Identification of a novel drug molecule for Neurodegenerative Disease from marine algae through In-silico analysis

doi:10.21203/rs.3.rs-2800050/v1

Download PDF

Research Article

Identification of a novel drug molecule for Neurodegenerative Disease from marine algae through In-silico analysis

https://doi.org/10.21203/rs.3.rs-2800050/v1

This work is licensed under a CC BY 4.0 License

Version 1

posted

You are reading this latest preprint version

Marine algae-derived compounds were reported for their neuroprotective activities, hence they can be utilised for treating neurodegenerative ailments like Alzheimer’s Disease and Parkinson’s Disease. Marine algae are potential sources of useful compounds for the pharmaceutical, medical and cosmetics industries. Molecular Docking study has been performed with the bioactive compounds reported from Phaeophyceae (Brown Algae), Rhodophyta (Red Algae) and Chlorophyta (Green Algae) with drug target protein Acetylcholinesterase (AChE). The docking study shows that the bioactive compound Tetrafucol (-14.37 kcal/mol) has good binding affinity and energy compared with other compounds such as Eckol (-13.78 kcal/mol), Phytol (-9.74 kcal/mol), Eckstonol (-12.56 kcal/mol), Phlorofucofuroeckol B (-13.307 kcal/mol) and the co-crystal (-11.63 kcal/mol) Further, Molecular Dynamics simulations study had been carried out with AChE complex with Phlorofucofuroeckol, Tetrafucol and cocrystal (Dihydrotanshinone I) for 100ns each. MD simulations study also supports the stability and flexibility of the bioactive compounds (Tetrafucol and Phlorofucofuroeckol) within the protein.

Acetylcholinesterase

Alzheimer’s Disease

Seaweeds

Molecular docking Molecular Dynamics simulations

Neurodegenerative Diseases

Neurodegenerative diseases are a varied category of neurological illnesses that impact neuron cells in different anatomical and physiological systems [1]. Neurological diseases are the second largest cause for fatality worldwide, accounting for 9 million fatalities and 16.5% of all deaths worldwide, and also the major reason of impairment accounting for 276 million DALYs worldwide, or 11.6% of all DALYs[2]. On the other hand, numerous research had shed light upon the anti-oxidant, anti-neuroinflammatory, and cholinesterase inhibitory activities of marine algae, as well as its ability to prevent neuronal death. Seaweed extracts reversed ethanol treatment effects on the levels of neurotransmitters in mice. They improved memory because they raised acetylcholine levels and inhibited cholinesterase[3]. Neurodegenerative disorders like Alzheimer's and Parkinson's continue to rise because of ageing and changing population demographics[4, 5]. Between 50 and 70 percent of all dementia cases are attributable to AD. Beta-amyloid (Aβ) deposits in senile plaques are thought to be responsible for disease progression of Alzheimer's disease (AD). Non-physiological Aβ depositions gradually alternate with physiological circumstances in neurons. Aβ is a complicated biological substance that interacts using various kinds of receptors and/or creates intractable assemblies [6].

Several reasons of neuronal loss are a hallmark of AD on neuropathological examination which remain unknown. As fibrillary braids and senile plaques form, they prevent signals from transducing among nerve cells, altering how the body works as a whole [7]. Lower than normal concentrations of neuro-mediators acetylcholine (ACh) and butyrylcholine (BCh) were found in the brains of AD patients. Hence, the blocking enzyme acetylcholinesterase (AChE), which is responsible for the breakdown of both acetylcholine and butyrylcholine, has emerged as a potential therapy for Alzheimer's disease [8]. Nerves and muscles have a high concentration of the cholinergic enzyme acetylcholinesterase (AChE) at their postsynaptic neuromuscular junctions. Hydrolysis of the natural sources of the neurotransmitter acetylcholine (ACh) in its constituent parts, acetic acid and choline, occurs very quickly [9]. To prevent ACh from propagating to and activating nearby receptors, AChE primarily blocks synaptic transmission and intercellular signalling amongst neurons. AChE is inhibited by organophosphates, which are included in many pesticides and nerve toxins [10]. An increasing cholinergic function has been the focus of recent attempts to treat AD, either by utilising cholinergic receptor agonists or inhibitors. Hence, in this work, we have performed molecular docking, molecular dynamics simulations and MM-PBSA research to identify marine bioactive compounds as inhibitors of AChE for neurodegenerative diseases.

