Funding for this work has been provided by the National Science Foundation:
Improving Undergraduate STEM Education Grant (Award #1712268)
Research Coordination Networks in Undergraduate in Undergraduate Biology Education (Award # 1920270)
We are grateful to Karsten Theis, PhD, Westfield University, for helpful discussions about Jmol.

The authors declare that they have no relevant or material financial interests that relate to the research described in this paper.

This protocol outlines a ten-step process for the modeling of an enzyme active site, applied to four popular programs for biomolecular modeling. The critical steps of the protocol are: identifying the ligands in the active site, selecting residues within 5 &#197; to define an active site, and showing the interactions of the enzyme with the active site ligands. Distinguishing the ligands relevant to the biological function is paramount, as this allows the user to define the amino acid residues within 5 &#197; that can play a role in binding the ligands. Finally, using the program to display molecular interactions allows the user to develop the skills necessary to understand the molecular interactions that promote binding.
A limitation of computer-based molecular modeling protocols is the dependence on specific commands and syntax. While biochemical protocols may be tolerant of small changes in procedure, computer-based investigations may yield wildly different final products if the procedure is not closely adhered to. This is particularly important when using command-line interfaces where program-specific syntax is required to achieve a certain output, and a seemingly insignificant change in punctuation or capitalization can cause a command to fail. There are various Wikis and manuals for each program, where a user can find and troubleshoot command-line inputs; the user should pay careful attention to the details of command syntax. Although most molecular visualization programs include undo commands, due to the complexity of the interfaces, the undo command doesn&#39;t always faithfully reverse the last executed step. Therefore, saving the current working state often is encouraged, especially for new users.
Further limitations can arise from the data used to create the model itself. While the standards inherent in the Protein Data Bank ensure a certain level of consistency, users of molecular visualization programs will often encounter unexpected effects in a protein rendering. First, most structures are determined using X-ray crystallography, which provides a single model of the protein; however, NMR structures are often composed of multiple models that can be visualized one at a time. Second, structures determined from crystallography or cryogenic electron microscopy experiments may contain atoms whose position can&#39;t be elucidated and appear as gaps in certain representations of the protein. Protein structures may have alternate conformations of side chains, which, when displayed in stick rendering, appear as two groups protruding off of the same amino acid backbone. Even short sections of backbone may have such alternative conformations, and sometimes ligands are superimposed in the active site in more than one binding conformation.
For a crystal structure, the deposited 3D coordinates include all components of the asymmetric unit, which provides enough information to reproduce the repeating unit of a protein crystal. Sometimes, this structure will contain additional protein chains compared to the biologically active form of the protein (e.g., fetal hemoglobin mutant, PDB ID: 4MQK). Conversely, some programs may not automatically load all chains of the biologically active unit. For example, the SARS-CoV2 main protease (PDB ID: 6Y2E) loads half of the biologically active dimer (made up of two protein chains) when fetched using the commands described in this protocol in ChimeraX, PyMOL, and Jmol. Though slight modification of the command will load the biologically active dimer, this consideration may not be straightforward for the novice modeling program user. A different issue that can arise is in the identification of the active site or substrate itself. Crystallographic experiments are carried out using a variety of molecules, which may be modeled into the final structure. For example, sulfate molecules may bind phosphate binding sites in the active site, or they may bind other regions that are not relevant to the mechanism. These molecules may obscure the correct identification of the active site itself and may even suggest to the student that they are part of the mechanism.
Presumably, the user will wish to apply this procedure to other active/binding sites. To apply this protocol in the future work involving the analysis of new protein active sites, the user will need to identify which of the bound ligands are relevant to function. Some ligands are not associated with protein function, and instead are a result of the solvent or crystallization conditions used to conduct the experiment (e.g., the potassium ion present in the 3FGU model). The key ligands should be identified by consulting the original manuscript. With practice and, where applicable, an understanding of the line command syntax, a user will be able to apply the protocol for the desired modeling program to any enzyme active site, and to model other macromolecules of their choice.
Identifying and analyzing bound substrates and ligands is central to the elucidation of molecular mechanisms and structure-based drug design efforts, which have directly led to improvements in treatments for disease, including acquired immunodeficiency syndrome (AIDS) and COVID-1947,48,49,50,51,52. While individual molecular visualization programs offer different interfaces and user experiences, most offer comparable features. It is important for the development of biomolecular visualization literacy that upper level biochemistry students become familiar with structure visualization and the tools to generate such images4,20,53. This allows students to move beyond the interpretation of two-dimensional images in textbooks and journal articles and to more easily develop their own hypotheses from structural data54, which will prepare developing scientists to address future public health issues and improve the understanding of biochemical processes.
In summary, this protocol details active site modeling using four leading free macromolecular modeling programs. Our community, BioMolViz, adopts a non-software-specific approach to biomolecular modeling. We specifically avoided a critique or comparison of program features, although a user sampling each program will likely find that they prefer certain aspects of macromolecular modeling in one program versus another. We invite readers to utilize the BioMolViz Framework, which details the biomolecular visualization-based learning goals and objectives targeted in this protocol, and explore resources for teaching and learning biomolecular visualization through the BioMolViz community website at&#160;http://biomolviz.org.

