Next Article in Journal
Portrayal of NLRP3 Inflammasome in Atherosclerosis: Current Knowledge and Therapeutic Targets
Next Article in Special Issue
VSpipe-GUI, an Interactive Graphical User Interface for Virtual Screening and Hit Selection
Previous Article in Journal
Dengue Virus Capsid Protein Facilitates Genome Compaction and Packaging
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
IJMS
Volume 24
Issue 9
10.3390/ijms24098161
ijms-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Identification of Catechins’ Binding Sites in Monomeric Aβ42 through Ensemble Docking and MD Simulations

by
Rohoullah Firouzi
1,*,
Shahin Sowlati-Hashjin
2,
Cecilia Chávez-García
3,4,
Mitra Ashouri
5,
Mohammad Hossein Karimi-Jafari
6 and
Mikko Karttunen
3,4,7,*
1
Department of Physical Chemistry, Chemistry and Chemical Engineering Research Center of Iran, Tehran 1496813151, Iran
2
Institute of Biomedical Engineering, University of Toronto, Toronto, ON M5S 3G9, Canada
3
Department of Chemistry, The University of Western Ontario, 1151 Richmond Street, London, ON N6A 5B7, Canada
4
The Centre of Advanced Materials and Biomaterials Research, The University of Western Ontario, 1151 Richmond Street, London, ON N6A 5B7, Canada
5
Department of Physical Chemistry, School of Chemistry, College of Science, University of Tehran, Tehran P.O. Box 14155-6619, Iran
6
Department of Bioinformatics, Institute of Biochemistry and Biophysics, University of Tehran, Tehran P.O. Box 14155-6619, Iran
7
Department of Physics and Astronomy, The University of Western Ontario, 1151 Richmond Street, London, ON N6A 3K7, Canada
*
Authors to whom correspondence should be addressed.
Int. J. Mol. Sci. 2023, 24(9), 8161; https://0-doi-org.brum.beds.ac.uk/10.3390/ijms24098161
Submission received: 14 March 2023 / Revised: 9 April 2023 / Accepted: 17 April 2023 / Published: 3 May 2023
(This article belongs to the Special Issue Drug Discovery of Compounds by Structural Design)
Browse Figures
Versions Notes

Abstract

:
The assembly of the amyloid-β peptide (Aβ) into toxic oligomers and fibrils is associated with Alzheimer’s disease and dementia. Therefore, disrupting amyloid assembly by direct targeting of the Aβ monomeric form with small molecules or antibodies is a promising therapeutic strategy. However, given the dynamic nature of Aβ, standard computational tools cannot be easily applied for high-throughput structure-based virtual screening in drug discovery projects. In the current study, we propose a computational pipeline—in the framework of the ensemble docking strategy—to identify catechins’ binding sites in monomeric Aβ42. It is shown that both hydrophobic aromatic interactions and hydrogen bonding are crucial for the binding of catechins to Aβ42. Additionally, it has been found that all the studied ligands, especially EGCG, can act as potent inhibitors against amyloid aggregation by blocking the central hydrophobic region of Aβ. Our findings are evaluated and confirmed with multi-microsecond MD simulations. Finally, it is suggested that our proposed pipeline, with low computational cost in comparison with MD simulations, is a suitable approach for the virtual screening of ligand libraries against Aβ.

1. Introduction

Intrinsically disordered proteins (IDPs) are very flexible biomolecules without a well-defined folded structure and typically have important roles in biological processes, in particular in cellular signaling and gene regulation [1,2,3,4]. Under certain conditions, some IDPs may aggregate into highly toxic oligomers. These oligomers are associated with a wide range of serious human diseases such as cancer, neurodegenerative diseases, autoimmune disorders, cardiovascular disease, and type II diabetes [4,5,6,7,8,9]. Thus, preventing or reducing aggregation of the IDPs involved in such diseases is as an effective therapeutic strategy.
In recent years, there have been efforts to design and synthesize small molecules and short peptides to block IDP aggregation at different stages along the aggregation pathway, in particular nucleation and oligomer formation [4,10,11,12,13,14,15,16,17]. Several studies have revealed that a large number of natural compounds derived from plants, animals and microorganisms have the potential to inhibit oligomerization [11,12,18,19,20]. For example, several computational and experimental observations have shown that polyphenolic plant compounds which occur naturally in fruit, vegetables, chocolate, and tea are capable of inhibiting IDP aggregation [12,13,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38].
Finding aggregation inhibitors and direct targeting of monomeric IDPs via small molecules is a very active area of research and a wide variety of computational techniques have been applied, yet there are many inherent difficulties [12,13,14,27,39,40,41,42,43,44,45,46]. For example, most docking algorithms employ the flexible ligand and rigid receptor paradigm [47,48,49]. However, IDPs display high conformational heterogeneity, and ligand binding causes large structural changes in the IDP conformations. To circumvent this problem, heterogeneous conformational ensembles of IDPs have been used for docking studies [27,38,42,50,51]. Nevertheless, challenges associated with generating and choosing a set of suitable conformations for docking still remain. It will also be interesting to see how and if Alphafold [52] will change the situation as it has already been applied to non-IDP-related binding problems [53], but IDPs appear to be a challenge even for Alphafold [54].
Several methods have been proposed for efficient sampling of IDP’s conformational space and constructing a representative conformational ensemble. Examples include replica-exchange- [55,56,57,58] and metadynamics-based methods [59,60,61], diffusion map approaches [62,63] and Markov state modeling [64,65,66,67]. For comparisons between the methods, see for example [43,68,69,70]. One alternative for effective exploration of the conformational space is the use of multiple conventional MD trajectories (replicas) with different initial conditions (different velocities or/and different starting configurations). The strategy of choosing the initial conditions controls the effectiveness of this approach and its ability to enhance conformational sampling performance [71,72,73,74,75,76]. We have recently proposed a new efficient algorithm for comprehensive exploring of the conformational space of IDPs, called Blockwise Excursion Sampling (BES) [75,76]. It uses simulated annealing (SA) to find different low-energy states of various regions of conformational space as optimal starting configurations for short conventional MD simulations. In BES, conformational sampling is based on many uncorrelated short MD simulations starting from different points of the accessible phase space. It has been shown that the protocol is successful in generating a diverse conformational library for IDP conformations in agreement with experimental data [75,76].
In this work, a computational pipeline in the framework of the ensemble docking strategy has been proposed to identify catechins’ binding sites on the full-length human amyloid-β (Aβ42) monomer which is involved in Alzheimer’s disease [2,5,11,12,27,39,41,43]. Catechins (or flavan-3-ols) are dietary polyphenolic compounds commonly found in green tea and their inhibitory effects on Aβ aggregation have been the subject of numerous experimental and computational investigations [21,28,36,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94]. There is experimental evidence indicating that the catechins are able to disturb Aβ aggregation and change the aggregation process toward the formation of non-toxic oligomers [28,37,77,78,80,81,85,86]. Several computational studies have proposed that green tea catechin EGCG interacts with Aβ42 through both multiple hydrophobic interactions and hydrogen bonding [28,79,84,85]. Those studies have suggested that π-stacking interactions with aromatic amino acids of the aromatic hydrophobic core region of Aβ42 (from Tyr10 to Phe20) affect Aβ42 aggregation and can, consequently, disrupt interchain interactions in Aβ42 protofibril structure and lead to the distortion of the protofibril structure [28,83,84,85,87,93]. Here, we applied the BES protocol to generate a reliable structural library for Aβ42 monomer. Through the ensemble docking approach, a catechin library was docked onto the surfaces of a library of Aβ42 monomers to identify the binding “hot spots” on the Aβ42 peptide. In order to further evaluate the docking results and the stability of complexes, the obtained structures with the largest binding energies for each Aβ42–catechin complex were used as the starting structures for long multi-microsecond MD simulations (total of 15 μs).

2. Results and Discussion

2.1. Docking Analysis

Structural Analysis and Identifying Ligand Binding Site
Determination of the important residues that are in close contact with the ligand is very important for the identification of potential inhibitors of Aβ42 aggregation. The distances between the heavy atoms of the ligand and the residues of Aβ42 were used to define the binding sites of Aβ42. Based on this assumption, a list of binding residues for each selected complex was generated for which at least one heavy atom of the residues falls within the distance cutoff of any ligand heavy atom. To assess the effect of the distance cutoff, five distances were evaluated: 3.0, 3.5, 4.0, 4.5, and 5.0 Å; the distances cutoff from 3.0 to 5.0 Å are commonly used to study ligand–protein binding interactions, such as hydrogen bonds, hydrophobic contacts, and aromatic interactions [22,95,96,97,98,99,100].
The number of contacts between each ligand and Aβ42 residues for the set-1 (as defined in Section 3.2) was counted for five different distance cutoffs and tabulated in Tables S1–S5. Here, we only show the results related to the distance cutoff of 5.0 Å in Table 1. It is very important to emphasize that in our structural analysis, none of the conformers selected from molecular docking alone were used to draw qualitative conclusions about the binding site. All the structural analyses in this study are based on the results given in Table 1, which were obtained from the statistical study of all the selected complexes based on ΔΔGbinding, which is illustrated in more detail in Section 3.2.
The first observation from the tables is that all the ligands have the most contacts with residues Tyr10, Phe19, and Phe20. Moreover, based on the ranked lists (Table 11 and Tables S1–S5), other aromatic residues (His13, His14, His6, and Phe4) show a relatively high number of contacts and thus, they contribute to stabilizing the interactions with catechins. Therefore, it seems that these polyphenolic compounds tend to interact with the aromatic residues through stacking and/or T-shaped interactions, as shown in the snapshots of Figure 1, Figure 2, Figure 3, Figure 4 and Figure 5. Another important observation is that the tendency of ligands to interact with Tyr10 correlates with the number of hydroxyl groups on the ligands. This is seen in the data in the tables: EGCG possesses the largest number of hydroxyl groups (8 OH’s) and has a greater tendency to interact with Tyr10 than with Phe19 or Phe20, i.e., the number of contacts between EGCG and Tyr10 is larger than those between EGCG and Phe19 or Phe20. Thus, the data appear to imply that, hydrophobic aromatic interactions and hydrogen bonding are both crucial for the binding process. Finally, a comparative look at the tabulated values immediately shows that all ligands have a tendency to associate with the hydrophobic region of Aβ42 spanning residues from Tyr10 to Phe20. This region contains most of the aromatic residues found in full-length Aβ. This region also encompasses the central hydrophobic region (16KLVFF20) that, based on many experimental and computational studies, is involved in the initiation of amyloid aggregation [11,12,13,22,40,101,102,103,104,105,106]. Therefore, our docking results show that all studied ligands, especially EGCG, can act as potent inhibitors against amyloid aggregation through blocking the central hydrophobic region. These findings are in agreement with experimental studies [28,37,78,79,80,83].
The distributions of binding affinities (kcal.mol−1) of all ligands in set-1 (with the cutoff value of 0.1 kcal.mol−1) are shown in Figure 1, Figure 2, Figure 3, Figure 4 and Figure 5. In addition, the figures show a couple of complexes with the highest binding affinities and for each complex the different types of aromatic interactions, such as π–π, XH–π (X = C, N, O), and lone pair–π interactions between the aromatic rings of the ligands, and the sidechain of aromatic residues have been highlighted by colored dashed lines for clarity. In all cases, at least one aromatic residue was in contact with the ligand and in most cases, catechin compounds possessing multiple aromatic rings were capable of interacting with several aromatic residues simultaneously. A comparative look at Figure 1, Figure 2, Figure 3, Figure 4 and Figure 5 shows that one or two rotatable aromatic rings of the ligands (phenyl ring containing R3 substitution and gallate moiety, as illustrated in Section 3.1) are essential to make the aromatic interactions between the ligands and the aromatic residues. It is reasonable to assume that these flexible rings can be adjusted during the docking process, to maximize the aromatic interaction formation. In conclusion, it seems that these aromatic interactions play an important role in binding to aggregation-prone regions of Aβ42 and are essential for high affinity and binding specificity.