Protein and Ligand Preparation

Leonel Pereira et al., 2021 reported that marine algae bioactive compounds (Bieckol, Phlorofucofuroeckol, Eckol, Sesquiterpenes, Phytol 2-phloroeckol, Eckstolonol, Halogenated Monoterpenes, Dieckol, 4-(3,5 Dihydroxyphenoxy)diphlorethohydroxycarmalol, Diphlorethohydroxycarmalol, Fucofuroeckol, Phlorofucofuroeckol B and Tetrafucol) have anti-neuronal activities [11]. These compounds were retrieved from PubChem database [12] and the composites were refined and redefined utilising ligprep module into Maestro [13], Schrödinger (Schrödinger Release 2014-2, 2014a). Before carrying out the docking studies, missing hydrogen atoms were added to restore original bond length and ordering, and OPLS force field was used to reduce the resulting energy [14]. Target protein Acetylcholinesterase (PDB id: 4M0E) was downloaded from Protein Data Bank [15]. Acetylcholinesterase degrades acetylcholine in nerve terminals, making it available for use by other neurotransmitters. Medications utilized to treat Alzheimer's illness and nerve gas utilized in warfare both aim for this area, making it vital target both for life-saving treatments and weapons of mass destruction[16].

Induced Fit Docking

The bioactive compounds and cocrystal (Dihydrotanshinone I) were subjected to molecular docking using Induced Fit docking method. For keeping receptor flexible throughout the docking procedures, Induced Fit Docking (IFD) protocol was employed [17][18]. The IFD docking was performed in three stages. To begin, the receptor was rigid while the ligand was permitted to freely bind to it. Ligand and protein were both given reduced radii according to the van der Waals (vdW) scaling factor of 0.5. Second, induced-fit protein-ligand complexes were created using "Prime" module. Refining of side chains and backbones of every structure from the previous phase was carried out. The residues within 4.0Å of ligands have been included in Prime refinement. Prime energy was used to rank refined complexes, and then receptor complexes of the lowest energy complexes were docked and scored using Glide [19]. An empirical scoring function was then used to rank the resulting binding postures. The ligand interaction profiles were analyzed using maestro ligand interaction and PyMol [20].

Molecular Dynamics Simulation

Molecular dynamics simulation was performed by GROMACS 2021 version [21–24]. The CHARMM27 force field was used to AChE protein, and CGenFF server was used to generate the ligand topology [25]. Moreover, water molecules were used to fix a box utilizing genbox program. To prepare the system for energy reduction, we used genion tool in Gromacs package to achieve neutrality. Minimizing energy, reaching equilibrium, and making MD products are the three parts of molecular dynamics simulation. The energy was minimized until it was less than 1000 kJ/mol as a whole for the system. Following that, systems underwent two independent runs in NVT and NPT ensembles for a total of 500,000 steps. The systems temperature was maintained for 310K using the V-rescale thermostat [26], and pressure was brought to 1 bar using the Berendsen method [27]. Under periodic boundary circumstances, MD productions for the systems under study were run for 90 ns. Using the Linear Constraint Solver (LINCS) algorithm, all of the bonds constrain at their equilibrium positions[28]. Particle-Mesh Ewald (PME) technique [29] was used to handle long-range electrostatic interactions, and a 12Å range cutoff was used to calculate nonbonded interactions. To evaluate produced trajectory files GROMACS packages (RMSD, RMSF and radius of gyration) were used. To foretell the protein's sustainability, fluctuations in overall protein and per residues were computed using gmx_mpi rms, gmx_mpi rmsf and gmx_mpi gyrate.