Understanding representations of the molecular world is critical to becoming an expert in the biomolecular sciences1, because the interpretation of such images is key to understanding biological function2. A learner&#39;s introduction to macromolecules usually comes in the form of two-dimensional textbook images of cell membranes, organelles, macromolecules, etc. but the biological reality is that these are three-dimensional structures, and an understanding of their properties requires ways to visualize and extract meaning from 3D models.
Accordingly, the development of biomolecular visual literacy in upper division molecular life science courses has gained attention, with a number of articles reporting on the importance and difficulties of teaching and assessing visualization skills1,3,4,5,6,7,8,9. The response to these articles has been an increase in the number of classroom interventions, typically within a semester in a single institution, wherein molecular visualization programs and models are used to target difficult concepts2,10,11,12,13,14,15. Additionally, researchers have sought to characterize how students use biomolecular visualization programs and/or models to approach a specific topic16,17,18,19. Our own group, BioMolViz, has described a Framework that subdivides overarching themes in visual literacy into learning goals and objectives to guide such interventions20,21, and we lead workshops that train faculty to use the Framework in the backward design of assessments to measure visual literacy skills22.
At the center of all of this work is a critical skill: the ability to manipulate structures of macromolecules using programs for biomolecular visualization. These tools were developed independently using a variety of platforms; therefore, they can be rather unique in their operation and use. This necessitates program-specific instructions, and the identification of a program that a user is comfortable with is important to facilitate continued implementation.
Beyond the very basics of manipulating structures in 3D (rotating, selecting, and altering the model), a major goal is to model the active site of a protein. This process allows a learner to develop their understanding in three overarching themes described by the BioMolViz Framework: molecular interactions, ligands / modifications, and structure-function relationships20,21.
Four popular choices of programs for biomolecular visualization include: Jmol/JSmol23, iCn3D24, PyMOL25, and UCSF Chimera26,27. We encourage those new to Chimera to use UCSF ChimeraX, the next generation of the Chimera molecular visualization program, which is the currently supported version of the program.
In this protocol, we demonstrate how to use each of these four programs to model the active site of human glucokinase with a bound substrate analog complex (PDB ID: 3FGU), and to display measurements to illustrate specific binding interactions28. The model represents a catalytic complex of the enzyme. To capture the active site in the pre-catalysis state, a non-hydrolyzable analog of ATP was bound to the glucokinase active site. This phosphoaminophosphonic acid-adenylate ester (ANP) contains a phosphorous-nitrogen bond instead of the usual phosphorous-oxygen linkage at this position. The active site also contains glucose (denoted BCG in the model) and magnesium (denoted MG). Additionally, there is a potassium ion (K) in the structure, resulting from potassium chloride used in the crystallization solvent. This ion is not critical for biological function and is located outside of the active site.
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/63170/63170fig01.jpg" /> 
Figure 1: ATP/ANP structures. Adenosine triphosphate (ATP) structure compared to the phosphoaminophosphonic acid-adenylate ester (ANP). <a href="https://www.jove.com/files/ftp_upload/63170/63170fig01large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
The protocol demonstrates the selection of the bound ligands of the substrate analog complex and the identification of active-site residues within 5 &#197; of the bound complex, which captures amino acids and water molecules capable of making relevant molecular interactions, including hydrophobic and van der Waals interactions.
The display is initially manipulated to show the majority of the protein in a cartoon representation, with the active site amino acid residues in stick representation to show the relevant atoms of the protein and highlight the molecular interactions. After step 3 of the protocol for each program, these representations have been applied and the view of the protein is similar across programs (Figure 2). At the end of the protocol, the protein cartoon is hidden to simplify the view, and focus on the active site.
<img alt="Figure 2" class="xfigimg" src="/files/ftp_upload/63170/63170fig02.jpg" /> 
Figure 2: Structure comparison across programs. Comparison of the structure of 3FGU in each program following the Adjust the Representation step (step 2 or 3 of each protocol). <a href="https://www.jove.com/files/ftp_upload/63170/63170fig02large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
CPK coloring is applied to the active site amino acids and bound ligands29,30. This coloring scheme distinguishes atoms of different chemical elements in molecular models shown in line, stick, ball and stick, and space-filling representations. Hydrogen is white, nitrogen is blue, oxygen is red, sulfur is yellow, and phosphorus is orange in the CPK coloring scheme. Traditionally, black is used for carbon, although in modern use, carbon coloring may vary.
Hydrogen atoms are not visible in crystal structures, although each of these programs is capable of predicting their location. Adding the hydrogen atoms to a large macromolecular structure can obscure the view, thus they are not displayed in this protocol. Accordingly, hydrogen bonds will be shown by measuring from the center of two heteroatoms (e.g., oxygen to oxygen, oxygen to nitrogen) in these structures.
