Crystal structure of dihydrofolate reductase from the filarial nematode W. bancrofti in complex with NADPH and folate

Keith Lange; Kathleen M. Frey; Tyler Eck; Cheryl A. Janson; Ueli Gubler; Nina M. Goodey

doi:10.1371/journal.pntd.0011303

Abstract

Lymphatic filariasis is a debilitating illness with an estimated 50 million cases as of 2018. The majority of cases are caused by the parasitic worm W. bancrofti and additional cases by the worms B. malayi and B. timori. Dihydrofolate reductase (DHFR) is an established target in the treatment of cancer, bacterial, and protozoal infections and may be a potential target for drugs targeting parasitic worm infections, including filariasis. Recent studies have shown that known antifolate compounds, including methotrexate, inhibit the activity of W. bancrofti DHFR (WbDHFR). However, the absence of structural information for filarial DHFRs has limited the study of more in-depth structure-function relationships. We report the structure of WbDHFR complexed with NADPH and folate using X-ray diffraction data measured to 2.47 Å resolution. The structure of WbDHFR reveals the usual DHFR fold and is currently only the second nematode DHFR structure in the Protein Data Bank. The equilibrium dissociation constants for NADPH (90 ± 29 nM) and folate (23 ± 4 nM) were determined by equilibrium titrations. The interactions of known antifolates with WbDHFR were analyzed using molecular docking programs and molecular dynamics simulations. Antifolates with a hydrophobic core and extended linker formed favorable interactions with WbDHFR. These combined data should now facilitate the rational design of filarial DHFR inhibitors, which in turn can be used to determine whether DHFR is a viable drug target for filariasis and whether existing antifolates may be repurposed for its treatment.

Author summary

Lymphatic filariasis is a disease commonly characterized by urogenital swelling, or scrotal hydrocele and lymphedema, which affects nearly 50 million people worldwide as of 2018. It is caused by parasitic worms transmitted through mosquitos. Dihydrofolate reductase (DHFR) is a ubiquitous enzyme involved in folate metabolism and provides building blocks for DNA synthesis. DHFR is an established pharmacological target in the treatment of cancer and infectious diseases through the use of DHFR specific inhibitors known as antifolates. Using recombinant DHFR from the parasitic worm W. bancrofti (Wb), we have recently shown that known antifolate compounds (i.e. methotrexate and others) potently inhibit this enzyme. However, the absence of structural information for WbDHFR has limited the investigation of in-depth structure-function relationships. We now report the first crystal structure of WbDHFR. Using in silico approaches, a series of known antifolates was docked into the active site of the enzyme to gain insights into their interactions with WbDHFR. Such data will facilitate the rational design of inhibitors specific for filarial DHFRs. The availability of such inhibitors will be crucial to establish DHFR as a valid target for the treatment of filariasis and will also help determine whether existing antifolates may be repurposed for treatment.

Citation: Lange K, Frey KM, Eck T, Janson CA, Gubler U, Goodey NM (2023) Crystal structure of dihydrofolate reductase from the filarial nematode W. bancrofti in complex with NADPH and folate. PLoS Negl Trop Dis 17(4): e0011303. https://doi.org/10.1371/journal.pntd.0011303

Editor: Denis Voronin, NIAID: National Institute of Allergy and Infectious Diseases, UNITED STATES

Received: October 14, 2022; Accepted: April 12, 2023; Published: April 27, 2023

Copyright: © 2023 Lange et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: The authors confirm that all data underlying the findings are fully available without restriction. All X-ray structure files are available from the Protein Data Bank database (accession number 8E4F).

Funding: The authors received no specific funding for this work.

Competing interests: The authors have declared that no competing interests exist.

Introduction

Lymphatic filariasis, also known as elephantiasis, is a mosquito-borne helminth infection prevalent in Sub-Saharan Africa and Southeast Asia. The disease is caused by the parasitic worms W. bancrofti (Wb), B. malayi (Bm), and B. timori [1]. Approximately 500 million people annually receive medications such as ivermectin, diethylcarbamazine, and albendazole to treat and prevent lymphatic filariasis infection [2]. Symptoms of lymphatic filariasis include swelling of the arms and legs, hydrocele, and fibrosis resulting in long-term disabilities. A significant proportion of patients carry co-infections with more than one parasite [1,3–5]. Some of these co-infections include malaria and schistosomiasis, a disease caused by another parasitic worm, S. mansoni (Sm) [6, 7].

Dihydrofolate reductase (DHFR) is a ubiquitous enzyme that catalyzes the conversion of dihydrofolate (DHF) to tetrahydrofolate, a key step in synthesizing pyrimidines such as thymidylate [8–12]. As the pyrimidine thymidylate is required for DNA synthesis, DHFR is a well-known drug target in cancer, bacterial and protozoal infections, and inflammatory diseases [13–16].

Literature reports have discussed the potential of DHFR as a drug target for treating helminth infections including filariasis [17–20]. Sharma and coworkers demonstrated that three antifolate compounds reduce Bm microfilariae motility by over 99% [17–18]. Upon treatment with folate, the authors observed motility returning, suggesting that inhibition of folate metabolism was responsible for the loss of motility. Supporting this is the fact that folate is structurally similar to the substrate DHF with the exception that in folate N8 and C7 are oxidized (Fig A in S1 Text). Furthermore, recent studies have demonstrated in vitro inhibition of filarial DHFRs from Wb and Bm by antifolate compounds [21, 22]. For example, methotrexate showed K_I values of 0.7 ± 0.1 nM and <0.5 ± 0.3 nM against WbDHFR and BmDHFR, respectively. While methotrexate also inhibits the human DHFR (HsDHFR), and therefore is not a selective antifilarial agent, these data nevertheless suggest that antifolates may represent treatment options for filariasis [22]. However, the absence of structural information for filarial DHFRs has made the investigation of structure-function relationships difficult. Serrão and coworkers recently reported the X-ray structure of S. mansoni DHFR (SmDHFR) (PDB code: 3VCO), the only other structure currently available for a nematode DHFR [23, 24].

Approximately 90% of filariasis cases are caused by Wb [25, 26]. Here we report the structure of WbDHFR in complex with its cofactor NADPH and folate determined by X-ray crystallography (Fig 1). The resulting structure made it possible to dock a series of known antifolates into the active site, analyze interactions using molecular dynamics simulations, and gain insights into their interactions with WbDHFR, as a possible guide for future drug design efforts.

Download:

Fig 1. Overview of experimental design and results.

The figure was created with BioRender.com.

https://doi.org/10.1371/journal.pntd.0011303.g001

Methods and materials

Expression and purification of WbDHFR

The open reading frame of WbDHFR including an engineered N-terminal His6-tag was expressed in the E. coli LOBSTR strain [27] in terrific broth (TB) with 100 μg/mL ampicillin at 37°C with shaking at 225 rpm. The starter culture was diluted 100-fold into 1 L of TB with 100 μg/mL ampicillin, grown to an OD₆₀₀ of 0.8–1.5 and induced with 1 mM IPTG at 37°C. Cells were collected after 4 hours of incubation by centrifugation at 5,000 rpm for 30 minutes at 4°C using a JA-10 rotor in an Avanti J-26S XP centrifuge. The resulting 12–16 grams of wet pellet that originated from 1 L of culture were stored at -80°C until use.

The pellet from 1 L of culture as prepared above was resuspended in 100 mL of Mtx_Eq. buffer (8 mM Na₂HPO₄, 2 mM KH₂PO₄, 300 mM NaCl, 0.5 mM DTT, 10% glycerol, pH 7.0) and sonicated on ice for 15 minutes at 45% amplitude with 15 seconds on / 45 seconds off pulsing using the ½ inch probe on the Sonic Dismembrator FB505. The lysate was clarified by centrifugation at 17,000 rpm for 25 minutes at 4°C in a JA-17 rotor. The lysate was loaded onto a 5 mL methotrexate-agarose resin column (Sigma Aldrich) that had been equilibrated with 10 column volumes of Mtx_Eq. buffer. The column was washed with ~200 mL of Mtx_Eq. buffer and WbDHFR was eluted with ~50 mL of 25 mM Tris, 300 mM NaCl, 5 mM folate, pH 8.6.

A nickel-nitriloacetic acid (Ni-NTA) column containing 10 mL of resin was equilibrated with 200 mL of 8 mM Na₂HPO₄, 2 mM KH₂PO₄, 300 mM NaCl, 10 mM imidazole at pH 7.4. The methotrexate-agarose column eluate was loaded directly onto the Ni-NTA column, and the column was washed with ~150 mL wash buffer (8 mM Na₂HPO₄, 2 mM KH₂PO₄, 300 mM NaCl, 35 mM imidazole pH 7.4). The WbDHFR was eluted with 50 mL of 8 mM Na₂HPO₄, 2 mM KH₂PO₄, 300 mM NaCl, and 500 mM imidazole at pH 7.4. The Ni-NTA eluate containing WbDHFR (~20 mL) was dialyzed against 4 L of 40 mM HEPES, 100 mM NaCl, pH 7.0 for one hour and against an additional 4 L of the same buffer overnight at 4°C. DHFR purity was assessed by SDS-PAGE (Fig B, part A in S1 Text). The concentration was determined at A₂₈₀ using the molar extinction coefficient 25,440 M^-1 cm^-1 [22]. The protein was concentrated to 20 mg/mL using an Amicon Ultra-15 10,000 MWCO centrifugal filter. Purification of WbDHFR yielded protein suitable for crystallization with a yield of 9–14 mg per liter of culture. The final WbDHFR preparation was filtered, aliquoted, and stored at -80°C.