Binding Free Energy Calculation (Mm/pbsa) Approach

The binding free energy calculation were done by using gmx_MMPBSA tool [30, 31]. Molecular Mechanics Poisson-Boltzmann Surface Area (MM-PBSA) based binding free energy was calculated using the g_mmpbsa program. The binding energy consists of vacuum molecular mechanics (MM) potential energy of the bonded and non-bonded interactions, polar and non-polar solvation energy [32]. Free energy for each individual entity (protein, ligand, protein-ligand complex) was represented by the general expression given below,

𝐺_𝑥 = 𝐸_𝑀𝑀 + 𝐺_{𝑝𝑜𝑙𝑎𝑟} + 𝐺_{𝑛𝑜𝑛𝑝𝑜𝑙𝑎𝑟}

Where, 𝑥 is the protein or ligand or protein-ligand complex,

𝐸_𝑀𝑀 is the average molecular mechanics potential energy in vacuum.

𝐺_{𝑝𝑜𝑙𝑎𝑟} and 𝐺_{𝑛𝑜𝑛𝑝𝑜𝑙𝑎𝑟} are the polar and non-polar solvation energies, respectively.

For understanding the characteristics of binding (ligand) and protein-ligand complex, molecular docking studies were performed. This computational study is the most commonly used technique in the drug discovery research field. In this present study, acetylcholinesterase AChE target was employed for the molecular docking studies with bioactive compounds from the brown marine algae.

Molecular Docking Analysis Of Marine Algae Bioactive Compounds With Ache

The results of the docking studies, namely, docking score and Glide energy have been portrayed in Table 1 which reflect the binding affinity of analysed ligands and the co-crystal. By observing the interactions of natural bioactive compounds and target protein, it was found that, except the co-crystal, remaining all 14 natural compounds produced significant number of hydrogen bond with the conserved active site residues Tyr72, Tyr124 and Phe295. In the docking results, all 14 compounds showed the higher binding energy (docking score and glide energy) compared with co-crystal inhibitor and also were found to have several hydrogen bonds interacting by catalytic sites of AChE. Among fourteen compounds, two compounds (Phlorofucofuroeckol and Tetrafucol) had higher docking score and glide energy compared with the co-crystal inhibitor of AChE (Table 1).

The co-crystal (Dihydrotanshinone I) forms hydrogen bonds with conserved active site residues Tyr72, Tyr124 and Phe295 with docking score and glide energy of -11.63 and − 45.71 kcal/mol, respectively. Similarly, phlorofucofuroeckol also forms hydrogen bonds with Tyr72 and Phe295 along with Gly122, Gly342, Tyr337, Arg296, Ser203 and Glu202 with docking score and glide energy of -16.79, and − 88.10 kcal/mol, respectively. Another compound, Tetrafucol forms a hydrogen bond with Tyr72 and Phe295 along with Ser293, His287 and Asn283 with docking score and glide energy of -14.37, and − 78.65 kcal/mol, respectively. Furthermore, hydrophobic interactions between residues like Tyr341, Phe297, Val294, Tyr337, Phe338, Tyr124, Trp286, Val365, Leu289 and Ala204 as well as π-π stacking were aided by the preservation of aromatic rings. Likewise, the compounds Bieckol, Eckol, Sesquiterpenes, 2-phloroeckol, Eckstolonol, Dieckol, 4-(3,5-Dihydroxyphenoxy) diphlorethohydroxycarmalol, Diphlorethohydroxycarmalol, Fucofuroeckol, Phlorofucofuroeckol B form hydrogen bonds with catalytic residues Tyr72, Tyr124 and Phe295 with the docking energy of -18.20, -13.78, -10.00, -14.97, -12.56, -16.76, -16.83, -17.22, -14.30 and − 13.30 kcal/mol, respectively. Interestingly, all marine algae bioactive compounds have common H-bond interactions with Tyr72, Tyr124 and Phe295, which are also similar to that of the co-crystal inhibitor. Docking results reveal that all marine algae bioactive compounds have strong binding affinities with AChE target protein. The ligand interaction plots of 2D maestro and 3D Pymol representation of protein-ligand complexes are shown in (Fig. 1). Based on the binding energy and key interactions profile analysis, the two best compounds Phlorofucofuroeckol and Tetrafucol complexes were chosen for molecular dynamics simulations to understand the structural and conformational stability. Results presented in Fig. 2 show the superposition analysis of protein-ligand docked complexes and it reveals that seaweed bioactive compounds bind at the active site of AChE target protein.

