modeling phosphoenolpyruvate in enolase enzyme from mycobacterium tuberculosis

Modeling Small Molecule Ligands in CryoEM Maps of Macromolecules

Cells accomplish their functions by carrying out innumerable chemical reactions simultaneously and independently, each meticulously regulated to ensure their survival and adaptability in response to environmental cues. This is achieved by molecular recognition, which enables biomolecules, especially proteins, to form transient or stable complexes with other macromolecules as well as small molecules or ligands1. Thus, protein-ligand interactions are fundamental to all processes in biology, which include the regulation of protein expression and activity, the recognition of substrates and cofactors by enzymes, as well as how cells perceive and relay signals1,2. A better understanding of the kinetic, thermodynamic, and structural properties of the protein-ligand complex reveals the molecular basis of ligand interaction and also facilitates rational drug design by optimizing drug interaction and specificity. An economical and faster approach to studying protein-ligand interaction is to use molecular docking, which is a computational method that virtually screens a diverse range of small molecules and predicts the binding mode and affinity of these ligands to target proteins3. However, experimental evidence from high-resolution structures determined by X-ray Diffraction (XRD), Nuclear Magnetic Resonance (NMR), or electron cryomicroscopy (cryoEM) provides the essential proof for such predictions and aids in the development of newer and more effective activators or inhibitors for a given target. This article uses the abbreviation &#39;cryoEM&#39; as the technique is commonly referred to. However, there is an ongoing debate on choosing the correct nomenclature, and recently, the term cryogenic-sample Electron Microscopy (cryoEM) has been proposed to indicate that the sample is at cryogenic temperature and imaged with electrons4. Similarly, the maps derived from cryoEM have been called electron potential, electrostatic potential, or Coulomb potential, and for simplicity, here we use cryoEM maps5,6,7,8,9,10.
Although XRD has been the gold standard technique in high-resolution structure determination of protein-ligand complexes, post resolution-revolution11 cryoEM has gained momentum, as indicated&#160;by the surge of Coulomb potential maps or cryoEM maps deposited in the Electron Microscopy Database (EMDB)12,13 over the last few years14. Owing to advancements in sample preparation, imaging, and data processing methods, the number of Protein Data Bank (PDB)14 depositions employing cryoEM increased from 0.7% to 17% between 2010 and 2020, with approximately 50% of reported structures in 2020 being determined at 3.5&#8197;&#197; resolution or better15,16. CryoEM has been rapidly adopted by the structural biology community, including the pharmaceutical industry, as it allows the study of flexible and non-crystalline biological macromolecules, especially membrane proteins and multi-protein complexes, at near-atomic resolution, overcoming the process of crystallization and obtaining well-diffracting crystals required for high-resolution structure determination by XRD.
Accurate modeling of the ligand in the cryoEM map is paramount, as it serves as a blueprint of the protein-ligand complex at a molecular level. There are several automated ligand-building tools used in X-ray crystallography&#160;that depend on the shape and topology of the ligand density in order to fit or build the ligand into the electron density17,18,19,20. Nevertheless, if the resolution is lower than 3 &#197;, these approaches tend to produce less desirable outcomes because the topological features on which they depend for recognition and building become less defined. In many instances, these methods have proven ineffective in accurately modeling ligands into cryoEM maps, as these maps have been determined in the low-to-medium resolution range, typically between 3.5 &#197;-5 &#197;17.