Program Overviews 
Downloadable Graphical User Interfaces (GUIs): PyMOL (Version 2.4.1), ChimeraX (Version 1.2.5), and Jmol (Version 1.8.0_301) are GUI-based molecular modeling tools. These three interfaces feature command lines to input typed code; many of the same capabilities are available through menus and buttons in the GUI. A common feature in the command line of these programs is that the user may load and re-execute previous commands using the up and down arrow keys on the keyboard.
Web-based GUIs: iCn3D (I-see-in-3D) is a WebGL-based viewer for interactive viewing of three-dimensional macromolecular structures and chemicals on the Web, without the need to install a separate application. It does not use a command line, though the full web version features an editable command log. JSmol is a JavaScript or HTML5 version of Jmol for use on a website or in a web browser window, and is very similar in operation to Jmol. JSmol can be used to create online tutorials, including animations.
Proteopedia31,32, FirstGlance in Jmol33, and the JSmol web interface (JUDE) at the Milwaukee School of Engineering Center for BioMolecular Modeling are examples of such Jmol-based online design environments34. The Proteopedia wiki is a teaching tool that allows the user to model a macromolecule structure and create pages featuring these models within the website35. The Proteopedia scene authoring tool, built using JSmol, integrates a GUI with additional features not available in the Jmol GUI.
Jmol and iCn3D are based on the Java programming language; JSmol uses either Java or HTML5, and PyMOL and ChimeraX are based on the Python programming language. Each of these programs loads protein data bank files, which can be downloaded from the RCSB Protein Data Bank under a 4-digit alphanumeric PDB ID36,37. The most common file types are Protein Data Bank (PDB) files containing the .pdb extension and Crystallographic Information File (CIF or mmCIF) containing the .cif extension. CIF has superseded PDB as the default file type for the Protein Data Bank, but both file formats function in these programs. There can be slight differences in the way the sequence/structure is displayed when using CIF as opposed to PDB files; however, the files function similarly and the differences will not be addressed in detail here. The Molecular Modeling Database (MMDB), a product of the National Center for Biotechnology Information (NCBI), is a subset of PDB structures to which categorical information has been associated (e.g., biological features, conserved protein domains)38. iCn3D, a product of the NCBI, is capable of loading PDB files containing the MMDB data.
To view a model, the user can download the desired file from the dedicated Protein Data Bank page for the structure (e.g.,&#160;https://www.rcsb.org/structure/3FGU), and then use the dropdown&#160;File menu of the program to open the structure. All of the programs are also capable of loading a structure file directly through the interface, and that method is detailed within the protocols.
The ChimeraX, Jmol, and PyMOL GUIs each contain one or more windows of the console that may be resized by dragging the corner. iCn3D and JSmol are entirely contained in a web browser. When using iCn3D, the user may need to scroll within the pop-up windows to reveal all menu items, depending on screen size and resolution.
The protocols detailed here provide a simple method to display the active site of the enzyme using each program. It should be noted that there are multiple ways to execute the steps in each program. For example, in ChimeraX, the same task may be executed using dropdown menus, the toolbar at the top, or the command line. Users interested in learning a specific program in detail are encouraged to explore the online tutorials, manuals, and Wikis available for these programs39,40,41,42,43,44,45,46.
Existing manuals and tutorials for these programs present the items in this protocol as discrete tasks. To display an active site, the user must synthesize the required operations from the various manuals and tutorials. This manuscript augments existing tutorials available by presenting a linear protocol for modeling a labeled active site with molecular interactions, providing the user with a logic for active site modeling that can be applied to other models and programs.
<img alt="Figure 3" class="xfigimg" src="/files/ftp_upload/63170/63170fig03.jpg" /> 
Figure 3: ChimeraX GUI. ChimeraX GUI interface with the dropdown menus, toolbar, structure viewer, and command line labeled. <a href="https://www.jove.com/files/ftp_upload/63170/63170fig03large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 4" class="xfigimg" src="/files/ftp_upload/63170/63170fig04.jpg" /> 
Figure 4: iCn3D GUI. iCn3D GUI interface with the dropdown menus, toolbar, structure viewer, command log, select sets pop-up, and sequence and annotations pop-up menus labeled. <a href="https://www.jove.com/files/ftp_upload/63170/63170fig04large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 5" class="xfigimg" src="/files/ftp_upload/63170/63170fig05.jpg" /> 
Figure 5: Jmol GUI. Jmol GUI interface with the dropdown menus, toolbar, structure viewer, pop-up menu, and console/command line labeled. <a href="https://www.jove.com/files/ftp_upload/63170/63170fig05large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 6" class="xfigimg" src="/files/ftp_upload/63170/63170fig06.jpg" /> 
Figure 6: PyMOL GUI. PyMOL GUI interface with the dropdown menus, structure viewer, names/object panel, mouse controls menu, and command line labeled. <a href="https://www.jove.com/files/ftp_upload/63170/63170fig06large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>