Crystallization of WbDHFR via sitting drop diffusion

WbDHFR was buffer exchanged using a 10 kDa MWCO centrifugal filter into 20 mM HEPES, 25 mM NaCl, 5 mM DTT, and 5 mM NADPH at pH 7.0 and incubated on ice for one hour. Several crystallization screens from Hampton Research and Jena Biosciences were evaluated using sitting drop vapor diffusion in MRC 96-well plates. The lower reservoir contained 50 μL of precipitant solution and drops were made with one microliter protein solution (20 mg/mL) and one microliter precipitant solution. The Jena Biosciences JBScreen Classic HTS I condition containing 100 mM tri-sodium citrate pH 5.6, 25% w/v PEG 4000, and 200 mM ammonium sulfate at 4°C grew tiny crystals and this condition was then optimized. Increasing the pH from 5.6 to 6.6 in 0.2 pH unit increments resulted in larger, hexagonal crystals. The largest crystals grew to full size in about three weeks using a precipitant reservoir of 100 mM tri-sodium citrate pH 6.2, 25% w/v PEG 4000, and 200 mM ammonium sulfate. Crystals were cryo-protected in the precipitant solution containing 15% (v/v) glycerol. After transferring the crystals to the cryosolution, they were immersed in liquid nitrogen and stored in a cryo-Dewar until data collection.

Data collection and structure determination

Diffraction data for all crystals were collected at the Brookhaven National Synchrotron Light Source II (NSLS-II) on beam line AMX (17-ID-1). A total of 1,800 0.2° images were collected spanning a phi range of 360°. All images were indexed, integrated, and scaled using XDS [28]. Molecular replacement (MR) using the previously solved structure of mouse DHFR (PDB code 2FZJ) with high homology (~40%) was used to obtain phases. The starting mouse model was modified using Sculptor in Phenix [29] and the WbDHFR input sequence also included the 13 amino acid loop described in previous work [22]. The modified model was then used for MR using program Phaser [30]. Programs Phenix [29] and COOT [31] were used to build and refine the structure. Several iterations of model building and refinement continued until acceptable R-factors and geometric parameters were achieved. Bond lengths, angles, and clashes were assessed using MolProbity [32]. An unbiased omit 2 F_o-F_c map was generated using the “Iterative Omit Map Tool” in Phenix (Fig C in S1 Text) [29]. The final structure was analyzed in PyMOL and Maestro [33, 34].

Molecular docking

The WbDHFR ternary structure with NADPH and folate bound served as the starting model for docking studies. The programs AutoDock VINA [35] and Glide (Schrödinger) [36] were used to dock compounds in parallel. Three-dimensional structure files (.sdf) for all antifolates were obtained from the PubChem database. AutoDockTools 1.5.6 was used to generate the appropriate input.pdbqt files for the docked ligands and WbDHFR. [37] The following center coordinates in Ångstroms were used for the docking grid in Autodock Vina [35]: x = 59.265, y = 17.612, z = -0.07. The models were analyzed in PyMOL [33]. To validate the docking methods, folate was first deleted from the ternary structure and then docked back into the active site of WbDHFR, with a resulting docking energy score of -9.1 kcal/mol (Vina) and -9.0 kcal/mol (Glide). The predicted low docking energy scores correlate with the low K_D (23 nM) that was determined for folate experimentally. Docking results also showed folate binding in a similar conformation to that observed in the experimentally determined ternary WbDHFR crystal structure, indicating that the docking approach was accurate. The antifolates methotrexate, pyrimethamine, trimethoprim, aminopterin, and trimetrexate were analyzed using molecular docking programs. The resulting docking models were used to predict contacts involved in drug-target interactions.

For comparison of docking scores obtained by another program, the same antifolates were docked into the WbDHFR crystal structure using the molecular docking program Glide [36]. The WbDHFR crystal structure was prepared using Protein Preparation in Maestro and the antifolates were prepared using Ligprep [34]. The docking grid was generated by specifying the 12 Å radius around folate in the WbDHFR active site; the docking grid coordinates in Ångstroms: X = 55.92, Y = 16.12, Z = -0.8. The antifolates were docked to the grid using Glide XP Precision Docking. Docking scores and poses for antifolates in the WbDHFR active site were examined in PyMOL [33] and Maestro [34].

Molecular dynamics simulations

The WbDHFR crystal structure, HsDHFR crystal structure (PDB code 2W3M), and the docking models for antifolates (methotrexate, aminopterin, trimetrexate, trimethoprim, pyrimethamine, and cycloguanil) were used for molecular dynamics (MD) simulations and analysis. Models were prepared by adding hydrogens, assigning charges, capping the termini, and deleting non-interacting water molecules using the Protein Preparation tool in Maestro [34]. The prepared models were energy minimized, then used to build the system for MD.

Systems for MD were prepared using System Builder in Desmond [38]. We predefined the solvent model as TIP4P. The system was neutralized by the addition of Na⁺ and Cl^- ions, simulating concentrations of 0.15 M. The force field system was Optimized Potentials for Liquid Simulations (OPLS) and OPLS was applied to each system. Water molecules and ions around the enzyme were built into the system within the calculated orthorhombic box volume for soluble proteins. The average calculated box volume was = 240,000 Å³. All models were relaxed before simulation. Desmond was used to conduct MD simulations under the following conditions: canonical ensemble (NVT); Berendsen thermostat (temperature = 300°K); and Berendsen barostat (pressure = 1.01325 bar); timescale = 10 nanoseconds (ns) [38]. We used 10 ns (10 picoseconds/interval) to allow for system equilibration confirmed by protein root mean square deviation (RMSD) plots. A total of 1000 snapshots (10 picoseconds/interval) were generated for each complex. Simulations were analyzed for equilibration and convergence by examining root mean square deviation (RMSD) versus simulation time plots. Trajectories from each simulation were analyzed using Simulation Interaction Analysis and Simulation Event Analysis programs in Desmond [38].

Determination of K_D of NADPH and folate

The dissociation constants (K_D) of the ligands NADPH and folate were determined by monitoring changes in tryptophan fluorescence upon increasing ligand concentrations. For dissociation constant determination, only the Ni-NTA affinity chromatography purification method (leaving out the methotrexate-agarose column step) was used to ensure WbDHFR was not bound to ligands during affinity studies. NADPH or folate was added to 400 nM WbDHFR at room temperature and fluorescence values were recorded using a Fluoromax-4 (Horiba Jobin Yvon) spectrofluorimeter (ex: 290 nm, em: 340 nm, with 5 mm and 10 mm slits, respectively). The tested ligand concentrations ranged from 0 to 10 μM for NADPH and 0 to 4 μM for folate. The titrations were performed by adding one microliter increments of ligand to a quartz cuvette that contained 1000 μL of 400 nM enzyme in 1X MTEN (50 mM MES, 25 mM Tris, 25 mM ethanolamine, 100 mM NaCl, 2 mM DTT) buffer at pH 6.0 and allowing equilibration for 10 seconds. The data were corrected for the inner filter effect using 200 nM L-tryptophan as described previously [39]. Fluorescence intensity values were graphed against ligand concentrations using Kaleidagraph and the data were fitted to the Morrison equation [40–42].

Results and discussion

Crystal optimization and structure determination

We here report the first crystal structure of DHFR from Wb, a parasitic nematode that causes lymphatic filariasis. The crystals obtained for data collection first appeared after five to eight days of incubation and continued to grow for three weeks (Fig B, part B in S1 Text). Crystals of WbDHFR grew in the presence of NADPH in the crystallization buffer. One crystal diffracted to amplitudes extending to 2.47 Å using native wavelengths (1.0 Å). The diffraction pattern confirmed that the WbDHFR crystals had a hexagonal lattice and space group P6₃22. The data set was 99.69% complete with acceptable R-factors (Table 1). The Matthew’s coefficient predicted one molecule in the asymmetric unit (ASU) with ~ 75% solvent content. These parameters were used to solve the phases via molecular replacement with Phaser [30]. The structure was deposited in the Protein Data Bank as 8E4F. Crystallization trials including either methotrexate or pyrimethamine did not result in crystal growth. In fact, neither co-crystallization nor soaking pre-existing crystals with antifolates provided crystals of sufficient quality for diffraction studies.

Download:

Table 1. Data collection and refinement statistics.

https://doi.org/10.1371/journal.pntd.0011303.t001

Analysis of the overall structure

The finalized crystal structure revealed a typical DHFR fold with four alpha helices and six parallel strands and one anti-parallel beta strand, similar to the human and bacterial orthologs of DHFR (Fig 2). One notable difference between the HsDHFR and WbDHFR orthologs is the absence of four amino acids near the C-terminus in WbDHFR (#160–164 in the human enzyme) [43]. The crystal structure reveals a ternary complex with both NADPH and folate present in the active site. NADPH was intentionally co-crystallized with WbDHFR while folate was used for the methotrexate-agarose column elution and remained protein-bound throughout the purification process. Omit electron density shows strong peaks at 1.0 sigma for folate in the active site (Fig C in S1 Text). The refined crystal structure of WbDHFR also shows the presence of two SO₄^2- ions bound to basic residues Arg-21, Arg-117, and Arg-149. These ions likely originated from the crystallization solution. According to our model, the donor NADPH-acceptor DHF distance in WbDHFR was estimated to be 3.5 Å, (Fig D in S1 Text) similar to the hydride transfer distances in E. coli DHFR (3.6 Å) and HsDHFR (3.0 Å) [44–45].

Download:

Fig 2. A ribbon diagram of WbDHFR (cyan) shows the bound complex with folate and NADPH (yellow) with the atom labels of folate and NADPH.