Table 1

Docking score, glide energy of seaweed bioactive compounds and the co-crystal docked complexes with AChE.
Compounds	Docking score (kcal\mol)	Glide energy (kcal\mol)	Hydrogen bond interactions (D-H…A)	Distances (Å)
Co-crystal (Dihydrotanshinone I)	-11.63	-45.71	(TYR124)OH-H…O20 (TYR72)OH-H…O21 (PHE295)N-H…O17	2.84 2.90 3.21
Bieckol	-18.20	-83.41	(HIS287)NE2-H...O17 (TYR124)OH-H...O18 (TYR124)OH-H...O8 (LEU76)N-H...O6 O6-H...(THR75)OG1 (THR75)N-H...O6 (TYR72)OH-H...O1	2.99 2.86 2.99 3.03 2.58 2.98 3.02
Phlorofucofuroeckol	-16.79	-88.10	(TYR337)OH-H…08 06-H…(ARG296)O (PHE295)N-H…O6 O6-H…(SER203)OG (O12)-H…(SER203)OG (011)-H…(GLU202)OE2 (GLY122)N-H…O12 (TYR72)-H…O9	2.98 2.98 2.97 2.76 3.23 2.87 3.01 2.76
Eckol	-13.78	-56.31	O6-H...(ARG96)O (PHE295)N-H...O6 O9-H...(SER293)O O8-H...(TRP286)O O4-H...(TYR72)OH	3.08 2.93 3.06 2.78 2.77
Sesquiterpenes	-10.00	-38.28	(PHE295)N-H…O2	2.89
Phytol	-9.74	-41.72	(THR75)N-H…O1	3.01
2-phloroeckol	-14.97	-59.34	O10-H...(TYR341)O (PHE295)N-H...O5 (PHE295)N-H...O2 (SER293)OG-H...O5 O9-H...(TRP286)O O11-H...(ASP74)OD1 O6-H...(ASP74)OD1	2.88 3.15 3.09 2.96 2.67 3.02 2.85
Eckstolonol	-12.56	-60.84	(PHE295)N-H...O5 (GLY121)N-H...O12 (THR75)N-H...O10 O6-H...(ASP74)OD2 O10-H...(ASP74)OD1	2.91 2.68 3.14 2.51 2.95
Halogenated Monoterpenes	-7.23	-32.08	---	---
Dieckol	-16.76	-76.85	(PHE295)N-H...O9 O12-H...(GLU292)OE2 (TYR124)OH-H...O11 (THR75)N-H...(O18)	2.82 2.77 2.86 3.05
4-(3,5-Dihydroxyphenoxy)diphlorethohydroxycarmalol	-16.83	-77.81	(PHE295)N-H...O7 (PHE295)N-H...O5 O5-H...(SER293)O (TYR133)OH-H...O15 (THR75)OG1-H...O13 O13-H...(THR75)O O6-H...(TYR72)OH	2.91 2.97 3.22 2.76 3.02 2.90 3.00
Diphlorethohydroxycarmalol	-17.22	-71.33	(PHE295)N-H...O7 (PHE295)N-H...O5 O5-H...(SER293)O (TYR133)OH-H...O15 (THR75)OG1-H...O13 O13-H...(THR75)O O6-H...(TYR72)OH	2.91 2.97 3.22 2.76 3.02 2.90 3.00
Fucofuroeckol	-14.30	-70.04	O10-H...ARG296)O (PHE295)N-H...O10 O6-H...(GLN291)O (TYR124)OH-H...O9 O9-H...(ASP74)OD2 O7-H...(TYR72)OH	2.70 3.05 3.12 3.08 2.67 3.05
Phlorofucofuroeckol B	-13.30	-80.24	O12-H...(PHE338)O O9-H...(LEU289)O (HIS287)ND1-H...O10 (HIS287)N-H...O13 O13-H...(ASN283)O (TYR124)OH-H...O8	3.21 2.76 2.83 3.22 2.90 2.78
Tetrafucol	-14.37	-78.65	(PHE295)N-H...O9 (PHE295)N-H...O5 (SER293)OG-H...O3 O4-H...(SER293)O (HIS287)ND1-H...O8 O12-H...(ASN283)O (TYR72)OH-H...O10	3.12 3.26 3.33 2.87 3.07 3.05 3.17

