Exploring the most promising anti ‐ Depressant drug targeting Microtubule Affinity Receptor Kinase 4 involved in Alzheimer’s Disease through molecular docking and molecular dynamics simulation

S Rehan Ahmad; Md Zeyaullah; Abdullah M AlShahrani; Mohammad Suhail Khan; Khursheed Muzammil; Faheem Ahmed; Adam Dawria; Ali Mohieldin; Haroon Ali; Abdelrhman A G Altijani

doi:10.1371/journal.pone.0301179

. 2024 Jul 25;19(7):e0301179. doi: 10.1371/journal.pone.0301179

Exploring the most promising anti ‐ Depressant drug targeting Microtubule Affinity Receptor Kinase 4 involved in Alzheimer’s Disease through molecular docking and molecular dynamics simulation

S Rehan Ahmad ^1,^*, Md Zeyaullah ^2,^*, Abdullah M AlShahrani ², Mohammad Suhail Khan ³, Khursheed Muzammil ³, Faheem Ahmed ³, Adam Dawria ³, Ali Mohieldin ³, Haroon Ali ³, Abdelrhman A G Altijani ³

Editor: Sajjad Ahmad⁴

PMCID: PMC11271900 PMID: 39052643

Abstract

Alzheimer’s Disease (AD) is the prevailing type of neurodegenerative illness, characterised by the accumulation of amyloid beta plaques. The symptoms associated with AD are memory loss, emotional variability, and a decline in cognitive functioning. To date, the pharmaceuticals currently accessible in the marketplace are limited to symptom management. According to several research, antidepressants have demonstrated potential efficacy in the management of AD. In this particular investigation, a total of 24 anti-depressant medications were selected as ligands, while the Microtubule Affinity Receptor Kinase 4 (MARK4) protein was chosen as the focal point of our study. The selection of MARK4 was based on its known involvement in the advancement of AD and other types of malignancies, rendering it a highly prospective target for therapeutic interventions. The initial step involved doing ADMET analysis, which was subsequently followed by molecular docking of 24 drugs. This was succeeded by molecular dynamics simulation and molecular mechanics generalised Born surface area (MMGBSA) calculations. Upon conducting molecular docking experiments, it has been determined that the binding affinities observed fall within the range of -5.5 kcal/mol to -9.0 kcal/mol. In this study, we selected six anti-depressant compounds (CID ID ‐ 4184, 2771, 4205, 5533, 4543, and 2160) based on their binding affinities, which were determined to be -9.0, -8.7, -8.4, -8.3, -8.2, and -8.2, respectively. Molecular dynamics simulations were conducted for all six drugs, with donepezil serving as the control drug. Various analyses were performed, including basic analysis and post-trajectory analysis such as free energy landscape (FEL), polarizable continuum model (PCM), and MMGBSA calculations. Based on the findings from molecular dynamics simulations and the MMGBSA analysis, it can be inferred that citalopram and mirtazapine exhibit considerable potential as anti-depressant agents. Consequently, these compounds warrant further investigation through in vitro and in vivo investigations in the context of treating AD.

Introduction

Alzheimer’s disease (AD) is a rapidly expanding neurodegenerative condition characterised by progressive and irreversible deterioration of the central nervous system, resulting in cognitive and behavioural deficits [1–3]. The process of diagnosing a condition relies on the analysis of historical case data, clinical evaluations, neuroimaging techniques, and blood analyses. The brain has two primary categories of aberrant proteins, namely amyloid-beta (Aβ) and tau. The disease advances as a consequence of the accumulation of these proteins [4–7]. At now, the therapeutic options for AD are limited to a total of five drugs. Among them, four belong to the class of cholinesterase inhibitors, while the last prescription, memantine, functions by inhibiting glutamate formation. Cholinesterase inhibitors are administered to patients diagnosed with mild to moderate AD, while memantine is prescribed for individuals in the mild to severe stages of AD [8–10].

In the present work, the researchers examined the possibility of microtubule affinity regulation kinase 4 (MARK4) as a target for AD. Previous studies have indicated an observed overexpression of MARK 4 in AD, and this overexpression has been linked to the progression of AD. The discovery of the crystal structure of MARK4 was facilitated by the utilisation of the pyrazolopyrimidine inhibitor [11]. The overexpression of MARK4 has been found to be accountable for the phosphorylation of the tau protein at the Ser262 location, a crucial step in the binding of microtubules to tau proteins [11]. In their study, Lund et al. 2014 observed the co-localization of phosphorylated MARK4 and phosphor-tau Ser262 in granulomatous (GVD) formations found in samples of AD [12]. The findings of this study proposed the development of a very effective MARK4 inhibitor for the treatment of AD [12]. In the context of this study, it is posited that MARK4 represents the most suitable candidate for addressing neurodegeneration in AD.

AD is widely regarded as a condition that lacks a definitive cure. To date, a total of 24 selective serotonin reuptake inhibitors (SSRIs) have been examined in the existing literature as potential treatments for some symptoms associated with major depressive disorder [13]. The development of AD is frequently associated with the early manifestation of depression, characterised by a chronic relapsing-remitting pattern that tends to worsen and become more extended as individuals grow older. This progression significantly heightens the probability of acquiring AD. Late-life depression has been found to be associated with reduced levels of neurotrophins, particularly brain-derived neurotrophic factor. Additionally, it is characterised by the activation of neuroinflammatory pathways, heightened secretion of pro-inflammatory cytokines and C-reactive protein, and an increased cortical amyloid burden. These factors collectively contribute to the process of neurodegeneration. Historically, there was a prevailing belief that the sole determinant of the symptoms associated with AD was cholinergic dysfunction, as evidenced by references [14–16]. Geeldenhuys, Van der Schyf, and Ramirez have highlighted the significance of the 5-Hydroxytryptamine (5-HT)1, 5-HT4, 5-HT6, and 5-HT7 receptor classes in the context of cognitive enhancement. In comparison to conventional anti-depressants such as tricyclics, selective serotonin reuptake inhibitors (SSRIs) exhibit a more desirable side effect profile and have been authorised as primary therapeutic interventions for depressive disorders [14–16]. There exists compelling data indicating that SSRIs have demonstrated a beneficial impact in delaying the onset of AD in those with a prior history of depression. In addition to the reduction of serotonin transporter function, newly developed serotonergic antidepressants such as vortioxetine exert an impact on serotonin receptors, specifically through the antagonism of 5-HT7 receptors. Vortioxetine significantly improved cognition when compared to other traditional anti-depressants in depressed adults with moderate AD when compared to placebo [17]. Based on the recent meta-analysis finding, sertraline and mirtazapine can be considered as an alternative treatment for depression in AD [18]. Even the combination of antipsychotics (such as risperidone and quetiapine) and mirtazapine can be used for the management of AD [19, 20].