(A). An amino acid sequence alignment of eight prokaryotic and eukaryotic DHFRs is shown (B). Amino acids corresponding to the Met20 loop are highlighted in red in A and in a red rectangle in B. Sequences of DHFR orthologs were obtained through UniProt and aligned with the multiple sequence alignment tool “ClustalW” within Jalview. Amino acids with similar chemical characteristics were colored using the “ClustalX” color scheme. Amino acids observed to make hydrogen bonds with either folate or NADPH are labeled “F” and “N”, respectively. The first amino acid residues for Hs, Mm and Sa DHFR are denoted as #2 because these sequence entries are derived from proteins purified from the organisms rather than cloned cDNA.

https://doi.org/10.1371/journal.pntd.0011303.g002

Interactions with folate

We determined the dissociation constant (K_D) for folate to be 23 ± 4 nM, indicating tight binding affinity (Fig 3A). The low K_D for folate was measured in the absence of NADPH and we expect that the K_D would be even lower if NADPH was included in the folate K_D determination due to the reported positive cooperative binding of folate [46]. Using tryptophan fluorescence for K_D determination limits the experiment to only one ligand due to the need to correct for inner filter effects. Interactions observed for folate and residues of WbDHFR include hydrogen bonds between the carboxylic acid of Glu-32 in WbDHFR and the N3 and NA2 atoms of folate (Fig 4A and 4B; see atom labels in Fig A in S1 Text). The interaction distances between the carboxylate of Glu-32 and the N3/NA2 atoms are 2.9 Å and 2.6 Å, respectively (Fig D in S1 Text). The NH₂ of Arg-72 hydrogen bonds with the O1 and O2 atoms of folate (Fig A in S1 Text), at distances of 3.0 Å and 2.7 Å, respectively (Fig D in S1 Text). Phe-36 is involved in π-stacking and makes van der Waals contacts with the para-benzoic acid group (PABA) of folate (Fig 4A and 4B). Phe-36 likely interacts with methotrexate in a similar manner in WbDHFR; Phe-36 is usually mutated to serine or tryptophan in methotrexate-resistant DHFR enzymes [47–48].

Download:

Fig 3.

Determination of K_Ds of folate (A) and NADPH (B) with WbDHFR using tryptophan fluorescence measurements. Approximately 400 nM of Ni-NTA purified WbDHFR was titrated with folate (0–4 μM; A) and with NADPH (0–10 μM; B) in separate experiments. Fluorescence intensity (ex. 290 nm, em. 340 nm) of the sample was recorded using a Fluoromax-4 spectrofluorimeter. Three trials were normalized, averaged, and the data were fitted to the Morrison equation using Kaleidagraph [41].

https://doi.org/10.1371/journal.pntd.0011303.g003

Download:

Fig 4.

WbDHFR (cyan) with amino acids involved in folate (A, B) and NADPH (C, D) binding are shown as light blue sticks. Three-dimensional representations (A and C) were generated in PyMOL while the two-dimensional images (B and D) were made in Maestro. Symbols used in B and D are described in E. The PDB entry 8E4F was used to generate these figures.

https://doi.org/10.1371/journal.pntd.0011303.g004

Interactions with NADPH

NADPH binds in the extended conformation identified in many DHFR structures [49]. Sequence differences in WbDHFR compared with other DHFRs result in minor interaction changes between NADPH and the co-factor binding site. The K_D of NADPH and WbDHFR (in the absence of folate) was determined using tryptophan fluorescence to be 90 ± 29 nM (Fig 3B). The K_D for NADPH binding to BmDHFR, a filarial nematode ortholog with 96% sequence identity to WbDHFR, was previously found to be 25 ± 24 nM [21]. These values are similar to the K_D (150 ± 20 nM) reported for the E. coli ortholog [50]. In DHFR homologs from H. sapiens, E. coli, and C. albicans (PDB codes 4KD7, 2ANO, 1AOE, respectively), the hydroxyl group and backbone NH of the highly conserved threonine, which corresponds to Val-58 in WbDHFR, form hydrogen bonds with the pyrophosphate region of NADPH. In contrast, WbDHFR contains a valine residue at position 58. The backbone NH of Val-58 forms a hydrogen bond with NADPH while the valine sidechain contacts folate via van der Waals interactions (Fig E in S1 Text).

In addition, HsDHFR and MmDHFR structures have a conserved arginine (Arg-77) that forms a sidechain π-cation interaction with the adenine ring. In WbDHFR, this arginine corresponds to Lys-79. Electron density for the sidechain of Lys-79 was disordered and this side chain could not be modeled and therefore probably does not interact with NADPH.

The nicotinamide ring of NADPH (Fig 2A) is involved in π-stacking interactions with the pteridine of folate. This interaction is commonly found in ternary complexes of DHFR and highlights the cooperativity between DHF and NADPH binding sites. This stacking interaction also enhances the interactions of folate and residues Glu-32, Phe-36, and Arg-72 (Fig 4) by positioning the pteridine to maintain proper interaction distances.

Comparison of WbDHFR and SmDHFR structures

To our knowledge, there is only one other nematode DHFR structure that is currently available in the PDB, the DHFR from Sm. In comparing WbDHFR with SmDHFR, we found that the structures are homologous (with an alignment RMSD of 1.324 Å) but differ in sequence; the amino acid sequence identity is only 32%. The alignment of the ternary WbDHFR structure with the structure of the apoenzyme SmDHFR revealed high structural similarity with the exception of the Met20 loop (Fig F in S1 Text). In SmDHFR, the Met20 loop is disordered while in WbDHFR, it exhibits a typical “closed” conformation (Fig F in S1 Text); these loop conformations are discussed in the article by Sawaya and Kraut [51]. The difference in the Met20 loop structures likely reflects the different binding states of the two enzyme preparations used; WbDHFR is bound to folate and NADPH while the SmDHFR structure is in the unbound apo state. The relatively high RMSD between the Wb and Sm DHFRs likely results from these differences. Other DHFRs from more evolutionarily distant organisms such as mouse DHFR and HsDHFR share greater sequence identity (40–41%) with WbDHFR but lower overall structural similarity (Table 2). In WbDHFR, Arg-72 interacts with folate’s polyglutamic acid tail (Fig 4A and 4B). Similarly, SmDHFR also has an arginine (Arg-67) in this position. In WbDHFR, Glu-32 interacts with the pteridine ring of the substrate through hydrogen bonding; in SmDHFR, this position is occupied by Asp-28, which likely forms similar interactions.

Download:

Table 2. The sequence identities and RMSD values for WbDHFR bound to NADPH and folate compared with 13 other DHFR structures.

Note: All structures are of the enzyme bound to cofactor NADPH and an antifolate except for SmDHFR (PDB 3VCO).

https://doi.org/10.1371/journal.pntd.0011303.t002

Given the similarities in folate binding motifs between the SmDHFR and WbDHFR, we hypothesize that novel antifolates with improved hydrophobic-binding capability could interact with both enzymes. As described in more detail below, there are differences in amino acid side chains between the HsDHFR antifolate binding site and those of the two nematode DHFR structures described here, presenting an opportunity for the design of dual inhibitors for WbDHFR and SmDHFR that would be selective against HsDHFR.

We further explored the amino acid sequence alignments, focusing specifically on the residues within the Met20 loop, a well-studied, catalytically significant structural element in the DHFR family of enzymes (Fig 2). The Met20 loop for WbDHFR is most similar to the Met20 loop in EcDHFR (residues 10–24) [51]. A seven amino acid long motif within the Met20 loop, containing two prolines that are spaced three residues apart (PWXLPAX) is clearly present in WbDHFR (Fig 2 and Table A in S1 Text). The same motif in SmDHFR is also seven amino acids long but contains only one proline (PWKIKKD). Previous literature has suggested that longer motif length is associated with lower conformational flexibility of the Met20 loop, preventing large-scale opening and closing loop motions [23] and thus influencing the catalytic cycle [52]. Based on these observations, we predict that the Met20 loops of both SmDHFR and WbDHFR undergo conformational motions during the catalytic cycle. In contrast, HsDHFR has a longer Met20 loop with 8 residues in the motif (PWPPLRNE) and does not undergo closed to occluded transitions [23, 51].

The sequence of the Met20 loop of WbDHFR compared to other DHFR orthologs (Fig 2) is largely similar with the two exceptions being Met-25 and Phe-28. Met-25, which is replaced by either leucine or isoleucine in other DHFR orthologs, makes nominal contacts with folate in both DHFR models from E. coli and W. bancrofti. Phe-28, a residue that is not conserved in other orthologs, does not make any contacts with folate but points toward the active site and could conceivably make hydrophobic contacts with a longer antifolate. Also, the Met20 loop is less flexible based on root mean square fluctuations (RMSFs) from the MD simulations for HsDHFR when compared to WbDHFR (Fig G in S1 Text). A more flexible loop, as observed for WbDHFR, may be able to sample more conformations and increase overall interactions with a longer antifolate that expands into this region.

Molecular docking of antifolate compounds

In previous studies, we tested antifolates as potential inhibitors of WbDHFR; methotrexate (K_I = 0.7 ± 0.1 nM) and structurally similar aminopterin (K_I = 2.1 ± 0.5 nM) were found to be the most potent inhibitors of those that were tested [22]. Other antifolates, including trimethoprim and pyrimethamine were low micromolar inhibitors. The crystal structure published here allowed us to extend these studies through molecular docking.

The docking poses of antifolates obtained from Vina and Glide were largely similar for a given antifolate (Fig H in S1 Text). The 2D ligand interaction diagrams for Glide and Vina also showed similar interactions (Figs I and J in S1 Text). VINA docking results predicted that folate analogs methotrexate and aminopterin have similar conformations and docking scores, -8.8 and -8.7 kcal/mol, respectively (Fig 5). These scores are also similar to the value predicted for folate (-9.1 kcal/mol). The Glide output energies for methotrexate (-9.1 kcal/mol) and aminopterin (-8.5 kcal/mol) also supported the VINA docking predictions (Table B in S1 Text). Methotrexate and aminopterin contain a modified pteridine ring, central hydrophobic linker, and polyglutamic acid tail, similar to the substrate DHF (Fig A in S1 Text). Here, we classified the traditional antifolates as having either a “longer” or “shorter” linker based on the number of “linker” atoms between pyrimidine/pteridine and polyglutamic acid pharmacophores. This classification includes atoms in the para-benzoic motif (8 atoms) but does not count the atoms in the pteridine ring or polyglutamic acid tail. Overall, the “shorter” linkers consist of 0–1 atoms while “longer” linkers are composed of >7 atoms. The longer linker in methotrexate and aminopterin is positioned to interact with nonpolar amino acids Phe-36, Leu-69, and Met-33 in the hydrophobic core of the active site of WbDHFR. Pyrimethamine has no such linker and trimethoprim has only a short linker consisting of one CH₂ group (Fig A in S1 Text). The docking predictions shown in Fig 5 suggested that these smaller antifolates made fewer interactions with the hydrophobic core of the enzyme active site.

