SJ is a recipient of the PhD studentship from DAE-TIFR, and the funding is acknowledged. KRV acknowledges DBT B-Life grant DBT/PR12422/MED/31/287/2014 and the support of the Department of Atomic Energy, Government of India, under Project Identification No. RTI4006.

Modeling Small Molecule Ligands in CryoEM Maps of Macromolecules

The authors have nothing to disclose.

The improvements in microscope hardware and software have resulted in an increase in the number of cryoEM structures in recent years. Although the highest resolution achieved at the moment in single particle cryoEM is 1.2 &#197;57,58,59, the majority of the structures are being determined around 3-4 &#197; resolution. Modeling ligands in medium to low-resolution maps can be tricky and often fraught with ambiguity. Given the wide...

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.

<table><tbody><tr><td>CCP4-8.0</td><td>Consortium of several institutes</td><td>https://www.ccp4.ac.uk</td><td>Free for academic users and includes Coot and list of tools developed for X-ray crystallography</td></tr><tr><td>CCP-EM</td><td>Consortium of several institutes</td><td>https://www.ccpem.ac.uk/download.php</td><td>Free for academic users and includes Coot, Relion and many others</td></tr><tr><td>Coot&amp;nbsp;</td><td>Paul Emsley, LMB, Cambridge</td><td>https://www2.mrc-lmb.cam.ac.uk/personal/pemsley/coot/</td><td>General software for model building but also available with other suites described above</td></tr><tr><td>DockinMap (Phenix)</td><td>Consortium of several institutes</td><td>https://phenix-online.org/documentation/reference/dock_in_map.html</td><td>Software inside the Phenix suite for docking model into cryoEM maps</td></tr><tr><td>Electron Microscopy Data Bank&amp;nbsp;</td><td>Consortium of several institutes</td><td>https://www.ebi.ac.uk/emdb/</td><td>Public Repository for Electron Microscopy maps</td></tr><tr><td>Falcon</td><td>Thermo Fisher Scientific&amp;nbsp;</td><td>https://assets.thermofisher.com/TFS-Assets/MSD/Technical-Notes/Falcon-3EC-Datasheet.pdf</td><td>Commercial, camera from Thermo Fisher&amp;nbsp;</td></tr><tr><td>Phenix&amp;nbsp;</td><td>Consortium of several institutes</td><td>https://phenix-online.org/download</td><td>Free for academic users and includes Coot</td></tr><tr><td>Protein Data Bank</td><td>Consortium of several institutes</td><td>https://rcsb.org</td><td>Public database of macromolecular structures</td></tr><tr><td>Pymol</td><td>Schrodinger</td><td>https://pymol.org/2/</td><td>Molecular viusalization tool. Educational version is free but comes with limitation. The full version can be obtained with a small fee.</td></tr><tr><td>Relion</td><td>MRC-LMB, Cambridge</td><td>https://relion.readthedocs.io/en/release-4.0/Installation.html</td><td>Software for cryoEM image processing, also available with CCP-EM</td></tr><tr><td>Titan Krios</td><td>Thermo Fisher Scientific&amp;nbsp;</td><td>https://www.thermofisher.com/in/en/home/electron-microscopy/products/transmission-electron-microscopes/krios-g4-cryo-tem.html?cid=msd_ls_xbu_xmkt_tem-krios_285811_gl_pso_gaw_tpne1c&amp;amp;&lt;br/&gt; gad_source=1&amp;amp;gclid=CjwKCAiA-P-rBhBEEiwAQEXhHyw5c8MKThmdA&lt;br/&gt; AkZesWC4FYQSwIQRk&lt;br/&gt; ZApkj08MfYG040DtiiuL8&lt;br/&gt; RihoCebEQAvD_BwE</td><td>Commercial, cryoTEM from Thermo Fisher</td></tr><tr><td>UCSF Chimera</td><td>UCSF, USA</td><td>https://www.cgl.ucsf.edu/chimera/download.html</td><td>General purpose software for display, analysis and more</td></tr><tr><td>UCSF Chimera X</td><td>UCSF, USA</td><td>https://www.cgl.ucsf.edu/chimerax/</td><td>General purpose software for display, analysis and more</td></tr></tbody></table>

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.

Example 1 
The enzyme enolase from M. tuberculosis catalyzes the penultimate step of glycolysis and converts 2-phosphoglycerate to phosphoenolpyruvate (PEP), which is an essential intermediate for several metabolic pathways44,45. CryoEM data for the apo-enolase and PEP-bound enolase samples were collected at the same pixel size of 1.07 &#197;, and image processing was performed with Relion 3.146,<s...