Upon reviewing the previous successful results of SSRIs in managing AD symptoms, the present study conducted molecular docking analyses for a total of 24 antidepressant compounds. Subsequently, utilising the top five binding affinities, molecular dynamics simulations were performed for all six SSRI drugs (CID ID ‐ 4184, 2771, 4205, 5533, 4543, and 2160). Both CID 2160 and CID 4543 were subjected to simulation as their binding affinities were found to be same. CID ID-3152 (Donepezil), an authorised medicine for AD, was selected as the control in our study. The primary objective of this study is to identify the most efficacious SSRI medication that specifically targets the MARK4 protein implicated in AD.

Methodology

1. Selection of SSRI drugs

We took 24 anti-depressant drugs from the literature based on their mode of action as well as easy availability and well establishment in the global market and it is shown in Table 1.

Table 1. Name of 24 anti-depressant drugs collected from literature.

Drug Name	Mode of action	CID ID	Reference
Sertraline	SRI	68617	13
Fluoxetine	SRI	3386	13
Escitalopram	SRI	146570	13
Fluvoxamine	SRI	5324346	13
Paroxetine	SRI	43815	13
Citalopram	SRI	2771	13
Agomelatine	M1,2 + 5-HT2c	82148	13
Mianserin	5-HT2, a1, a2	4184	13
Reboxetine	NRI	127151	13
Trazodone	5-HT2, a1, SRI	5533	13
Venlafaxine	SRI+NRI	5656	13
Bupropion	NRI+DRI	444	13
Duloxetine	SRI+NRI	60835	13
Mirtazapine	5-HT2, 5-HT3, a2	4205	13
Tranylcypromine	Irreversible MAOI	5530	13
Moclobemide	Reversible MAOI	4235	13
Phenelzine	Irreversible MAOI	3675	13
Isocarboxazid	Irreversible MAOI	3759	13
Amitriptyline	SRI+NRI	2160	13
Trimipramine	SRI+NRI	5584	13
Imipramine	SRI+NRI	3696	13
Clomipramine	SRI	2801	13
Dosulepin	SRI+NRI	5284550	13
Nortriptyline	NRI	4543	13

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2. ADMET analysis of 24 SSRI drugs

The pharmacokinetics and Lipinski’s rule are central to accelerating drug discovery and development. As 24 SSRI drugs are already in the market, but still we performed ADMET analysis with the SWISS ADME server which is shown in S1 Table and we even plotted the radar graph for the the binding affinity of the top six drugs and Donepezil.

3. Molecular docking of 24 SSRI drugs

Molecular docking was performed with AutoDock 4.2.6. First, we converted PDB structures to PDBQT format, added Gasteiger charges to the ligands, and then loaded MARK4-associated protein (5ES1) to AutoDock MGL tools. A grid box with x, y, and z directions was generated to cover the active sites of the target protein, keeping enough space for the rotational and translational movement in the ligands and keeping the grid spacing at 0.375 Å, and flexible docking was performed. The outputs were analysed and visualised in AutoDock 4.2.6.

4. Molecular dynamics simulation of 6 SSRI drugs

Desmond, a program from Schrödinger LLC [21], was used to assess the effect of the solvation model on the binding affinity between the best-screened ligands and target protein. Molecular docking is a static representation of interactions between protein and ligand in a vacuum [22]. In contrast, molecular dynamics simulations use Newton’s classical equation of motion to compute atom motions over time. An attempt was made to assess the binding status of all complexes with the target protein (MARK4; PDB ID:5ES1) in the physiological environment predicted using simulations [23, 24]. The target protein complexes with the drugs were subjected to molecular dynamics simulations for 100 ns each. Pre-processing of the protein-ligand complexes was done using the Protein Preparation Wizard of Maestro software to optimise and minimise the structure of the protein-ligand complexes. All of the systems were prepared using the System Builder tool. TIP3P water (Transferable Intermolecular Interaction Potential 3 Points) was chosen as a solvent model with an orthorhombic box. The simulation was run using the OPLS 2005 force field [25]. To simulate physiological conditions, 0.15 M sodium chloride (NaCl) was added to the solvated complexes to neutralise the system. The NPT ensemble was used throughout the simulation, with a temperature of 300 K and a pressure of 1 atm.

5. Molecular Mechanics Generalised Born Surface Area (MMGBSA) calculations

The binding free energies (Gobind) of the anchored complexes were calculated using the first Molecular Mechanics Generalized Born Surface Area (MMGBSA) module (Schrodinger suite, LLC, New York). Binding free energies were calculated using the OPLS 2005 force field, VSGB solvent model and rotamer search method [26]. After performing MD, 10 sec was used to select the MD orbital frame. The total free binding energy is calculated using the below-mentioned formula:

Δ Gobind = Gcomplex - (G-protein + Gligand)

ΔGobind = binding free energy, Gcomplex = free energy of the complex, G-protein = free energy of the target protein, and Gligand = free energy of the ligand.

Ethical Statements: This article does not contain any studies involving human or animal participants performed by any of the authors.

Results

1. Pharmacokinetics and drug likeliness profile of 24 SSRI drugs

Lipinski’s is one of the most essential drug discovery and development factors. In this study all the 24 drugs are available in the market, which means drugs follow Lipinski’s rule.In this study, we generated radar plots for the binding affinity of the top six drugs (CID ID ‐ 4184, 2771, 4205, 5533, 4543, and 2160) lies between -9 to -8.2 kcal/mol as well as radar plot of Donepezil which is already approved for AD. We use the SWISS-ADME web server for which the requirement is the SMILES notation of the selected compounds, which is used as an input based on the database predicting their pharmacokinetic property and plotted the results as a radar plot through which we can analyse the compound’s lipophilic property, size, polarity, insolubility, instauration and flex properties. The more it is towards the centre, the more it is considered a good result. The radar plots are shown as Figs 1–7.