Download:

Fig 5. Results of molecular docking of folate and antifolates into the WbDHFR active site using AutoDock Vina.

Hydrogen bonds between the co-crystallized folate (A) and WbDHFR (PDB: 8E4F) are denoted as yellow dashes. Green dashes indicate potential hydrogen bonds between the docked antifolates and WbDHFR. In panel A, co-crystallized folate (yellow) and docked folate (orange) exhibit comparable conformations (See Fig A in S1 Text for structures). Larger inhibitors that resemble folate, i.e. methotrexate (green; B) and aminopterin (pink; E) and smaller antifolates pyrimethamine (magenta; C), trimethoprim (dark red, D), and trimetrexate (yellow, F), are shown. The energy scores from AutoDock Vina and Schrödinger Glide for docked ligands are shown in G.

https://doi.org/10.1371/journal.pntd.0011303.g005

The docking results provide insight for WbDHFR-antifolate interactions but there are several limitations to these models. The docking scores only provide relative estimates of binding affinities and must be validated through binding assays. We observed some correlations between the docking scores and affinity measurements. For example, the experimentally determined K_I values for methotrexate and pyrimethamine (0.7± 0.1 nM and 15 ± 6 μM) qualitatively correlated with the Vina (-8.8 kcal/mol and -7.9 kcal/mol) and Glide (-9.1 kcal/mol and -7.0 kcal/mol) estimated binding energies. The scoring functions for Vina and Glide are similar and rely on summing contributions of hydrophobic and hydrophilic interactions. The score differences may be due to the different force fields, OPLS and AMBER, which Glide and Vina use, respectively. While our docking comparison suggests that the OPLS force field works better for the WbDHFR structure, the docking scores only provide relative estimates of binding. The predicted absolute energies from molecular docking may not reflect experimentally determined K_I values due to assumptions such as rigid-body protein receptor, no solvation, and simplified general scoring [53, 54]. However, the trend in the predicted binding energies from Vina and Glide docking programs agree that antifolates with longer linkers can make more contacts with WbDHFR compared to shorter linker antifolates.

MD simulations

The MD simulations provided additional insights into the binding interactions between WbDHFR and the antifolates. System equilibration of the WbDHFR and HsDHFR simulations was confirmed through plotting RMSD over time (Figs K and L in S1 Text). Similar to the crystal structure, the MD simulations showed strong interactions between folate and Glu-32, Ile-10, and Arg-72. Additional interactions included the stacking interaction with Phe-36 and several van der Waals contacts with hydrophobic residues. The simulations provided the relative fraction or percentage of time during which the interaction was observed over 10 nanoseconds; strong interactions are those that were observed >50% of the time in the simulation.

The crystal structure, K_I data, and molecular docking scores suggest that antifolates with longer linkers have better affinity for WbDHFR. When comparing total protein-ligand contacts for all MD simulations, we found that antifolates with longer linkers make more overall hydrophobic contacts in the simulation compared to antifolates with shorter linkers. Fig 6 shows a comparison of the interaction fractions for key residues for simulations conducted for WbDHFR and all antifolates. When comparing the interaction fractions, antifolates with longer linkers made more hydrophobic contacts for longer fractions or periods of time in the simulation.

Download:

Fig 6. Interaction fractions depicted for WbDHFR and various antifolate types.

Antifolates with longer (A), shorter (B), or moderate length linkers (C) were grouped to show hydrophobic binding patterns. Residues interacting with the antifolate are listed on the x-axis; interaction fraction on the y-axis describes the fraction of time each interaction is observed in the simulation (fraction of 1.0 = interaction is observed in 100% of snapshots). In cases where the interaction fraction is >1.0, the residues (or atoms) may be involved in more than one interaction. Common interactions are color-coded including hydrogen bonds (green), hydrophobic contacts (purple), ionic bonds (pink), and water bridges (blue) (D). Comparison of interaction fractions shows that the long linker antifolates make more hydrophobic contacts.

https://doi.org/10.1371/journal.pntd.0011303.g006

MD simulations were also used to compare the WbDHFR and HsDHFR Met20 loop regions. Using average root mean square fluctuation (RMSF) values for residues within the Met20 loop, we found that WbDHFR undergoes more fluctuations compared to HsDHFR (Fig G in S1 Text). This finding supports the hypothesis that HsDHFR adopts a closed conformation, while WbDHFR undergoes conformational motions during the catalytic cycle.

Insights for rational design of WbDHFR inhibitors

Experimental data have demonstrated that traditional antifolate substrate analogs can inhibit WbDHFR. Subsequent molecular docking predictions using the WbDHFR X-ray structure corroborated this finding and revealed potential strategies for drug design. As described below, there are amino acid side chain differences between the folate binding sites of HsDHFR and WbDHFR (Fig 7), suggesting opportunities for developing WbDHFR selective inhibitors. There are several differences between the folate binding sites of WbDHFR and HsDHFR (PDB 2W3M). Docking the antifolates to HsDHFR and WbDHFR with Glide and Vina shows that methotrexate, a HsDHFR-tailored inhibitor, is predicted to bind with high affinity to both enzymes (Table C in S1 Text). Glide predicts that trimetrexate has a higher affinity for WbDHFR than HsDHFR. Two hydrophilic residues that form side chain hydrogen bonds to folate in the human structure (Gln-35 and Asn-64) have hydrophobic counterparts in the WbDHFR structure (Ala-37 and Phe-66) (Fig 7). One similarity between the WbDHFR and SmDHFR is that they share active site hydrophobic residues unique to these nematode orthologs such as Met-33 and Phe-66 in WbDHFR and the corresponding Met-29 and Phe-61 in SmDHFR. Therefore, small molecules with hydrophobic linker motifs may serve as dual inhibitors for both SmDHFR and WbDHFR enzymes while lacking affinity for the human enzyme.

Download:

Fig 7. The folate binding sites of the NADPH and folate bound DHFR structures from Wb (cyan; PDB 8E4F) and human (green; PDB 2W3M) were aligned using PyMOL.

Key differences in the binding pocket are shown.

https://doi.org/10.1371/journal.pntd.0011303.g007

An antifolate where the carbonyl O atom (Fig A in S1 Text) has been replaced with a nonpolar functional group or compounds with longer and more hydrophobic polyglutamic acid tails could be more selective for WbDHFR over HsDHFR. Based on the docking predictions shown in Fig 5 an effective strategy to develop antifolates targeting WbDHFR could exploit the side chains of residues Glu-32, Arg-72, Tyr-120, and Ser-61 and the backbone carbonyls of Ile-10 and Ile-114.

The active site of WbDHFR contains hydrophobic regions consisting of residues Phe-66, Ala-37, and Val-58 (Fig 7). These residues may also be used to anchor aliphatic substitutions in novel antifolates, since they are not present in the human ortholog, in which hydrophilic side chains occupy these positions. The predictions shown here and in previous studies have noted the importance of linker length and hydrophobic moieties connecting the diaminopyrimidine pharmacophore in the design of antifolates [44, 55, 56]. In further support of this importance, our docking models for both trimetrexate and trimethoprim (both containing a trimethoxyphenyl functional group) predict a more negative docking score for trimetrexate (-8.6 kcal/mol) than for the shorter trimethoprim (-7.6 kcal/mol). This may again be due to the linker length of trimetrexate and its subsequent ability to extend further, thus taking advantage of additional hydrophobic residues such as Leu-69 and Phe-66 (Fig 5). We determined the IC₅₀ value of trimetrexate experimentally to be 0.49 ± 0.16 μM (Fig M in S1 Text), which lies between the previously determined IC₅₀ values for trimethoprim and methotrexate (83 ± 25 μM and 0.018 ± 0.003 μM, respectively). The docking results and the biochemical data suggest that interactions with the more hydrophobic residues in the active site of WbDHFR are important for efficient inhibition.

We now have the crystal structure of WbDHFR with folate and NADPH bound in the active site. Our studies were limited to mostly computational methods when exploring the linker length of effective antifolate inhibitors for WbDHFR. Therefore, future experiments may involve solving the crystal structure of WbDHFR bound to antifolates with different linker lengths. In summary, inspection of the WbDHFR crystal structure, docking predictions, and results from MD simulations suggest that novel antifolates designed to contain a central hydrophobic linker directed to interact with the now well-established WbDHFR target regions could help make antifolate therapy a reality in the treatment of filarial nematode infections.

Supporting information

S1 Text.

