بررسی سرقت ادبی مقاله با سرویس iThenticate

Posted by مدیر پرتال دانلود مقالات علمی 25/01/1397 Categories: نكات و ابزارهايي براي نوشتن مقاله معتبر

چک کردن پلاجیاریزم (PLAGIARISM) مقالات

چک کردن پلاجیاریزم  مقاله

احتمالا تاکنون برای شما رخ داده است که مقاله ای که نگارش نموده اید به دلیل درصدی شباهت متنی در مجلات معتبر در همان ابتدای فرایند ارزیابی توسط سردبیر یا داوران مجله رد شده باشد یا برای بازبینی برگشت داده شده است. حتی بیشتر مواقع نیز بدون ذکر دلیل مشخصی مقاله شما مناسب برای انتشار، شناخته نشده است.

آمار نشان داده است که یکی از دلایل اصلی رد شدن مقالات انگلیسی نویسندگان فارسی زبان در مجلات معتبر علمی بین المللی و مجلات انگلیسی زبان داخلی تشابه متنی است که به سرقت ادبی یا Plagiarism  مشهور است. در اکثر موارد این اتفاق سهوی پیش می آید و نویسنده با رعایت تمامی مضامین اخلاقی و اصول نگارش ، مقاله را تکمیل کرده است.

بررسی سرقت ادبی مقاله با سرویس iThenticate از نظر پلاجیاریزم (PLAGIARISM)

شناسایی و گزارش سرقت ادبی توسط نرم افزار های پیشرفته با ساختار پیچیده انجام می شود. به نحوی که تمامی جملات و عبارات مشابه در متن های مقالات و کتابهای در دست چاپ و همچنین مقالات چاپ شده در زمینه مرتبط در زمان کوتاهی مورد اسکن و مقایسه قرار می گیرد.

در پایان، بخش های مشابه بعنوان کپی یا سرقت ادبی معرفی میشود و بدان معناست که نویسنده در این بخش ها را از علم سایرین بدون اینکه خود فکر و اندیشه ای داشته باشد، استفاده کرده است. برای جلوگیری از سرقت ادبی، شما همیشه باید با توجه به دستورالعمل نگارش علمی و بدون خیانت به اصل موضوع به یافته های نویسنده مرجع رفرنس دهید.

درصد تشابه مقاله

با خدمات سایت دانلودپیپر می توانید بررسی سرقت ادبی مقاله خود را با سایت مرجع iThenticate.com چک کنید و براحتی سرقت ادبی مقاله خود را متوجه شوید و سپس آن را رفع کنید. تشخیص سرقت ادبی در این سایت بخاطر دیتابیسی که شامل میلیون ها مقاله منتشر شده و میلیاردها صفحات وب از دقیق ترین درصد سرقت ادبی می باشد.

هزینه بررسی هر مقاله و ارسال PDF گزارش 7 هزار تومان است.

 

Comments

25/07/1397
sako mirzaie
View User Profile for sako mirzaie

re: بررسی سرقت ادبی مقاله با سرویس iThenticate

Abstract

Folate antagonists are classified as important and valuable therapeutic agents against infection, neoplastic and inflammatory diseases. Dihydrofolate reductase (DHFR) is a biological target of two well-defined folate antagonists, classical and non-classical inhibitors. DHFR catalyzes the reduction of 7, 8-dihydrofolate to 5, 6, 7, 8-tetrahydrofolate benefits of NADPH as a cofactor. With the point to recognize new chemicals to be utilized for further structure-based drug design, a set of 67753 molecules including chemicals and natural products from the Zinc Database have been screened through docking method. The high ranked compound with regard to methotrexate (MTX), resulted to be three compounds comprises of ZINC29236925, ZINC31169388 and ZINC01629864, which further investigated by molecular dynamics (MD) simulation, Poisson−Boltzmann surface area method (MM-PBSA) and QMMM calculations. PCA analysis revealed that upon inhibitor binding, the DHFR folding is changed. Our QMMM data suggested that a structure with ZINC ID of ZINC31169388 has the stronger interaction with DHFR active site and could be a good candidate for biological assessment and additional advanced improvement. Also, ADME prediction demonstrated that all physicochemical parameters of ZINC31169388 are within the satisfactory range described for human treatment.

 

 

Keywords: Dihydrofolate reductase, drug design, Zinc Database, molecular dynamics, natural products.

