f) incorporating additional residues such as His85 and Tyr382 (chain B) and His195His391Ser204Ser393 (chain A)resembling the second cluster of the main simulation. The second replica network (227-1000Fig. S9g) adds Pro121 and Phe122 (chain B) and Gln205 (chain A)while losing His391 and Asn388reflecting continued structural adaptation. These observations highlight a possibility of a highly dynamic water-mediated networkwith residues and water molecules rearranging to accommodate structural and environmental demands.

Overallthis analysis underscores the crucial role of MD simulations in revealing binding water molecules. Our MD simulations shed light on the indispensable involvement of water molecules in the binding of polyphenols to proteinsa fact frequently disregarded in in silico studiesespecially when dealing with low-resolution X-ray or cryo-EM structures. Additionallythey underscore that the crystal structure does not necessarily grasp the most representative pose of the protein-polyphenol complexrevealing the flexible nature of polyphenol binding observed in MD simulation.

Despite inherent limitations and uncertainties associated with MD simulationssuch as the choice of force fieldsolvent modelsimulation timeand sampling methodour consistent observation of dynamic and flexible polyphenol binding (Fig. 1)mediated by several water moleculesreinforces the robustness of these findings [16].

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Interaction contact maps between glutamate dehydrogenase residues and XEG for the main a and replica b simulationsdepicting the presence of hydrophobic (yellow)hydrogen bonds (green)and rare π-cation (purple) interactions. c–k Dynamic binding of epicatechin-3-gallate (XEG) to glutamate dehydrogenase (GDH). Analysis of the water-mediated binding for the first cluster (snapshots 0-598) simulationshowing the c entire medoid (409) snapshotd zoomed-in binding siteand e the Bridge2 output of water-mediated H-bonding interactions. f–h panels corresponding to the second cluster (snapshots 599-745) and i–k panels corresponding to the third cluster (snapshots 746-1000). Blue cartoons in panels represent the backbone of GDH and sticks with grey carbons the amino-acid residues forming H-bonding interactions (cyan lines) with XEG. Sticks with green carbons represent the XEG ligand.The numbers on the edges represent the average number of water molecules bridging the H-bonding interaction

Influence of crystallographic resolution on water binding dynamics: comparative analysis of transthyretin-resveratrol complexes

We performed a comparative analysis of two TTR-resveratrol structuresone resolved at high resolution (PDB ID 7Q9OR = 1.35 Å) and the other at lower resolution (PDB ID 1DVSR = 2.00 Å)using 1 µs MD simulations to evaluate how crystallographic resolution impacts structural completeness. This analysis focused particularly on water-mediated hydrogen-bond networks and assessed the ability of MD simulations to compensate for any missing hydration details in lower-resolution structures.

In the high-resolution structure (PDB ID 7Q9OR = 1.35 Å)six water oxygen atoms are identified within three hydration layers around the resveratrol ligandwhereas in the lower-resolution structure (PDB ID 1DVSR = 2.00 Å)only two such water oxygens are present (Fig. 9ab). In the lower-resolution structureone of the water molecules bridges further toward the side chain atoms of Glu54A.

Despite relatively high RMSD values in certain regions during the MD simulationslikely due to the inherent flexibility of the loop regions and peripheral domains (Fig. S12a-d)the core structure and ligand-binding interactions remained stable throughout the simulations (Fig. 9c–e). Time-dependent analyses of H-bond networks and key interaction patterns further confirmed this stability (Fig. S12e-k). Interaction contact maps for both high- and low-resolution starting structures across main and replica simulations revealed consistent and conserved interaction profiles over time.

This highlights the robustness of the TTR-resveratrol complex in maintaining critical ligand interactionseven amidst structural flexibility or when derived from lower-quality experimental data. Specificallyin both high- and low-resolution starting structuresthe two direct hydrogen bonds with Ser117 and hydrophobic interactions with residues such as Leu108 and Ala110 were preservedwhile additional water molecules entered the binding site during the simulations (Fig. 9c–eS11e-k). Using a hydrogen-bond cutoff occupancy of \(\ge\)50%the same symmetrical water-mediated networkinvolving Ser117Thr106Glu54and Lys15was consistently observed across all main and replica simulationswith minimal variation in the average number of bridging water molecules (Fig. 9e and S12i-k).