Fig A. Structures and atom labelling of WbDHFR ligands folate, NADPH and antifolates methotrexate (MTX), pyrimethamine (PYR), trimethoprim (TMP), aminopterin (AMP), trimetrexate (TMX), and dihydrofolate. Structures were obtained from the ligand summary pages in the Protein Data Bank. Fig B. (A) SDS-PAGE (4–20% polyacrylamide gel) of purification fractions for WbDHFR. MWM–molecular weight markers; MTA–methotrexate agarose; FT–flow through. (B) Microscopic image of WbDHFR crystals grown by sitting-drop vapor diffusion. Fig C. Omit electron density for active site residues of WbDHFR, folate, and NADPH. Electron density is contoured to 1.0 sigma. Fig D. Distances in Å between atoms of folate (yellow) and residues of WbDHFR (cyan) that form hydrogen bonds are shown. The two amino acids responsible for forming hydrogen bonds with folate, Glu-32 and Arg-72, are shown as sticks. The predicted distance of the hydride transfer from NADPH to folate is also shown. The distances were measured in PyMOL. Fig E. Alignment of ternary structures of HsDHFR and WbDHFR demonstrates the similarity of ligand conformations for both folate and NADPH. The ternary structure for HsDHFR (PDB: 2W3M, green) was obtained from the PDB and was aligned with WbDHFR in PyMOL. The conformation of folate for both WbDHFR and HsDHFR is very similar; however, the adenine moiety in NADPH is differently positioned in the two structures. Fig F. Alignment of SmDHFR apoenzyme (PDB: 3VCO, magenta) and the WbDHFR ternary structure (PDB: 8E4F, cyan) done in PyMOL. Folate and NADPH from WbDHFR are shown in yellow. The Met20 of the ternary WbDHFR structure is in the closed conformation while the Met20 loop of SmDHFR is in the disordered conformation. Table A: Met20 loop, B-factor, and steady state parameter data for four DHFR homologs. Fig G. Comparison of root mean square fluctuation (RMSF) values for residues within the Met20 loop region (5–35) in Angstroms: WbDHFR (blue) and HsDHFR (red). WbDHFR shows more fluctuation in the Met20 loop region whereas HsDHFR shows less fluctuation. Fig H. Alignment of docking poses of antifolates obtained from Glide and Vina. The docking poses of the antifolates from Glide (green) and Vina (violet) with the receptor WbDHFR (cyan). The NADPH cofactor is seen as yellow. Table B: Summary of interactions observed in molecular docking models obtained from both Autodock and Glide. Fig I. Ligplot images generated for docking models obtained from Autodock Vina. Fig J. Ligand interaction diagrams generated for docking models obtained from Glide. A) WbDHFR-Folic Acid; B) WbDHFR-Methotrexate; C) WbDHFR-Aminopterin; D) WbDHFR-Pyrimethamine; E) WbDHFR-Trimethoprim; F) WbDHFR-Trimetrexate. See Fig 4 for legend. Fig K: Root mean square deviation (RMSD) values per time in nanoseconds (ns) for all WbDHFR molecular dynamics (MD) simulations conducted at 10 ns. RMSD values for the Cα backbone of WbDHFR (blue) and ligand (red) show equilibration by the end of 10 ns. According to Desmond Simulation Interaction Reports, changes of the order of 1–3 Å for the Cα backbone are acceptable for small, globular proteins such as WbDHFR. Fig L. Root mean square deviation (RMSD) values per time in nanoseconds (ns) for the HsDHFR molecular dynamics (MD) simulations conducted at 10 ns. RMSD values for the Cα backbone of WbDHFR (blue) and ligand (red) show equilibration by the end of 10 ns. According to Desmond Simulation Interaction Reports, changes of the order of 1–3 Å for the Cα backbone are acceptable for small, globular proteins such as WbDHFR. Table C. Comparison of docking scores of antifolates to HsDHFR and WbDHFR using both Glide and Vina. Fig M. Evaluation of trimetrexate as an inhibitor of WbDHFR. Enzyme activity plot of WbDHFR against a titration of trimetrexate from 100 μM down to 1.6 nM (see method above). The resulting plot is the average of four independent measurements with error bars representing standard error. The data was fitted to a sigmoidal curve in KaleidaGraph software V 4.5.4 where an IC₅₀ of 491 ± 161 nM was observed. Using the Cheng-Prusoff equation and a Michaelis-Menten constant of 3.7 μM, the K_I was calculated to be 33 ± 11 nM.

https://doi.org/10.1371/journal.pntd.0011303.s001

(DOCX)

Acknowledgments

We thank Drs. Nicole Bertoletti, Daniel Czyzyk, and Karen S. Anderson for their generous assistance with access to NSLS-II beam time. This research used resources AMX (17-ID-1) of the National Synchrotron Light Source II (NSLS-II), a U.S. Department of Energy (DOE) Office of Science User Facility operated for the DOE Office of Science by Brookhaven National Laboratory under Contract No. DE-SC0012704.