2. Molecular docking analysis of 24 SSRI drugs

First, molecular docking was conducted to validate the intermolecular interactions between the MARK4 protein and 24 drugs. The docking results were arranged in the order of lowest binding energy with each target protein, as shown in Table 2. The binding affinity of the top six drugs lies between -9 to -8.2 kcal/mol. After analysing the binding modes for the top six protein-ligand complexes, 5SE1-2160 formed an h-bond interaction with ASP196 with a bond length of 2.8 Å. 5SE1-2771 formed an h-bond interaction with ILE62 with a bond length of 2.8 Å. 5SE1-3152 formed an h-bond interaction with GLY65 and ILE62 with bond lengths of 4.0 Å and 3.9 Å, respectively. 5SE1-4184 formed a bond length with ASN183, which was 4.3 Å. 5SE1-4205 formed an h-bond interaction with GLU182 ASN183 with bond lengths of 2.8 Å and 3.3 Å, respectively. 5SE1-5533 formed an h-bond interaction with ASP196 with a bond length of 2.7 Å. The compound 4553 showed no h-bond interaction with the active binding site of the protein. Besides this h-bond interaction, all the compounds show salt bridges, as shown in the ligand interaction figures. Moreover, the compound 5533 additionally showed halogen interaction. These six drugs are considered further for molecular dynamics simulation, and we considered donepezil as a control, as it is an established drug for AD. The conformation of docked Sertraline, Fluoxetine, Escitalopram, Fluvoxamine, Paroxetine and Citalopram with the MARK4 is shown in the Figs 8–14. The suitable docked pose was selected by mimicking the crystal structure of the MARK4 complex with pyrazolopyrimidine inhibitor (PDB: 5ES1). The docked complex where Sertraline, Fluoxetine, Escitalopram, Fluvoxamine, Paroxetine and Citalopram was present at the same position occupied by pyrazolopyrimidine inhibitor in the crystal structure, was selected for further analysis. Binding free energy of Sertraline, Fluoxetine, Escitalopram, Fluvoxamine, Paroxetine and Citalopram to the MARK4 was found to be ‐ 9 kcal/mol,-8.7kcal/mol, -8.4kcal/mol, -8.3kcal/mol, -8.2 kcal/mol and -8.2 kcal/mol respectively. All six drugs form several close interactions to active site residues of MARK4 such as Ile62, Lys85, Val116, Met132, Tyr134, Ala135, Leu185, Ala195 and Asp196, and forming one hydrogen bonds with Lys85, and several non-covalent interactions such as Alkyl, Pi-Alkyl and Van der Waals interactions offered by the protein MARK4. It is found that all six drugs are placed at the same position where co-crystal ligand pyrazolopyrimidine inhibitor is placed and it is interacting with the same active site residues, to which co-crystal ligand pyrazolopyrimidine inhibitor is interacting with. The analysis of docked conformations clearly indicates that all six drugs bind deeper into the cavity, and perhaps decrease the accessibility of MARK4 which may be responsible modulation of its biological functions.

Table 2. Binding affinity of the 24 docked complex.

Drug Name	CID ID	Binding Affinity
Mianserin	4184	-9
Citalopram	2771	-8.7
Mirtazapine	4205	-8.4
Trazodone	5533	-8.3
Nortriptyline	4543	-8.2
Amitriptyline	2160	-8.2
Sertraline	68617	-8.1
Paroxetine	43815	-8
Trimipramine	5584	-7.9
Dosulepin	5284550	-7.8
Reboxetine	127151	-7.7
Imipramine	3696	-7.7
Fluoxetine	3386	-7.3
Escitalopram	146570	-7.3
Duloxetine	60835	-7.3
Clomipramine	2801	-7.3
Agomelatine	82148	-7.3
Isocarboxazid	3759	-6.8
Venlafaxine	5656	-6.7
Tranylcypromine	5530	-6.7
Bupropion	444	-6.5
Fluvoxamine	5324346	-6.3
Moclobemide	4235	-5.5
Phenelzine	3675	-5.3

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Fig 8 — Ligand targeting the protein MARK4 as Fig (8) 4184 (9) 2771 (10) 4205 (11) 5533 (12) 4543 (13) 2160 (14) 3152.

Fig 14 — Ligand targeting the protein MARK4 as Fig (8) 4184 (9) 2771 (10) 4205 (11) 5533 (12) 4543 (13) 2160 (14) 3152.

Fig 9 — Ligand targeting the protein MARK4 as Fig (8) 4184 (9) 2771 (10) 4205 (11) 5533 (12) 4543 (13) 2160 (14) 3152.

Fig 10 — Ligand targeting the protein MARK4 as Fig (8) 4184 (9) 2771 (10) 4205 (11) 5533 (12) 4543 (13) 2160 (14) 3152.

Fig 11 — Ligand targeting the protein MARK4 as Fig (8) 4184 (9) 2771 (10) 4205 (11) 5533 (12) 4543 (13) 2160 (14) 3152.

Fig 12 — Ligand targeting the protein MARK4 as Fig (8) 4184 (9) 2771 (10) 4205 (11) 5533 (12) 4543 (13) 2160 (14) 3152.

Fig 13 — Ligand targeting the protein MARK4 as Fig (8) 4184 (9) 2771 (10) 4205 (11) 5533 (12) 4543 (13) 2160 (14) 3152.

3. Molecular dynamics of 24 SSRI drugs

Based on the top six binding free energy of Sertraline, Fluoxetine, Escitalopram, Fluvoxamine, Paroxetine and Citalopram to the MARK4 which was found to be -9 kcal/mol,-8.7kcal/mol, -8.4kcal/mol, -8.3kcal/mol, -8.2 kcal/mol and -8.2 kcal/mol respectively from 24 drugs, we proceed towards further analysis which include molecular dynamics simulations at 100ns followed by MMGBSA. A molecular dynamics simulation was performed to assess the dynamic behaviour of the protein. The simulation system’s stability was assessed by examining the Root Mean Square Deviations (RMSD) values throughout the trajectory. The RMSD number calculates the difference between the two structures and shows how stable the simulated system is. A lower RMSD number implies a more minor difference between the two systems. Additionally, a significant factor for examining the dynamics of the protein-ligand complexes is the stability of the simulated system, which is indicated by a less prominent variation in the RMSD value. The kinetics and the binding interactions of protein-ligand complexes can be better understood using RMSD plots, and it also sheds light on the stability and viability of these complexes as prospective therapeutic targets. Root mean square deviation RMSD plot of protein concerning ligands for 100ns MD simulation, showing most of the complexes are stable throughout the simulation trajectory, but compound 5533 showed less stability near 77–78 ns time frame; similarly, compound 4205 showed the same. The compound 4184 showed less stability at 55-56ns. The compounds 2771 and 4205, along with 5SE1, showed very stable at the end of the simulation as in the controlled 3152 compounds. The controlled compound 3152 showed a slight instability at 25–40 ns, but after 65 ns, it regained stability and maintained it till 100 ns. The compound 4205 shows a jump in stability at 75ns, and after that, it maintains that stability till 100ns. The RMSD plot for the rest of the protein-ligand complex showed stability, but the compound 5533 is very unstable throughout the trajectory and attained an RMSD value of 14–16 Å. The compound 4184-5SE1 showed stability throughout the trajectory with RMSD value ranging between 1–2 Å. All the compounds showed stability till 45 ns properly. After that, they attained conformational changes, which formed a stronger interaction between the protein and ligand and RMSD plot is shown as Fig 15.