2.2. Analysis of MD Simulations

In order to evaluate the docking results, microsecond-scale MD simulations were performed on Aβ42-L (L = C, EC, ECG, EGC, EGCG) structures for which the largest binding energies were obtained by the docking procedure. The initial and final MD structures are provided in Figure S1 which show that the ligands maintain their interactions with Aβ42 throughout the simulation. Root mean square deviation (RMSD) of Aβ42 indicates that in all cases the system has reached steady state (Table S6 and Figure S2). The stability of the Aβ42-L complexes is also reflected in the relatively low radius of gyration (~1.0 nm) of Aβ42 (Table S6 and Figure S3). Moreover, the steady solvent accessible surface area (SASA; with <7% fluctuations) observed for the chains provides further support for the stability and compactness of the system (Table S6 and Figure S4).
The docking results suggest that Tyr10, Phe19, and to some extent Phe20, are the main residues involved in ligand binding. Average distances between these residues and the five ligands from the MD simulations are provided in Table 2. In agreement with the results from our docking procedure, with the exception of C for which the distance is ~1.0 nm, the average distance between the ligands and all three residues is ~0.5 nm (Table 2 and Figures S5–S9). The time evolution of these distances and the end-to-end distance for Aβ42 are shown in Figures S5–S9.
Inspection of hydrogen bonds between the ligands and Aβ42 reveals that hydrogen bonding plays a more important role in binding of EGCG and EGC than with the other three ligands (Table 3). Binding of C seems to have the least dependence on the hydrogen bonding among the ligands and relies on the π–π, XH–π (X = C, O) interactions. Average number of Aβ42∙∙∙ligand, Aβ42∙∙∙solvent, and ligand∙∙∙solvent hydrogen bonds are collected in Table 3.
Figures S10–S14 depict the contributions of Aβ42 residues that involve hydrogen bonding with the ligands. A comparison among the five Aβ42∙∙∙ligand systems reveals that there are a few residues in the Aβ42 sequence that frequently form hydrogen bonds with the five different catechins (e.g., Glu22, Asp23, and Ala42). These intransient hydrogen bonds alternate between the ligand and the residues, which may suggest that hydrogen bonds have a less important role in the binding of the ligands to Aβ42 compared to stacking interactions. The average number of hydrogen bonds between all the amino acids of the peptide and ligands are depicted in Figure S15. Similar conclusion has been previously made for Aβ42 protofibrils and EGCG, where EGCG was shown to bind to Aβ42 monomer through hydrophobic, π–π stacking, and hydrogen bonds [79,83,84,85,87,107]. Moreover, same study identifies Asp1, Glu22, and Ala42 residues to form the most hydrogen bonds with EGCG, which except for Asp1, agrees with our observation (Figure S14). It should be noted, however, that while we have examined disordered structures in this study, Li et al. [107] considered fibrils and 25 EGCG molecules.
Finally, the secondary structure of Aβ42 in the presence of catechins was also evaluated and plotted using the VMD visualization software (Figure 6). As is evident from the figure, ‘turn’ and ‘coil’ are the main observed secondary structures, followed by the helices and β-strands. In some instances, relatively short-lived fast-alternating helices (α-helix and 310 helix) as well as β-strands are formed. The propensity of certain regions of Aβ42 toward a specific type of secondary structure varies with the ligand type. For example, the N-terminal domain is mainly unstructured (turn and coil) in the presence of ECG, while a persistent β-strand appears in the region when EGC is bound. Similarly, residues 14–18 form a β-strand or turn-coil in the presence of EGCG but mainly form short-lived α-helices. As is apparent from the figure, different catechins have different impacts on the secondary structure of Aβ42, nevertheless, they all seem to impose local secondary structures across the Aβ42 chain, potentially impacting the formation and stability of amyloid fibrils.

3. Methods

3.1. Protein and Ligand Library Preparation

A library of Aβ42 (see Figure 7 for a snapshot and the amino acid sequence) structures was generated using the BES protocol. Briefly, 2000 excursion chains were performed such that all excursion chains were started from a fully extended structure; excursion chain refers to a sequence of MD and SA blocks in the BES protocol, for details and terminology please see [75,76]. It is interesting to note that it has already been shown that the chemical shift calculated for the conformational ensemble has a good agreement with experimental data [75,76].
Each excursion chain included five successive SA and MD blocks with maximum temperatures of 700, 600, 500, 400, and 350 K for the SA blocks. The relaxation time for each MD block was set to 120 ps and the last 100 ps were used to generate representative structures. In the next step, an average (mean) structure over the MD trajectory was obtained, and the root mean deviation (RMSD) was used as a criterion to identify the configuration in the MD trajectory that is structurally closest to the average structure. The selected structure was then energy minimized using the conjugate gradient method and used as a representative structure. As a result, for each MD block (five blocks in each excursion chain) one representative structure was derived. The final structural library included a total of 10,000 representative structures. Scheme 1 summarizes the procedure. For more technical details and a complete description of the BES protocol see [75,76].
For this study, we selected five main catechins found in green tea: (1) (+)-catechin (C), (2) (-)-epicatechin (EC), (3) (-)-epigallocatechin (EGC), (4) (-)-epicatechin-3-gallate (ECG), and (5) (-)-epigallocatechin-3-gallate (EGCG). Their chemical structures are shown in Figure 8 and their initial structures were taken from the ZINC database [108]. This was followed by optimization of the molecular geometries for all of them using the B3LYP exchange and correlation functional [109] and the 6-31+G(d,p) basis set. The GAMESS package [110] was used for geometry optimization.

3.2. Docking Setup

Docking simulations were performed with AutoDock Vina (version 1.1.2) software [111]. The docking search space for exploring ligand binding conformations around each representative Aβ42 structure was defined using a rectangular box centered at the center of mass of Aβ42 with a minimal distance of 12 Å from Aβ42 to the edges of the box. Therefore, depending on the size and shape of Aβ42 configuration, an optimized docking box was determined individually for each Aβ42. Each docking run generates nine optimal Aβ42-ligand bound conformations and overall, a total of 90,000 (10,000 × 9) poses were generated for each catechin compound. The different poses in each run are rank-ordered by the Vina score, a quantity that correlates with the binding free energy. The top-scoring pose in each run achieves the lowest free energy of binding in the complex.
Very recently, it has been shown that the correct pose (the pose with the lowest RMSD from the corresponding experimental pose) is usually predicted by Vina but sometimes, does not get the top score in the Vina ranking [112,113]. To avoid the problem and to capture the correct poses, it is recommended that except for the top-ranked pose, some important lower-ranked poses for each docking run should be identified and selected for post-docking analysis. For more discussions about ranking, see also [114,115,116,117]. For this purpose, the differences in the binding free energies between the top-ranked pose and lower-ranked poses were calculated for selected high-ranked modelled complexes for each docking run, ΔΔGbinding (= ΔGtop pose – ΔGlower-ranked pose). Different cutoff values for the ΔΔGbinding threshold (0.1, 0.2 and 0.3 kcal.mol−1) were used for selecting docking complexes, since the optimal selection of complexes is not known. For larger ΔΔGbinding cutoff values, more complexes were selected. For example, with the cutoff value of 0.1 kcal.mol−1, 17,431 (10,000 × 1 (top-ranked poses from each run) + 7431 (lower-ranked poses near top poses with the cutoff)) complexes were selected for docking of EGCG, while 29,923 and 42,833 complexes were selected with the cutoff values of 0.2 and 0.3 kcal.mol−1, respectively. The number of selected docking complexes for all ligands and the corresponding cutoff values of ΔΔGbinding are provided and labelled as “set-n” in Table 4. Since the results for different sets are very similar, we only present the results of set-1 (with the cutoff value of 0.1) and the results for the other two sets can be found in the Supporting Information. It should be emphasized that we employed the Vina ranking score just for selecting high-ranked docking poses for the structural analyses. The binding energies associated with these poses do not provide further insights.