References

1. Taylor MJ, Hoerauf A, Bockarie M. Lymphatic filariasis and onchocerciasis. The Lancet. 2010;376(9747):1175–85. pmid:20739055
- View Article
- PubMed/NCBI
- Google Scholar
2. King CL, Suamani J, Sanuku N, Cheng Y-C, Satofan S, Mancuso B, et al. A trial of a triple-drug treatment for lymphatic filariasis. New England Journal of Medicine. 2018;379(19):1801–10. pmid:30403937
- View Article
- PubMed/NCBI
- Google Scholar
3. Supali T, Djuardi Y, Bradley M, Noordin R, Rückert P, Fischer PU. Impact of six rounds of mass drug administration on Brugian filariasis and soil-transmitted helminth infections in eastern Indonesia. PLoS Negl Trop Dis. 2013;7(12):e2586. pmid:24349595
- View Article
- PubMed/NCBI
- Google Scholar
4. Tummalapalli SR, Bhat R, Chojnowski A, Prorok M, Kreiss T, Goldberg R, et al. Discovery of a Stress-Activated Protein Kinase Inhibitor for Lymphatic Filariasis. ACS medicinal chemistry letters. 2018;9(3):210–4. pmid:29541362
- View Article
- PubMed/NCBI
- Google Scholar
5. Ramaiah KD, Ottesen EA. Progress and impact of 13 years of the global programme to eliminate lymphatic filariasis on reducing the burden of filarial disease. PLoS Negl Trop Dis. 2015;8(11):e3319. Epub 2014/11/21. [pii]. pmid:25412180.
- View Article
- PubMed/NCBI
- Google Scholar
6. Kinung’hi SM, Magnussen P, Kaatano GM, Kishamawe C, Vennervald BJ. Malaria and helminth co-infections in school and preschool children: a cross-sectional study in Magu district, north-western Tanzania. PloS one. 2014;9(1):e86510. pmid:24489732
- View Article
- PubMed/NCBI
- Google Scholar
7. Hartgers F, Yazdanbakhsh M. Co-infection of helminths and malaria: modulation of the immune responses to malaria. Parasite immunology. 2006;28(10):497–506. pmid:16965285
- View Article
- PubMed/NCBI
- Google Scholar
8. Cody V. Structural Analysis of Dihydrofolate Reductase and Thymidylate Synthase from Mammalian and Pathogenic Organisms. Current Enzyme Inhibition. 2012;8(2):124–39.
- View Article
- Google Scholar
9. Benkovic SJ, Hammes-Schiffer S. A perspective on enzyme catalysis. Science. 2003;301(5637):1196–202. Epub 2003/08/30. [pii]. pmid:12947189.
- View Article
- PubMed/NCBI
- Google Scholar
10. Benkovic SJ, Wagner CR. Dihydrofolate reductase: a paradigm for drug design. Protein Des Dev New Ther Vaccines. 1990:237–49. An 1991:424799.
- View Article
- Google Scholar
11. Goodey NM, Benkovic SJ. Allosteric regulation and catalysis emerge via a common route. Nat Chem Biol. 2008;4(8):474–82. Epub 2008/07/22. [pii] pmid:18641628.
- View Article
- PubMed/NCBI
- Google Scholar
12. Lee J, Goodey NM. Catalytic contributions from remote regions of enzyme structure. Chem Rev. 2011;111(12):7595–624. Epub 2011/09/20. pmid:21923192.
- View Article
- PubMed/NCBI
- Google Scholar
13. Beierlein JM, Frey KM, Bolstad DB, Pelphrey PM, Joska TM, Smith AE, et al. Synthetic and crystallographic studies of a new inhibitor series targeting Bacillus anthracis dihydrofolate reductase. Journal of medicinal chemistry. 2008;51(23):7532–40. pmid:19007108
- View Article
- PubMed/NCBI
- Google Scholar
14. Bolstad DB, Bolstad ES, Frey KM, Wright DL, Anderson AC. Structure-based approach to the development of potent and selective inhibitors of dihydrofolate reductase from Cryptosporidium. Journal of medicinal chemistry. 2008;51(21):6839–52. pmid:18834108
- View Article
- PubMed/NCBI
- Google Scholar
15. Frey KM, Georgiev I., Donald B. R. and Anderson A. C. Predicting resistance mutations using protein design algorithms. Proc Natl Acad Sci USA. 2010;107:13707–12. pmid:20643959
- View Article
- PubMed/NCBI
- Google Scholar
16. Kumar VP, Cisneros JA, Frey KM, Castellanos-Gonzalez A, Wang Y, Gangjee A, et al. Structural studies provide clues for analog design of specific inhibitors of Cryptosporidium hominis thymidylate synthase–dihydrofolate reductase. Bioorganic & medicinal chemistry letters. 2014;24(17):4158–61. pmid:25127103
- View Article
- PubMed/NCBI
- Google Scholar
17. Sharma M, Chauhan PMS. Dihydrofolate reductase as a therapeutic target for infectious diseases: opportunities and challenges. Future MedChem. 2012;4(10):1335–65.
- View Article
- Google Scholar
18. Sharma RD, Bag S, Tawari NR, Degani MS, Goswami K, Reddy MVR. Exploration of 2, 4-diaminopyrimidine and 2, 4-diamino-s-triazine derivatives as potential antifilarial agents. Parasitology. 2013;140(08):959–65. pmid:23552564
- View Article
- PubMed/NCBI
- Google Scholar
19. Jaffe JJ. Dihydrofolate reductase from filarial worms and schistosomes. Annals of the New York Academy of Sciences. 1971;186:113. pmid:4399847
- View Article
- PubMed/NCBI
- Google Scholar
20. Bhoj PS, Rao S, Bahekar SP, Agrawal NR, Togre NS, Sharma R, et al. In vitro apoptotic effect on human lymphatic filarial parasite by piperidine derivatives and thymidine reversal study. Parasitology research. 2020;119(1):165–75. pmid:31807868
- View Article
- PubMed/NCBI
- Google Scholar
21. Perez-Abraham R, Sanchez KG, Alfonso M, Gubler U, Siekierka JJ, Goodey NM. Expression, purification and enzymatic characterization of Brugia malayi dihydrofolate reductase. Protein Expr Purif. 2016;128:81–5. pmid:27544923.
- View Article
- PubMed/NCBI
- Google Scholar
22. Tobias AM, Toska D, Lange K, Eck T, Bhat R, Janson CA, et al. Expression, purification, and inhibition profile of dihydrofolate reductase from the filarial nematode Wuchereria bancrofti. PloS one. 2018;13(5):e0197173. pmid:29787565
- View Article
- PubMed/NCBI
- Google Scholar
23. Serrão VHB, Romanello L, Cassago A, de Souza JRT, Cheleski J, DeMarco R, et al. Structure and kinetics assays of recombinant Schistosoma mansoni dihydrofolate reductase. Acta tropica. 2017;170:190–6. pmid:28288799
- View Article
- PubMed/NCBI
- Google Scholar
24. Serrão VHB, Scortecci JF, Pereira HDM. Characterization of Schistosoma mansoni Dihydrofolate Reductase (DHFR). Schistosoma mansoni: Methods and Protocols. 2020:159–72. pmid:32452003
- View Article
- PubMed/NCBI
- Google Scholar
25. de Albuquerque AL, de Araújo TA, de Melo DCTV, Paiva MHS, de Melo FL, de Oliveira CMF, et al. Development of a molecular xenomonitoring protocol to assess filariasis transmission. Experimental Parasitology. 2020;215:107918. pmid:32464220
- View Article
- PubMed/NCBI
- Google Scholar
26. Mak J, editor Epidemiology of lymphatic filariasis. Ciba Foundation Symposium 127-Filariasis: Filariasis: Ciba Foundation Symposium 127; 2007: Wiley Online Library.
27. Andersen KR, Leksa NC, Schwartz TU. Optimized E. coli expression strain LOBSTR eliminates common contaminants from His-tag purification. Proteins: Structure, Function, and Bioinformatics. 2013;81(11):1857–61. pmid:23852738
- View Article
- PubMed/NCBI
- Google Scholar
28. Karplus PA, Diederichs K. Linking crystallographic model and data quality. Science. 2012;336(6084):1030–3. pmid:22628654
- View Article
- PubMed/NCBI
- Google Scholar
29. Adams PD, Afonine PV, Bunkóczi G, Chen VB, Davis IW, Echols N, et al. PHENIX: a comprehensive Python-based system for macromolecular structure solution. Acta Crystallographica Section D: Biological Crystallography. 2010;66(2):213–21.
- View Article
- Google Scholar
30. McCoy AJ, Grosse-Kunstleve RW, Adams PD, Winn MD, Storoni LC, Read RJ. Phaser crystallographic software. Journal of applied crystallography. 2007;40(4):658–74. pmid:19461840
- View Article
- PubMed/NCBI
- Google Scholar
31. Emsley P, Lohkamp B, Scott WG, Cowtan K. Features and development of Coot. Acta Crystallographica Section D: Biological Crystallography. 2010;66(4):486–501. pmid:20383002
- View Article
- PubMed/NCBI
- Google Scholar
32. Chen VB, Arendall WB, Headd JJ, Keedy DA, Immormino RM, Kapral GJ, et al. MolProbity: all-atom structure validation for macromolecular crystallography. Acta Crystallographica Section D: Biological Crystallography. 2010;66(1):12–21. pmid:20057044
- View Article
- PubMed/NCBI
- Google Scholar
33. Schrödinger L. The PyMOL molecular graphics system. Version. 2010;1(5):0.
- View Article
- Google Scholar
34. Schrödinger Release 2021–2: Maestro, Schrödinger, LLC, New York, NY, 2021.
35. Trott O, Olson AJ. AutoDock Vina: Improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading J Comput Chem. 2010;31(2):455–61. pmid:19499576
- View Article
- PubMed/NCBI
- Google Scholar
36. Schrödinger Release 2021–2: Glide, Schrödinger, LLC, New York, NY, 2021.
37. Morris GM, Huey R, Lindstrom W, Sanner MF, Belew RK, Goodsell DS, et al. AutoDock4 and AutoDockTools4: Automated docking with selective receptor flexibility. Journal of computational chemistry. 2009;30(16):2785–91. pmid:19399780
- View Article
- PubMed/NCBI
- Google Scholar
38. Schrödinger Release 2023–1: Desmond Molecular Dynamics System. Maestro-Desmond Interoperability Tools, Schrödinger, New York, NY, 2021.
- View Article
- Google Scholar
39. Birdsall B, King RW, Wheeler MR, Lewis CA Jr, Goode SR, Dunlap RB, et al. Correction for light absorption in fluorescence studies of protein-ligand interactions. Analytical biochemistry. 1983;132(2):353–61. pmid:6625170
- View Article
- PubMed/NCBI
- Google Scholar
40. Stone SR, Morrison JF. Catalytic mechanism of the dihydrofolate reductase reaction as determined by pH studies. Biochemistry. 1984;23(12):2753–8. pmid:6380573
- View Article
- PubMed/NCBI
- Google Scholar
41. Williams JW, Morrison JF. [17] The kinetics of reversible tight-binding inhibition. Methods in enzymology. 63: Academic Press; 1979. p. 437–67.
42. Morrison J. Kinetics of the reversible inhibition of enzyme-catalysed reactions by tight-binding inhibitors. Biochimica et Biophysica Acta (BBA)-Enzymology. 1969;185(2):269–86. pmid:4980133
- View Article
- PubMed/NCBI
- Google Scholar
43. Lee J, Yennawar NH, Gam J, Benkovic SJ. Kinetic and structural characterization of dihydrofolate reductase from Streptococcus pneumoniae. Biochemistry. 2010;49(1):195–206. pmid:19950924
- View Article
- PubMed/NCBI
- Google Scholar
44. Stojković V, Perissinotti LL, Willmer D, Benkovic SJ, Kohen A. Effects of the donor–acceptor distance and dynamics on hydride tunneling in the dihydrofolate reductase catalyzed reaction. Journal of the American Chemical Society. 2012;134(3):1738–45. pmid:22171795
- View Article
- PubMed/NCBI
- Google Scholar
45. Francis K, Sapienza PJ, Lee AL, Kohen A. The effect of protein mass modulation on human dihydrofolate reductase. Biochemistry. 2016;55(7):1100–6. pmid:26813442
- View Article
- PubMed/NCBI
- Google Scholar
46. Feeney J, Birdsall B, Kovalevskaya NV, Smurnyy YD, Navarro Peran EM, Polshakov VI. NMR structures of apo L. casei dihydrofolate reductase and its complexes with trimethoprim and NADPH: contributions to positive cooperative binding from ligand-induced refolding, conformational changes, and interligand hydrophobic interactions. Biochemistry. 2011;50(18):3609–20. pmid:21410224
- View Article
- PubMed/NCBI
- Google Scholar
47. Evenson DA, Adams J, McIvor RS, Wagner CR. Methotrexate resistance of mouse dihydrofolate reductase: effect of substitution of phenylalanine-31 by serine or tryptophan. Journal of medicinal chemistry. 1996;39(9):1763–6. pmid:8627598
- View Article
- PubMed/NCBI
- Google Scholar
48. Nakano T, Spencer HT, Appleman JR, Blakley RL. Critical role of phenylalanine 34 of human dihydrofolate reductase in substrate and inhibitor binding and in catalysis. Biochemistry. 1994;33(33):9945–52. pmid:8061003
- View Article
- PubMed/NCBI
- Google Scholar
49. Frey KM, Liu J, Lombardo MN, Bolstad DB, Wright DL, Anderson AC. Crystal structures of wild-type and mutant methicillin-resistant Staphylococcus aureus dihydrofolate reductase reveal an alternate conformation of NADPH that may be linked to trimethoprim resistance. Journal of molecular biology. 2009;387(5):1298–308. pmid:19249312
- View Article
- PubMed/NCBI
- Google Scholar
50. Appleman J, Howell E, Kraut J, Blakley R. Role of aspartate 27 of dihydrofolate reductase from Escherichia coli in interconversion of active and inactive enzyme conformers and binding of NADPH. Journal of Biological Chemistry. 1990;265(10):5579–84. pmid:2108144
- View Article
- PubMed/NCBI
- Google Scholar
51. Sawaya MR, Kraut J. Loop and subdomain movements in the mechanism of Escherichia coli dihydrofolate reductase: crystallographic evidence. Biochemistry. 1997;36(3):586–603. pmid:9012674
- View Article
- PubMed/NCBI
- Google Scholar
52. Bhabha G, Ekiert DC, Jennewein M, Zmasek CM, Tuttle LM, Kroon G, et al. Divergent evolution of protein conformational dynamics in dihydrofolate reductase. Nature structural & molecular biology. 2013;20(11):1243. pmid:24077226
- View Article
- PubMed/NCBI
- Google Scholar
53. Pagadala N, Syed K, Tuszynski J. Software for molecular docking: a review. Biophys Rev. 2017;9:91–102. pmid:28510083
- View Article
- PubMed/NCBI
- Google Scholar
54. Pantsar T, Poso A. Binding affinity via docking: fact and fiction. Molecules. 2018;23(8):1899. pmid:30061498
- View Article
- PubMed/NCBI
- Google Scholar
55. Ng H-L, Chen S, Chew E-H, Chui W-K. Applying the designed multiple ligands approach to inhibit dihydrofolate reductase and thioredoxin reductase for anti-proliferative activity. European journal of medicinal chemistry. 2016;115:63–74. pmid:26994844
- View Article
- PubMed/NCBI
- Google Scholar
56. Wright DL, Anderson AC. Antifolate agents: a patent review (2006–2010). Expert opinion on therapeutic patents. 2011;21(9):1293–308. pmid:21619471
- View Article
- PubMed/NCBI
- Google Scholar

[ref1] 1. Taylor MJ, Hoerauf A, Bockarie M. Lymphatic filariasis and onchocerciasis. The Lancet. 2010;376(9747):1175–85. pmid:20739055
View Article
PubMed/NCBI
Google Scholar

[2] View Article

[3] PubMed/NCBI

[4] Google Scholar

[ref2] 2. King CL, Suamani J, Sanuku N, Cheng Y-C, Satofan S, Mancuso B, et al. A trial of a triple-drug treatment for lymphatic filariasis. New England Journal of Medicine. 2018;379(19):1801–10. pmid:30403937
View Article
PubMed/NCBI
Google Scholar