Fig 15 — RMSD analysis of MD simulation trajectory of 5es1 in complex with (A) 4184, (B) 2771, (C) 4205, (D) 5533, (E) 4543 (F) 2160 (G) 3152.

The root-mean-square-fluctuation (RMSF) is used to describe flexibility differences among residues. The backbone RMSF of each residue of the three systems, was calculated to analyse the backbone structure’s flexibility. The vital amino acids between 10–25 show higher flexibility with an RMSF value of 0.5–0.6 nm. The average rmsf value is 0.35nm throughout the trajectory. The compound 2771 4205 showed the most negligible fluctuations throughout the trajectory compared to the controlled compound 3152. Amino acid residues of 5SE1: THR61, ILE62, Gly63, SER96, LEU97, GLN98, LYS99, LEU100, and PHE101 showed more flexibility in these two compounds 2160 and 4553 and their rmsf value ranging between 0.15–0.52 nm. This indicated that the conformational changes of the systems at the end of simulations were relatively small. The RMSF is shown in Fig 16.

Fig 16 — RMSF of protein (5es1) in complex with (A) 4184, (B) 2771, (C) 4205, (D) 5533, (E) 4543 (F) 2160 (G) 3152.

A protein’s volume and shape are described by the radius of gyration (Rg), which offers information on the compactness and stability of the protein’s structure. A higher Rg value indicates an expanded system, which suggests a less dense and compact protein structure. A stable folded protein, on the other hand, usually shows a constant Rg value. Changes in the Rg value can signal structural changes in the protein, such as unfolding or denaturation, which can happen under various circumstances or due to various external factors. After analysing the plot, it was observed that the compactness of the compound 2771, 4205 is much less compared to the controlled 3152 with Rg value ranges between 3.2–3.75nm. This indicates that the compound 2771 and 4205 are better compact than 3152. The rest of the compounds showed compactness values ranging between 3-4nm, whereas the compound 5533 showed very similar compactness values to 3152 and all the results are shown in Fig 17.

Fig 17 — Radius of gyration of 5SE1 in complex with (A) 4184, (B) 2771, (C) 4205, (D) 5533, (E) **4553**, (F) 2160, (G) 3152.

The solvent-accessible surface area (SASA) plot analysis of the molecular dynamic simulation was used to gauge the protein’s environmental exposure over time. The plot revealed information about the dynamics and structural changes of the protein during the simulation and is shown in Fig 18. It was feasible to pinpoint areas of the protein with varying solvent exposure by keeping track of changes in SASA levels. This could point to protein-ligand interactions, structural rearrangements, or solvent-shielding effects. The SASA plot analysis helped to understand the behaviour of the protein better and offered vital data for researching its relationships and function in a dynamic context. In this study, after analysing solvent-accessible surface area for all the proteins and the compounds throughout the simulation, it was observed that all the systems have SASA values ranging between 53.96 nm2 and 302.11 nm2. Compounds 2771 and 4205 showed excellent solvent accessibility with 54–150 nm2 values. Overall, it can be concluded from this, which is comparatively reasonable compared to the controlled 3152 proteins, and the compound has excellent accessibility to the solvent environment.

Fig 18 — SASA of 5es1 in complex with (A) 4184, (B) 2771, (C) 4205, (D) 5533, (E) 4543 (F) 2160 (G) 3152.

The hydrogen bond interaction between the protein and ligand changes throughout the trajectory at 0ns, there are approx. 1–2 h-bond, and the interaction increases near 17–18 ns due to some conformational changes of the compounds along with the conformational changes of the binding site residues. After 45ns, the interaction starts falling at 100ns; it maintained 2–3 interactions. The hydrogen bond interaction is shown in S1 Fig. The ligand interaction with the protein’s binding site is shown in Figs 19–25. In all these images, it is observed that most of the compounds form h-bonds with the protein binding site and also show salt bridges. The compound 5533 also showed halogen bond interaction with the Ala135 residue of the protein’s binding site.

Residue contact map and the mean smallest distance between C-alpha atoms of each amino acid residue 5SE1 corresponding to docked compounds. The residue contact map is analysed to calculate the smallest distance between c-alpha atoms that unify protein-ligand docked complexes that influence secondary structure elements to know their allosteric effects on the protein. The protein-ligand complex 5SE1-2771 5SE1-4205 showed atomic distance as shown in the above plots. From the above comparative analysis, it is evident that the interacting amino acids of the complexes were near form strong binding and stable conformations of the ligands towards the active pocket of 5SE1 and it is shown in Figs 26–32.

The Free Energy Landscape (FEL) displayed an intricate energy landscape with several basins and transition states, pointing to a system with a rich conformational diversity. Numerous shallow wells for the compounds with protein signify metastable states, whereas substantial barriers signify transitions that require much energy. Notably, the conformation with the lowest energy is the most stable. According to this FEL study, the system investigates several structural configurations linked to a different energy level. Understanding the system’s behaviour, including possible binding paths, conformational changes, and the thermodynamics driving molecular dynamics, is essential for comprehending biological processes and logical drug design. These insights into the energy landscape give vital information about the system’s behaviour. After analysing the FEL plot, it was observed that the FEL achieved global minima of C-alpha backbone atoms of proteins concerning RMSD and rGyr. The FEL forecast for deterministic behaviour of 5SE1 to lowest energy state owing to its high stability and best conformation at 2771 and 4205 bound state compared to the controlled 3152 bound state. The result of FEL is shown in Figs 33–39.

Principal Component investigation (PCA) was used to examine the MD simulations of the 5SE1 protein and certain chemicals’ trajectories. The main aim was to understand atoms’ complex, erratic motions in amino acid residues. This procedure revealed instances when the protein structure suffered deformation due to greater flexibility, shedding light on the variety in trajectories. This was accomplished by tracking the motion of internal coordinates in a three-dimensional space, recording data throughout 100 nanoseconds, and presenting the data in a scatter pattern. Later, the idea of orthogonal sets or Eigenvectors was used to understand the controlled motion of each trajectory. In particular, the PCA analysis revealed an intriguing finding: when one advances into higher modes, especially approaching PC4. The result of PCA is shown in Figs 40–46.