Molecular Dynamics Simulations Of The Best Compounds And Co-crystal Complex

To understand the structural stability of the AChE complexes, MD simulations were performed for 100 ns which included AChE-Phlorofucofuroeckol and AChE-Tetrafucol as well as AChE-co-crystal complexes. The RMSD, RMSF, and Rgyr graphs were used to assess the steadiness of the complexes. From MD outcomes of the RMSD as shown in Fig. 3, it can be seen that the trajectory of the AChE in complex with the Phlorofucofuroeckol compound showed lesser structural deviations from the initial to 80 ns and the rmsd value is ~ 0.15–0.2 nm. Afterwards, it reached stable convergence till 100 ns trajectories. Similarly, AChE-Tetrafucol complex also showed very lesser deviations during the course of the MD simulations and the average rmsd value is ~ 0.15 nm in the overall MD simulations. Whereas, AChE-co-crystal complex showed higher structural deviations during the 0–52 ns simulation and it is observed that rmsd is ~ 0.25–0.3 nm. Finally, it reached convergence and then gradually increased in the rmsd at 80–100 ns, rmsd being 0.3–0.35 nm, respectively.

For predicting the dynamic behaviour of residual vacillation with docked complexes all through simulation, root mean square fluctuation analysis was carried out with AChE-Phlorofucofuroeckol and AChE-Tetrafucol as well as AChE-co-crystal complexes. The RMSF graph in Fig. 4 shows that upon binding of bioactive compounds (Tetrafucol and Phlorofucofuroeckol) simulated complexes have lesser residual fluctuations at the active site of AChE. In particular, catalytic sites residues of Tyr72, Tyr124 and Phe295 regions do not have fluctuations as in the co-crystal bound complex. Radius of gyration governs the mass-weight of atoms collection and mutual centre of mass during a molecular dynamics simulation. Rgyr was plotted as per Cα atoms of the protein. Results in Fig. 5 indicate that AChE-Phlorofucofuroeckol and AChE-Tetrafucol complex structures were stable and folded throughout the MD run and rg values range is 2.30 to 2.32 nm. AChE-Co-crystal complex showed fewer structural deviation and Rgyr value is 2.26 to 2.20 nm. The total number of hydrogen bond contacts was also analysed for the 100 ns simulation run. Phlorofucofuroeckol marine bioactive compound with AChE protein has formed 6 H-bonds whereas Tetrafucol compound has 9 H-bonds with target protein and it maintains these throughout MD simulations. The cocrystal has 3 H-bonds, which depict that, the our best bioactive compounds show strong stability with AChE protein (Fig. 6).

Free Energy Calculations

To understand the affinity of ligand binding with AChE protein in energetic parameters, MM-PBSA approach was used to calculate the binding free energy (Table 2). In this calculation, only enthalpy terms are incorporated, due to high computational complexity and entropic parameters not included. The known cocrystal inhibitor has − 14.88 (± 9.93) kJ/mol of binding energy whereas best bioactive compounds Pholorofucofuroeckol and Tetrafucol showed more favourable binding energy − 37.55 (± 5.58) kJ/mol and − 14.92 (± 16.45) kJ/mol respectively. The higher binding affinity in the Pholorofucofuroeckol and Tetrafucol is due to more favorable van dar Waal and electrostatics energetics. In addition, polar solvation effects played a significant role in ligand binding in the Pholorofucofuroeckol.