[6] View Article

[7] PubMed/NCBI

[8] Google Scholar

[ref3] 3. Supali T, Djuardi Y, Bradley M, Noordin R, Rückert P, Fischer PU. Impact of six rounds of mass drug administration on Brugian filariasis and soil-transmitted helminth infections in eastern Indonesia. PLoS Negl Trop Dis. 2013;7(12):e2586. pmid:24349595
View Article
PubMed/NCBI
Google Scholar

[10] View Article

[11] PubMed/NCBI

[12] Google Scholar

[ref4] 4. Tummalapalli SR, Bhat R, Chojnowski A, Prorok M, Kreiss T, Goldberg R, et al. Discovery of a Stress-Activated Protein Kinase Inhibitor for Lymphatic Filariasis. ACS medicinal chemistry letters. 2018;9(3):210–4. pmid:29541362
View Article
PubMed/NCBI
Google Scholar

[14] View Article

[15] PubMed/NCBI

[16] Google Scholar

[ref5] 5. Ramaiah KD, Ottesen EA. Progress and impact of 13 years of the global programme to eliminate lymphatic filariasis on reducing the burden of filarial disease. PLoS Negl Trop Dis. 2015;8(11):e3319. Epub 2014/11/21. [pii]. pmid:25412180.
View Article
PubMed/NCBI
Google Scholar

[18] View Article

[19] PubMed/NCBI

[20] Google Scholar

[ref6] 6. Kinung’hi SM, Magnussen P, Kaatano GM, Kishamawe C, Vennervald BJ. Malaria and helminth co-infections in school and preschool children: a cross-sectional study in Magu district, north-western Tanzania. PloS one. 2014;9(1):e86510. pmid:24489732
View Article
PubMed/NCBI
Google Scholar

[22] View Article

[23] PubMed/NCBI

[24] Google Scholar

[ref7] 7. Hartgers F, Yazdanbakhsh M. Co-infection of helminths and malaria: modulation of the immune responses to malaria. Parasite immunology. 2006;28(10):497–506. pmid:16965285
View Article
PubMed/NCBI
Google Scholar

[26] View Article

[27] PubMed/NCBI

[28] Google Scholar

[ref8] 8. Cody V. Structural Analysis of Dihydrofolate Reductase and Thymidylate Synthase from Mammalian and Pathogenic Organisms. Current Enzyme Inhibition. 2012;8(2):124–39.
View Article
Google Scholar

[30] View Article

[31] Google Scholar

[ref9] 9. Benkovic SJ, Hammes-Schiffer S. A perspective on enzyme catalysis. Science. 2003;301(5637):1196–202. Epub 2003/08/30. [pii]. pmid:12947189.
View Article
PubMed/NCBI
Google Scholar

[33] View Article

[34] PubMed/NCBI

[35] Google Scholar

[ref10] 10. Benkovic SJ, Wagner CR. Dihydrofolate reductase: a paradigm for drug design. Protein Des Dev New Ther Vaccines. 1990:237–49. An 1991:424799.
View Article
Google Scholar

[37] View Article

[38] Google Scholar

[ref11] 11. Goodey NM, Benkovic SJ. Allosteric regulation and catalysis emerge via a common route. Nat Chem Biol. 2008;4(8):474–82. Epub 2008/07/22. [pii] pmid:18641628.
View Article
PubMed/NCBI
Google Scholar

[40] View Article

[41] PubMed/NCBI

[42] Google Scholar

[ref12] 12. Lee J, Goodey NM. Catalytic contributions from remote regions of enzyme structure. Chem Rev. 2011;111(12):7595–624. Epub 2011/09/20. pmid:21923192.
View Article
PubMed/NCBI
Google Scholar

[44] View Article

[45] PubMed/NCBI

[46] Google Scholar

[ref13] 13. Beierlein JM, Frey KM, Bolstad DB, Pelphrey PM, Joska TM, Smith AE, et al. Synthetic and crystallographic studies of a new inhibitor series targeting Bacillus anthracis dihydrofolate reductase. Journal of medicinal chemistry. 2008;51(23):7532–40. pmid:19007108
View Article
PubMed/NCBI
Google Scholar

[48] View Article

[49] PubMed/NCBI

[50] Google Scholar

[ref14] 14. Bolstad DB, Bolstad ES, Frey KM, Wright DL, Anderson AC. Structure-based approach to the development of potent and selective inhibitors of dihydrofolate reductase from Cryptosporidium. Journal of medicinal chemistry. 2008;51(21):6839–52. pmid:18834108
View Article
PubMed/NCBI
Google Scholar

[52] View Article

[53] PubMed/NCBI

[54] Google Scholar

[ref15] 15. Frey KM, Georgiev I., Donald B. R. and Anderson A. C. Predicting resistance mutations using protein design algorithms. Proc Natl Acad Sci USA. 2010;107:13707–12. pmid:20643959
View Article
PubMed/NCBI
Google Scholar

[56] View Article

[57] PubMed/NCBI

[58] Google Scholar

[ref16] 16. Kumar VP, Cisneros JA, Frey KM, Castellanos-Gonzalez A, Wang Y, Gangjee A, et al. Structural studies provide clues for analog design of specific inhibitors of Cryptosporidium hominis thymidylate synthase–dihydrofolate reductase. Bioorganic & medicinal chemistry letters. 2014;24(17):4158–61. pmid:25127103
View Article
PubMed/NCBI
Google Scholar

[60] View Article

[61] PubMed/NCBI

[62] Google Scholar

[ref17] 17. Sharma M, Chauhan PMS. Dihydrofolate reductase as a therapeutic target for infectious diseases: opportunities and challenges. Future MedChem. 2012;4(10):1335–65.
View Article
Google Scholar

[64] View Article

[65] Google Scholar

[ref18] 18. Sharma RD, Bag S, Tawari NR, Degani MS, Goswami K, Reddy MVR. Exploration of 2, 4-diaminopyrimidine and 2, 4-diamino-s-triazine derivatives as potential antifilarial agents. Parasitology. 2013;140(08):959–65. pmid:23552564
View Article
PubMed/NCBI
Google Scholar

[67] View Article

[68] PubMed/NCBI

[69] Google Scholar

[ref19] 19. Jaffe JJ. Dihydrofolate reductase from filarial worms and schistosomes. Annals of the New York Academy of Sciences. 1971;186:113. pmid:4399847
View Article
PubMed/NCBI
Google Scholar

[71] View Article

[72] PubMed/NCBI

[73] Google Scholar

[ref20] 20. Bhoj PS, Rao S, Bahekar SP, Agrawal NR, Togre NS, Sharma R, et al. In vitro apoptotic effect on human lymphatic filarial parasite by piperidine derivatives and thymidine reversal study. Parasitology research. 2020;119(1):165–75. pmid:31807868
View Article
PubMed/NCBI
Google Scholar

[75] View Article

[76] PubMed/NCBI

[77] Google Scholar

[ref21] 21. Perez-Abraham R, Sanchez KG, Alfonso M, Gubler U, Siekierka JJ, Goodey NM. Expression, purification and enzymatic characterization of Brugia malayi dihydrofolate reductase. Protein Expr Purif. 2016;128:81–5. pmid:27544923.
View Article
PubMed/NCBI
Google Scholar

[79] View Article

[80] PubMed/NCBI

[81] Google Scholar

[ref22] 22. Tobias AM, Toska D, Lange K, Eck T, Bhat R, Janson CA, et al. Expression, purification, and inhibition profile of dihydrofolate reductase from the filarial nematode Wuchereria bancrofti. PloS one. 2018;13(5):e0197173. pmid:29787565
View Article
PubMed/NCBI
Google Scholar

[83] View Article

[84] PubMed/NCBI

[85] Google Scholar

[ref23] 23. Serrão VHB, Romanello L, Cassago A, de Souza JRT, Cheleski J, DeMarco R, et al. Structure and kinetics assays of recombinant Schistosoma mansoni dihydrofolate reductase. Acta tropica. 2017;170:190–6. pmid:28288799
View Article
PubMed/NCBI
Google Scholar

[87] View Article

[88] PubMed/NCBI

[89] Google Scholar

[ref24] 24. Serrão VHB, Scortecci JF, Pereira HDM. Characterization of Schistosoma mansoni Dihydrofolate Reductase (DHFR). Schistosoma mansoni: Methods and Protocols. 2020:159–72. pmid:32452003
View Article
PubMed/NCBI
Google Scholar

[91] View Article

[92] PubMed/NCBI

[93] Google Scholar

[ref25] 25. de Albuquerque AL, de Araújo TA, de Melo DCTV, Paiva MHS, de Melo FL, de Oliveira CMF, et al. Development of a molecular xenomonitoring protocol to assess filariasis transmission. Experimental Parasitology. 2020;215:107918. pmid:32464220
View Article
PubMed/NCBI
Google Scholar

[95] View Article

[96] PubMed/NCBI

[97] Google Scholar

[ref26] 26. Mak J, editor Epidemiology of lymphatic filariasis. Ciba Foundation Symposium 127-Filariasis: Filariasis: Ciba Foundation Symposium 127; 2007: Wiley Online Library.