 

 

 

 

 

 

 

 

 

 

 

 

1. Introduction

Human dihydrofolate reductase (hDHFR), an oxidoreductase enzyme converts dihydrofolate into tetrahydrofolate, and at the lower reaction rate, catalyzes the conversion of folate to tetrahydrofolate [1]. hDHFR has a critical role in cellular metabolism and growth. Tetrahydrofolate is involved in amino acids, lipids, pyrimidines, and purines biosynthesis [1, 2]. So, it is a valuable target for some drugs including MTX, pyrimethamine, and trimethoprim that act by blocking competitively and irreversibly this enzyme in parasitic, bacterial or malignant cells [3]. The importance of hDHFR and its role in DNA replication and un-controlled cell deviation in cancers has been studied for over 50 years [4]. Furthermore, hDHFR is an excellent target for the infectious disease as the prior studies exhibited outstanding selectivity for the parasite over the host enzyme [4]. hDHFR inhibitors are divided by classical and non-classical antifolates. The classical inhibitors possess p-aminobenzoylglutamic moiety and are closely similar to the folic acid.  MTX, a common and typical inhibitor, is categorized in classical inhibitors.  Various cancer [5], rheumatoid arthritis [6], inflammatory bowel diseases [7], asthma [8] and psoriasis [9] are the examples of diseases treated by MTX. In non-classical inhibitors, p-aminobenzoylglutamic moiety has been replaced with a lipophilic part. These inhibitors, in comparison with classical one, have raised potency, higher solubility in lipid and consequently have enhanced cellular uptake. However, due to decreased selectivity, usually, they have a higher toxicity [4]. Trimetrexate [10], piritrexim [11] and pemetrexed [12] belong to non-classical inhibitors. hDHFR is an alpha/beta secondary structure enzyme with chiefly parallel beta sheets and also, with two binding sites, one for NADPH and one site for folate [4]. In various species, DHFR has some unique features in view of its structural motifs. However, in human, hDHFR operates as an RNA-binding protein, which can bind to its relevant mRNA and causes the translational repression [13, 14]. As mentioned above, MTX is worthwhile and valuable as an anti-neoplastic and immune-suppressive agent because it inhibits the proliferation of rapidly dividing malignant and immune-responsive cells. However, resistance to MTX has been pointed out in cultured human and rodent cells, often because of amplification of the DHFR gene, which can defeat the therapeutic properties of DHFR inhibitors [4, 15]. On the other hand, MTX administration can cause some light to severe side effects including hepatotoxicity, bone marrow suppression [16], rarely hypersensitivity pneumonitis [17] and opportunistic infections [18]. Since the majority of the MTX is eliminated unchanged by the kidneys, the toxicity of this drug in patients with the impaired kidney function or dehydration is elevated [18, 19]. Recently, Hsu et al. described a case of psoriatic plaque ulceration stimulated by low-dose MTX and highlighted the risk factors and probable mechanisms of toxicity [20]. So, drug design and development against hDHFR has not been stopped yet and the world needs to try to develop the new potent and effective drugs. Today, computers have become an integral part of research in biological science and medicine. Computational methods such as molecular mechanics, quantum mechanics, molecular docking and molecular dynamics are commonly engaged to design new hits and drugs. [21, 22]. Anyhow, drug design is time consuming and costly. Prior studies have shown that the discovery of a new drug require $ 800 million and needs around 20 years. [22-24]. Natural products afford the invaluable framework for medicinal chemists, to design and identify the new hits against infectious and cancerous cells. A statistical study from 1939 up to now, revealed that natural products are extensively applied to discover the US FDA approved drugs [25]. In the current study, we employed the molecular docking, molecular dynamics and quantum mechanics/molecular mechanics (QMMM) investigations to discover the new compounds among the analogues of MTX, and natural products, with potent inhibitory effects and reduced side effects against hDHFR.

 

2. Methods

2-1. Preparing hDHFR and Chemical Structures Preparation

The 3D structure of hDHFR (PDB code: 2W3M) was downloaded from the data bank named Brookhaven protein [3]. The molecules of water, along with non-polar hydrogen atoms, were all cleared away. Gasteiger–Marsili method was used to allot the charges to the system atoms [26].