<table><tbody><tr><td>ChimeraX (Version 1.2.5) https://www.rbvi.ucsf.edu/chimerax/</td><td></td><td></td><td></td></tr><tr><td>Computer</td><td></td><td></td><td>Any</td></tr><tr><td>iCn3D (web-based only: https://www.ncbi.nlm.nih.gov/Structure/icn3d/full.html)</td><td></td><td></td><td></td></tr><tr><td>Java (for Jmol) https://java.com/en/download/</td><td></td><td></td><td></td></tr><tr><td>Jmol (Version 1.8.0_301) http://jmol.sourceforge.net/</td><td></td><td></td><td></td></tr><tr><td>Mouse (optional)</td><td></td><td></td><td>Any</td></tr><tr><td>PyMOL (Version 2.4.1 - educational): https://pymol.org/2 educational use only version: https://pymol.org/edu/?q=educational</td><td></td><td></td><td></td></tr></tbody></table>

modeling an enzyme active site using molecular visualization freeware

Biomolecular visualization skills are paramount to understanding key concepts in the biological sciences, such as structure-function relationships and molecular interactions. Various programs allow a learner to manipulate 3D structures, and biomolecular modeling promotes active learning, builds computational skills, and bridges the gap between two dimensional textbook images and the three dimensions of life. A critical skill in this area is to model a protein active site, displaying parts of the macromolecule that can interact with a small molecule, or ligand, in a way that shows binding interactions. In this protocol, we describe this process using four freely available macromolecular modeling programs: iCn3D, Jmol/JSmol, PyMOL, and UCSF ChimeraX. This guide is intended for students seeking to learn the basics of a specific program, as well as instructors incorporating biomolecular modeling into their curriculum. The protocol enables the user to model an active site using a specific visualization program, or to sample several of the free programs available. The model chosen for this protocol is human glucokinase, an isoform of the enzyme hexokinase, which catalyzes the first step of glycolysis. The enzyme is bound to one of its substrates, as well as a non-reactive substrate analog, which allows the user to analyze interactions in the catalytic complex.