3.3. MD Simulation Setup

All-atom MD simulations were performed on the peptide with five different ligands. The force field parameters for each ligand were created using the Antechamber program in the Ambertools19 package [118] and described by the General Amber Force Field (GAFF) [119] using AM1-BCC charges [120]. The Amberff99SB*-ILDNP force field [121] and the TIP3P water model [122] were adopted for the protein and water, respectively. Each protein was placed in a dodecahedral box such that the distance from the edges of the box to every atom in the protein was at least 1 nm and 150 mM of KCl was added to reproduce physiological conditions. Overall charge neutrality was preserved by adding 3 K+ counterions. The GROMACS 2016.3 [123]. package was used for all simulations. Each system was energy minimized using the method of steepest descents. This was followed by a pre-equilibration in the canonical ensemble, i.e., at constant particle number, volume, and temperature, for 100 ps. The Lennard–Jones potential was truncated using a shift function between 1.0 and 1.2 nm. Electrostatic interactions were calculated using the particle-mesh Ewald method (PME) [124,125] with a real space cutoff of 1.2 nm. The temperature was set to 310 K with the V-rescale algorithm [126] and pressure was kept at 1 atm using the Parrinello–Rahman barostat [127]. Bonds involving hydrogens were constrained using the linear constraint solver (P-LINCS) algorithm [128]. Pre-equilibration was followed by a production run of 3 μs with a time step of 2 fs for each of the five peptide-ligand systems.

4. Conclusions

In this work, binding of various well-known catechins present in green tea to the amyloid-β peptide (Aβ) has been predicted and analyzed. For this purpose, a computational pipeline in the framework of the ensemble docking strategy has been proposed in which a structurally heterogeneous ensemble of conformations of Aβ42 is used. The ensemble is generated by the Blockwise Excursion Sampling (BES) protocol [75,76] in which the conformational sampling is performed on the basis of many uncorrelated short-time MD simulations starting from different reasonable points of the accessible phase space.
It was observed that all green tea catechins compounds tended to interact with the aromatic residues through stacking and/or T-shaped interactions and, because of this, all compounds show a high tendency to interact with the hydrophobic region of Aβ42 spanning residues from Tyr10 to Phe20, the region with the highest number of the aromatic residues in full-length Aβ42. This region also encompasses the central hydrophobic core (CHC, residues 16–20) that, based on many experimental and computational studies, plays a key role in the aggregation process of Aβ42. Therefore, the docking results indicate that all studied ligands, especially EGCG, can act as potent inhibitors against amyloid aggregation by blocking the central hydrophobic core. Additionally, it has been suggested that both hydrophobic aromatic interactions and hydrogen bonding are crucial for the binding of catechins to Aβ42.
To evaluate the obtained findings in binding of catechin compounds to Aβ42, long multi-microsecond MD simulations were performed. It was shown that the present docking protocol is highly successful in identifying catechins’ binding sites in monomeric Aβ42, in agreement with previous MD simulations and some recent experimental observations for similar Aβ42–catechin complexes [28,77,78,79,83,84,85,87,93]. Finally, we suggested that our proposed pipeline with low computational cost in comparison with MD simulations is a suitable approach for high-throughput structure-based virtual screening of ligand libraries against the intrinsically disordered proteins (IDPs), such as Aβ. The execution of our proposed docking protocol for each ligand took up to a week using a standard Intel Core i7 desktop computer, while MD simulations for each ligand required, on average, approximately six weeks on a single compute node of the Compute Canada clusters, containing 24 CPU cores and 4 NVIDIA Tesla P100 GPUs.

Supplementary Materials

The following supporting information can be downloaded at: https://0-www-mdpi-com.brum.beds.ac.uk/article/10.3390/ijms24098161/s1, Reference [129] are cited in the Supplementary Materials.

Author Contributions

Conceptualization, R.F., S.S.-H., C.C.-G., M.A., M.H.K.-J. and M.K.; methodology, R.F., S.S.-H., C.C.-G., M.A., M.H.K.-J. and M.K; software, R.F., S.S.-H., C.C.-G., M.A., M.H.K.-J. and M.K; validation, R.F., S.S.-H., C.C.-G., M.A., M.H.K.-J. and M.K; formal analysis, R.F., S.S.-H., C.C.-G., M.A., M.H.K.-J. and M.K.; investigation, R.F., S.S.-H., C.C.-G., M.A., M.H.K.-J. and M.K.; resources, R.F., S.S.-H., C.C.-G., M.A., M.H.K.-J. and M.K.; data curation, R.F., S.S.-H., C.C.-G., M.A., M.H.K.-J. and M.K.; writing—original draft preparation, R.F., S.S.-H. and C.C.-G.; writing—review and editing, R.F., S.S.-H., C.C.-G., M.A., M.H.K.-J. and M.K.; visualization, C.C.-G.; supervision, R.F. and M.K.; project administration, R.F., S.S.-H. and M.K.; funding acquisition, M.K. All authors have read and agreed to the published version of the manuscript.

Funding

C.C.-G. thanks the Province of Ontario Trillium Scholarship Program, and M.K. thanks the Natural Sciences and Engineering Research Council of Canada (NSERC) and the Canada Research Chairs Program for funding. Computing facilities were provided by SHARCNET and Compute Canada.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