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.

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 ...

modeling-ligands-into-maps-derived-from-electron-cryomicroscopy

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JoVE Journal

Biochemistry

1.2K Views.  Tata Institute of Fundamental Research. This protocol introduces the tools available for modeling small-molecule ligands in cryoEM maps of macromolecules.

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

High-resolution Single Particle Analysis from Electron Cryo-microscopy Images Using SPHIRE

SPHIRE (SPARX for High-Resolution Electron Microscopy) is a novel open-source, user-friendly software suite for the semi-automated processing of single particle electron cryo-microscopy (cryo-EM) data. The protocol presented here describes in detail how to obtain a near-atomic resolution structure starting from cryo-EM micrograph movies by guiding users through all steps of the single particle structure determination pipeline. These steps are controlled from the new SPHIRE graphical user interface and require minimum user intervention. Using this protocol, a 3.5 &#197; structure of TcdA1, a Tc toxin complex from Photorhabdus luminescens, was derived from only 9500 single particles. This streamlined approach will help novice users without extensive processing experience and a priori structural information, to obtain noise-free and unbiased atomic models of their purified macromolecular complexes in their native state.

This paper presents a protocol for processing cryo-EM images using the software suite SPHIRE. The present protocol can be applied for nearly all single particle EM projects that target near-atomic resolution.

SPHIRE (SPARX for High-Resolution Electron Microscopy) is a novel open-source, user-friendly software suite for the semi-automated processing of single ...

Measuring Biomolecular DSC Profiles with Thermolabile Ligands to Rapidly Characterize Folding and Binding Interactions

Differential scanning calorimetry (DSC) is a powerful technique for quantifying thermodynamic parameters governing biomolecular folding and binding interactions. This information is critical in the design of new pharmaceutical compounds. However, many pharmaceutically relevant ligands are chemically unstable at the high temperatures used in DSC analyses. Thus, measuring binding interactions is challenging because the concentrations of ligands and thermally-converted products are constantly changing within the calorimeter cell. Here, we present a protocol using thermolabile ligands and DSC for rapidly obtaining thermodynamic and kinetic information on the folding, binding, and ligand conversion processes. We have applied our method to the DNA aptamer MN4 that binds to the thermolabile ligand cocaine. Using a new global fitting analysis that accounts for thermolabile ligand conversion, the complete set of folding and binding parameters are obtained from a pair of DSC experiments. In addition, we show that the rate constant for thermolabile ligand conversion may be obtained with only one supplementary DSC dataset. The guidelines for identifying and analyzing data from several more complicated scenarios are presented, including irreversible aggregation of the biomolecule, slow folding, slow binding, and rapid depletion of the thermolabile ligand.

We present a protocol for rapid characterization of biomolecular folding and binding interactions with thermolabile ligands using differential scanning calorimetry.

Differential scanning calorimetry (DSC) is a powerful technique for quantifying thermodynamic parameters governing biomolecular folding and binding ...

Semi-automated Biopanning of Bacterial Display Libraries for Peptide Affinity Reagent Discovery and Analysis of Resulting Isolates

Biopanning bacterial display libraries is a proven technique for peptide affinity reagent discovery for recognition of both biotic and abiotic targets. Peptide affinity reagents can be used for similar applications to antibodies, including sensing and therapeutics, but are more robust and able to perform in more extreme environments. Specific enrichment of peptide capture agents to a protein target of interest is enhanced using semi-automated sorting methods which improve binding and wash steps and therefore decrease the occurrence of false positive binders. A semi-automated sorting method is described herein for use with a commercial automated magnetic-activated cell sorting device with an unconstrained bacterial display sorting library expressing random 15-mer peptides. With slight modifications, these methods are extendable to other automated devices, other sorting libraries, and other organisms. A primary goal of this work is to provide a comprehensive methodology and expound the thought process applied in analyzing and minimizing the resulting pool of candidates. These techniques include analysis of on-cell binding using fluorescence-activated cell sorting (FACS), to assess affinity and specificity during sorting and in comparing individual candidates, and the analysis of peptide sequences to identify trends and consensus sequences for understanding and potentially improving the affinity to and specificity for the target of interest.