Table 2

Binding Free Energy Calculations of AChE-cocrystal and best compounds using MMPBSA method.
Protein –Ligand Complexes	ΔE VdW (kJ/mol)	ΔE Elec (kJ/mol)	ΔEGB (kJ/mol)	ΔESURF (kJ/mol)	ΔGGAS (kJ/mol)	Binding Free Energy (ΔG) (kJ/mol)
Co-crystal (Dihydrotanshinone I)	-20.37 (± 12.07)	-16.82 (± 12.84)	25.02 (± 14.72)	-2.71 (± 1.55)	-37.20 (± 22.28)	-14.88 (± 9.93)
Tetrafucol	-18.79 (± 20.27)	-18.69 (± 21.46)	25.08 (± 27.40)	-2.53 (± 2.72)	-37.47 (± 40.89)	-14.92 (± 16.45)
Pholorofucofuroeckol	-50.38 (± 4.54)	-38.93 (± 9.48)	57.76 (± 7.48)	-6.00 (± 0.56)	-89.31 (± 10.86)	-37.55 (± 5.58)

Bioactive compounds derived from Marine algae have the neuroprotective activity that might be employed to treat neurodegenerative diseases such as Alzheimer’s Disease and Parkinson’s Disease. These diseases might be linked to the activity of acetylcholinesterase (AChE) enzyme. To understand the binding characteristics with drug target protein AChE, the molecular docking study has been carried out with the bioactive compounds reported from Phaeophyceae (Brown Algae), Rhodophyta (Red Algae) and Chlorophyta (Green Algae). The docking studies show that among the bioactive compounds, Phlorofucofuroeckol and Tetrafucol have the binding energy − 16.79 and − 14.37 kcal/mol, with good binding affinity and energy values compared with other compounds such as Eckol (-13.78 kcal/mol), Phytol (-9.74 kcal/mol), Eckstonol (-12.56 kcal/mol), Phlorofucofuroeckol B (-13.30 kcal/mol) and the cocrystal (-11.63 kcal/mol). These results depict that Phlorofucofuroeckol and Tetrafucol should further be subjected as potential lead compounds for the Neurodegenerative Diseases treatment.

Acknowledgments

The authors DV and AKD thank AMET University for providing the computational facilities for doing the docking work. AKD thanks AMET University for providing Research Associate Fellowship. DV thanks SRMIST for the facilities provided.

Funding

The authors declare that no funds, grants, or other support were received during the preparation of this manuscript.

Conflicts of interest

The authors declare no conflicts of interest.

Data Availability Statement

All data generated or analyzed during this study are included in this published article