After analysing all the molecular docking results using PyMOL, MD Simulation trajectory analysis, it can be concluded that the citalopram and mirtazapine showed the best results in all aspects as we compared them with control 3152. It was observed that during simulation for 100ns, binding of these compounds to the active site of protein caused some conformational changes of the residues of the protein, due to which, from a reported time frame, it obtained stability till 100ns and binds appropriately as compared to earlier.

4. Molecular Mechanics Generalised Born Surface Area (MMGBSA) calculations

Further protein-ligand binding affinity was validated by calculating the complexes’ MMGBSA binding free energies.MM/GBSA is emerging as a valuable and practical approach to predicting the binding energy. The Prime MM/GBSA module of the Schrodinger Suite was used to calculate the binding free energy of receptor-ligand complexes. After analysing the MMGBSA plot, it was observed that for most of the protein-ligand complexes, the values ranged between -40-50 region as shown in orange colour in the plot. Based on the MMGBSA result we can observe that the binding value for both 2771 and 4205 is between -30–40 which is very similar to the 5SE1-3152 controlled compound whose MMGBSA binding value also ranged between -30-40, respectively. The binding value for 4184, 5533, 4543 and 2160 ranged between -40-50 which is not close to the control drug (donepezil). The MMGBSA result is shown in Fig 47.

Fig 47 — MMGBSA analysis of 5es1 with the ligands (A) 4184, (B) 2771, (C) 4205, (D) 5533, (E) 4543 (F) 2160 (G) 3152.

Discussion

Molecular docking is a widely recognised technique in the field of structure-based drug design, enabling the examination of interactions between tiny molecules and molecular targets. By employing this methodology, a more comprehensive comprehension of the behaviour of small molecules within certain protein targets can be achieved, facilitating the ability to forecast the structure of ligand-receptor complexes. This capability holds significant importance in the realm of pharmaceutical research. The repurposing of pharmaceuticals has consistently demonstrated efficacy in the treatment of many medical issues and illnesses. SSRIs have demonstrated favourable outcomes in the treatment of AD. The present study employed molecular docking and subsequent molecular dynamics simulations to assess the interaction between SSRI medications and MARK4 protein. The objective was to identify the most favourable SSRI drug candidate capable of selectively targeting the overexpressed MARK4 protein in AD. The MMGBSA method was employed to calculate the binding energy of the top six medications, taking into account their binding affinity. This approach was utilised to verify the docking results. The results of the analysis indicated that these pharmaceuticals consistently and reliably interacted with the active site residues of MARK4 over the whole simulation trajectory. Mirtazapine, a pharmaceutical compound that specifically targets the MARK4 enzyme, exhibits potential for further exploration in various research settings, including in-vitro, in-vivo, and clinical investigations pertaining to AD. Previous studies conducted by Shamsi et al. in 2020 have demonstrated the binding affinity of donepezil, rivastigmine tartrate, and other cholinergic inhibitors to the active site of the MARK4 enzyme [27]. According to a recent study conducted by Waseem et al. in 2021, it has been observed that the protein irisin, which plays a critical role in the decline of memory associated with AD, has a binding affinity towards MARK4 and contributes to the preservation of its stability. The user has provided a numerical reference [28]. According to recent investigations [29], the molecules ganoderic acid A and ganoderenic acid B, produced from Ganoderma lucidium, have shown significant potential in their interaction with MARK4. According to the latest investigation, galantamine has been identified as a potentially effective inhibitor for MARK 4 [30]. Based on our investigation, bolstered by previous research, it has been determined that mirtazapine, which exhibits affinity for MARK4, is regarded as the most auspicious SSRI medication among the 24 SSRI medications under consideration. Prior to conducting clinical trials, it is imperative to validate our findings through in vitro and in vivo research.

Conclusion

Currently, there are just five medications for AD that have been approved and SSRIs are used as off-label drugs in AD. In this study we presents a novel prediction that citalopram and mirtazapine are the most promising SSRIs from the list of 24 drugs which is targeting the MARK4 protein, which is related with AD. However, further validation is required through rigorous in vitro and in vivo investigations.

Supporting information

S1 Fig

H-bond occupancy plot of the protein 5SE1 in complex with (A) 4184, (B) 2771, (C) 4205, (D) 5533, (E) 4543 (F) 2160 (G) 3152.

(DOCX)

pone.0301179.s001.docx^{(203.9KB, docx)}

S1 Table

(DOCX)

pone.0301179.s002.docx^{(17.8KB, docx)}

S1 Dataset. Minimal data set definition.

(DOCX)

pone.0301179.s003.docx^{(14.9KB, docx)}

Acknowledgments

I, Dr. S Rehan Ahmad, and Md. Zeyaullah would like to express our sincere gratitude to our esteemed co-authors for their invaluable scientific contributions to this project. Without their dedication, expertise, and collaborative spirit, the accomplishment of this endeavour would not have been possible. Each co-author has brought unique insights, skills, and perspectives that have enriched the research process and outcomes. Furthermore, we extend our heartfelt appreciation to the authorities of the concerned colleges and universities for their support and assistance throughout the duration of this project. This research was made possible by the collective efforts and unwavering commitment of all involved researchers, and we are deeply thankful for their contributions.

Abbreviation

AD: Alzheimer’s Disease
SSRI: selective serotonin reuptake inhibitors
MARK4: microtubule affinity regulation kinase 4
RMSD: root mean square deviation
RMSF: root mean square fluctuation
SASA: solvent Accessible Surface Area
FEL: free Energy Landscape
PCA: Principal Component investigation
MMGBSA: molecular mmechanics generalised Born ssurface area

Data Availability

All relevant data are within the manuscript.

Funding Statement

Deanship of Scientific Research at King Khalid University, Kingdom of Saudi Arabia, Research Project Grant Number RGP2/355/44.

References

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PLoS One. doi: 10.1371/journal.pone.0301179.r001

Decision Letter 0

Sajjad Ahmad

19 Jan 2024

PONE-D-23-35218Exploring a Promising Anti-Depressant Drug Targeting Microtubule Affinity Receptor Kinase 4 Inhibition in Alzheimer's Disease through Molecular Docking and Dynamics SimulationPLOS ONE

Dear Dr. Ahmad,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

==============================

The manuscript overall quality is good but still needs major changes as suggested by the reviewer.

==============================

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Kind regards,

Sajjad Ahmad

Academic Editor

PLOS ONE

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Research Project Grant Number RGP2/355/44.”

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Additional Editor Comments:

The reviewer raised some concerns on the methodology of the manuscript. However, i still think the paper has the potential to be published. I will give one chance to the authors to improve the manuscript in light of the reviewer comments and send the manuscript for second round of revisions after incorporating all the changes.