Biopanning bacterial display libraries is a proven technique for discovery of peptide affinity reagents, a robust alternative to antibodies. The semi-automated sorting method herein has streamlined biopanning to decrease the occurrence of false positives. Here we illustrate the thought process and techniques applied in evaluating candidates and minimizing downstream analysis.

Biopanning bacterial display libraries is a proven technique for peptide affinity reagent discovery for recognition of both biotic and abiotic targets. ...

Analyzing Dynamic Protein Complexes Assembled On and Released From Biolayer Interferometry Biosensor Using Mass Spectrometry and Electron Microscopy

In vivo, proteins are often part of large macromolecular complexes where binding specificity and dynamics ultimately dictate functional outputs. In this work, the pre-endosomal anthrax toxin is assembled and transitioned into the endosomal complex. First, the N-terminal domain of a cysteine mutant lethal factor (LFN) is attached to a biolayer interferometry (BLI) biosensor through disulfide coupling in an optimal orientation, allowing protective antigen (PA) prepore to bind (Kd 1 nM). The optimally oriented LFN-PAprepore complex then binds to soluble capillary morphogenic gene-2 (CMG2) cell surface receptor (Kd 170 pM), resulting in a representative anthrax pre-endosomal complex, stable at pH 7.5. This assembled complex is then subjected to acidification (pH 5.0) representative of the late endosome environment to transition the PAprepore into the membrane inserted pore state. This PApore state results in a weakened binding between the CMG2 receptor and the LFN-PApore and a substantial dissociation of CMG2 from the transition pore. The thio-attachment of LFN to the biosensor surface is easily reversed by dithiothreitol. Reduction on the BLI biosensor surface releases the LFN-PAprepore-CMG2 ternary complex or the acid transitioned LFN-PApore complexes into microliter volumes. Released complexes are then visualized and identified using electron microscopy and mass spectrometry. These experiments demonstrate how to monitor the kinetic assembly/disassembly of specific protein complexes using label-free BLI methodologies and evaluate the structure and identity of these BLI assembled complexes by electron microscopy and mass spectrometry, respectively, using easy-to-replicate sequential procedures.

Here we present a protocol to monitor the assembly and disassembly of the anthrax toxin using biolayer interferometry (BLI). Following assembly/disassembly on the biosensor surface, the large protein complexes are released from the surface for visualization and identification of components of the complexes using electron microscopy and mass spectrometry, respectively.

In vivo, proteins are often part of large macromolecular complexes where binding specificity and dynamics ultimately dictate functional outputs. In this ...

Analysis of &#946;-Amyloid-induced Abnormalities on Fibrin Clot Structure by Spectroscopy and Scanning Electron Microscopy

This article presents methods for generating in vitro fibrin clots and analyzing the effect of beta-amyloid (A&#946;) protein on clot formation and structure by spectrometry and scanning electron microscopy (SEM). A&#946;, which forms neurotoxic amyloid aggregates in Alzheimer&#39;s disease (AD), has been shown to interact with fibrinogen. This A&#946;-fibrinogen interaction makes the fibrin clot structurally abnormal and resistant to fibrinolysis. A&#946;-induced abnormalities in fibrin clotting may also contribute to cerebrovascular aspects of the AD pathology such as microinfarcts, inflammation, as well as, cerebral amyloid angiopathy (CAA). Given the potentially critical role of neurovascular deficits in AD pathology, developing compounds which can inhibit or lessen the A&#946;-fibrinogen interaction has promising therapeutic value. In vitro methods by which fibrin clot formation can be easily and systematically assessed are potentially useful tools for developing therapeutic compounds. Presented here is an optimized protocol for in vitro generation of the fibrin clot, as well as analysis of the effect of A&#946; and A&#946;-fibrinogen interaction inhibitors. The clot turbidity assay is rapid, highly reproducible and can be used to test multiple conditions simultaneously, allowing for the screening of large numbers of A&#946;-fibrinogen inhibitors. Hit compounds from this screening can be further evaluated for their ability to ameliorate A&#946;-induced structural abnormalities of the fibrin clot architecture using SEM. The effectiveness of these optimized protocols is demonstrated here using TDI-2760, a recently identified A&#946;-fibrinogen interaction inhibitor.

Presented here are two methods that can be used individually or in combination to analyze the effect of beta-amyloid on fibrin clot structure. Included is a protocol for creating an in vitro fibrin clot, followed by clot turbidity and scanning electron microscopy methods.

This article presents methods for generating in vitro fibrin clots and analyzing the effect of beta-amyloid (A&#946;) protein on clot formation and ...