A successfully executed protocol for each of the programs will result in a molecular model zoomed in on the active site, with active site residues and ligands shown as sticks, the protein cartoon hidden, and ligands displayed with a contrasting color scheme. Interacting amino acid residues should be labeled with their identifiers, and hydrogen bonding and ionic interactions shown with lines. The presence of these features can be determined by visual inspection of the model.
To facilitate this inspection and enable the user to determine whether they have performed the steps of the protocol correctly, we&#39;ve provided animated figures that present an image of the structure following each step. For ChimeraX, iCn3D, Jmol, and PyMOL, this is illustrated in Figures 7-10, respectively.
Figure 7: ChimeraX protocol output. Animated figure illustrating steps 1.1-1.8 of the ChimeraX protocol. <a href="https://www.jove.com/files/ftp_upload/63170/Figure 7.mp4" target="_blank">Please click here to download this figure.</a>
Figure 8: iCn3D protocol output. Animated figure illustrating steps 2.1-2.8 of the iCn3D protocol. <a href="https://www.jove.com/files/ftp_upload/63170/Figure 8.mp4" target="_blank">Please click here to download this figure.</a>
Figure 9: Jmol protocol output. Animated figure illustrating steps 3.1-3.8 of the Jmol protocol. <a href="https://www.jove.com/files/ftp_upload/63170/Figure 9.mp4" target="_blank">Please click here to download this figure.</a>
Figure 10: PyMOL protocol output. Animated figure illustrating steps 4.1-4.8 of the PyMOL protocol. <a href="https://www.jove.com/files/ftp_upload/63170/Figure 10.mp4" target="_blank">Please click here to download this figure.</a>
The most common error that can influence the outcome of these protocols is erroneous selection, resulting in part of the structure being displayed in an undesired rendering. This is typically a result of mis-clicking, either on the structure itself, or in one of the display menu buttons. An example of a suboptimal result would be a model containing residues outside of the active site displayed as sticks. The user can begin to analyze if this error has occurred by visually inspecting the residues displayed as sticks and ensuring that they are in the proximity of the active site ligands. An advanced method to evaluate whether or not the displayed residues are within 5&#197; of the active site ligands is to use the measurement tools built into each program to measure the distance between a nearby ligand and the active site residue. The measurement tools are beyond the scope of this manuscript; however, we encourage interested users to explore the many online tutorials detailing this type of analysis.
We present a specific example of a sub-optimal execution of this protocol, resulting from a mis-click on the names/objects panel in the PyMOL. This error displays the entire protein as sticks, rather than showing only the active site using this representation, as illustrated in Figure 11.
<img alt="Figure 11" class="xfigimg" src="/files/ftp_upload/63170/63170fig11.jpg" /> 
Figure 11: Negative result. Example of a negative result. Mis-selecting the full cartoon in PyMOL and displaying sticks. <a href="https://www.jove.com/files/ftp_upload/63170/63170fig11large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
To troubleshoot, the user will need to hide the sticks for the entire model (labeled 3FGU in the names/object panel), and then show the stick representation for only the selection named &#34;active,&#34; using the hide and show buttons/commands in PyMOL. Recovering the model from this type of error is relatively straightforward once the user is able to create appropriate selections for different parts of the model and display and hide them effectively. It is tempting to restart the protocol and work through the steps another time; however, we encourage the user to not be afraid to go &#34;off script&#34; and experiment with the model. In our experience, working through display errors facilitates progress in understanding the modeling program.
A side-by-side display of the final output from a successfully-executed protocol for each program is shown in Figure 12. The views are oriented similarly to allow the user to compare the appearance of the models created in different programs.
<img alt="Figure 12" class="xfigimg" src="/files/ftp_upload/63170/63170fig12.jpg" /> 
Figure 12: Final structure comparison across programs. Comparison of the structure of each active site rendering at the end of the protocol. A: ChimeraX, B: iCn3D, C: Jmol, D: PyMOL. The PyMOL active site label includes all active site residues and the ligands. The other outputs have only hydrogen bonded side chains labeled. <a href="https://www.jove.com/files/ftp_upload/63170/63170fig12large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>

Watch this Scientific Journal Video about Modeling an Enzyme Active Site using Molecular Visualization Freeware at JoVE.com

Modeling an Enzyme Active Site using Molecular Visualization Freeware

A key skill in biomolecular modeling is displaying and annotating active sites in proteins. This technique is demonstrated using four popular free programs for macromolecular visualization: iCn3D, Jmol, PyMOL, and UCSF ChimeraX.

Biomolecular visualization skills are paramount to understanding key concepts in the biological sciences, such as structure-function relationships and ...

modeling-an-enzyme-active-site-using-molecular-visualization-freeware

Research

JoVE Journal

Biochemistry

9.5K Views.  The University of Texas at Austin. Het visualiseren van biologische macromoleculen is een kritische vaardigheid voor studenten en professionals in de biologische wetenschappen. In dit protocol laten we zien hoe we de actieve plaats van het enzym glucokinase kunnen modelleren met behulp van vier vrij beschikbare programma's voor moleculaire modellering. Deze tutorial belicht verschillende stappen van het protocol voor elk programma, waaronder het selecteren van gebonden liganden en het gebruik van de liganden om aminozuren en w...

Video: Modeling an Enzyme Active Site using Molecular Visualization Freeware

Unraveling Entropic Rate Acceleration Induced by Solvent Dynamics in Membrane Enzymes

Enzyme catalysis evolved in an aqueous environment. The influence of solvent dynamics on catalysis is, however, currently poorly understood and usually neglected. The study of water dynamics in enzymes and the associated thermodynamical consequences is highly complex and has involved computer simulations, nuclear magnetic resonance (NMR) experiments, and calorimetry. Water tunnels that connect the active site with the surrounding solvent are key to solvent displacement and dynamics. The protocol herein allows for the engineering of these motifs for water transport, which affects specificity, activity and thermodynamics. By providing a biophysical framework founded on theory and experiments, the method presented herein can be used by researchers without previous expertise in computer modeling or biophysical chemistry. The method will advance our understanding of enzyme catalysis on the molecular level by measuring the enthalpic and entropic changes associated with catalysis by enzyme variants with obstructed water tunnels. The protocol can be used for the study of membrane-bound enzymes and other complex systems. This will enhance our understanding of the importance of solvent reorganization in catalysis as well as provide new catalytic strategies in protein design and engineering.