It is important to note that our pose selection positions the catechol moiety of resveratrol facing outward from the proteina deliberate choice based on the existence of multiple poses documented for this complex (Fig. 9ab) [65].

Moreovera back-mapping approach was employed to analyze the observed water molecules in the high-resolution electron density map of the complex (PDB ID: 7Q9O). By superposing MD snapshots with the electron density mapit was possible to validate the placement of discussed water molecules in relation to the crystal structure. The electron density mapcarved within 5 Å of the resveratrol ligandrevealed that the modelled crystal water locations were consistently occupied by waters from the MD simulationunderscoring the relevance of these molecules in the binding and stabilization of the complex. These findings elaborate on the functional importance of water molecules in mediating interactions between transthyretin and resveratroladding to a cohesive picture of their structural and dynamic contributions to complex stability.

In conclusionour results demonstrate that starting from a lower-resolution structureMD simulations are capable of reproducing the same water-mediated bonding patterns observed with high-resolution structures. This highlights the ability of MD simulations to provide reliable and reproducible insights into protein-ligand dynamics and molecular interactionseven when initial crystallographic data are of lower quality. It reinforces the value of MD simulations as a robust tool for investigating biomolecular systems under varying experimental conditions.

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Water-mediated H-bonds were observed during the MD simulations of a TTR-resveratrol structure. ab The lower-resolution crystal structure (1DVS) contains two water molecules within the binding site. One forms a water-mediated H-bond to Glu54A. cd During MD simulationsan extensive water-mediated H-bond network was formed within the binding siteincluding residues from chains A and C. A frame from a 930\(^{th}\) ns is chosen for visualization. e Bridge2 output of the resveratrol water-mediated H-bonding. Values on the edges represent the average number of bridging water molecules during the main MD simulation of the low resolution structure. Corresponding figures from remaining simulations are deposited in Supporting Information Fig. S11i-k. Blue cartoons represent the backbone of H-bonding amino-acid residuesand sticks with grey carbons their side-chains. Resveratrol (STL) is presented by sticks of green carbonsand waters by balls-and-sticks representation (red oxygenswhite hydrogens). Direct H-bonds are shown with purple dotted linesand water mediated ones with cyan dotted lines. f 7Q9O in yellow-colored cartoon model with green stick model ligand is overlaid with 2fo-fc electron density map in light-blue mesh. Crystal-modelled waters that were fitted to the electron density are emphasized by dark-blue spheres. Our MD snapshot is superposed (rose-colored cartoon model with light-pink stick model ligand) with MD TIP3 waters of the inspected snapshot in element-colored stick model (red oxygen and white hydrogens). It can clearly be observed that all modelled crystal water locations are also occupied by MD TIP3 waters

MD simulation of the binding of quercetin and isoquercetin to sirtuin 6: the effect of glycosylation on dynamic polyphenol binding

Glycosylation plays a pivotal role in modulating the binding of polyphenols to proteinsinfluencing their biological activities and bioavailability [6667]. It serves as a natural mechanism for plants to regulate the activity and availability of polyphenols in response to environmental changes or stress conditions. Given the rapid metabolism of polyphenols upon their absorptionunderstanding the impact of glycosylation on polyphenol binding becomes crucial. Moreoverglycosylation emerges as a valuable biotechnological tool for modifying the properties and functions of polyphenols [68].

In generalglycosylation tends to diminish the binding affinity of polyphenols to proteins through a reduction in hydrophobicity and an increase in steric hindrance [6970]. Neverthelessglycosylation mayin certain caseselevate the binding affinity of specific polyphenols to particular proteins. This enhancement can occur through increased solubilityimproved stabilityheightened selectivityand the formation of specific interactions with amino-acid residues within the protein structure [68].

Sirtuin 6 (SIRT6) stands out as a critical NAD\(^{+}\)-dependent protein deacylase homodimeric enzymeplaying a crucial role in metabolic regulation and maintaining chromatin homeostasis. Its activation has been linked to the protection against a spectrum of metabolic and age-related diseaseswhile its inhibition is associated with anticarcinogenic effects. The activation of SIRT6 has been attributed to quercetina polyphenol that binds to the isoform-specific acyl-binding channel of the protein. The complex formed between SIRT6 and quercetin (PDB ID 6QCD) represents a rare instance where the structures of both the polyphenol itself and its direct glycosylated derivativeisoquercetin (PDB ID 6QCE)are available in the PDB [71]. Isoquercetina glycosylated derivative of quercetinhas demonstrated a heightened selectivity as an activator of SIRT6 due to the sugar moiety preventing its binding to alternative sites in remaining SIRTs. Notablyin the crystal structurethe aglyconic part of isoquercetin retains an identical binding pose as the parent moleculewhile the glycoside is oriented towards the cofactor in the isoquercetin (HW2) moleculeforming an H-bond with the ribose moiety [71].