GAMESS package and AutoDock Vina (version 1.1.2) were used under a free academic license for ligands preparation and docking simulations. Produced and analyzed data are available upon request. MD simulations were performed using GROMACS 2016.3. Produced and analyzed data are available upon request.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Dyson, H.J.; Wright, P.E. Intrinsically unstructured proteins and their functions. Nat. Rev. Mol. Cell Biol. 2005, 6, 197–208. [Google Scholar] [CrossRef] [PubMed]
  2. Van der Lee, R.; Buljan, M.; Lang, B.; Weatheritt, R.J.; Daughdrill, G.W.; Dunker, A.K.; Fuxreiter, M.; Gough, J.; Gsponer, J.; Jones, D.T.; et al. Classification of Intrinsically Disordered Regions and Proteins. Chem. Rev. 2014, 114, 6589–6631. [Google Scholar] [CrossRef] [PubMed]
  3. Davey, N.E. The functional importance of structure in unstructured protein regions. Curr. Opin. Struct. Biol. 2019, 56, 155–163. [Google Scholar] [CrossRef] [PubMed]
  4. Dobson, C.M.; Knowles, T.P.J.; Vendruscolo, M. The Amyloid Phenomenon and Its Significance in Biology and Medicine. Cold Spring Harb. Perspect. Biol. 2020, 12, a033878. [Google Scholar] [CrossRef]
  5. Uversky, V.N.; Oldfield, C.J.; Dunker, A.K. Intrinsically Disordered Proteins in Human Diseases: Introducing the D2 Concept. Annu. Rev. Biophys. 2008, 37, 215–246. [Google Scholar] [CrossRef]
  6. Uversky, V.N. Intrinsic Disorder, Protein–Protein Interactions, and Disease. Adv. Protein Chem. Struct. Biol. 2018, 110, 85–121. [Google Scholar]
  7. Owen, M.C.; Gnutt, D.; Gao, M.; Wärmländer, S.K.T.S.; Jarvet, J.; Gräslund, A.; Winter, R.; Ebbinghaus, S.; Strodel, B. Effects of in vivo conditions on amyloid aggregation. Chem. Soc. Rev. 2019, 48, 3946–3996. [Google Scholar] [CrossRef]
  8. La Rosa, C.; Condorelli, M.; Compagnini, G.; Lolicato, F.; Milardi, D.; Do, T.N.; Karttunen, M.; Pannuzzo, M.; Ramamoorthy, A.; Fraternali, F.; et al. Symmetry-breaking transitions in the early steps of protein self-assembly. Eur. Biophys. J. 2020, 49, 175–191. [Google Scholar] [CrossRef]
  9. Strodel, B. Energy Landscapes of Protein Aggregation and Conformation Switching in Intrinsically Disordered Proteins. J. Mol. Biol. 2021, 433, 167182. [Google Scholar] [CrossRef]
  10. Metallo, S.J. Intrinsically disordered proteins are potential drug targets. Curr. Opin. Chem. Biol. 2010, 14, 481–488. [Google Scholar] [CrossRef]
  11. Nie, Q.; Du, X.-G.; Geng, M.-Y. Small molecule inhibitors of amyloid β peptide aggregation as a potential therapeutic strategy for Alzheimer’s disease. Acta Pharmacol. Sin. 2011, 32, 545–551. [Google Scholar] [CrossRef]
  12. Lemkul, J.A.; Bevan, D.R. The Role of Molecular Simulations in the Development of Inhibitors of Amyloid β-Peptide Aggregation for the Treatment of Alzheimer’s Disease. ACS Chem. Neurosci. 2012, 3, 845–856. [Google Scholar] [CrossRef] [PubMed]
  13. Härd, T.; Lendel, C. Inhibition of Amyloid Formation. J. Mol. Biol. 2012, 421, 441–465. [Google Scholar] [CrossRef] [PubMed]
  14. Heller, G.T.; Sormanni, P.; Vendruscolo, M. Targeting disordered proteins with small molecules using entropy. Trends Biochem. Sci. 2015, 40, 491–496. [Google Scholar] [CrossRef] [PubMed]
  15. Heller, G.T.; Aprile, F.A.; Vendruscolo, M. Methods of probing the interactions between small molecules and disordered proteins. Cell. Mol. Life Sci. 2017, 74, 3225–3243. [Google Scholar] [CrossRef] [PubMed]
  16. Ruan, H.; Sun, Q.; Zhang, W.; Liu, Y.; Lai, L. Targeting intrinsically disordered proteins at the edge of chaos. Drug Discov. Today 2019, 24, 217–227. [Google Scholar] [CrossRef]
  17. Firouzi, R.; Noohi, B. Identification of key stabilizing interactions of amyloid-β oligomers based on fragment molecular orbital calculations on macrocyclic β-hairpin peptides. Proteins 2022, 90, 229–238. [Google Scholar] [CrossRef]
  18. Tang, C.; Ye, Y.; Feng, Y.; Quinn, R.J. TCM, brain function and drug space. Nat. Prod. Rep. 2016, 33, 6–25. [Google Scholar] [CrossRef]
  19. Rodrigues, T.; Reker, D.; Schneider, P.; Schneider, G. Counting on natural products for drug design. Nat. Chem. 2016, 8, 531–541. [Google Scholar] [CrossRef]
  20. Habtemariam, S. Natural Products in Alzheimer’s Disease Therapy: Would Old Therapeutic Approaches Fix the Broken Promise of Modern Medicines? Molecules 2019, 24, 1519. [Google Scholar] [CrossRef]
  21. Porat, Y.; Abramowitz, A.; Gazit, E. Inhibition of Amyloid Fibril Formation by Polyphenols: Structural Similarity and Aromatic Interactions as a Common Inhibition Mechanism. Chem. Biol. Drug Des. 2006, 67, 27–37. [Google Scholar] [CrossRef] [PubMed]
  22. Ehrnhoefer, D.E.; Bieschke, J.; Boeddrich, A.; Herbst, M.; Masino, L.; Lurz, R.; Engemann, S.; Pastore, A.; Wanker, E.E. EGCG redirects amyloidogenic polypeptides into unstructured, off-pathway oligomers. Nat. Struct. Mol. Biol. 2008, 15, 558–566. [Google Scholar] [CrossRef] [PubMed]
  23. Jiang, L.; Liu, C.; Leibly, D.; Landau, M.; Zhao, M.; Hughes, M.P.; Eisenberg, D.S. Structure-based discovery of fiber-binding compounds that reduce the cytotoxicity of amyloid beta. eLife 2013, 2, e00857. [Google Scholar] [CrossRef] [PubMed]
  24. Stefani, M.; Rigacci, S. Protein Folding and Aggregation into Amyloid: The Interference by Natural Phenolic Compounds. Int. J. Mol. Sci. 2013, 14, 12411. [Google Scholar] [CrossRef] [PubMed]
  25. Zheng, Q.; Kebede, M.T.; Kemeh, M.M.; Islam, S.; Lee, B.; Bleck, S.D.; Wurfl, L.A.; Lazo, N.D. Inhibition of the Self-Assembly of Aβ and of Tau by Polyphenols: Mechanistic Studies. Molecules 2019, 24, 2316. [Google Scholar] [CrossRef] [PubMed]
  26. Murakami, K.; Irie, K. Three Structural Features of Functional Food Components and Herbal Medicine with Amyloid β42 Anti-Aggregation Properties. Molecules 2019, 24, 2125. [Google Scholar] [CrossRef]
  27. Zhu, M.; De Simone, A.; Schenk, D.; Toth, G.; Dobson, C.M.; Vendruscolo, M. Identification of small-molecule binding pockets in the soluble monomeric form of the Aβ42 peptide. J. Chem. Phys. 2013, 139, 035101. [Google Scholar] [CrossRef]
  28. Fusco, G.; Sanz-Hernandez, M.; Ruggeri, F.S.; Vendruscolo, M.; Dobson, C.M.; De Simone, A. Molecular determinants of the interaction of EGCG with ordered and disordered proteins. Biopolymers 2018, 109, e23117. [Google Scholar] [CrossRef]
  29. Jia, L.; Zhao, W.; Sang, J.; Wang, W.; Wei, W.; Wang, Y.; Zhao, F.; Lu, F.; Liu, F. Inhibitory Effect of a Flavonoid Dihydromyricetin against Aβ40 Amyloidogenesis and Its Associated Cytotoxicity. ACS Chem. Neurosci. 2019, 10, 4696–4703. [Google Scholar] [CrossRef]
  30. Tomaselli, S.; La Vitola, P.; Pagano, K.; Brandi, E.; Santamaria, G.; Galante, D.; D’Arrigo, C.; Moni, L.; Lambruschini, C.; Banfi, L.; et al. Biophysical and in Vivo Studies Identify a New Natural-Based Polyphenol, Counteracting Aβ Oligomerization in Vitro and Aβ Oligomer-Mediated Memory Impairment and Neuroinflammation in an Acute Mouse Model of Alzheimer’s Disease. ACS Chem. Neurosci. 2019, 10, 4462–4475. [Google Scholar] [CrossRef]
  31. Windsor, P.K.; Plassmeyer, S.P.; Mattock, D.S.; Bradfield, J.C.; Choi, E.Y.; Miller, B.R.; Han, B.H. Biflavonoid-Induced Disruption of Hydrogen Bonds Leads to Amyloid-β Disaggregation. Int. J. Mol. Sci. 2021, 22, 2888. [Google Scholar] [CrossRef] [PubMed]
  32. Henríquez, G.; Gomez, A.; Guerrero, E.; Narayan, M. Potential Role of Natural Polyphenols against Protein Aggregation Toxicity: In Vitro, In Vivo, and Clinical Studies. ACS Chem. Neurosci. 2020, 11, 2915–2934. [Google Scholar] [CrossRef] [PubMed]
  33. Nam, G.; Hong, M.; Lee, J.; Lee, H.J.; Ji, Y.; Kang, J.; Baik, M.-H.; Lim, M.H. Multiple reactivities of flavonoids towards pathological elements in Alzheimer’s disease: Structure–activity relationship. Chem. Sci. 2020, 11, 10243–10254. [Google Scholar] [CrossRef]
  34. Pandey, S.N.; Rangra, N.K.; Singh, S.; Arora, S.; Gupta, V. Evolving Role of Natural Products from Traditional Medicinal Herbs in the Treatment of Alzheimer’s Disease. ACS Chem. Neurosci. 2021, 12, 2718–2728. [Google Scholar] [CrossRef] [PubMed]
  35. Tuzimski, T.; Petruczynik, A. Determination of Anti-Alzheimer’s Disease Activity of Selected Plant Ingredients. Molecules 2022, 27, 3222. [Google Scholar] [CrossRef] [PubMed]
  36. Afzal, O.; Dalhat, M.H.; Altamimi, A.S.A.; Rasool, R.; Alzarea, S.I.; Almalki, W.H.; Murtaza, B.N.; Iftikhar, S.; Nadeem, S.; Nadeem, M.S.; et al. Green Tea Catechins Attenuate Neurodegenerative Diseases and Cognitive Deficits. Molecules 2022, 27, 7604. [Google Scholar] [CrossRef]
  37. Ahmed, R.; Huang, J.; Lifshitz, R.; Pomier, K.M.; Melacini, G. Structural determinants of the interactions of catechins with Aβ oligomers and lipid membranes. J. Biol. Chem. 2022, 298, 101502. [Google Scholar] [CrossRef]