Before starting a molecular docking study, to loosen up any closed contact, hDHFR was minimized in GROMACS 5.0.4 through the steepest descent approach [27]. For our virtual screening study, MTX was chosen as a positive control, and the structures with 40 percent similar to the control (5800 structures), AnalytiCon Discovery natural product library (5154 structures) and IBScreen natural product library (56799 structures) totally 67753 structures were retrieved from ZINC database [28]. Afterward, by using the method of Gustier–Marsili, the charges of molecules were all considered. Lastly, the 3D structures were utilized for molecular docking studies.

2-2. Molecular Docking Studies

Molecular docking of the selected molecules from ZINC library (67753 structures) to the active site of hDHFR was performed by AutoDock Vina [29]. This docking software works based on the empirical scoring functions, and is capable to compute the grid maps automatically.  For molecular docking runs, the default parameters were allocated [29]. The crystal structure of hDHFR had a folate as a substrate; thence, its neighboring residues were defined as the binding site [3]. hDHFR has a nicotinamide adenine dinucleotide phosphate (NADPH) as a cofactor (Figure 1) and this coenzyme was used in its reduced form in all docking studies. All pre-arranged parameters were apportioned for docking simulations. For each run, in consonance with a scoring function of AutoDock Vina, the pose with the minimum free energy of binding was picked for MD investigation.

2-3. MD Simulation on RT

The MD runs were performed with GROMACS 5.1 package and AMBER 99SB force field [27]. Totally five independent runs including free hDHFR, hDHFR: MTX, hDHFR: ZINC29236925, hDHFR: ZINC31169388, hDHFR: ZINC01629864 complexes were introduced to MD investigations. The correct pKa of hDHFR residues was considered by PROPKA 2.0 server to allocate the right ionization conditions of hDHFR ionizable moieties [30]. The relative charges and topology records of NADPH and inhibitors were estimated by ACPYPE, a module in a ANTECHAMBER package [31]. With regard to the selected Amber force field in all our MD simulations, the compatible TIP3P water model in a cubic periodic box was employed [32]. To neutralize the system containing MTX, ZINC29236925, ZINC31169388, ZINC01629864, five, three, three and four Na+ ions were added, respectively. Each system was minimized energetically with the steepest descent integrator followed by conjugate gradient algorithm to achieve a maximum force below 1000 kJ mol-1 nm-1 on any atom. The cutoff for computing the short-range, non-bonded interactions of van der Waals and electrostatic interactions were assigned 1.4 and 0.9 nm, respectively. Particle mesh Ewald (PME) algorithm was operated treat the Long-range electrostatic interactions[33, 34]. The temperature of 300 K, with the coupling time constant of 0.1 ps [35], and pressure of 1 bar (coupling constant of 2 ps) [36] were appointed for each MD run. A Berendsen coupling with an additional term [27], and Parrinello-Rahman barostat [36] were conducted for the temperature and pressure coupling, respectively. To restrict the bond lengths with a 2 fs time step, Linear Constraint Solver (LINCS) was occupied [37]. Each system was equilibrated under a fixed volume (NVT) ensemble (100 ps) and a fixed pressure (NPT) ensemble (100 ps) [38]. The equilibrated system from each run was subjected to 30 ns MD simulation. The trajectories were examined using VMD software [39] the standard tools applied in the GROMACS software.

2-4. Essential dynamics (ED) or principal component analysis (PCA) on all MD trajectories

ED or it’s another name, “PCA” analysis was carried out on all MD trajectories by two useful GROMACS codes, g_covar and g_anaeig. By ED investigation, the identification of dominant motions of DHFR in the presence and/or absence of our hits is possible. Based on the first two eigenvectors, consist of principal component 1 (PC1) and principal component 2 (PC2), a 2D projection was produced for each MD trajectory [40].

2-5. Binding Free Energy Computation of Free and Complex hDHFR

Modified Molecular Mechanics–Poisson Boltzmann Surface Area (MM-PBSA) is free software, and engaged to estimate the free energy of binding between two distinct groups in a complex. Lately, MM-PBSA algorithm has been applied as a scoring function in computational drug design and discovery [41]. In this survey, MM-PBSA method was employed to calculate the intermolecular interaction free energy between hits and/or crystal inhibitor, and hDHFR. The free energy of binding is extracted from the following equation:

ΔGbinding = Gcomplex – (Gprotein + Gligand)

Where Gcomplex is the absolute free energy of the DHFR–inhibitor complex, Gprotein and Gligand are the whole free energies of the detached form of DHFR and the relevant hits in the solvent, respectively [41, 42].