Przedborski S, Vila M, Jackson-lewis V, et al (2003) Series Introduction : Neurodegeneration : What is it and where are we ? Find the latest version : Neurodegeneration : What is it and where are we ? 111:3–10. https://doi.org/10.1172/JCI200317522.
Federation TW, Neurological N (2020) World Brain Day 2020 – Challenges and Opportunities in India. 2020–2021. https://doi.org/10.4103/AOMD.AOMD
Chaudhari SM, Patel KY, Badole SL (2013) Punica granatum (Pomegranate Fruit): In Cancer Treatment. In: Polyphenols in Human Health and Disease. Elsevier, pp 1393–1400
Skovronsky DM, Lee VM-Y, Trojanowski JQ (2006) NEURODEGENERATIVE DISEASES: New Concepts of Pathogenesis and Their Therapeutic Implications. Annu Rev Pathol Mech Dis 1:151–170. https://doi.org/10.1146/annurev.pathol.1.110304.100113
Garcia-Ayllon M-S, Cauli O, Silveyra M-X, et al (2008) Brain cholinergic impairment in liver failure. Brain 131:2946–2956. https://doi.org/10.1093/brain/awn209
Sadigh-Eteghad S, Sabermarouf B, Majdi A, et al (2015) Amyloid-Beta: A Crucial Factor in Alzheimer’s Disease. Med Princ Pract 24:1–10. https://doi.org/10.1159/000369101
Wimo A, Prince M (2010) Design, Dignity, Dementia: Dementia-Related Design and the Built Environment. Dementia I:96
Cavdar H, Senturk M, Guney M, et al (2019) Inhibition of acetylcholinesterase and butyrylcholinesterase with uracil derivatives: kinetic and computational studies. J Enzyme Inhib Med Chem 34:429–437. https://doi.org/10.1080/14756366.2018.1543288
McHardy SF, Wang H-YL, McCowen S V., Valdez MC (2017) Recent advances in acetylcholinesterase Inhibitors and Reactivators: an update on the patent literature (2012-2015). Expert Opin Ther Pat 27:455–476. https://doi.org/10.1080/13543776.2017.1272571
McGleenon, Dynan, Passmore (1999) Acetylcholinesterase inhibitors in Alzheimer’s disease. Br J Clin Pharmacol 48:471–480. https://doi.org/10.1046/j.1365-2125.1999.00026.x
Pereira L, Valado A (2021) The Seaweed Diet in Prevention and Treatment of the Neurodegenerative Diseases. Mar Drugs 19:128. https://doi.org/10.3390/md19030128
Kim S, Chen J, Cheng T, et al (2023) PubChem 2023 update. Nucleic Acids Res 51:D1373–D1380. https://doi.org/10.1093/nar/gkac956
Schrödinger Release 2014-2 (2014) LigPrep, Schrödinger, LLC, New York, NY, 2014-2.
Halgren T (2007) New Method for Fast and Accurate Binding-site Identification and Analysis. Chem Biol Drug Des 69:146–148. https://doi.org/10.1111/j.1747-0285.2007.00483.x
Berman HM, Westbrook J, Feng Z, et al (2000) The Protein Data Bank. Nucleic Acids Res. 28:235–242
Cheung J, Gary EN, Shiomi K, Rosenberry TL (2013) Structures of Human Acetylcholinesterase Bound to Dihydrotanshinone I and Territrem B Show Peripheral Site Flexibility. ACS Med Chem Lett 4:1091–1096. https://doi.org/10.1021/ml400304w
Halgren TA, Murphy RB, Friesner RA, et al (2004) Glide : A New Approach for Rapid , Accurate Docking and Scoring . 2 . Enrichment Factors in Database Screening. 2:1750–1759. https://doi.org/10.1021/jm030644s
Sherman W, Beard HS, Farid R (2006) Use of an induced fit receptor structure in virtual screening. Chem. Biol. Drug Des. 67:83–84
Farid R, Day T, Friesner RA, Pearlstein RA (2006) New insights about HERG blockade obtained from protein modeling, potential energy mapping, and docking studies. Bioorg Med Chem 14:3160–3173. https://doi.org/10.1016/j.bmc.2005.12.032
DeLano W (2002) Pymol: An open-source molecular graphics tool. CCP4 Newsl Protein Crystallogr 700:
Abraham MJ, Murtola T, Schulz R, et al (2015) GROMACS: High performance molecular simulations through multi-level parallelism from laptops to supercomputers. SoftwareX 1–2:19–25. https://doi.org/10.1016/j.softx.2015.06.001
Hess B, Kutzner C, van der Spoel D, Lindahl E (2008) GROMACS 4: Algorithms for Highly Efficient, Load-Balanced, and Scalable Molecular Simulation. J Chem Theory Comput 4:435–447. https://doi.org/10.1021/ct700301q
Abraham MJ, Murtola T, Schulz R, et al (2015) Gromacs: High performance molecular simulations through multi-level parallelism from laptops to supercomputers. SoftwareX 1–2:19–25. https://doi.org/10.1016/j.softx.2015.06.001
Lemkul J (2019) From Proteins to Perturbed Hamiltonians: A Suite of Tutorials for the GROMACS-2018 Molecular Simulation Package [Article v1.0]. Living J Comput Mol Sci 1:1–53. https://doi.org/10.33011/livecoms.1.1.5068
Vanommeslaeghe K, Hatcher E, Acharya C, et al (2010) CHARMM General Force Field (CGenFF): A force field for drug-like molecules compatible with the CHARMM all-atom additive biological force fields. J Comput Chem 31:671–690. https://doi.org/10.1002/jcc.21367.CHARMM
Bussi G, Donadio D, Parrinello M (2007) Canonical sampling through velocity rescaling. J Chem Phys 126:. https://doi.org/10.1063/1.2408420
Berendsen HJC, Postma JPM, Van Gunsteren WF, et al (1984) Molecular dynamics with coupling to an external bath. J Chem Phys 81:3684–3690. https://doi.org/10.1063/1.448118
Hess B, Bekker H, Berendsen HJC, Fraaije JGEM (1997) LINCS: A Linear Constraint Solver for molecular simulations. J Comput Chem 18:1463–1472. https://doi.org/10.1002/(SICI)1096-987X(199709)18:12<1463::AID-JCC4>3.0.CO;2-H
Petersen HG (1995) Accuracy and efficiency of the particle mesh Ewald method. J Chem Phys 103:3668–3679. https://doi.org/10.1063/1.470043
Valdés-Tresanco MS, Valdés-Tresanco ME, Valiente PA, Moreno E (2021) Gmx_MMPBSA: A New Tool to Perform End-State Free Energy Calculations with GROMACS. J Chem Theory Comput 17:6281–6291. https://doi.org/10.1021/acs.jctc.1c00645
Changhao Wang, Peter H. Nguyen, Kevin Pham, Danielle Huynh, Thanh-Binh Nancy Le, Hongli Wang, Pengyu Ren and RL (2019) Calculating Protein-Ligand Binding Affinities with MMPBSA: Method and Error Analysis. Physiol Behav 176:139–148. https://doi.org/10.1002/jcc.24467.Calculating
Varvara S, Popa M, Rustoiu G, et al (2009) Evaluation of some amino acids as bronze corrosion inhibitors in aqueous solution. Stud Univ Babes-Bolyai Chem 2:73–85