Channels for the transportation of water molecules in enzymes influence active site solvation and catalysis. Herein we present a protocol for the engineering of these additional catalytic motifs based on in silico computer modeling and experiments. This will enhance our understanding of the influence of solvent dynamics on enzyme catalysis.

Enzyme catalysis evolved in an aqueous environment. The influence of solvent dynamics on catalysis is, however, currently poorly understood and usually ...

Chemistry

Analyzing Protein Architectures and Protein-Ligand Complexes by Integrative Structural Mass Spectrometry

Proteins are an important class of biological macromolecules that play many key roles in cellular functions including gene expression, catalyzing metabolic reactions, DNA repair and replication. Therefore, a detailed understanding of these processes provides critical information on how cells function. Integrative structural MS methods offer structural and dynamical information on protein complex assembly, complex connectivity, subunit stoichiometry, protein oligomerization and ligand binding. Recent advances in integrative structural MS have allowed for the characterization of challenging biological systems including large DNA binding proteins and membrane proteins. This protocol describes how to integrate diverse MS data such as native MS and ion mobility-mass spectrometry (IM-MS) with molecular dynamics simulations to gain insights into a helicase-nuclease DNA repair protein complex. The resulting approach provides a framework for detailed studies of ligand binding to other protein complexes involved in important biological processes.

Mass spectrometry (MS) has emerged as an important tool for the investigation of structure and dynamics of macromolecular assemblies. Here, we integrate MS-based approaches to interrogate protein complex formation and ligand binding.

Proteins are an important class of biological macromolecules that play many key roles in cellular functions including gene expression, catalyzing ...

Deciphering the Structural Effects of Activating EGFR Somatic Mutations with Molecular Dynamics Simulation

Numerous somatic mutations occurring in the epidermal growth factor receptor (EGFR) family (ErbB) of receptor tyrosine kinases (RTK) have been reported from cancer patients, although relatively few have been tested and shown to cause functional changes in ErbBs. The ErbB receptors are dimerized and activated upon ligand binding, and dynamic conformational changes of the receptors are inherent for induction of downstream signaling. For two mutations shown experimentally to alter EGFR function, A702V and the &#916;746ELREA750 deletion mutation, we illustrate in the following protocol how molecular dynamics (MD) simulations can probe the (1) conformational stability of the mutant tyrosine kinase structure in comparison with wild-type EGFR; (2) structural consequences and conformational transitions and their relationship to observed functional changes; (3) effects of mutations on the strength of binding ATP as well as for binding between the kinase domains in the activated asymmetric dimer; and (4) effects of the mutations on key interactions within the EGFR binding site associated with the activated enzyme. The protocol provides a detailed step-by-step procedure as well as guidance that can be more generally useful for investigation of protein structures using MD simulations as a means to probe structural dynamics and the relationship to biological function.

The objective of this protocol is to use molecular dynamics simulations to examine the dynamic structural changes that occur due to activating mutations of the EGFR kinase protein.

Numerous somatic mutations occurring in the epidermal growth factor receptor (EGFR) family (ErbB) of receptor tyrosine kinases (RTK) have been reported ...

Structural Analyses of An Epidermal Growth Factor Receptor-Specific Single-Chain Fragment Variable via An In Silico Approach

Single-chain fragment variable (scFv) antibodies were previously constructed of variable light and heavy chains joined by a (Gly4-Ser) 3 linker. The linker was created using molecular modeling software as a loop structure. Here, we introduce a protocol forin silico analysis of a complete scFv antibody that interacts with the epidermal growth factor receptor (EGFR). The homology modeling, with Pyrx of protein-protein docking and molecular dynamic simulation of the interacting scFv antibody and EGFR First, the authors used a protein structure modeling program and Python for homology modeling, and the antibody scFv structure was modeled for homology. The investigators downloaded Pyrx software as a platform in the docking study. The Molecular dynamic simulation was run using modeling software. Results show that when the MD simulation was subjected to energy minimization, the protein model had the lowest binding energy (-5.4 kcal/M). In addition, the MD simulation in this study showed that the docked EGFR-scFv antibody was stable for 20-75 ns when the movement of the structure increased sharply to 7.2 &#197;. In conclusion, in silicoanalysiswas performed, and the molecular docking and molecular dynamics simulations of the scFv antibody proved the effectiveness of the designed immune-therapeutic drug scFv as a specific drug therapy for EGFR.

Structural Analyses of An Epidermal Growth Factor Receptor-Specific Single-Chain Fragment Variable via An In Silico Approach

This antibody homology modeling prediction protocol is followed by antibody-receptor Pyrx docking and molecular dynamic simulation. These three primary methods are used to visualize the accurate antibody-receptor binding areas and the binding stability of the final structure.