  38. Dehabadi, M.H.; Caflisch, A.; Ilie, I.M.; Firouzi, R. Interactions of Curcumin’s Degradation Products with the Aβ42 Dimer: A Computational Study. J. Phys. Chem. B 2022, 126, 7627–7637. [Google Scholar] [CrossRef]
  39. Rezaei-Ghaleh, N.; Blackledge, M.; Zweckstetter, M. Intrinsically Disordered Proteins: From Sequence and Conformational Properties toward Drug Discovery. ChemBioChem 2012, 13, 930–950. [Google Scholar] [CrossRef]
  40. Attanasio, F.; Convertino, M.; Magno, A.; Caflisch, A.; Corazza, A.; Haridas, H.; Esposito, G.; Cataldo, S.; Pignataro, B.; Milardi, D.; et al. Carnosine Inhibits Aβ42 Aggregation by Perturbing the H-Bond Network in and around the Central Hydrophobic Cluster. ChemBioChem 2013, 14, 583–592. [Google Scholar] [CrossRef]
  41. Nguyen, P.; Derreumaux, P. Understanding Amyloid Fibril Nucleation and Aβ Oligomer/Drug Interactions from Computer Simulations. Acc. Chem. Res. 2014, 47, 603–611. [Google Scholar] [CrossRef] [PubMed]
  42. Heller, G.T.; Bonomi, M.; Vendruscolo, M. Structural Ensemble Modulation upon Small-Molecule Binding to Disordered Proteins. J. Mol. Biol. 2018, 430, 2288–2292. [Google Scholar] [CrossRef] [PubMed]
  43. Bhattacharya, S.; Xu, L.; Thompson, D. Revisiting the earliest signatures of amyloidogenesis: Roadmaps emerging from computational modeling and experiment. WIREs Comput. Mol. Sci. 2018, 8, e1359. [Google Scholar] [CrossRef]
  44. Nguyen, P.H.; Ramamoorthy, A.; Sahoo, B.R.; Zheng, J.; Faller, P.; Straub, J.E.; Dominguez, L.; Shea, J.-E.; Dokholyan, N.V.; De Simone, A.; et al. Amyloid Oligomers: A Joint Experimental/Computational Perspective on Alzheimer’s Disease, Parkinson’s Disease, Type II Diabetes, and Amyotrophic Lateral Sclerosis. Chem. Rev. 2021, 121, 2545–2647. [Google Scholar] [CrossRef] [PubMed]
  45. Boopathi, S.; Poma, A.B.; Garduño-Juárez, R. An Overview of Several Inhibitors for Alzheimer’s Disease: Characterization and Failure. Int. J. Mol. Sci. 2021, 22, 10798. [Google Scholar] [CrossRef]
  46. Löhr, T.; Kohlhoff, K.; Heller, G.T.; Camilloni, C.; Vendruscolo, M. A Small Molecule Stabilizes the Disordered Native State of the Alzheimer’s Aβ Peptide. ACS Chem. Neurosci. 2022, 13, 1738–1745. [Google Scholar] [CrossRef]
  47. Meng, X.-Y.; Zhang, H.-X.; Mezei, M.; Cui, M. Molecular Docking: A Powerful Approach for Structure-Based Drug Discovery. Curr. Comput.-Aided Drug Des. 2011, 7, 146–157. [Google Scholar] [CrossRef]
  48. Craig, I.R.; Essex, J.W.; Spiegel, K. Ensemble Docking into Multiple Crystallographically Derived Protein Structures: An Evaluation Based on the Statistical Analysis of Enrichments. J. Chem. Inf. Model. 2010, 50, 511–524. [Google Scholar] [CrossRef]
  49. Forli, S.; Huey, R.; Pique, M.E.; Sanner, M.F.; Goodsell, D.S.; Olson, A.J. Computational protein–ligand docking and virtual drug screening with the AutoDock suite. Nat. Protoc. 2016, 11, 905–919. [Google Scholar] [CrossRef]
  50. Limongelli, V.; Marinelli, L.; Cosconati, S.; Braun, H.A.; Schmidt, B.; Novellino, E. Ensemble-Docking Approach on BACE-1: Pharmacophore Perception and Guidelines for Drug Design. ChemMedChem 2007, 2, 667–678. [Google Scholar] [CrossRef]
  51. Tóth, G.; Gardai, S.J.; Zago, W.; Bertoncini, C.W.; Cremades, N.; Roy, S.L.; Tambe, M.A.; Rochet, J.-C.; Galvagnion, C.; Skibinski, G.; et al. Targeting the Intrinsically Disordered Structural Ensemble of α-Synuclein by Small Molecules as a Potential Therapeutic Strategy for Parkinson’s Disease. PLoS ONE 2014, 9, e87133. [Google Scholar] [CrossRef]
  52. Ruff, K.M.; Pappu, R.V. AlphaFold and Implications for Intrinsically Disordered Proteins. J. Mol. Biol. 2021, 433, 167208. [Google Scholar] [CrossRef]
  53. Yuan, Q.; Chen, S.; Rao, J.; Zheng, S.; Zhao, H.; Yang, Y. AlphaFold2-aware protein-DNA binding site prediction using graph transformer. Brief. Bioinformatics 2022, 23, bbab564. [Google Scholar] [CrossRef]
  54. Wilson, C.J.; Choy, W.-Y.; Karttunen, M. AlphaFold2: A role for disordered protein prediction? Int. J. Mol. Sci. 2022, 23, 4591. [Google Scholar] [CrossRef]
  55. Ball, K.A.; Phillips, A.H.; Wemmer, D.E.; Head-Gordon, T. Differences in β-strand Populations of Monomeric Aβ40 and Aβ42. Biophys. J. 2013, 104, 2714–2724. [Google Scholar] [CrossRef]
  56. Rosenman, D.J.; Wang, C.; García, A.E. Characterization of Aβ Monomers through the Convergence of Ensemble Properties among Simulations with Multiple Force Fields. J. Phys. Chem. B 2016, 120, 259–277. [Google Scholar] [CrossRef] [PubMed]
  57. Ziegler, Z.; Schmidt, M.; Gurry, T.; Burger, V.; Stultz, C.M. Mollack: A web server for the automated creation of conformational ensembles for intrinsically disordered proteins. Bioinformatics 2016, 32, 2545–2547. [Google Scholar] [CrossRef] [PubMed]
  58. Ball, K.A.; Phillips, A.H.; Nerenberg, P.S.; Fawzi, N.L.; Wemmer, D.E.; Head-Gordon, T. Homogeneous and Heterogeneous Tertiary Structure Ensembles of Amyloid-β Peptides. Biochemistry 2011, 50, 7612–7628. [Google Scholar] [CrossRef]
  59. Do, T.N.; Choy, W.-Y.; Karttunen, M. Accelerating the Conformational Sampling of Intrinsically Disordered Proteins. J. Chem. Theory Comput. 2014, 10, 5081–5094. [Google Scholar] [CrossRef] [PubMed]
  60. Camilloni, C.; Vendruscolo, M. Statistical Mechanics of the Denatured State of a Protein Using Replica-Averaged Metadynamics. J. Am. Chem. Soc. 2014, 136, 8982–8991. [Google Scholar] [CrossRef] [PubMed]
  61. Zerze, G.H.; Miller, C.M.; Granata, D.; Mittal, J. Free Energy Surface of an Intrinsically Disordered Protein: Comparison between Temperature Replica Exchange Molecular Dynamics and Bias-Exchange Metadynamics. J. Chem. Theory Comput. 2015, 11, 2776–2782. [Google Scholar] [CrossRef]
  62. Zheng, W.; Rohrdanz, M.A.; Clementi, C. Rapid Exploration of Configuration Space with Diffusion-Map-Directed Molecular Dynamics. J. Phys. Chem. B 2013, 117, 12769–12776. [Google Scholar] [CrossRef]
  63. Fujisaki, H.; Moritsugu, K.; Mitsutake, A.; Suetani, H. Conformational change of a biomolecule studied by the weighted ensemble method: Use of the diffusion map method to extract reaction coordinates. J. Chem. Phys. 2018, 149, 134112. [Google Scholar] [CrossRef]
  64. Noé, F.; Clementi, C. Kinetic Distance and Kinetic Maps from Molecular Dynamics Simulation. J. Chem. Theory Comput. 2015, 11, 5002–5011. [Google Scholar] [CrossRef]
  65. Paul, A.; Samantray, S.; Anteghini, M.; Khaled, M.; Strodel, B. Thermodynamics and kinetics of the amyloid-β peptide revealed by Markov state models based on MD data in agreement with experiment. Chem. Sci. 2021, 12, 6652–6669. [Google Scholar] [CrossRef]
  66. Cukier, R.I. Generating Intrinsically Disordered Protein Conformational Ensembles from a Database of Ramachandran Space Pair Residue Probabilities Using a Markov Chain. J. Phys. Chem. B 2018, 122, 9087–9101. [Google Scholar] [CrossRef]
  67. Lin, Y.-S.; Bowman, G.R.; Beauchamp, K.A.; Pande, V.S. Investigating How Peptide Length and a Pathogenic Mutation Modify the Structural Ensemble of Amyloid Beta Monomer. Biophys. J. 2012, 102, 315–324. [Google Scholar] [CrossRef]
  68. Barducci, A.; Pfaendtner, J.; Bonomi, M. Tackling sampling challenges in biomolecular simulations. Methods Mol. Biol. 2015, 1215, 151–171. [Google Scholar]
  69. Ilie, I.M.; Caflisch, A. Simulation Studies of Amyloidogenic Polypeptides and Their Aggregates. Chem. Rev. 2019, 119, 6956–6993. [Google Scholar] [CrossRef]
  70. Nasica-Labouze, J.; Nguyen, P.H.; Sterpone, F.; Berthoumieu, O.; Buchete, N.-V.; Coté, S.; De Simone, A.; Doig, A.J.; Faller, P.; Garcia, A.; et al. Amyloid β Protein and Alzheimer’s Disease: When Computer Simulations Complement Experimental Studies. Chem. Rev. 2015, 115, 3518–3563. [Google Scholar] [CrossRef]
  71. Nakajima, N.; Nakamura, H.; Kidera, A. Multicanonical Ensemble Generated by Molecular Dynamics Simulation for Enhanced Conformational Sampling of Peptides. J. Phys. Chem. B 1997, 101, 817–824. [Google Scholar] [CrossRef]
  72. Caves, L.S.; Evanseck, J.D.; Karplus, M. Locally accessible conformations of proteins: Multiple molecular dynamics simulations of crambin. Protein Sci. 1998, 7, 649–666. [Google Scholar] [CrossRef] [PubMed]
  73. Harada, R.; Takano, Y.; Baba, T.; Shigeta, Y. Simple, yet powerful methodologies for conformational sampling of proteins. Phys. Chem. Chem. Phys. 2015, 17, 6155–6173. [Google Scholar] [CrossRef] [PubMed]
  74. Harada, R.; Shigeta, Y. Efficient Conformational Search Based on Structural Dissimilarity Sampling: Applications for Reproducing Structural Transitions of Proteins. J. Chem. Theory Comput. 2017, 13, 1411–1423. [Google Scholar] [CrossRef]