[ref27] 27. Andersen KR, Leksa NC, Schwartz TU. Optimized E. coli expression strain LOBSTR eliminates common contaminants from His-tag purification. Proteins: Structure, Function, and Bioinformatics. 2013;81(11):1857–61. pmid:23852738
View Article
PubMed/NCBI
Google Scholar

[100] View Article

[101] PubMed/NCBI

[102] Google Scholar

[ref28] 28. Karplus PA, Diederichs K. Linking crystallographic model and data quality. Science. 2012;336(6084):1030–3. pmid:22628654
View Article
PubMed/NCBI
Google Scholar

[104] View Article

[105] PubMed/NCBI

[106] Google Scholar

[ref29] 29. Adams PD, Afonine PV, Bunkóczi G, Chen VB, Davis IW, Echols N, et al. PHENIX: a comprehensive Python-based system for macromolecular structure solution. Acta Crystallographica Section D: Biological Crystallography. 2010;66(2):213–21.
View Article
Google Scholar

[108] View Article

[109] Google Scholar

[ref30] 30. McCoy AJ, Grosse-Kunstleve RW, Adams PD, Winn MD, Storoni LC, Read RJ. Phaser crystallographic software. Journal of applied crystallography. 2007;40(4):658–74. pmid:19461840
View Article
PubMed/NCBI
Google Scholar

[111] View Article

[112] PubMed/NCBI

[113] Google Scholar

[ref31] 31. Emsley P, Lohkamp B, Scott WG, Cowtan K. Features and development of Coot. Acta Crystallographica Section D: Biological Crystallography. 2010;66(4):486–501. pmid:20383002
View Article
PubMed/NCBI
Google Scholar

[115] View Article

[116] PubMed/NCBI

[117] Google Scholar

[ref32] 32. Chen VB, Arendall WB, Headd JJ, Keedy DA, Immormino RM, Kapral GJ, et al. MolProbity: all-atom structure validation for macromolecular crystallography. Acta Crystallographica Section D: Biological Crystallography. 2010;66(1):12–21. pmid:20057044
View Article
PubMed/NCBI
Google Scholar

[119] View Article

[120] PubMed/NCBI

[121] Google Scholar

[ref33] 33. Schrödinger L. The PyMOL molecular graphics system. Version. 2010;1(5):0.
View Article
Google Scholar

[123] View Article

[124] Google Scholar

[ref34] 34. Schrödinger Release 2021–2: Maestro, Schrödinger, LLC, New York, NY, 2021.

[ref35] 35. Trott O, Olson AJ. AutoDock Vina: Improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading J Comput Chem. 2010;31(2):455–61. pmid:19499576
View Article
PubMed/NCBI
Google Scholar

[127] View Article

[128] PubMed/NCBI

[129] Google Scholar

[ref36] 36. Schrödinger Release 2021–2: Glide, Schrödinger, LLC, New York, NY, 2021.

[ref37] 37. Morris GM, Huey R, Lindstrom W, Sanner MF, Belew RK, Goodsell DS, et al. AutoDock4 and AutoDockTools4: Automated docking with selective receptor flexibility. Journal of computational chemistry. 2009;30(16):2785–91. pmid:19399780
View Article
PubMed/NCBI
Google Scholar

[132] View Article

[133] PubMed/NCBI

[134] Google Scholar

[ref38] 38. Schrödinger Release 2023–1: Desmond Molecular Dynamics System. Maestro-Desmond Interoperability Tools, Schrödinger, New York, NY, 2021.
View Article
Google Scholar

[136] View Article

[137] Google Scholar

[ref39] 39. Birdsall B, King RW, Wheeler MR, Lewis CA Jr, Goode SR, Dunlap RB, et al. Correction for light absorption in fluorescence studies of protein-ligand interactions. Analytical biochemistry. 1983;132(2):353–61. pmid:6625170
View Article
PubMed/NCBI
Google Scholar

[139] View Article

[140] PubMed/NCBI

[141] Google Scholar

[ref40] 40. Stone SR, Morrison JF. Catalytic mechanism of the dihydrofolate reductase reaction as determined by pH studies. Biochemistry. 1984;23(12):2753–8. pmid:6380573
View Article
PubMed/NCBI
Google Scholar

[143] View Article

[144] PubMed/NCBI

[145] Google Scholar

[ref41] 41. Williams JW, Morrison JF. [17] The kinetics of reversible tight-binding inhibition. Methods in enzymology. 63: Academic Press; 1979. p. 437–67.

[ref42] 42. Morrison J. Kinetics of the reversible inhibition of enzyme-catalysed reactions by tight-binding inhibitors. Biochimica et Biophysica Acta (BBA)-Enzymology. 1969;185(2):269–86. pmid:4980133
View Article
PubMed/NCBI
Google Scholar

[148] View Article

[149] PubMed/NCBI

[150] Google Scholar

[ref43] 43. Lee J, Yennawar NH, Gam J, Benkovic SJ. Kinetic and structural characterization of dihydrofolate reductase from Streptococcus pneumoniae. Biochemistry. 2010;49(1):195–206. pmid:19950924
View Article
PubMed/NCBI
Google Scholar

[152] View Article

[153] PubMed/NCBI

[154] Google Scholar

[ref44] 44. Stojković V, Perissinotti LL, Willmer D, Benkovic SJ, Kohen A. Effects of the donor–acceptor distance and dynamics on hydride tunneling in the dihydrofolate reductase catalyzed reaction. Journal of the American Chemical Society. 2012;134(3):1738–45. pmid:22171795
View Article
PubMed/NCBI
Google Scholar

[156] View Article

[157] PubMed/NCBI

[158] Google Scholar

[ref45] 45. Francis K, Sapienza PJ, Lee AL, Kohen A. The effect of protein mass modulation on human dihydrofolate reductase. Biochemistry. 2016;55(7):1100–6. pmid:26813442
View Article
PubMed/NCBI
Google Scholar

[160] View Article

[161] PubMed/NCBI

[162] Google Scholar

[ref46] 46. Feeney J, Birdsall B, Kovalevskaya NV, Smurnyy YD, Navarro Peran EM, Polshakov VI. NMR structures of apo L. casei dihydrofolate reductase and its complexes with trimethoprim and NADPH: contributions to positive cooperative binding from ligand-induced refolding, conformational changes, and interligand hydrophobic interactions. Biochemistry. 2011;50(18):3609–20. pmid:21410224
View Article
PubMed/NCBI
Google Scholar

[164] View Article

[165] PubMed/NCBI

[166] Google Scholar

[ref47] 47. Evenson DA, Adams J, McIvor RS, Wagner CR. Methotrexate resistance of mouse dihydrofolate reductase: effect of substitution of phenylalanine-31 by serine or tryptophan. Journal of medicinal chemistry. 1996;39(9):1763–6. pmid:8627598
View Article
PubMed/NCBI
Google Scholar

[168] View Article

[169] PubMed/NCBI

[170] Google Scholar

[ref48] 48. Nakano T, Spencer HT, Appleman JR, Blakley RL. Critical role of phenylalanine 34 of human dihydrofolate reductase in substrate and inhibitor binding and in catalysis. Biochemistry. 1994;33(33):9945–52. pmid:8061003
View Article
PubMed/NCBI
Google Scholar

[172] View Article

[173] PubMed/NCBI

[174] Google Scholar

[ref49] 49. Frey KM, Liu J, Lombardo MN, Bolstad DB, Wright DL, Anderson AC. Crystal structures of wild-type and mutant methicillin-resistant Staphylococcus aureus dihydrofolate reductase reveal an alternate conformation of NADPH that may be linked to trimethoprim resistance. Journal of molecular biology. 2009;387(5):1298–308. pmid:19249312
View Article
PubMed/NCBI
Google Scholar

[176] View Article

[177] PubMed/NCBI

[178] Google Scholar

[ref50] 50. Appleman J, Howell E, Kraut J, Blakley R. Role of aspartate 27 of dihydrofolate reductase from Escherichia coli in interconversion of active and inactive enzyme conformers and binding of NADPH. Journal of Biological Chemistry. 1990;265(10):5579–84. pmid:2108144
View Article
PubMed/NCBI
Google Scholar

[180] View Article

[181] PubMed/NCBI

[182] Google Scholar

[ref51] 51. Sawaya MR, Kraut J. Loop and subdomain movements in the mechanism of Escherichia coli dihydrofolate reductase: crystallographic evidence. Biochemistry. 1997;36(3):586–603. pmid:9012674
View Article
PubMed/NCBI
Google Scholar

[184] View Article

[185] PubMed/NCBI

[186] Google Scholar

[ref52] 52. Bhabha G, Ekiert DC, Jennewein M, Zmasek CM, Tuttle LM, Kroon G, et al. Divergent evolution of protein conformational dynamics in dihydrofolate reductase. Nature structural & molecular biology. 2013;20(11):1243. pmid:24077226
View Article
PubMed/NCBI
Google Scholar

[188] View Article

[189] PubMed/NCBI

[190] Google Scholar

[ref53] 53. Pagadala N, Syed K, Tuszynski J. Software for molecular docking: a review. Biophys Rev. 2017;9:91–102. pmid:28510083
View Article
PubMed/NCBI
Google Scholar

[192] View Article

[193] PubMed/NCBI

[194] Google Scholar

[ref54] 54. Pantsar T, Poso A. Binding affinity via docking: fact and fiction. Molecules. 2018;23(8):1899. pmid:30061498
View Article
PubMed/NCBI
Google Scholar

[196] View Article

[197] PubMed/NCBI

[198] Google Scholar

[ref55] 55. Ng H-L, Chen S, Chew E-H, Chui W-K. Applying the designed multiple ligands approach to inhibit dihydrofolate reductase and thioredoxin reductase for anti-proliferative activity. European journal of medicinal chemistry. 2016;115:63–74. pmid:26994844
View Article
PubMed/NCBI
Google Scholar

[200] View Article

[201] PubMed/NCBI

[202] Google Scholar

[ref56] 56. Wright DL, Anderson AC. Antifolate agents: a patent review (2006–2010). Expert opinion on therapeutic patents. 2011;21(9):1293–308. pmid:21619471
View Article
PubMed/NCBI
Google Scholar

[204] View Article

[205] PubMed/NCBI

[206] Google Scholar

Figures

Abstract

Author summary

Introduction

Methods and materials

Expression and purification of WbDHFR

Crystallization of WbDHFR via sitting drop diffusion

Data collection and structure determination

Molecular docking

Molecular dynamics simulations

Determination of KD of NADPH and folate

Results and discussion

Crystal optimization and structure determination

Analysis of the overall structure

Interactions with folate

Interactions with NADPH

Comparison of WbDHFR and SmDHFR structures

Molecular docking of antifolate compounds

MD simulations

Insights for rational design of WbDHFR inhibitors

Supporting information

S1 Text.

Acknowledgments

References

Determination of K_D of NADPH and folate