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Reviewers' comments:

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Comments to the Author

1. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: No

**********

2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: No

**********

3. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: No

**********

4. Is the manuscript presented in an intelligible fashion and written in standard English?

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Reviewer #1: Yes

**********

5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: Overall, the study presents a well-structured and comprehensive investigation into the potential of antidepressants as a treatment for Alzheimer's disease (AD). The authors have clearly identified a need for more effective AD therapies and have explored a promising therapeutic target, MARK4, which is involved in both AD and cancer progression. The use of molecular docking, molecular dynamics simulation, and MMGBSA to assess the binding affinity and potential efficacy of various antidepressants is a rigorous approach that provides valuable insights.

However, there are a few areas which need to be addressed

The selection of 24 antidepressants for this study warrants further explanation. The authors rely heavily on a 2013 study that itself drew from outdated references, raising concerns about the study's novelty and the relevance of the chosen antidepressants. The 2013 study, which served as the primary basis for selecting the antidepressants, appears to have cherry-picked compounds based on limited evidence and without considering more recent advancements in antidepressant research. Given the significant advancements in antidepressant development over the past decade, it is surprising that the authors chose to exclusively rely on compounds identified in a 2013 study.

Moreover, the authors should conduct additional literature reviews to identify newer and more relevant evidence supporting their choice of antidepressants. This could involve examining recent studies that have investigated the potential of antidepressants in treating Alzheimer's disease or exploring the mechanisms by which these compounds interact with MARK4.

The authors' reliance on ADMET analysis to narrow down the pool of potential antidepressants is also a significant concern. While ADMET studies provide valuable insights into the pharmacokinetic and toxicological properties of compounds, they should not be used solely as exclusionary criteria to filter out potential drug candidates.

The authors' reliance on molecular docking to identify promising antidepressants warrants further scrutiny. While molecular docking provides a valuable starting point for identifying potential drug interactions, it is essential to carefully validate the results to ensure their robustness and reproducibility. The authors should compare the predicted binding modes from docking with those obtained from X-ray crystallography as reported in PDB. This will help to establish the accuracy of the docking simulations and ensure that the proposed binding interactions are biologically plausible.

Justifiy in details the criteria for selecting the top six antidepressants for molecular dynamics simulation and MMGBSA.

Provide a more detailed comparison of the molecular dynamics and MMGBSA results for the six antidepressants.

**********

6. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

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Reviewer #1: Yes: Shafi Ullah Khan

**********

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PLoS One. 2024 Jul 25;19(7):e0301179. doi: 10.1371/journal.pone.0301179.r002

Author response to Decision Letter 0

8 Feb 2024

Comments to the Author

1. Is the manuscript technically sound, and do the data support the conclusions?

Reviewer #1: No

________________________________________

2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: No

________________________________________

3. Have the authors made all data underlying the findings in their manuscript fully available?

Reviewer #1: No

________________________________________

4. Is the manuscript presented in an intelligible fashion and written in standard English?

Reviewer #1: Yes

________________________________________

5. Review Comments to the Author

However, there are a few areas which need to be addressed

We would like to thanks the reviewer for spending value time and effort in reviewing the manuscript. We modified the manuscript based on the comment and we also modified conclusion to have better clarity and removed extra MMGBSA figure (Figure 11) to avoid confusion

Answer 1- We would like to thanks the reviewer for valuable comment. We selected 24 anti-depressants based on their mode of action as well as commonly used in the global market. We added one column in the table related to mode of action in Table 1.

Answer 2 - Thanks for the valuable comment. Based on the comment we added the sentence “Based on the recent meta-analysis finding, sertraline and mirtazapine can be considered as an alternative treatment for depression in AD [18]. Even the combination of antipsychotics (such as risperidone and quetiapine) and mirtazapine can be used for the management of AD [19,20]”.

Answer 3 - Thanks for valuable comment and we apologize for the confusion. We filter out the drugs based on the binding affinities not on ADMET studies. We plotted the radar plot only for top 6 drugs which we got from molecular docking and donepezil. Based on the comment we even added a ADMET analysis table as supplementary 2 and modified the sentence in methodology “As 24 SSRI drugs are already in the market, but still we performed ADMET analysis with the SWISS ADME server which is shown in supplementary 2 and we even plotted the radar graph for the the binding affinity of the top six drugs and donepezil. Even we made changes in result section “In this study, we generated radar plots for the binding affinity of the top six drugs (CID ID - 4184, 2771, 4205, 5533, 4543, and 2160) lies between -9 to -8.2 kcal/mol as well as radar plot of Donepezil which is already approved for AD”.

Answer 4 - Based on the comment we added “The conformation of docked Sertraline, Fluoxetine, Escitalopram, Fluvoxamine, Paroxetine and Citalopram with the MARK4 is shown in the Figure 2. The suitable docked pose was selected by mimicking the crystal structure of the MARK4 complex with pyrazolopyrimidine inhibitor (PDB: 5ES1). The docked complex where Sertraline, Fluoxetine, Escitalopram, Fluvoxamine, Paroxetine and Citalopram was present at the same position occupied by pyrazolopyrimidine inhibitor in the crystal structure, was selected for further analysis. Binding free energy of Sertraline, Fluoxetine, Escitalopram, Fluvoxamine, Paroxetine and Citalopram to the MARK4 was found to be - 9 kcal/mol,-8.7kcal/mol, -8.4kcal/mol, -8.3kcal/mol, -8.2 kcal/mol and -8.2 kcal/mol respectively. All six drugs forms several close interactions to active site residues of MARK4 such as Ile62, Lys85, Val116, Met132, Tyr134, Ala135, Leu185, Ala195 and Asp196, and forming one hydrogen bonds with Lys85, and several non-covalent interactions such as Alkyl, Pi-Alkyl and Van der Waals interactions offered by the protein MARK4. It is found that all six drugs are placed at the same position where co-crystal ligand pyrazolopyrimidine inhibitor is placed and it is interacting with the same active site residues, to which co-crystal ligand pyrazolopyrimidine inhibitor is interacting with. The analysis of docked conformations clearly indicates that all six drugs binds deeper into the cavity, and perhaps decrease the accessibility of MARK4 which may be responsible modulation of its biological functions “in the result section of molecular docking.

Justifiy in details the criteria for selecting the top six antidepressants for molecular dynamics simulation and MMGBSA.