Single-chain fragment variable (scFv) antibodies were previously constructed of variable light and heavy chains joined by a (Gly4-Ser) 3 linker. The ...

Genetics

Visualizing Single Molecular Complexes In Vivo Using Advanced Fluorescence Microscopy

Full insight into the mechanisms of living cells can be achieved only by investigating the key processes that elicit and direct events at a cellular level. To date the shear complexity of biological systems has caused precise single-molecule experimentation to be far too demanding, instead focusing on studies of single systems using relatively crude bulk ensemble-average measurements. However, many important processes occur in the living cell at the level of just one or a few molecules; ensemble measurements generally mask the stochastic and heterogeneous nature of these events. Here, using advanced optical microscopy and analytical image analysis tools we demonstrate how to monitor proteins within a single living bacterial cell to a precision of single molecules and how we can observe dynamics within molecular complexes in functioning biological machines. The techniques are directly relevant physiologically. They are minimally-perturbative and non-invasive to the biological sample under study and are fully attuned for investigations in living material, features not readily available to other single-molecule approaches of biophysics. In addition, the biological specimens studied all produce fluorescently-tagged protein at levels which are almost identical to the unmodified cell strains ("genomic encoding"), as opposed to the more common but less ideal approach for generating significantly more protein than would occur naturally ('plasmid expression'). Thus, the actual biological samples which will be investigated are significantly closer to the natural organisms, and therefore the observations more relevant to real physiological processes.

Visualizing Single Molecular Complexes In Vivo Using Advanced Fluorescence Microscopy

Here we demonstrate the protocols for performing single-molecule fluorescence microscopy on living bacterial cells to enable functional molecular complexes to be detected, tracked and quantified.

Full insight into the mechanisms of living cells can be achieved only by investigating the key processes that elicit and direct events at a cellular ...

Biology

Crystallization and Structural Determination of an Enzyme:Substrate Complex by Serial Crystallography in a Versatile Microfluidic Chip

The preparation of well diffracting crystals and their handling before their X-ray analysis are two critical steps of biocrystallographic studies. We describe a versatile microfluidic chip that enables the production of crystals by the efficient method of counter-diffusion. The convection-free environment provided by the microfluidic channels is ideal for crystal growth and useful to diffuse a substrate into the active site of the crystalline enzyme. Here we applied this approach to the CCA-adding enzyme of the psychrophilic bacterium Planococcus halocryophilus in the presented example. After crystallization and substrate diffusion/soaking, the crystal structure of the enzyme:substrate complex was determined at room temperature by serial crystallography and the analysis of multiple crystals directly inside the chip. The whole procedure preserves the genuine diffraction properties of the samples because it requires no crystal handling.

A versatile microfluidic device is described that enables the crystallization of an enzyme using the counter-diffusion method, the introduction of a substrate in the crystals by soaking, and the 3D structure determination of the enzyme:substrate complex by a serial analysis of crystals inside the chip at room temperature.

The preparation of well diffracting crystals and their handling before their X-ray analysis are two critical steps of biocrystallographic studies. We ...

Modeling Small Molecule Ligands in CryoEM Maps of Macromolecules

Modeling Ligands into Maps Derived from Electron Cryomicroscopy

Deciphering the protein-ligand interactions in a macromolecular complex is crucial for understanding the molecular mechanism, underlying biological processes, and drug development. In recent years, cryogenic sample electron microscopy (cryoEM) has emerged as a powerful technique to determine the structures of macromolecules and to investigate the mode of ligand binding at near-atomic resolution. Identifying and modeling non-protein molecules in cryoEM maps is often challenging due to anisotropic resolution across the molecule of interest and inherent noise in the data. In this article, the readers are introduced to various software and methods currently used for ligand identification, model building, and refinement of atomic coordinates using selected macromolecules. One of the simplest ways to identify the presence of a ligand, as illustrated with the enolase enzyme, is to subtract the two maps obtained with and without the ligand. The extra density of the ligand is likely to stand out in the difference map even at a higher threshold. There are instances, as shown in the case of metabotropic Glutamate receptor mGlu5, when such simple difference maps cannot be generated. The recently introduced method of deriving the Fo-Fc omit map can serve as a tool for validating and demonstrating the presence of the ligand. Finally, using the well-studied &#946;-galactosidase as an example, the effect of resolution on modeling the ligands and solvent molecules in cryoEM maps is analyzed, and an outlook on how cryoEM can be used in drug discovery is presented.

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

This protocol introduces the tools available for modeling small-molecule ligands in cryoEM maps of macromolecules.

Deciphering the protein-ligand interactions in a macromolecular complex is crucial for understanding the molecular mechanism, underlying biological ...