 

 

 2-6. ADME/T prediction

The four physic-chemical parameters including absorption, distribution, metabolism, and excretion (ADME/T) parameters of all preferred hits were figured out using QikProp module. This code is a speedy, authentic, straightforward and simple to use absorption, distribution, metabolism, and excretion (ADME/T) estimation program constructed by Professor William L. Jorgensen [43]. This software, which is embedded in Schrödinger’s Maestro Molecular modeling suit, determines substantially meaningful descriptors and pharmaceutically related properties of individual or in-batch organic molecules.

2-8. A combination of quantum mechanics and molecular mechanics (QMMM) studies

Density functional theory (DFT) estimations have been executed through Jaguar included in Schrödinger’s Maestro Molecular modeling suit. The hits were treated at the quantum mechanical (QM) level and the DHFR at the MM level. MTX, ZINC29236925, ZINC31169388, ZINC01629864 molecules were optimized in free and DHFR bound states, with hybrid DFT using Becke's three parameter exchange potential and Lee-Yang-Parr correlation functional (B3LYP) gradient amended exchange-correlation functional in combination with 6-31  G* basis set [44, 45].  In the case of hit bound DHFR, the complex from the last snapshot of MD was introduced to QMMM calculations. OPLS 2005 force field was set in the MM part of QMMM studies. Highest occupied molecular orbital (HOMO), lowest unoccupied molecular orbital (LUMO) and energy gaps were assessed. The free binding energy of the complex was extracted using the following equation:

Ebinding = Ecomplex- Efree_receptor- Efree_ligand

 

3. Results and discussion

3-1. Molecular docking studies

At present, virtual screening research is an irrefutable part of a process of drug design and discovery. In this technique, the elemental inputs are the target structure, either experimentally identified or computationally predicted, and a library composed of small molecules available with the aid of purchase or synthesis [46]. In the current study, 67753 structures (chemical and natural product) derived from ZINC database, were occupied in our virtual screening. Table 1 shows the structures, zinc database code and anticipated binding free energy of the best three poses predicted by AutoDock Vina. Compound with zinc database code of ZINC29236925 and IUPAC name of Dipropyl 2-[2-(4-{N-methyl[(2,4-diamino-4,4a-dihydropteridin-6-yl)methyl]amino}cyclohexyl)-2-oxoethyl]glutarate has the lowest free binding energy (-14.24 kcal/mol). As seen in Table 1, the docking energy of MTX is -12.89, which is higher than ZINC29236925. Figure 2 shows the interaction mode between ZINC29236925 and hDHFR active site. Based on this Figure, the backbones of Ile 7 and Val 115 interact with an NH2 moiety of Dihydropteridine via hydrogen bonding. Another hydrogen bonding also has been formed between the side chain of Glu 30 from hDHFR and the NH2 moiety of Dihydropteridine. Residue Asn 64 also interacts with two carbonyl moieties of ZINC29236925 due to hydrogen bonding. Details of the interaction between MTX and hDHFR residues are illustrated in Figure 3. Residues Glu 30, Val 115, Ile 7, Asn 64 and Arg 70 interact with MTX via hydrogen bonding (Figure 3). Klon et al., in 2002 demonstrated that two hDHFR influential inhibitors, 6-([5-quinolylamino]methyl)-2,4-diamino-5-methylpyrido[2,3-d ]pyrimidine (SRI-9439) and (Z)-6-(2-[2,5-dimethoxyphenyl]ethen-1-yl)-2,4-diamino-5-methylpyrido[2,3-d]pyrimidine (SRI-9662), interact with the carbonyl oxygen atoms of Ile7 backbone and Val115 [47]. Glu 30, which interacts with MTX and the compounds ZINC29236925, ZINC31169388 and ZINC01629864 is extremely conserved in the active site of all vertebral DHFRs [47, 48]. This residue participates in a “tightly constrained polar pocket” that has been observed in all other DHFR structures determined to date [48-50]. The interaction modes between ZINC31169388 and ZINC01629864 with hDHFR are shown in Figure 4 and 5, respectively. Phe 34 interacts with pyrocatechol group of ZINC31169388 via π-π stacking. Also, the side chains of residues Gln 35 and Arg 70 form the two hydrogen bonds with the hydroxyl moiety of p-hydroxybenzoic acid from ZINC31169388 (Figure 4). Upon Figure 5, ZINC01629864 interacts with Glu 30, Phe 31 and Asn 64. The hydrogen bond and salt bridge also are formed between Lys 63 and the carboxyl group of ZINC01629864.