To explore the influence of glycosylation on polyphenol behavior and activityMD simulations again emerge as a vital tool. By comparing the structural dynamics of aglycon and glycon structures through MD simulationswe sought to elucidate how glycosylation affects the binding of polyphenols. In our simulationsboth dimers of SIRT6 were consideredwith each dimere binding the corresponding flavonoid.

Specific RMSD values (Fig. S13a-d) for this simulated system appear large due to the inclusion of the entire dimer in the simulationwith structural contributions from 81 residues in β-sheets166 in α-helicesand 324 in loop regions. The N-terminal and C-terminal ends also span 17 and 28 residuesrespectivelywhich impacts RMSD values. Moreoverfor protein-only RMSD plotsall backbone-C atoms were usedwhile other RMSD plots included all atoms. For examplethe average RMSD for a 100 ns backbone calculation (main aglycone trajectory) was 4.58 Å ± 0.61whereas considering only helical structures reduced the RMSD to 3.94 Å ± 0.52. Overallwe therefore consider the SIRT6 systems stableand confirm this with the below described time-dependent interaction analyses.

Performing a time-dependent analysis of protein-ligand interactionsthe simulations of the aglycone (quercetin) system showed notable consistencywith key hydrophobic residues such as Phe82Phe86Val115and Phe86maintaining high occupancies and substantial frame overlap across the main and replica simulations (Fig. S13e,f). Hydrogen bonding interactionsparticularly with Pro62 and Leu186were also present in both simulationsalthough their occupancies and timing displayed some variability. In contrastthe glycone (isoquercetin) simulations displayed greater variability. Interactions with residues like Val115Ile185 and Leu186but additional interactionssuch as those involving Glu74emerged in the glycone system (Fig. S12g,h). H-bonding of e.g. the main chain of Phe64 showed more fluctuationslikely due to the introduction of heightened flexibility by the glycone moiety.

To complement the above analysisBridge2 was used to investigate water-mediated H-bonding networks in the last 400 ns of the simulation. This analysis revealed a high number of water-mediated interactions between the flavonoid ligands and the receptormany of which showed frequent occupancy (\(\ge\)50%) (Fig. 10). While comparing the water-mediated H-bond networks formed in the main and replica simulationswe observed deviations from the strong correspondence observed in previous TTR-resveratrol simulations. This suggests a more pronounced transient nature of water-mediated H-bondsresulting in rapid fluctuations of H-bonding patterns.

Interestinglyin simulations containing isoquercetinwe did not observe frequent interactions between the glycan moiety and the AR6 cofactoras observed in the static crystal structure. Howeverour analysis revealed the formation of intricate water-mediated H-bond networks involving the polar sugar moietyindicating the potential for glycosylation to introduce new interactions with proteinsprimarily mediated by water molecules (Fig. 10e–h). This underscores the role of glycosylation in modulating protein-ligand interactions and emphasizes the significance of water-mediated H-bonds in fine tuning the protein interactions with polyphenols. For instancein the main isoquercetin-SIRT6 MD simulationwe observed the formation of an extensive water-mediated H-bond network with the glycan moietyencompassing amino-acid residues around Asp187 (Fig.  10ceg). In the corresponding replica MD simulationa water network was formed between the glycan moiety and Phe86Asp116and Arg121 (Fig. 10dfh).

Here wethereforeobservethat glycosylation can play a pivotal role in influencing the binding pose and orientation of the aglyconic polyphenol portion within the protein binding sitethrough altered formation of noncovalent interactions and especially through water-mediated networks by its polar glyconic part.