Answer 5. Based on the comment, we added “Based on the top six binding free energy of Sertraline, Fluoxetine, Escitalopram, Fluvoxamine, Paroxetine and Citalopram to the MARK4 which was found to be -9 kcal/mol,-8.7kcal/mol, -8.4kcal/mol, -8.3kcal/mol, -8.2 kcal/mol and -8.2 kcal/mol respectively from 24 drugs, we proceed towards further analysis which include molecular dynamics simulations at 100ns followed by MMGBSA” in the result section of molecular dynamics.

Provide a more detailed comparison of the molecular dynamics and MMGBSA results for the six antidepressants.

Answer 6 Based on the comment we modified the MMGBSA result “After analysing the MMGBSA plot, it was observed that for most of the protein-ligand complexes, the values ranged between -40-50 region as shown in orange colour in the plot. Based on the MMGBSA result we can observe that the binding value for both 2771 and 4205 is between -30 -40 which is very similar to the 5SE1-3152 controlled compound whose MMGBSA binding value also ranged between -30-40, respectively. The binding value for 4184, 5533, 4543 and 2160 ranged between -40-50 which is not close to the control drug (donepezil). The MMGBSA result is shown in Figure 11.” We removed the Figure 11 related to MMGBSA to avoid confusion.

________________________________________

6. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

Attachment

Submitted filename: Response to Reviewer.docx

pone.0301179.s004.docx^{(18.9KB, docx)}

PLoS One. doi: 10.1371/journal.pone.0301179.r003

Decision Letter 1

Sajjad Ahmad

8 Mar 2024

PONE-D-23-35218R1Exploring the most promising Anti - Depressant Drug targeting Microtubule Affinity Receptor Kinase 4 involved in Alzheimer’s Disease Through Molecular Docking and Molecular Dynamics SimulationPLOS ONE

Dear Dr. Ahmad,

==============================

Most of the comments are addressed. Minor revisions needed to improve the figures quality.

==============================

Please submit your revised manuscript by Apr 22 2024 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

Please include the following items when submitting your revised manuscript:

A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'.
A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.
An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

We look forward to receiving your revised manuscript.

Kind regards,

Sajjad Ahmad

Academic Editor

PLOS ONE

Journal Requirements:

Please review your reference list to ensure that it is complete and correct. If you have cited papers that have been retracted, please include the rationale for doing so in the manuscript text, or remove these references and replace them with relevant current references. Any changes to the reference list should be mentioned in the rebuttal letter that accompanies your revised manuscript. If you need to cite a retracted article, indicate the article’s retracted status in the References list and also include a citation and full reference for the retraction notice.

Additional Editor Comments:

Minor revisions needed. Improve figures quality.

[Note: HTML markup is below. Please do not edit.]

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: All comments have been addressed

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

Reviewer #1: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: N/A

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

Reviewer #1: No

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

Reviewer #1: (No Response)

**********

6. Review Comments to the Author

Reviewer #1: While the author has addressed previous reviewer comments and their work is suitable for publication, the figures require revision due to insufficient resolution, making it difficult to decipher details like legends and color schemes.

**********

7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: Yes: Shafi Ullah Khan

**********

PLoS One. 2024 Jul 25;19(7):e0301179. doi: 10.1371/journal.pone.0301179.r004

Author response to Decision Letter 1

11 Mar 2024

Respected Editor Sir,

We have uploaded all figure as separate file. Please find the attachments and do the needful.

with regards

Dr. S Rehan Ahmad

Assistant Professor ,

Department of Zoology ,

Hiralal Mazumdar Memorial College for Women ,

Govt. Aided College , Govt. of West Bengal

Dakshineswar , Kolkata , West Bengal , India -700035

Mobile No. +91-8335999175 / +91-9332094070

E-Mail : zoologist.rehan@gmail.com

Scopus Id : 57226372929

Attachment

Submitted filename: Response to Reviewer.docx

pone.0301179.s005.docx^{(18.9KB, docx)}

PLoS One. doi: 10.1371/journal.pone.0301179.r005

Decision Letter 2

Sajjad Ahmad

13 Mar 2024

Exploring the most promising Anti - Depressant Drug targeting Microtubule Affinity Receptor Kinase 4 involved in Alzheimer’s Disease Through Molecular Docking and Molecular Dynamics Simulation

PONE-D-23-35218R2

Dear Dr. Ahmad,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

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Kind regards,

Sajjad Ahmad

Academic Editor

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Sajjad Ahmad

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

S1 Fig

H-bond occupancy plot of the protein 5SE1 in complex with (A) 4184, (B) 2771, (C) 4205, (D) 5533, (E) 4543 (F) 2160 (G) 3152.

(DOCX)

pone.0301179.s001.docx^{(203.9KB, docx)}

S1 Table

(DOCX)

pone.0301179.s002.docx^{(17.8KB, docx)}

S1 Dataset. Minimal data set definition.

(DOCX)

pone.0301179.s003.docx^{(14.9KB, docx)}

Attachment

Submitted filename: Response to Reviewer.docx

pone.0301179.s004.docx^{(18.9KB, docx)}

Attachment

Submitted filename: Response to Reviewer.docx

pone.0301179.s005.docx^{(18.9KB, docx)}

Data Availability Statement

All relevant data are within the manuscript.

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PERMALINK

Exploring the most promising anti ‐ Depressant drug targeting Microtubule Affinity Receptor Kinase 4 involved in Alzheimer’s Disease through molecular docking and molecular dynamics simulation

S Rehan Ahmad

Md Zeyaullah

Abdullah M AlShahrani

Mohammad Suhail Khan

Khursheed Muzammil

Faheem Ahmed

Adam Dawria

Ali Mohieldin

Haroon Ali

Abdelrhman A G Altijani

Roles

Abstract

Introduction

Methodology

1. Selection of SSRI drugs

Table 1. Name of 24 anti-depressant drugs collected from literature.

2. ADMET analysis of 24 SSRI drugs

3. Molecular docking of 24 SSRI drugs

4. Molecular dynamics simulation of 6 SSRI drugs

5. Molecular Mechanics Generalised Born Surface Area (MMGBSA) calculations

Results

1. Pharmacokinetics and drug likeliness profile of 24 SSRI drugs

Fig 1. Radar plots of six SSRI drugs ‐ (1) 4184 (2) 2771 (3) 4205 (4) 5533 (5) 4543 (6) 2160 (7)3152.

Fig 7. Radar plots of six SSRI drugs ‐ (1) 4184 (2) 2771 (3) 4205 (4) 5533 (5) 4543 (6) 2160 (7)3152.

Fig 2. Radar plots of six SSRI drugs ‐ (1) 4184 (2) 2771 (3) 4205 (4) 5533 (5) 4543 (6) 2160 (7)3152.