Expression and Purification of the Human Lipid-sensitive Cation Channel TRPC3 for Structural Determination by Single-particle Cryo-electron Microscopy

Transient receptor potential channels (TRPCs) of the canonical TRP subfamily are nonselective cation channels that play an essential role in calcium homeostasis, particularly store-operated calcium entry, which is critical to maintaining proper function of synaptic vesicle release and intracellular signaling pathways. Accordingly, TRPC channels have been implicated in a variety of human diseases including cardiovascular disorders such as cardiac hypertrophy, neurodegenerative disorders such as Parkinson&#39;s disease, and neurologic disorders such as spinocerebellar ataxia. Therefore, TRPC channels represent a potential pharmacologic target in human diseases. However, the molecular mechanisms of gating in these channels are still unclear. The difficulty in obtaining large quantities of stable, homogeneous, and purified protein has been a limiting factor in structure determination studies, particularly for mammalian membrane proteins such as the TRPC ion channels. Here, we present a protocol for the large-scale expression of mammalian ion channel membrane proteins using a modified baculovirus gene transfer system and the purification of these proteins by affinity and size-exclusion chromatography. We further present a protocol to collect single-particle cryo-electron microscopy images from purified protein and to use these images to determine the protein structure. Structure determination is a powerful method for understanding the mechanisms of gating and function in ion channels.

This protocol describes techniques used to determine ion channel structures by cryo-electron microscopy, including a baculovirus system used to efficiently express genes in mammalian cells with minimum effort and toxicity, protein extraction, purification, and quality checking, sample grid preparation and screening, as well as data collection and processing.

Transient receptor potential channels (TRPCs) of the canonical TRP subfamily are nonselective cation channels that play an essential role in calcium ...

Single Particle Cryo-Electron Microscopy: From Sample to Structure

Cryo-electron microscopy (cryoEM) is a powerful technique for structure determination of macromolecular complexes, via single particle analysis (SPA). The overall process involves i) vitrifying the specimen in a thin film supported on a cryoEM grid; ii) screening the specimen to assess particle distribution and ice quality; iii) if the grid is suitable, collecting a single particle dataset for analysis; and iv) image processing to yield an EM density map. In this protocol, an overview for each of these steps is provided, with a focus on the variables which a user can modify during the workflow and the troubleshooting of common issues. With remote microscope operation becoming standard in many facilities, variations on imaging protocols to assist users in efficient operation and imaging when physical access to the microscope is limited will be described.

Structure determination of macromolecular complexes using cryoEM has become routine for certain classes of proteins and complexes. Here, this pipeline is summarized (sample preparation, screening, data acquisition and processing) and readers are directed towards further detailed resources and variables that may be altered in the case of more challenging specimens.

Cryo-electron microscopy (cryoEM) is a powerful technique for structure determination of macromolecular complexes, via single particle analysis (SPA). The ...

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 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 ...

Simplified Volumetric Models as an Effective Strategy for Segmenting Actin Networks in Cryo-Electron Tomograms

Efficient methods for the extraction of features of interest remain one of the biggest challenges for the interpretation of cryo-electron tomograms. Various automated approaches have been proposed, many of which work well for high-contrast datasets where the features of interest can be easily detected and are clearly separated from one another. Our inner ear stereocilia cryo-electron tomographic datasets are characterized by a dense array of hexagonally packed actin filaments that are frequently cross-connected. These features make automated segmentation very challenging, further aggravated by the high-noise environment of cryo-electron tomograms and the high complexity of the densely packed features. Using prior knowledge about the actin bundle organization, we have placed layers of a highly simplified ball-and-stick actin model to first obtain a global fit to the density map, followed by regional and local adjustments of the model. We show that volumetric model building not only allows us to deal with the high complexity, but also provides precise measurements and statistics about the actin bundle. Volumetric models also serve as anchoring points for local segmentation, such as in the case of the actin-actin cross connectors. Volumetric model building, particularly when further augmented by computer-based automated fitting approaches, can be a powerful alternative when conventional automated segmentation approaches are not successful.

Here, we present a protocol to place simplified volumetric models into noisy, complex, tomographic 3D volumes. This allows the fast segmentation of actin filament densities, detection of systematic filament bending and of gaps in hair bundle filaments, as well as convenient quantification of volumetric model properties, such as distances.

Efficient methods for the extraction of features of interest remain one of the biggest challenges for the interpretation of cryo-electron tomograms. ...