  75. Salehi, N.; Amininasab, M.; Firouzi, R.; Karimi-Jafari, M.H. Efficient construction of a diverse conformational library for amyloid-β as an intrinsically disordered protein. J. Mol. Graph. Model. 2019, 88, 183–193. [Google Scholar] [CrossRef]
  76. Dehabadi, M.H.; Firouzi, R. Constructing conformational library for amyloid-β42 dimers as the smallest toxic oligomers using two CHARMM force fields. J. Mol. Graph. Model. 2022, 115, 108207. [Google Scholar] [CrossRef]
  77. Ahmed, R.; VanSchouwen, B.; Jafari, N.; Ni, X.; Ortega, J.; Melacini, G. Molecular Mechanism for the (-)-Epigallocatechin Gallate-Induced Toxic to Nontoxic Remodeling of Aβ Oligomers. J. Am. Chem. Soc. 2017, 139, 13720–13734. [Google Scholar] [CrossRef]
  78. Lopez del Amo, J.M.; Fink, U.; Dasari, M.; Grelle, G.; Wanker, E.E.; Bieschke, J.; Reif, B. Structural Properties of EGCG-Induced, Nontoxic Alzheimer’s Disease Aβ Oligomers. J. Mol. Biol. 2012, 421, 517–524. [Google Scholar] [CrossRef]
  79. Wang, S.-H.; Liu, F.-F.; Dong, X.-Y.; Sun, Y. Thermodynamic Analysis of the Molecular Interactions between Amyloid β-Peptide 42 and (-)-Epigallocatechin-3-gallate. J. Phys. Chem. B 2010, 114, 11576–11583. [Google Scholar] [CrossRef]
  80. Sinha, S.; Du, Z.; Maiti, P.; Klärner, F.-G.; Schrader, T.; Wang, C.; Bitan, G. Comparison of Three Amyloid Assembly Inhibitors: The Sugar scyllo-Inositol, the Polyphenol Epigallocatechin Gallate, and the Molecular Tweezer CLR01. ACS Chem. Neurosci. 2012, 3, 451–458. [Google Scholar] [CrossRef]
  81. Mori, T.; Koyama, N.; Tan, J.; Segawa, T.; Maeda, M.; Town, T. Combined treatment with the phenolics (-)-epigallocatechin-3-gallate and ferulic acid improves cognition and reduces Alzheimer-like pathology in mice. J. Biol. Chem. 2019, 294, 2714–5444. [Google Scholar] [CrossRef]
  82. Lantz, R.; Busbee, B.; Wojcikiewicz, E.P.; Du, D. Flavonoids with Vicinal Hydroxyl Groups Inhibit Human Calcitonin Amyloid Formation. Chem. Eur. J. 2020, 26, 13063–13071. [Google Scholar] [CrossRef] [PubMed]
  83. Minh Hung, H.; Nguyen, M.T.; Tran, P.-T.; Truong, V.K.; Chapman, J.; Quynh Anh, L.H.; Derreumaux, P.; Vu, V.V.; Ngo, S.T. Impact of the Astaxanthin, Betanin, and EGCG Compounds on Small Oligomers of Amyloid Aβ40 Peptide. J. Chem. Inf. Model. 2020, 60, 1399–1408. [Google Scholar] [CrossRef] [PubMed]
  84. Zhan, C.; Chen, Y.; Tang, Y.; Wei, G. Green Tea Extracts EGCG and EGC Display Distinct Mechanisms in Disrupting Aβ42 Protofibril. ACS Chem. Neurosci. 2020, 11, 1841–1851. [Google Scholar] [CrossRef] [PubMed]
  85. Acharya, A.; Stockmann, J.; Beyer, L.; Rudack, T.; Nabers, A.; Gumbart, J.C.; Gerwert, K.; Batista, V.S. The Effect of (-)-Epigallocatechin-3-Gallate on the Amyloid-β Secondary Structure. Biophys. J. 2020, 119, 349–359. [Google Scholar] [CrossRef]
  86. Chen, T.; Yang, Y.; Zhu, S.; Lu, Y.; Zhu, L.; Wang, Y.; Wang, X. Inhibition of Aβ aggregates in Alzheimer’s disease by epigallocatechin and epicatechin-3-gallate from green tea. Bioorg. Chem. 2020, 105, 104382. [Google Scholar] [CrossRef]
  87. Fang, M.; Zhang, Q.; Guan, P.; Su, K.; Wang, X.; Hu, X. Insights into Molecular Mechanisms of EGCG and Apigenin on Disrupting Amyloid-Beta Protofibrils Based on Molecular Dynamics Simulations. J. Phys. Chem. B 2022, 126, 8155–8165. [Google Scholar] [CrossRef]
  88. Ono, K.; Hamaguchi, T.; Naiki, H.; Yamada, M. Anti-amyloidogenic effects of antioxidants: Implications for the prevention and therapeutics of Alzheimer’s disease. Biochim. Biophys. Acta Mol. Basis Dis. 2006, 1762, 575–586. [Google Scholar] [CrossRef]
  89. Ono, K.; Yoshiike, Y.; Takashima, A.; Hasegawa, K.; Naiki, H.; Yamada, M. Potent anti-amyloidogenic and fibril-destabilizing effects of polyphenols in vitro: Implications for the prevention and therapeutics of Alzheimer’s disease. J. Neurochem. 2003, 87, 172–181. [Google Scholar] [CrossRef]
  90. Ahmed, R.; Akcan, M.; Khondker, A.; Rheinstädter, M.C.; Bozelli, J.C.; Epand, R.M.; Huynh, V.; Wylie, R.G.; Boulton, S.; Huang, J.; et al. Atomic resolution map of the soluble amyloid beta assembly toxic surfaces. Chem. Sci. 2019, 10, 6072–6082. [Google Scholar] [CrossRef]
  91. Takai, E.; Uda, K.; Yoshida, T.; Zako, T.; Maeda, M.; Shiraki, K. Cysteine inhibits the fibrillisation and cytotoxicity of amyloid-β 40 and 42: Implications for the contribution of the thiophilic interaction. Phys. Chem. Chem. Phys. 2014, 16, 3566–3572. [Google Scholar] [CrossRef] [PubMed]
  92. Gonçalves, P.B.; Sodero, A.C.; Cordeiro, Y. Green Tea Epigallocatechin-3-gallate (EGCG) Targeting Protein Misfolding in Drug Discovery for Neurodegenerative Diseases. Biomolecules 2021, 11, 767. [Google Scholar] [CrossRef] [PubMed]
  93. Pervin, M.; Unno, K.; Ohishi, T.; Tanabe, H.; Miyoshi, N.; Nakamura, Y. Beneficial Effects of Green Tea Catechins on Neurodegenerative Diseases. Molecules 2018, 23, 1297. [Google Scholar] [CrossRef] [PubMed]
  94. Martinez Pomier, K.; Ahmed, R.; Melacini, G. Catechins as Tools to Understand the Molecular Basis of Neurodegeneration. Molecules 2020, 25, 3571. [Google Scholar] [CrossRef]
  95. Pérez-Nueno, V.I.; Rabal, O.; Borrell, J.I.; Teixidó, J. APIF: A New Interaction Fingerprint Based on Atom Pairs and Its Application to Virtual Screening. J. Chem. Inf. Model. 2009, 49, 1245–1260. [Google Scholar] [CrossRef]
  96. Tan, L.; Batista, J.; Bajorath, J. Rationalization of the Performance and Target Dependence of Similarity Searching Incorporating Protein−Ligand Interaction Information. J. Chem. Inf. Model. 2010, 50, 1042–1052. [Google Scholar] [CrossRef]
  97. Batista, J.; Tan, L.; Bajorath, J. Atom-Centered Interacting Fragments and Similarity Search Applications. J. Chem. Inf. Model. 2010, 50, 79–86. [Google Scholar] [CrossRef]
  98. Nishio, M.; Umezawa, Y.; Fantini, J.; Weiss, M.S.; Chakrabarti, P. CH–π hydrogen bonds in biological macromolecules. Phys. Chem. Chem. Phys. 2014, 16, 12648–12683. [Google Scholar] [CrossRef]
  99. Weisel, M.; Bitter, H.-M.; Diederich, F.; So, W.V.; Kondru, R. PROLIX: Rapid Mining of Protein–Ligand Interactions in Large Crystal Structure Databases. J. Chem. Inf. Model. 2012, 52, 1450–1461. [Google Scholar] [CrossRef]
  100. Li, G.-B.; Yang, L.-L.; Wang, W.-J.; Li, L.-L.; Yang, S.-Y. ID-Score: A New Empirical Scoring Function Based on a Comprehensive Set of Descriptors Related to Protein–Ligand Interactions. J. Chem. Inf. Model. 2013, 53, 592–600. [Google Scholar] [CrossRef]
  101. Chimon, S.; Shaibat, M.A.; Jones, C.R.; Calero, D.C.; Aizezi, B.; Ishii, Y. Evidence of fibril-like β-sheet structures in a neurotoxic amyloid intermediate of Alzheimer’s β-amyloid. Nat. Struct. Mol. Biol. 2007, 14, 1157–1164. [Google Scholar] [CrossRef] [PubMed]
  102. Berhanu, W.M.; Hansmann, U.H.E. Side-chain hydrophobicity and the stability of Aβ16–22 aggregates. Protein Sci. 2012, 21, 1837–1848. [Google Scholar] [CrossRef] [PubMed]
  103. Crespi, G.A.N.; Hermans, S.J.; Parker, M.W.; Miles, L.A. Molecular basis for mid-region amyloid-β capture by leading Alzheimer’s disease immunotherapies. Sci. Rep. 2015, 5, 9649. [Google Scholar] [CrossRef] [PubMed]
  104. Riek, R.; Eisenberg, D.S. The activities of amyloids from a structural perspective. Nature 2016, 539, 227–235. [Google Scholar] [CrossRef] [PubMed]
  105. Liu, H.; Morris, C.; Lantz, R.; Kent, T.W.; Elbassal, E.A.; Wojcikiewicz, E.P.; Du, D. Residue-Specific Dynamics and Local Environmental Changes in Aβ40 Oligomer and Fibril Formation. Angew. Chem. Int. Ed. 2018, 57, 8017–8021. [Google Scholar] [CrossRef]
  106. Sonar, K.; Mancera, R.L. Characterization of the Conformations of Amyloid Beta 42 in Solution That May Mediate Its Initial Hydrophobic Aggregation. J. Phys. Chem. B 2022, 126, 7916–7933. [Google Scholar] [CrossRef]
  107. Li, F.; Zhan, C.; Dong, X.; Wei, G. Molecular mechanisms of resveratrol and EGCG in the inhibition of Aβ42 aggregation and disruption of Aβ42 protofibril: Similarities and differences. Phys. Chem. Chem. Phys. 2021, 23, 18843–18854. [Google Scholar] [CrossRef]
  108. Irwin, J.J.; Shoichet, B.K. ZINC—A Free Database of Commercially Available Compounds for Virtual Screening. J. Chem. Inf. Model. 2005, 45, 177–182. [Google Scholar] [CrossRef]
  109. Stephens, P.J.; Devlin, F.J.; Chabalowski, C.F.; Frisch, M.J. Ab Initio Calculation of Vibrational Absorption and Circular Dichroism Spectra Using Density Functional Force Fields. J. Phys. Chem. 1994, 98, 11623–11627. [Google Scholar] [CrossRef]
  110. Schmidt, M.W.; Baldridge, K.K.; Boatz, J.A.; Elbert, S.T.; Gordon, M.S.; Jensen, J.H.; Koseki, S.; Matsunaga, N.; Nguyen, K.A.; Su, S.; et al. General atomic and molecular electronic structure system. J. Comput. Chem. 1993, 14, 1347–1363. [Google Scholar] [CrossRef]
  111. 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. 2010, 31, 455–461. [Google Scholar] [CrossRef] [PubMed]