The first step in 3D structure determination of a protein-ligand complex by cryoEM involves either co-purifying the ligand with the protein (when the ligand has a high binding affinity to the protein) or incubating the protein solution with the ligand for a specific duration before grid preparation. Subsequently, a small sample volume is placed onto a plasma-cleaned holey TEM grid, followed by flash freezing in liquid ethane and ultimately imaging with a cryo-TEM. The 2D projection images from hundreds of thousands to millions of individual particles are averaged to reconstruct a 3-dimensional (3D) Coulomb potential map of the macromolecule. Identifying and modeling ligands and solvent molecules in these maps pose significant challenges in many cases due to the anisotropic resolution across the map (i.e., the resolution is not uniform across the macromolecule), flexibility in the region where the ligand is bound, and the noise in the data. Many of the modeling, refinement, and visualization tools that were developed for XRD are now being adapted for use in cryoEM for the same purposes18, 19, 20, 21. In this article, an overview of various methods and software currently used to identify ligands, build models, and refine the coordinates derived from cryoEM is presented. A step-by-step protocol has been provided to illustrate the processes involved in modeling ligands using specific protein-ligand complexes with varying resolution and complexity.
The first step in modeling ligands in cryoEM maps includes the identification of the ligand density (non-protein) in the map. If ligand binding does not induce any conformational change in the protein, then calculating a simple difference map between the protein-ligand complex and the apo-protein essentially highlights the regions of extra density, suggesting the presence of the ligand. Such differences can be observed immediately, as it just requires two maps, and even intermediate maps during the process of 3D refinement can be used to check if the ligand is present. Additionally, if the resolution is high enough (&#60;3.0 &#197;), then the difference map can also provide insights into the location of water molecules as well as ions interacting with the ligand and the protein residues.
In the absence of the apo-protein map, it is now possible to use Servalcat22, which is available as a standalone tool and has also been integrated into the CCP-EM software suite23,24 as part of the Refmac refinement and in CCP4 8.0 release25,26. Servalcat allows the calculation of an FSC weighted difference (Fo-Fc) map using the unsharpened half-maps and the apoprotein model as input. The Fo-Fc omit map represents the disparity between the experimental map (Fo) and the map derived from the model (Fc). In the absence of a ligand in the model, a positive density in a Fo-Fc map that overlaps with the experimental EM map typically suggests the presence of the ligand. The assumption here is that the protein chain is well-fitted in the map, and the remaining positive density indicates the location of the ligand. However, it is important to meticulously examine whether the positive density stems from modeling inaccuracies, such as the wrong rotamer of a protein side-chain.
The second step involves obtaining or creating a cartesian coordinate file of the ligand with well-defined geometry from the available chemical information. Standard ligands (for example, ATP and NADP+) that are already available in the CCP4 monomer library can be used for refinement by retrieving the coordinate and geometry files via their monomer accession code. However, for unknown or non-standard ligands, various tools are available to create the geometry files. Some of the examples include the eLBOW27 - (electronic ligand builder and optimization workbench) in Phenix28, Lidia - an in-built tool in Coot29, JLigand/ACEDRG30,31, CCP-EM23,24, Ligprep32-a module of Glide within the Schrodinger suite. The ligand coordinate file is then fitted in the density, guided by both the experimental cryoEM map and the difference map in Coot. This is followed by real-space refinement in Phenix28 or reciprocal refinement in Refmac33. A Linux workstation or a laptop equipped with a good graphics card and the above-mentioned software is required. Most of these programs are included in various suites. CCP-EM24&#160;and Phenix28 are freely available to academic users and include a variety of tools that are used in this article, including Coot, Refmac533,34,35,36, Servalcat, phenix.real_space_refine, etc. Similarly,&#160;Chimera37, and ChimeraX38 provide free licenses to academic users.