Fig 3. Radar plots of six SSRI drugs ‐ (1) 4184 (2) 2771 (3) 4205 (4) 5533 (5) 4543 (6) 2160 (7)3152.

Fig 4. Radar plots of six SSRI drugs ‐ (1) 4184 (2) 2771 (3) 4205 (4) 5533 (5) 4543 (6) 2160 (7)3152.

Fig 5. Radar plots of six SSRI drugs ‐ (1) 4184 (2) 2771 (3) 4205 (4) 5533 (5) 4543 (6) 2160 (7)3152.

Fig 6. Radar plots of six SSRI drugs ‐ (1) 4184 (2) 2771 (3) 4205 (4) 5533 (5) 4543 (6) 2160 (7)3152.

2. Molecular docking analysis of 24 SSRI drugs

Table 2. Binding affinity of the 24 docked complex.

Fig 8. Docked images of ligand-protein complex.

Fig 14. Docked images of ligand-protein complex.

Fig 9. Docked images of ligand-protein complex.

Fig 10. Docked images of ligand-protein complex.

Fig 11. Docked images of ligand-protein complex.

Fig 12. Docked images of ligand-protein complex.

Fig 13. Docked images of ligand-protein complex.

3. Molecular dynamics of 24 SSRI drugs

Fig 15.

Fig 16.

Fig 17.

Fig 18.

Fig 19. Protein-ligand contact summary of the target protein MARK4 with the ligands (19) 4184, (20) 2771, (21) 4205, (22) 5533, (23) 4543 (24) 2160 (25) 3152.

Fig 25. Protein-ligand contact summary of the target protein MARK4 with the ligands (19) 4184, (20) 2771, (21) 4205, (22) 5533, (23) 4543 (24) 2160 (25) 3152.

Fig 20. Protein-ligand contact summary of the target protein MARK4 with the ligands (19) 4184, (20) 2771, (21) 4205, (22) 5533, (23) 4543 (24) 2160 (25) 3152.

Fig 21. Protein-ligand contact summary of the target protein MARK4 with the ligands (19) 4184, (20) 2771, (21) 4205, (22) 5533, (23) 4543 (24) 2160 (25) 3152.

Fig 22. Protein-ligand contact summary of the target protein MARK4 with the ligands (19) 4184, (20) 2771, (21) 4205, (22) 5533, (23) 4543 (24) 2160 (25) 3152.

Fig 23. Protein-ligand contact summary of the target protein MARK4 with the ligands (19) 4184, (20) 2771, (21) 4205, (22) 5533, (23) 4543 (24) 2160 (25) 3152.

Fig 24. Protein-ligand contact summary of the target protein MARK4 with the ligands (19) 4184, (20) 2771, (21) 4205, (22) 5533, (23) 4543 (24) 2160 (25) 3152.

Fig 26. Residue contact map of 5es1 in complex with (26) 4184, (27) 2771, (28) 4205, (29) 5533, (30) 4543 (31) 2160 (32) 3152.

Fig 32. Residue contact map of 5es1 in complex with (26) 4184, (27) 2771, (28) 4205, (29) 5533, (30) 4543 (31) 2160 (32) 3152.

Fig 27. Residue contact map of 5es1 in complex with (26) 4184, (27) 2771, (28) 4205, (29) 5533, (30) 4543 (31) 2160 (32) 3152.

Fig 28. Residue contact map of 5es1 in complex with (26) 4184, (27) 2771, (28) 4205, (29) 5533, (30) 4543 (31) 2160 (32) 3152.

Fig 29. Residue contact map of 5es1 in complex with (26) 4184, (27) 2771, (28) 4205, (29) 5533, (30) 4543 (31) 2160 (32) 3152.

Fig 30. Residue contact map of 5es1 in complex with (26) 4184, (27) 2771, (28) 4205, (29) 5533, (30) 4543 (31) 2160 (32) 3152.

Fig 31. Residue contact map of 5es1 in complex with (26) 4184, (27) 2771, (28) 4205, (29) 5533, (30) 4543 (31) 2160 (32) 3152.

Fig 33. Free energy landscape of 5es1 in complex with (33) 4184, (34) 2771, (35) 4205, (36) 5533, (37) 4543 (38) 2160 (39) 3152.

Fig 39. Free energy landscape of 5es1 in complex with (33) 4184, (34) 2771, (35) 4205, (36) 5533, (37) 4543 (38) 2160 (39) 3152.

Fig 34. Free energy landscape of 5es1 in complex with (33) 4184, (34) 2771, (35) 4205, (36) 5533, (37) 4543 (38) 2160 (39) 3152.

Fig 35. Free energy landscape of 5es1 in complex with (33) 4184, (34) 2771, (35) 4205, (36) 5533, (37) 4543 (38) 2160 (39) 3152.

Fig 36. Free energy landscape of 5es1 in complex with (33) 4184, (34) 2771, (35) 4205, (36) 5533, (37) 4543 (38) 2160 (39) 3152.

Fig 37. Free energy landscape of 5es1 in complex with (33) 4184, (34) 2771, (35) 4205, (36) 5533, (37) 4543 (38) 2160 (39) 3152.

Fig 38. Free energy landscape of 5es1 in complex with (33) 4184, (34) 2771, (35) 4205, (36) 5533, (37) 4543 (38) 2160 (39) 3152.

Fig 40. PCA of 5es1 in complex with (40) 4184, (41) 2771, (42) 4205, (43) 5533, (44) 4543 (45) 2160 (46) 3152.

Fig 46. PCA of 5es1 in complex with (40) 4184, (41) 2771, (42) 4205, (43) 5533, (44) 4543 (45) 2160 (46) 3152.

Fig 41. PCA of 5es1 in complex with (40) 4184, (41) 2771, (42) 4205, (43) 5533, (44) 4543 (45) 2160 (46) 3152.

Fig 42. PCA of 5es1 in complex with (40) 4184, (41) 2771, (42) 4205, (43) 5533, (44) 4543 (45) 2160 (46) 3152.

Fig 43. PCA of 5es1 in complex with (40) 4184, (41) 2771, (42) 4205, (43) 5533, (44) 4543 (45) 2160 (46) 3152.

Fig 44. PCA of 5es1 in complex with (40) 4184, (41) 2771, (42) 4205, (43) 5533, (44) 4543 (45) 2160 (46) 3152.

Fig 45. PCA of 5es1 in complex with (40) 4184, (41) 2771, (42) 4205, (43) 5533, (44) 4543 (45) 2160 (46) 3152.

4. Molecular Mechanics Generalised Born Surface Area (MMGBSA) calculations

Fig 47.

Discussion

Conclusion

Supporting information

Acknowledgments

Abbreviation