3-2. MD simulations

The major and fundamental challenge in virtual screening is the scoring the clustered poses.

A good and suitable scoring function could be able to predict the binding energy precisely and rank the ligands in accordance with their affinity [51]. Unfortunately, most of these scoring functions, coupled with molecular docking software, are not capable of re-generating the binding affinity, using these scoring functions is restricted to screening of databases of a large wide variety of ligands. In order to anticipate and calculate the binding affinity of small molecule inhibitors, a variety of post-docking approaches have been developed. These methods range from simple consensus scoring to free energy perturbation [52-55]. To validate the scoring function of the docking procedure, and to investigate the conformation changes of hDHFR in the absence and/or presence of selected hits, MD simulations were utilized. In overall, five independent MD runs were conducted, and MD results of MTX were utilized as a control. To study and investigate the conformational variations, and to check the stability of the free hDHFR, hDHFR: MTX, hDHFR: ZINC29236925, hDHFR: ZINC31169388, hDHFR:  ZINC01629864 complexes, the root mean square deviation (RMSD) of the backbone atomic position was computed (Figure 6). With regard to this figure, RMSD value of free hDHFR was increased to 1.5 Å at 2830 ps. Then, it was decreased to 1.1 Å at 3160 ps. To detect the conformational changes during MD simulation, the snapshots of the structures at various times were extracted and subsequently superimposed. It was observed that the systems equilibrate around the first 2000 ps of simulation and this behavior was common to all MD simulations except ZINC01629864 bound hDHFR. Figure 7 demonstrates the superposed structures of free hDHFR at 2840 and 9550 ps. The difference between the free hDHFR conformation at 2840 ps and 9550 ps is related to the three distinct loops (Figure 7). The first loop, loop A, is comprised of Ser 41, Ser 42, Val 43, Glu 44, Gly 45 and Lys 46. Loop B is composed of Lys 173 and Gly 174. The residues Lys 18- Asp 21 construct the last loop, loop C. It has been shown that Lys 18-Pro 25 are slightly mobile in hDHFR [56]. In the presence of MTX, the RMSD value is increased to 1.3 Å at 2140 ps. Then it decreased to 0.8 Å at 7960 with some variations. However, in the presence of ZINC29236925 in the hDHFR active site, the RMSD was reached to steady state at 12240 ps (Figure 6). As depicted in Figure 6, the RMSD variation of hDHFR: MTX is lower than hDHFR: ZINC01629864 complex. With reference to this figure, upon binding of two natural products, ZINC31169388 and ZINC01629864, into the active site of hDHFR, the RMSD of protein backbone is increased to the higher values. This raising is remarkable for ZINC01629864: hDHFR complex. The root mean square fluctuation (RMSF) on each amino acid residue for all five systems was also calculated during 30000 ps (Figure 8). The RMSF value of Asn 19 in the free form of hDHFR is 2.1 Å. These values are for hDHFR: MTX and hDHFR: ZINC29236925 complex are reduced to 1.7 and 1.5 Å, respectively. RMSF value of Asn 19 in ZINC31169388 : hDHFR complex is reduced to 1.2 Å, while this value for ZINC01629864 bound hDHFR is 2.1 Å (as seen in the free hDHFR) (Figure 8). The lower RMSF indicates the lower residue flexibility and movement. Asn 19 is located in a hairpin turn, covers the active site of the enzyme, and probably protecting the substrate from solvent interaction, as seen this matter for  Escherichia coli [57, 58]. This region, which is known as Met-20 loop, includes residue 9–24, squeeze against the nicotinamide moiety of the cofactor and covers the active site of E.coli DHFR [59]. Figure 9, which is produced by the multiple alignments of human and E.coli DHFR sequences, demonstrates the existence of such hairpin turn in human enzyme. Based on Figure 8, upon binding the MTX, ZINC29236925, and ZINC31169388 into the hDHFR active site, the flexibility and movement of the hairpin turn, in comparison with the free enzyme is restricted. Based on the binding of MTX