Visual Dynamics: Simplifying Molecular Dynamics Simulations

New Features in Visual Dynamics 3.0

Visual Dynamics (VD) is a web tool that aims to facilitate the use and application of Molecular Dynamics (MD) executed in Gromacs, allowing users without computational familiarity to run short-time simulations for validation, demonstration, and teaching purposes. It is true that quantum methods are the most accurate. However, there is currently no computational feasibility to carry out the experiments that MD performs. The tool described here has continuously received improvements over the course of the last couple of years. This protocol will describe what is needed to run a simulation in VD with a protein-ligand complex previously prepared in ACPYPE and some general directions on the other simulation models available. For the detailed simulation, the FK506-binding protein from Plasmodium vivax complexed with the inhibitor D5 (PDB ID: 4mgv) will be used, and all files used will be provided. Note that this protocol will tell every option to be used to achieve the same results presented, but these options are not necessarily the only ones available.

Author Spotlight: Streamlining Visual Dynamics to Simplify Molecular Dynamics Simulations Using Gromacs

Visual Dynamics is an open-source tool that accelerates implementations and learning in molecular dynamics simulation using Gromacs. The presented protocol will guide you through the steps to perform a protein-ligand simulation prepared in ACPYPE with ease and general steps to other simulation models.

Visual Dynamics (VD) is a web tool that aims to facilitate the use and application of Molecular Dynamics (MD) executed in Gromacs, allowing users without ...

Analysis of Group IV Viral SSHHPS Using In Vitro and In Silico Methods

Alphaviral enzymes are synthesized in a single polypeptide. The nonstructural polyprotein (nsP) is processed by its nsP2 cysteine protease to produce active enzymes essential for viral replication. Viral proteases are highly specific and recognize conserved cleavage site motif sequences (~6-8 amino acids). In several Group IV viruses, the nsP protease(s) cleavage site motif sequences can be found in specific host proteins involved in generating the innate immune responses and, in some cases, the targeted proteins appear to be linked to the virus-induced phenotype. These viruses utilize short stretches of homologous host-pathogen protein sequences (SSHHPS) for targeted destruction of host proteins. To identify SSHHPS the viral protease cleavage site motif sequences can be inputted into BLAST and the host genome(s) can be searched. Cleavage initially can be tested using the purified nsP viral protease and fluorescence resonance energy transfer (FRET) substrates made in E. coli. The FRET substrates contain cyan and yellow fluorescent protein and the cleavage site sequence (CFP-sequence-YFP). This protease assay can be used continuously in a plate reader or discontinuously in SDS-PAGE gels. Models of the bound peptide substrates can be generated in silico to guide substrate selection and mutagenesis studies. CFP/YFP substrates have also been utilized to identify protease inhibitors. These in vitro and in silico methods can be used in combination with cell-based assays to determine if the targeted host protein affects viral replication.

We present a general protocol for identifying short stretches of homologous host-pathogen protein sequences (SSHHPS) embedded in the viral polyprotein. SSHHPS are recognized by viral proteases and direct the targeted destruction of specific host proteins by several Group IV viruses.

Alphaviral enzymes are synthesized in a single polypeptide. The nonstructural polyprotein (nsP) is processed by its nsP2 cysteine protease to produce ...

Immunology and Infection

Exploring Caspase Mutations and Post-Translational Modification by Molecular Modeling Approaches

Apoptosis is a type of programmed cell death that eliminates damaged cells and controls the development and tissue homeostasis of multicellular organisms. Caspases, a family of cysteine proteases, play a key role in apoptosis initiation and execution. The maturation of caspases and their activity is fine-tuned by post-translational modifications in a highly dynamic fashion. To assess the effect of post-translational changes, potential sites are routinely mutated with residues persistent to any modifications. For example, the serine residue is replaced with alanine or aspartic acid. However, such substitutions could alter the caspase active site's conformation, leading to disturbances in catalytic activity and cellular functions. Moreover, mutations of other amino acid residues located in critical positions could also break the structure and functions of caspases and lead to apoptosis perturbation. To avoid the difficulties of employing mutated residues, molecular modeling approaches can be readily applied to estimate the potential effect of amino acid substitutions on caspase structure. The present protocol allows the modeling of both the wild-type caspase and its mutant forms with the biomolecular simulation package (Amber) and supercomputer facilities to test the effect of mutations on the protein structure and function.

The present protocol uses a biomolecular simulation package and describes the molecular dynamics (MD) approach for modeling the wild-type caspase and its mutant forms. The MD method allows for assessing the dynamic evolution of the caspase structure and the potential effect of mutations or post-translational modifications.

Apoptosis is a type of programmed cell death that eliminates damaged cells and controls the development and tissue homeostasis of multicellular organisms. ...

Modeling an Enzyme Active Site using Molecular Visualization Freeware

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