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Water molecules mediate a large number of interactions between flavonoids and SIRT6 as observed during MD simulations. a Water-mediated H-bonds formed with quercetin (QUE) in the main MD simulation. b Water-mediated H-bonds formed with QUE in the replica MD simulation. c Water-mediated H-bonds formed with isoquercetin (HW2) in the main MD simulation. d Water-mediated H-bonds formed with HW2 in the replica MD simulation. eg 3D view of the formation of a frequent (occupancy \(\ge\) 50%) water-mediated H-bond network involving the sugar moiety of HW2 as observed in the main MD simulation. fh 3D view of the formation of a frequent (occupancy \(\ge\) 50%) water-mediated H-bond network involving the sugar moiety of HW2 as observed in the replica MD simulation

Our study was initiated by analyzing polyphenol-protein interactions within the entire Protein Data Bank (PDB)which revealed a diverse array of polyphenolic structures encompassing smaller hydroxybenzenes and phenolic acidsto larger and distinct flavonoidsstilbenesand lignans (Fig. 2). Across all these classespolyphenols engage in a variety of noncovalent interactions with their protein targets - predominantly in hydrophobic interactions with aromatic rings or linkersas well as in direct or water-mediated H-bonds with aromatic hydroxyl groups orwhen presentcarboxylic acid moieties (Fig. 7). Additionallynegatively charged carboxylic acids often form salt bridges. Frequent interactions also include \(\uppi\)-stacking and \(\uppi\)-cation interactions formed with aromatic rings. The catechol moiety of numerous polyphenols further facilitates metal complexation (Fig. 2). While similar overall interaction patterns are present among different polyphenolic classesexceptions exist (Fig. 6); for instancesalt-bridge interactions are specific for charged moieties such as phenolic acids and hydroxycinnamic acid derivatives. Water-mediated hydrogen bonds are notably prevalent in larger polyphenols with abundant aromatic hydroxyl groups arranged in a planar and rigid structure (flavonoidsstilbenes). Another exception is the occurrence of \(\uppi\)-stacking interactionswhich are less frequent in phenolic acids compared to remaining polyphenolslikely due to the electron-withdrawing effects of the carboxylic acid moiety (Fig. 6).

In the second part of our studywe explored various polyphenol-protein complexes using MD simulations to uncover the intricate dynamics of their interactions. Our exploration revealed dynamic binding patterns characterized by the initial influx of water molecules into the binding siteprominently observed in the XEG-GDH complexas well as in the binding of QUE and HW2 to SIRT6. These findings underscore the limitations of static crystal structures in capturing the most representative poses of protein-polyphenol complexes. Notablywater-mediated interactions emerge as crucial in polyphenol-protein binding due to the presence of multiple polyphenoloic polar groupsrendering the overall interaction patterns highly dynamic. Water molecules actively participate in mediating transient H-bonds between the ligand and protein residues via established water networks. The latter possibly contributes to the remarkable variability in the binding pattern of polyphenolsas well as to their promiscuous binding. The observed flexibility therefore advocates water-mediated H-bond and conserved water molecule analysis in polyphenol-protein interaction study.

Converselythe comparison between high and low-resolution crystal structures of TTR-resveratrolcoupled with extensive MD simulationshighlights the robustness of carefully curated MD simulations. These simulations reveal consistent dynamicsdespite the difference in resolution of the initial structural data.

Our investigations into the influence of glycosylation on polyphenol binding hint to its role in modulating interactions with proteins. Glycosylationfunctioning as a natural polyphenolic regulatory mechanism in plantscan impact the overall binding of the aglyconic part of the polyphenol by facilitating the formation of intricate water-interaction networks between the glyconic part of the polyphenol and the proteineither promoting or abolishing the activity.

In contrast to synthetic drugswhich frequently exhibit stable and specific binding modes [72]polyphenol binding seems to lack the typical stability seen in ligand-protein complexes representing synthetic actives. Insteadthe dynamic nature of polyphenol interactionsprimarily driven by the propensity to form water-mediated interactionsunderscores the intricate interplay among the ligandproteinand solvent environments. This emphasizes the necessity for comprehensive dynamical studies aimed at elucidating the molecular mechanisms underlying polyphenol-protein recognition.

In essenceour comprehensive analyses contribute to a deeper understanding of the nuanced interplay between polyphenols and proteins. This knowledge not only enhances our grasp of the molecular mechanismsbut also provides a foundation for future studies aimed at harnessing the therapeutic potential of polyphenols through informed drug design.