and ZINC29236925 to the enzyme, the same reduction in RMSF value was seen for Glu 30 upon inhibitor binding. X-ray crystallography experiment showed that Glu30 and Trp24 interact with the pteridine ring N8 of MTX via a conserved water molecule. This mode on interaction has been seen in the majority of DHFR structures [60]. MTX also forms a hydrogen bond comprising the benzoyl keto moiety and residue Asn64 [60]. RMSF values of Lys 63 and Asn 64 in the complex state is lower than free hDHFR (except ZINC31169388). The clarification and explanation of the mechanisms of protein folding remains a conspicuous challenge in biochemistry. These processes figure out the conformational search for the compact native structure within the microsecond to even few seconds timescale, starting from an assumed spatial random coil [61]. Figure 10 exhibits the temporal radius of gyration (Rg) of free and inhibitor-bound hDHFR. Rg is the mass-weighted scalar length of each atom from the center-of-mass. This factor also is an indicator of protein stability and our results in Figure 10 reveal that all systems are stable during MD simulations. For free hDHFR, the Rg values fluctuate around 16.3 Å. For hDHFR: MTX and hDHFR: ZINC29236925 complex, these fluctuations were decreased to ~ 15.8 Å. Anyway, binding of ZINC01629864 to the hDHFR decreased the Rg of the enzyme from 16.5 to 15.8 Å. Based on Figure 10, upon binding of the studied inhibitor into the hDHFR active site, the Rg of the enzyme is decreased and fixed around 15.8 Å. This data also showed that in the presence of inhibitor, hDHFR has no tendency to be unfolded (Figure 10). Furthermore, the Rg was measured as a characteristic to limit the energetically available conformational space of attached compounds. This approach is raised from the simple theory that a high binding affinity needs an increasing in buried surface area of the protein that is attained by an expanded bound ligand. As mentioned above, upon binding the enzyme to the studied inhibitors, reduction in Rg was occurred, which could be in relation with decreasing in the solvent accessible surface area [62]. To investigate the stability of hydrogen bonds with the active site of the protein, MD analysis of hDHFRs in complex with the hits were monitored during the trajectory period. Hydrogen bond profiles between the hits and hDHFRs were calculated using the g_hbond utility of GROMACS. As can be seen in Figure 11A, the average hydrogen bond formation between hDHFR and MTX was 3.4. Further analysis also exhibited that ZINC29236925 comprises 1.5 average H_bonds during the simulation period (Figure 11B) while ZINC01629864 showed the highest H_bond interactions with 3 H_bonds on average during the trajectory period (Figure 11D). The lowest H_bond between protein and inhibitor was seen for ZINC31169388 (Figure 11C). The higher formation of H_bond between protein and inhibitor is in correlation with the higher stability of complex.  This proposes that the performance and capability of these compounds can expertly inhibit hDHFR. Also, it can be suggested that at least the formation of one hydrogen bond is critical to anchor the inhibitor in the hDHFR binding site. One of the worthwhile deliberations of hydrophobic interactions within a protein is a solvent accessible surface area or SASA. Hydrophobic interactions shaped among non-polar amino acids assure the stability of globular proteins in solution by covering the non-polar residues in hydrophobic cores, far from the aqueous environment [63]. The SASA profiles of free DHFR, along with MTX, ZINC29236925, ZINC31169388, and ZINC01629864 bound DHFR are illustrated in Figure 12. As can be deciphered from this figure, the free and MTX bound DHFR has approximately the same SASA. It indicates upon binding of MTX to the DHFR active site, has no influence on protein folding. Anyhow, due to ZINC29236925, ZINC31169388 and ZINC01629864 binding, the SASA is increased. So, it can be suggested that the hits binding into the DHFR active site, can stimulate the enzyme unfolding, as the hydrophobic core of protein is exposed to the surrounding solvent environment.