Author Spotlight: Exploring Cellular Processes by Modeling Ligands in Cryo-EM Maps

Modeling Ligands into Maps Derived from Electron Cryomicroscopy

We are interested in understanding the physiological processes that occur across the cell, particularly at the membrane interface. We use Cryo-EM as a main technique and work on various interesting and challenging biological problems studying multiple macromolecular complexes. Determining the structures of membrane proteins and other labile complexes was challenging couple of decades ago.However, technical advances in Cryo-EM, a novel detergent and lipid mimetic and with endowments paved the way for rapid structure determination of these complexes. These structures can now be obtained at high resolution and potentially used in drug discovery. In single particle Cryo-EM, challenges include acquiring stable and homogeneous sample, embedding the sample in random orientation in thin ice, processing a large number of images with low signal to noise ratio, and accurately determining the angular orientation and classification during 3D reconstruction.Identifying and modeling small molecules in cryo-EM maps of ligand protein complexes is challenging due to inherent noise in the data and isotropic and low map resolution, sample heterogeneity, and ligand flexibility. This protocol is a step-by-step guide for identifying and modeling ligands and solvent molecules in low to medium resolution Cryo-EM.

Modeling Phosphoenolpyruvate in Enolase Enzyme from Mycobacterium tuberculosis

Begin by downloading the unsharpened half-maps of the apo-enolase from additional data in EMDB. Then open ChimeraX and click on Open in the toolbar. Select the half-maps of apo-enolase.Type this in the command line to combine the half-maps and obtain the apo-enolase unsharpened map. Adjust the threshold of the map to 0.0595 to reduce noise. Then type this command to rename the combined map.Click on Open. Select the PEP enolase unsharpened half-maps and type this command in the command line. Adjust the threshold of the map to 0.0721 to reduce noise and rename the map.Then display the apo-enolase and PEP enolase unsharpened maps and click on Fit in the Map panel to compute a difference map. Type this command in the command line to subtract the PEP enolase map from the apo-enolase map and adjust threshold. Color the subtracted map in green, denoting positive density.Next, open PDB and download the coordinates of mycobacterium tuberculosis octameric enolase. Then in ChimeraX, click on Open from the toolbar and select the file name. Click Molecule Display, Hide Atoms, and Show Cartoon.Type this command to rename the model. Select the model from the Model panel. Click on right mouse from the toolbar.Then click Move Model and position the model in proximity to the PEP enolase map. Align the model with respect to the map. Fit this model into the PEP enolase unsharpened map by typing this command in the command line.Check the fit. Adjust the color and structure representation. Open Coot from the terminal by typing coot&Click on File, then Open Coordinates, and select Apo-enolase.pdb.Next, download the PEP bound enolase sharpened map from EMDB. Display this map in Coot by clicking on File and Open Map. Scroll the middle mouse button to set the threshold to 7.00 sigma.Click on Validate, then Unmodeled Blobs. In the new window, type 7.00 as the RMSD and click Find Blobs. Locate the unmodeled ligand density near the active site residues, serine 42, lysine 386, and arginine 364.Next, click on File, Get Monomer, and enter PEP to get the PEP Monomer model file from the Coot Monomer Library. Move the PEP molecule to the density using the Rotate Translate Zone Chain Molecule option in the sidebar menu. Then click on Real Space Refined Zone in the sidebar menu to fit the PEP molecule in the density.Assess the fit and click on Accept when done. Use the Merge Molecule option in the Edit tab to merge the fitted ligand with the apo-enolase model and save the model as PEP enolase.pdb. Then click on Calculate, NCS Tools, followed by NCS Ligands to add ligands to the rest of the monomers in the coordinate file.For the protein with NCS option, select the coordinate file apo-enolase. pdb and chain ID A as NCS master chain. Next, for the molecule containing ligand, select apo-enolase.pdb as the coordinate file, chain ID J, and residue number 121. Click on Find Candidate Positions. Click on the individual candidate ligands and analyze the fit visually.Then model two magnesium ions in the active site density by clicking on Place Atom at the pointer and selecting MG from the Pointer Atom Type list. Also, add the magnesium ions in the symmetry-related monomers. Assess the fit for the symmetry-related magnesium atoms.Save the model by clicking on File, then Save Coordinates. Select file name and typing enolase plus PEP plus MG.pdb. Then open Phoenix GUI.Run a real space refinement job using default parameters with the saved model and PEP bound sharpened map. To visualize the modeled ligand, open PyMOL, click on File, and Open to load the refined model from the Phoenix refinement job. Also load the PEP bound sharpened map.Rename the map as PEP sharpened by clicking on Actions and then Rename. Then select the ligands by clicking on Display followed by Sequence. Type this in the command line and press Enter.This carves the map density around the selection. Click on the last box, C, next to the mesh_ligand object to change the color of the ligand mesh to blue. Display the active site residues interacting with the PEP and magnesium ions in stick and sphere representation and enolase enzyme in cartoon representation.Set mesh size. Type this in the command line to perform ray tracing and save the image as a PNG file. During glycolysis, enolase converts two phosphoglycerate to phosphoenolpyruvate, a vital intermediate for various metabolic pathways.The difference map of the apo enzyme and PEP bound enzyme showed a distinct ligand density. Also, extra density was observed in the active site of the modeled protein. Phosphoenolpyruvate and two magnesium ions were modeled in the density observed in the vicinity of the ligand.Phosphoenolpyruvate formed hydrogen bonds with several active site residues, such as lysine 386 and arginine 364. The magnesium ions formed metal coordination bonds with aspartate 241, glutamate 283, aspartate 310, and the phosphate of phosphoenolpyruvate.

modeling-phosphoenolpyruvate-enolase-enzyme-from-mycobacterium

Research

JoVE Journal

Biochemistry

206 vistas. Comience por descargar los semimapas sin enfocar de la apo-enolasa de datos adicionales en EMDB. A continuación, abra ChimeraX y haga clic en Abrir en la barra de herramientas. Seleccione los semimapas de apo-enolasa.Escriba esto en la línea de comandos para combinar los medios mapas y obtener el mapa sin enfocar de apo-enolasa. Ajuste el umbral del mapa a 0,0595 para reducir el ruido. A continuación, escriba este comando para cambiar el nombre del mapa combinado.Haga clic en...