  112. Nguyen, N.T.; Nguyen, T.H.; Pham, T.N.H.; Huy, N.T.; Bay, M.V.; Pham, M.Q.; Nam, P.C.; Vu, V.V.; Ngo, S.T. Autodock Vina Adopts More Accurate Binding Poses but Autodock4 Forms Better Binding Affinity. J. Chem. Inf. Model. 2020, 60, 204–211. [Google Scholar] [CrossRef] [PubMed]
  113. Bagheri, S.; Behnejad, H.; Firouzi, R.; Karimi-Jafari, M.H. Using the Semiempirical Quantum Mechanics in Improving the Molecular Docking: A Case Study with CDK2. Mol. Inf. 2020, 39, 2000036. [Google Scholar] [CrossRef] [PubMed]
  114. Cole, J.; Davis, E.; Jones, G.; Sage, C.R. Molecular Docking—A Solved Problem? In Comprehensive Medicinal Chemistry III; Chackalamannil, S., Rotella, D., Ward, S.E., Eds.; Elsevier: Oxford, UK, 2017; pp. 297–318. [Google Scholar]
  115. Liu, J.; Wang, R. Classification of Current Scoring Functions. J. Chem. Inf. Model. 2015, 55, 475–482. [Google Scholar] [CrossRef] [PubMed]
  116. Leach, A.R.; Shoichet, B.K.; Peishoff, C.E. Prediction of Protein−Ligand Interactions. Docking and Scoring:  Successes and Gaps. J. Med. Chem. 2006, 49, 5851–5855. [Google Scholar] [CrossRef]
  117. Jain, A.N. Scoring Functions for Protein-Ligand Docking. Curr. Protein Pept. Sci. 2006, 7, 407–420. [Google Scholar]
  118. Case, D.A.; Cheatham Iii, T.E.; Darden, T.; Gohlke, H.; Luo, R.; Merz, K.M., Jr.; Onufriev, A.; Simmerling, C.; Wang, B.; Woods, R.J. The Amber biomolecular simulation programs. J. Comput. Chem. 2005, 26, 1668–1688. [Google Scholar] [CrossRef]
  119. Wang, J.; Wolf, R.M.; Caldwell, J.W.; Kollman, P.A.; Case, D.A. Development and testing of a general amber force field. J. Comput. Chem. 2004, 25, 1157–1174. [Google Scholar] [CrossRef]
  120. Jakalian, A.; Jack, D.B.; Bayly, C.I. Fast, efficient generation of high-quality atomic charges. AM1-BCC model: II. Parameterization and validation. J. Comput. Chem. 2002, 23, 1623–1641. [Google Scholar] [CrossRef]
  121. Aliev, A.E.; Kulke, M.; Khaneja, H.S.; Chudasama, V.; Sheppard, T.D.; Lanigan, R.M. Motional timescale predictions by molecular dynamics simulations: Case study using proline and hydroxyproline sidechain dynamics. Proteins. 2014, 82, 195–215. [Google Scholar] [CrossRef]
  122. Jorgensen, W.L.; Chandrasekhar, J.; Madura, J.D.; Impey, R.W.; Klein, M.L. Comparison of simple potential functions for simulating liquid water. J. Chem. Phys. 1983, 79, 926–935. [Google Scholar] [CrossRef]
  123. Abraham, M.J.; Murtola, T.; Schulz, R.; Páll, S.; Smith, J.C.; Hess, B.; Lindahl, E. GROMACS: High performance molecular simulations through multi-level parallelism from laptops to supercomputers. SoftwareX 2015, 1–2, 19–25. [Google Scholar] [CrossRef]
  124. Darden, T.; York, D.; Pedersen, L. Particle mesh Ewald: An N⋅log(N) method for Ewald sums in large systems. J. Chem. Phys. 1993, 98, 10089–10092. [Google Scholar] [CrossRef]
  125. Essmann, U.; Perera, L.; Berkowitz, M.L.; Darden, T.; Lee, H.; Pedersen, L.G. A smooth particle mesh Ewald method. J. Chem. Phys. 1995, 103, 8577–8593. [Google Scholar] [CrossRef]
  126. Bussi, G.; Donadio, D.; Parrinello, M. Canonical sampling through velocity rescaling. J. Chem. Phys. 2007, 126, 014101. [Google Scholar] [CrossRef] [PubMed]
  127. Parrinello, M.; Rahman, A. Polymorphic transitions in single crystals: A new molecular dynamics method. J. Appl. Phys. 1981, 52, 7182–7190. [Google Scholar] [CrossRef]
  128. Hess, B. P-LINCS:  A Parallel Linear Constraint Solver for Molecular Simulation. J. Chem. Theory Comput. 2008, 4, 116–122. [Google Scholar] [CrossRef]
  129. Eisenhaber, F.; Lijnzaad, P.; Argos, P.; Sander, C.; Scharf, M. The double cubic lattice method: Efficient approaches to numerical integration of surface area and volume and to dot surface contouring of molecular assemblies. J. Comput. Chem. 1995, 16, 273–284. [Google Scholar] [CrossRef]
Ijms 24 08161 g001 550
Figure 1. The histogram of binding affinity (kcal.mol−1) of the C ligand in set-1 (with the cutoff value of 0.1 kcal.mol−1, as illustrated in Section 3.2). The binding affinities were divided to bins of 0.1 kcal/mol, and the numbers on top of the bins show the number of complexes in each bin. The three aromatic residue hotspots (i.e., Tyr10, Phe19, and Phe20, see Figure 7 for sequence) and interacting aromatic residues with the ligand for some complexes with the highest binding affinities are shown in licorice representation. Favorable aromatic interactions between the ligand and peptide are depicted with orange dashed lines.
Figure 1. The histogram of binding affinity (kcal.mol−1) of the C ligand in set-1 (with the cutoff value of 0.1 kcal.mol−1, as illustrated in Section 3.2). The binding affinities were divided to bins of 0.1 kcal/mol, and the numbers on top of the bins show the number of complexes in each bin. The three aromatic residue hotspots (i.e., Tyr10, Phe19, and Phe20, see Figure 7 for sequence) and interacting aromatic residues with the ligand for some complexes with the highest binding affinities are shown in licorice representation. Favorable aromatic interactions between the ligand and peptide are depicted with orange dashed lines.
Ijms 24 08161 g001
Ijms 24 08161 g002 550
Figure 2. The histogram of binding affinity (kcal.mol−1) of the EC ligand in set-1 (with the cutoff value of 0.1 kcal.mol−1, as illustrated in Section 3.2). The binding affinities were divided to bins of 0.1 kcal.mol−1, and the numbers on top of the bins show the number of complexes in each bin. The three aromatic residue hotspots (i.e., Tyr10, Phe19, and Phe20, see Figure 7 for sequence) and interacting aromatic residues with the ligand for some complexes with the highest binding affinities are shown in licorice representation. Favorable aromatic interactions between the ligand and peptide are depicted with orange dashed lines.
Figure 2. The histogram of binding affinity (kcal.mol−1) of the EC ligand in set-1 (with the cutoff value of 0.1 kcal.mol−1, as illustrated in Section 3.2). The binding affinities were divided to bins of 0.1 kcal.mol−1, and the numbers on top of the bins show the number of complexes in each bin. The three aromatic residue hotspots (i.e., Tyr10, Phe19, and Phe20, see Figure 7 for sequence) and interacting aromatic residues with the ligand for some complexes with the highest binding affinities are shown in licorice representation. Favorable aromatic interactions between the ligand and peptide are depicted with orange dashed lines.
Ijms 24 08161 g002
Ijms 24 08161 g003 550
Figure 3. The histogram of binding affinity (kcal.mol−1) of the ECG ligand in set-1 (with the cutoff value of 0.1 kcal.mol−1, as illustrated in Section 3.2). The binding affinities were divided to bins of 0.1 kcal.mol−1, and the numbers on top of the bins show the number of complexes in each bin. The three aromatic residue hotspots (i.e., Tyr10, Phe19, and Phe20, see Figure 7 for sequence) and interacting aromatic residues with the ligand for some complexes with the highest binding affinities are shown in licorice representation. Favorable aromatic interactions between the ligand and peptide are depicted with orange dashed lines.
Figure 3. The histogram of binding affinity (kcal.mol−1) of the ECG ligand in set-1 (with the cutoff value of 0.1 kcal.mol−1, as illustrated in Section 3.2). The binding affinities were divided to bins of 0.1 kcal.mol−1, and the numbers on top of the bins show the number of complexes in each bin. The three aromatic residue hotspots (i.e., Tyr10, Phe19, and Phe20, see Figure 7 for sequence) and interacting aromatic residues with the ligand for some complexes with the highest binding affinities are shown in licorice representation. Favorable aromatic interactions between the ligand and peptide are depicted with orange dashed lines.
Ijms 24 08161 g003
Ijms 24 08161 g004 550
Figure 4. The histogram of binding affinity (kcal.mol−1) of the EGC ligand in set-1 (with the cutoff value of 0.1 kcal.mol−1, as illustrated in Section 3.2). The binding affinities were divided to bins of 0.1 kcal.mol−1, and the numbers on top of the bins show the number of complexes in each bin. The three aromatic residue hotspots (i.e., Tyr10, Phe19, and Phe20, see Figure 7 for sequence) and interacting aromatic residues with the ligand for some complexes with the highest binding affinities are shown in licorice representation. Favorable aromatic interactions between the ligand and peptide are depicted with orange dashed lines.
Figure 4. The histogram of binding affinity (kcal.mol−1) of the EGC ligand in set-1 (with the cutoff value of 0.1 kcal.mol−1, as illustrated in Section 3.2). The binding affinities were divided to bins of 0.1 kcal.mol−1, and the numbers on top of the bins show the number of complexes in each bin. The three aromatic residue hotspots (i.e., Tyr10, Phe19, and Phe20, see Figure 7 for sequence) and interacting aromatic residues with the ligand for some complexes with the highest binding affinities are shown in licorice representation. Favorable aromatic interactions between the ligand and peptide are depicted with orange dashed lines.