No competing interests reported.

Download PDF

Version 1

posted

You are reading this latest preprint version

Identification of a novel drug molecule for Neurodegenerative Disease from marine algae through In-silico analysis

Status:

Version 1

Abstract

Figures

Introduction

Computational Methods

Protein and Ligand Preparation

Induced Fit Docking

Molecular Dynamics Simulation

Binding Free Energy Calculation (Mm/pbsa) Approach

𝐺_𝑥 = 𝐸_𝑀𝑀 + 𝐺_{𝑝𝑜𝑙𝑎𝑟} + 𝐺_{𝑛𝑜𝑛𝑝𝑜𝑙𝑎𝑟}

Results & Discussion

Molecular Docking Analysis Of Marine Algae Bioactive Compounds With Ache

Molecular Dynamics Simulations Of The Best Compounds And Co-crystal Complex

Free Energy Calculations

Conclusions

Declarations

References

Additional Declarations

Status:

Version 1

Identification of a novel drug molecule for Neurodegenerative Disease from marine algae through In-silico analysis

Status:

Version 1

Abstract

Figures

Introduction

Computational Methods

Protein and Ligand Preparation

Induced Fit Docking

Molecular Dynamics Simulation

Binding Free Energy Calculation (Mm/pbsa) Approach

𝐺𝑥 = 𝐸𝑀𝑀 + 𝐺𝑝𝑜𝑙𝑎𝑟 + 𝐺𝑛𝑜𝑛𝑝𝑜𝑙𝑎𝑟

Results & Discussion

Molecular Docking Analysis Of Marine Algae Bioactive Compounds With Ache

Molecular Dynamics Simulations Of The Best Compounds And Co-crystal Complex

Free Energy Calculations

Conclusions

Declarations

References

Additional Declarations

Status:

Version 1

𝐺_𝑥 = 𝐸_𝑀𝑀 + 𝐺_{𝑝𝑜𝑙𝑎𝑟} + 𝐺_{𝑛𝑜𝑛𝑝𝑜𝑙𝑎𝑟}