3-3. ED/ PCA analysis

In this study, to identify and also a deep understanding of the conformational transitions and structure motions, the independent PCA/ ED analysis on each MD trajectory was conducted. In our PCA analysis, which is a mathematical and statistical concept, more distribution of circles (Figure 13) represents the more conformational changes in the free or hit bound DHFR structure. As depicted in Figure 13, the computed MD-derived eigenvectors for all systems are relatively diverse, which illustrating the difference in DHFR structural motion between the studied systems. The most distributions of circles in Figure 13 belong to two systems containing ZINC31169388 and ZINC01629864. This figure also shows that in the presence of the inhibitor, the conformational changes of DHFR are increased. However, the lowest circle distributions in the PCA plot is related to free and ZINC29236925 bound DHFR.

 

 

3-4. Energy Analysis of the Complexes

The binding free energies for the four hDHFR: inhibitor complexes were calculated (Table 2) using the MM-PBSA method. The snapshots were derived from the last 10 ns of MD trajectories and then, the binding energies were estimated. The calculated binding free energies were -93.87 and -152.2  kJ.mol-1 for hDHFR: MTX and hDHFR: ZINC29236925 complex, respectively.  The binding free energies of two natural products with the codes of ZINC31169388 and ZINC01629864 had -116.6 and -131.5, respectively. Based on Table 2, the van der Waals interaction is more favorable for hDHFR: ZINC29236925 with the value of -240.587 kJ.mol-1. MTX, in comparison with the other inhibitors, has the highest polar-salvation energy (677.992 kJ.mol-1). This energy is calculated by solving the Poisson–Boltzmann equation [64]. Besides the atomic radius, dielectric constant and grid resolution, the molecular surface definition may affect the polar-solvation energy [41]. However, hDHFR: MTX has the favorable electrostatic energy (Table 2). This energy term is calculated by applying Coulomb’s law with atomic charges taken from the molecular mechanic's force field [65]. Therefore, the results depend on the charges used for the receptor and the ligand. Dielectric constant (ɛ) determination is critical to achieving precise electrostatic energy. It has been proposed that the optimum value of ɛ depends on the characteristics of the binding site by means that a highly charged binding site requires a higher ɛ than a hydrophobic site [66, 67]. However, often the results are best for ɛ = 2 – 4 [68-70] and we assigned 2 for ɛ in all our MMPBSA calculations. The results shown in Table 2 suggest that the optimizations of electrostatic interactions between the hDHFR and inhibitors including ZINC29236925, ZINC31169388, and ZINC01629864, may lead to the potent inhibitors.

3-5. Computational ADME/T estimations

Using QikProp, the preferred hits (four high ranked compounds in Table 1) were more examined for their drug-like behavior by examination of their pharmacokinetic parameters. For these hits, predicted octanol/water partition coefficient, predicted blockage of hERG K+ channel [71], predicted apparent Caco-2 cell permeability in nm/s [72], predicted brain/blood partition coefficient [73, 74], percentage of human oral absorption and predicted central nervous system activity [75], were predicted and included in Table 3. ZINC31169388 has the highest oral absorption (80 percent) and lowest Lipinski and Jorgensen rules violation. ZINC29236925 and MTX have three and two violations from Lipinski rules, respectively. None of the studied compounds had toxicity against the central nervous system. With regard to the ADME/T prediction results, the pharmacokinetic properties of the ZINC31169388 are within the acceptable range (Table 3). Hence, this compound was found computationally suitable for additional and further drug development process.

3-6. QMMM analysis on the hits in free and bound states

Following the MD simulations, the last frame of the inhibitor-bound DHFR was extracted and introduced to the QMMM calculation. Also, the free form of hits was conducted for DFT calculations. Electronic molecular features such as electron density, frontier molecular orbital density fields, such as LUMO, HOMO, and molecular electrostatic map, have been shown to be advantageous and beneficial in QSAR studies to explain biological activities and molecular properties [76, 77]. HOMO/LUMO values are very important for charge transfer in the chemical reactions. Molecules with higher HOMO levels tend to be potent nucleophiles. On the contrary, the molecules with low LUMO energies tend to be influential electrophiles. In the free state of MTX, the energy gap is 0.008 eV (Table 4), which is the lowest energy gap among all studies inhibitors. The lesser energy gap between the HOMO and their corresponding LUMO indicates the chemical reactivity of the molecules. After binding the MTX to DHFR, its energy gap was increased and reached 0.138 eV. ZINC31169388 had the highest energy gap in its free state. Anyway, following the enzyme binding, its energy gap was decreased to 0.055 eV. This value was the lowest energy gap in the DHFR bound state among all hits (Table 4). The QMMM derived binding free energy of MTX and ZINC29236925 were -726.44 and -734.48 kcal/mol, respectively. ZINC31169388 had the lowest binding energy (-794.19 kcal/mol), which is correspondence with the highest inhibitory potential of this hit. ZINC01629864 with the free binding energy of -531.15 kcal/mol, had the highest binding energy. The frontier orbitals of MTX, ZINC29236925, ZINC31169388, and ZINC01629864 in free and bound states are depicted in Figure 14A and 14B. In this Figure, the positive electron density is shown in red color and negative electron density is presented in blue. In the free state of MTX, HOMO plot is scattered on carboxyl moiety. In the bound state, it is transferred to the pteridine core. The mentioned core in the DHFR active site is orientated near the residues Glu 30, Val 115 and Ile 7 and can donate the electron to them (Figure 14A). ZINC31169388, a hit with the lowest QMMM derived binding energy, exhibited its HOMO orbital on the pyrocatechol ring (Figure 14B). This ring is positioned close to Glu 30 and can give the electron to it. As illustrated in Figure 14B, in the bound state, the LUMO map of ZINC31169388 is placed on the phenol ring. So, it can accept the electron from its neighbor residue (Phe 31) and make a π-π interaction with it.

 4. Conclusion

Careful approaches of calculating the affinity of a small ligand interacting with the biological target are needed to spread and develop the finding of novel leads. This can be achieved by a profoundly realization of the physical chemistry and the implementation of attentive methods. We believe that deadly diseases have remained un-curable because we don’t know the molecular mechanism of their pathogenicity. Nowadays, biological reaction and interaction simulation by the computer can help us to overcome such diseases. In this study, structure-based virtual, as well as, MD simulations were carried out against hDHFR to introduce and propose novel chemical and natural products as potent inhibitors. Also, the mechanism of enzyme inhibition and conformational changes of cofactor bound hDHFR in the absence or in the presence of the hits were investigated. Our data supply alluring information which may be conducive in attains a deeper comprehension of the molecular interactions between the hDHFR and classical and non-classical inhibitors. Our results showed that the strong interaction between Glu 30 from DHFR and all studied four hits (MTX, ZINC29236925, ZINC31169388, and ZINC01629864) was maintained throughout the MD simulation. Our data confirmed this conclusion that Glu 30 acts as an anchoring point for the substrate, classical and non-classical DHFR inhibitors. Also, π -π stacking interactions with Phe 31 and Phe 34, and hydrophobic interactions with Ile 7 and Val 115 are important for stabilizing the hits in the hDHFR active site. Previously, it has been reported that Arg 70 makes a salt bridge (a combination of hydrogen bonding and electrostatic interactions) with a wide range of classical DHFR inhibitors. Among all hits, ZINC31169388 make a hydrogen bond with the Arg 70. RMSD analysis showed that upon binding of two natural products, ZINC31169388 and ZINC01629864, into the active site of hDHFR, the RMSD of protein backbone is increased to the higher values. Based on RMSF analysis, the binding of MTX and ZINC29236925 into the DHFR active site, the RMSF value of Glu 30 is decreased. Glu 30 is a critical and important residue in the catalytic activity of the enzyme. A conserved Met-20 loop, which comprises of residues 9-24 and seals the DHFR active site, prevents the interaction of the substrate with the surrounding solvent. Our results also exhibited that by the binding of MTX, ZINC29236925 and ZINC31169388 into the enzyme active site, the RMSF value of Met-20 loop is decreased. Binding of ZINC31169388 and ZINC01629864 to the active site changed the DHFR folding, as they increased the SASA of the enzyme. PCA analysis revealed that in the presence of the hits, the conformational changes of DHFR are increased. With regard to QMMM computation, ZINC31169388 had the lowest free energy. ADME prediction also showed that on the contrary of MTX, all of the pharmacokinetic properties of ZINC31169388 are in within the acceptable range. So, this hit is suitable for additional and further drug development process.

 
2 + 3 =  

Site Map | Printable View | دانلود کتاب مقاله پایان نامه | تهیه استاندارد | پسورد دانشگاه |© شرکت هوشمند رایانه طاها

دانلود کتاب
پسورد دانشگاه ها
تبادل لینک رایگان

امتیاز بدهید!
دانلود پیپر را در گوگل محبوب کنید.

آموزش های مهندسی برق
آموزش های مهندسی صنایع
آموزش های مهندسی عمران
آموزش های مهندسی مکانیک
آموزش های مهندسی نرم افزار
آموزش های اصول مقاله نویسی
● آموزش های رایگان
● تبلیغات دیجیتال هوشمند
● استخدام در فرادرس

لینک دوستان ( نحوه ی تبادل لینک و مشاهده سایر لینک ها )