Ijms 24 08161 g004
Ijms 24 08161 g005 550
Figure 5. The histogram of binding affinity (kcal.mol−1) of the EGCG ligand in set-1 (with the cutoff value of 0.1 kcal.mol−1, as illustrated in Section 3.2). The binding affinities were divided to bins of 0.1 kcal.mol−1, and the numbers on top of the bins show the number of complexes for each bin. The three aromatic residue hotspots (i.e., Tyr10, Phe19, and Phe20, see Figure 7 for sequence) and interacting aromatic residues with the ligand for some complexes with the highest binding affinities are shown in licorice representation. Favorable aromatic interactions between the ligand and peptide are depicted with orange dashed lines.
Figure 5. The histogram of binding affinity (kcal.mol−1) of the EGCG ligand in set-1 (with the cutoff value of 0.1 kcal.mol−1, as illustrated in Section 3.2). The binding affinities were divided to bins of 0.1 kcal.mol−1, and the numbers on top of the bins show the number of complexes for each bin. The three aromatic residue hotspots (i.e., Tyr10, Phe19, and Phe20, see Figure 7 for sequence) and interacting aromatic residues with the ligand for some complexes with the highest binding affinities are shown in licorice representation. Favorable aromatic interactions between the ligand and peptide are depicted with orange dashed lines.
Ijms 24 08161 g005
Ijms 24 08161 g006 550
Figure 6. Secondary structure evolution of Aβ42 with time is shown in the presence of catechins. Turn, β-strand, α-helix, 310 helix, random coil, and isolated bridge are shown in teal, yellow, blue, pink, white, and beige, respectively.
Figure 6. Secondary structure evolution of Aβ42 with time is shown in the presence of catechins. Turn, β-strand, α-helix, 310 helix, random coil, and isolated bridge are shown in teal, yellow, blue, pink, white, and beige, respectively.
Ijms 24 08161 g006
Ijms 24 08161 g007 550
Figure 7. A random structure of Amyloid β42. Aβ42 sequence: 1DAEFRHDSG 10YEVHHQKLVF 20FAEDVGSNKG 30AIIGLMVGGV 40VI42A. Asp1 (N-terminus) and Ala42 (C-terminus) are shown using van der Waals radii. Other residues are represented in licorice. Acidic, basic, polar, and non-polar amino acids are shown in red, blue, green, and white, respectively.
Figure 7. A random structure of Amyloid β42. Aβ42 sequence: 1DAEFRHDSG 10YEVHHQKLVF 20FAEDVGSNKG 30AIIGLMVGGV 40VI42A. Asp1 (N-terminus) and Ala42 (C-terminus) are shown using van der Waals radii. Other residues are represented in licorice. Acidic, basic, polar, and non-polar amino acids are shown in red, blue, green, and white, respectively.
Ijms 24 08161 g007
Ijms 24 08161 sch001 550
Scheme 1. Flowchart of steps taken in this study.
Scheme 1. Flowchart of steps taken in this study.
Ijms 24 08161 sch001
Ijms 24 08161 g008 550
Figure 8. The 2D and 3D representations of catechins considered in this study.
Figure 8. The 2D and 3D representations of catechins considered in this study.
Ijms 24 08161 g008
Table
Table 1. The number of contacts between the amino acid residues of Aβ42 (For the full amino acid sequence of Aβ42, see Section 3.1) and each ligand for set-1 (as defined in Section 3.2) and the distance cutoff of 5.0 Å. The aromatic residues are in bold typeface and the three most favorable aromatic residue hotspots are highlighted in light blue.
Table 1. The number of contacts between the amino acid residues of Aβ42 (For the full amino acid sequence of Aβ42, see Section 3.1) and each ligand for set-1 (as defined in Section 3.2) and the distance cutoff of 5.0 Å. The aromatic residues are in bold typeface and the three most favorable aromatic residue hotspots are highlighted in light blue.
CECECGEGCEGCG
ResidueIDPopulationResidueIDPopulationResidueIDPopulationResidueIDPopulationResidueIDPopulation
PHE196290PHE196194PHE197425TYR107107TYR108585
PHE206242PHE206083PHE207304PHE197010PHE198302
TYR106014TYR105967TYR107187PHE206931PHE208282
GLN155911GLN155800GLN157058GLN156792GLN158159
LYS165586LEU175497HIS146641HIS136456HIS137861
LEU175581LYS165456HIS136620HIS146438HIS147760
VAL185423VAL185418LEU176578LYS166300VAL127712
HIS135386HIS145403VAL186545VAL126254LYS167546
HIS145381HIS135375LYS166545LEU176251LEU177538
VAL125279VAL125214VAL126495VAL186212VAL187463
GLU114785GLU114776GLU115988GLU115730GLU117189
ALA214721ALA214671ALA215763ALA215459ALA216624
GLY94225GLY94282GLY95452GLY95250GLY96552
GLU224223GLU224244SER85246SER85198SER86389
SER84122SER84177ARG55178ARG55144ARG56294
ARG54075ARG54119GLU225176GLU225051HIS66105
HIS63934HIS63999HIS64978HIS64989GLU225944
ASP73715ASP73829ASP74792ASP74670ASP75906
VAL243710PHE43694PHE44623PHE44522PHE45621
PHE43686VAL243688VAL244612VAL244373VAL245316
ASP233430ASP233414ASP234334ASP234127ASP234988
SER263110SER263150SER263998SER263871SER264651
ASN272975ASN273001ASN273808ASN273660ASN274429
GLY252927GLY252952GLY253751GLY253598GLY254363
LYS282766LYS282717GLU33548GLU33387GLU34314
GLU32685GLU32676LYS283512LYS283259LYS284067
ILE312473ILE312501ALA303044ILE312928ALA303506
ALA302427ALA302458ILE313014ALA302910ILE313471
GLY292310GLY292336GLY292903GLY292808ALA23358
ILE322254ILE322298ILE322808ILE322689GLY293320
LEU342110LEU342095ALA22753ALA22595ILE323261
ALA22040ALA22068LEU342549LEU342397LEU342977
MET351875MET351889MET352415MET352185MET352792
VAL361822VAL361810VAL362284VAL362104VAL362595
GLY331672GLY331729GLY332139GLY332004ASP12452
VAL391559VAL391569VAL392038ASP11872GLY332451
ASP11416ASP11414ASP12008VAL391867VAL392423
VAL401392GLY371412VAL401824GLY371635GLY382126
GLY371373VAL401353ILE411805VAL401620ILE412103
ILE411372GLY381346GLY371792GLY381591VAL402094
GLY381305ILE411310GLY381761ILE411557GLY372090
ALA42870ALA42837ALA421202ALA421060ALA421466
Table
Table 2. Average Aβ42 end-to-end distance (D1–A42) and distances between EGCG and Y10, F19, F20 residues (the amino acid sequence is provided in the caption of Figure 7, in Section 3.1). Standard deviations are provided in parentheses.
Table 2. Average Aβ42 end-to-end distance (D1–A42) and distances between EGCG and Y10, F19, F20 residues (the amino acid sequence is provided in the caption of Figure 7, in Section 3.1). Standard deviations are provided in parentheses.
Distance (nm)
CECECGEGCEGCG
end-to-end1.10 (0.66)1.69 (0.73)1.30 (0.48)1.77 (0.60)1.54 (0.59)
Ligand∙∙∙Tyr101.08 (0.45)0.47 (0.40)0.54 (0.18)0.29 (0.08)0.70 (0.22)
Ligand∙∙∙Phe190.97 (0.54)0.95 (0.51)0.55 (0.25)0.50 (0.20)0.40 (0.23)
Ligand∙∙∙Phe200.74 (0.53)0.72 (0.39)0.44 (0.24)0.44 (0.20)0.36 (0.26)
Table
Table 3. Average number of hydrogen bonds (donor∙∙∙acceptor ≤ 0.35 nm and α (∠(hydrogen–donor–acceptor) ≤ 30°). Standard deviations are provided in parentheses.
Table 3. Average number of hydrogen bonds (donor∙∙∙acceptor ≤ 0.35 nm and α (∠(hydrogen–donor–acceptor) ≤ 30°). Standard deviations are provided in parentheses.
Number of Hydrogen Bonds (SD)
CECECGEGCEGCG
Aβ∙∙∙Ligand2 (2)2 (1)4 (2)4 (1)5 (2)
Aβ∙∙∙Solvent109 (17)114 (20)108 (21)114 (21)112 (8)
Ligand∙∙∙Solvent6 (2)5 (3)7 (4)5 (3)7 (3)
Table
Table 4. The number of selected docking complexes for each ligand with the different ΔΔGbinding cutoffs.
Table 4. The number of selected docking complexes for each ligand with the different ΔΔGbinding cutoffs.
ΔΔGbinding (kcal.mol−1) CutoffCECEGCECGEGCG
Set-10.113,49513,47815,64615,02717,431
Set-20.220,76820,72224,81123,32729,923
Set-30.330,54430,71235,75834,10542,833
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Firouzi, R.; Sowlati-Hashjin, S.; Chávez-García, C.; Ashouri, M.; Karimi-Jafari, M.H.; Karttunen, M. Identification of Catechins’ Binding Sites in Monomeric Aβ42 through Ensemble Docking and MD Simulations. Int. J. Mol. Sci. 2023, 24, 8161. https://0-doi-org.brum.beds.ac.uk/10.3390/ijms24098161

AMA Style

Firouzi R, Sowlati-Hashjin S, Chávez-García C, Ashouri M, Karimi-Jafari MH, Karttunen M. Identification of Catechins’ Binding Sites in Monomeric Aβ42 through Ensemble Docking and MD Simulations. International Journal of Molecular Sciences. 2023; 24(9):8161. https://0-doi-org.brum.beds.ac.uk/10.3390/ijms24098161

Chicago/Turabian Style

Firouzi, Rohoullah, Shahin Sowlati-Hashjin, Cecilia Chávez-García, Mitra Ashouri, Mohammad Hossein Karimi-Jafari, and Mikko Karttunen. 2023. "Identification of Catechins’ Binding Sites in Monomeric Aβ42 through Ensemble Docking and MD Simulations" International Journal of Molecular Sciences 24, no. 9: 8161. https://0-doi-org.brum.beds.ac.uk/10.3390/ijms24098161

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop