This work has been supported by the The Funda&#231;&#227;o Oswaldo Cruz (Fiocruz), the Funda&#231;&#227;o para o Desenvolvimento Cient&#237;fico e Tecnol&#243;gico em Sa&#250;de (Fiotec), the Instituto Nacional de Ci&#234;ncia e Tecnologia de Epidemiologia da Amaz&#244;nia Ocidental - INCT-EpiAmO, the Funda&#231;&#227;o Rond&#244;nia de Amparo ao Desenvolvimento das A&#231;&#245;es Cient&#237;ficas e Tecnol&#243;gicas e &#224; Pesquisa do Estado de Rond&#244;nia (FAPERO), the Coordena&#231;&#227;o de Aperfei&#231;oamento de Pessoal de N&#237;vel Superior (CAPES) and the Conselho Nacional de Desenvolvimento Cient&#237;fico e Tecnol&#243;gico (CNPq).

1. Accessing the software and new user registration
<ol>
	<li>Visit the Visual Dynamics (VD) web page. Click on the +Register icon at the top right to create an account. Register to use the software. 
	NOTE: Only institutional email addresses are allowed. The user will receive an email notification once their registration is approved.</li>
	<li>Click on Login at the top right to access the system login screen. Fill in the username/email and password fields and click Log in. After logging in, the user will have access to their simulation submission area. They can also view tutorials and usage statistics for VD.</li>
</ol>
2. Apoenzyme simulation submission
<ol>
	<li>Click on New Simulation in the left sidebar. On the screen that appears, click on the button APO, which refers to the apoenzyme.</li>
	<li>Upload the free protein 4mvg.pdb file. Select the AMBER94 force field (or any other appropriate option). 
	NOTE: 4mvg.pdb can be obtained from Supplementary File 1 or downloaded directly from the Protein Data Bank (PDB).</li>
	<li>Select the TIP3P Water Model. Select the Cubic Box. Select 0.5 nm distance between protein and box edge. 
	NOTE: The options selected are just suggestions. All other options work in VD.</li>
	<li>Check the option Run in Our Servers to execute the simulation with the submitted files and parameters. After clicking on Run simulation, the user will see the evolution of the simulation steps on the screen and will receive an email notification about the simulation status upon completion.</li>
</ol>
3. Submission of Simulation of Enzyme Complexed with Ligand Prepared in ACPYPE
<ol>
	<li>Using UCSF Chimera20, open the protein-ligand complex 4mgv.pdb41, under Select, click Residue, and set the code to D5I. Then, under File, click Save PDB, select Save Selected Atoms Only, set the file name to ligand.pdb, and click Save.</li>
	<li>Submit the file ligand.pdb generated in the previous step to the Bio2Byte ACPYPE server42, from the output files, ligand_NEW.itp and ligand_NEW.pdb will be the ones used and provided in this experiment. 
	NOTE: The ligand_NEW.itp and ligand_NEW.pdb files can be obtained from Supplementary File 2, and Supplementary File 3.</li>
	<li>Click on New Simulation on the left sidebar. Click on the button Protein + Ligand (prepared in ACPYPE).</li>
	<li>Upload the free protein 4mvg.pdb file. Select the Ligand Files prepared in ACPYPE: ligand_NEW.itp and ligand_NEW.pdb. Select the AMBER94 force field.</li>
	<li>Select the TIP3P Water Model. Select the Cubic Box. Select 0.5 nm distance between protein and box edge. The options selected are just suggestions. All other options work in VD.</li>
	<li>Check the option Run in Our Servers to execute the simulation with the submitted files and parameters. After clicking on Run simulation, the user will see the evolution of the simulation steps on the screen and will receive an email notification about the simulation status upon completion.</li>
</ol>
4. Accessing the simulation results
<ol>
	<li>Click on My Simulations at the left sidebar.</li>
	<li>Click on Download MDP Files to download the simulation configuration files used by the platform to the user&#39;s computer.</li>
	<li>Download simulation elements as described below.
	<ol>
		<li>Click Commands to download the list of commands executed by the platform to the user&#39;s computer.</li>
		<li>Click GROMACS Log to download the log file containing .gmx command outputs in sequence to the user&#39;s computer.</li>
		<li>Click Results to download files generated by .gmx commands, such as _npt.gro, _pr.edr, _pr.tpr, _pr_PBC.gro, and pr_PBC.xtc to the user&#39;s computer.</li>
		<li>Click Figure Graphics to download graphs for analyzing each simulation step in image and .xvg format to the user&#39;s computer.</li>
	</ol>
	</li>
</ol>

Visual Dynamics: Simplifying Molecular Dynamics Simulations

<ol>
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ECMAScript 2020 language specification, 11th edition Available from: <a target="_blank" href="https://www.ecma-international.org/publications-and-standards/standards/ecma-262/">https://www.ecma-international.org/publications-and-standards/standards/ecma-262/</a> (2023)</li><li>Pieper, U., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=ModBase,+a+database+of+annotated+comparative+protein+structure+models,+and+associated+resources.">ModBase, a database of annotated comparative protein structure models, and associated resources.</a> Nucleic Acids Res. 39, D465-D474 (2011).</li><li>Kiefer, F., Arnold, K., Kunzli, M., Bordoli, L., Schwede, T. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+SWISS-MODEL+Repository+and+associated+resources.">The SWISS-MODEL Repository and associated resources.</a> Nucleic Acids Res. 37, D387-D392 (2009).</li><li>De Vries, S. 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V., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=QwikMD+-+Integrative+molecular+dynamics+toolkit+for+novices+and+experts.">QwikMD - Integrative molecular dynamics toolkit for novices and experts.</a> Sci Rep. 6 (1), 26536 (2016).</li><li>Rodr&#237;guez Mart&#237;nez, A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> ASGARD. 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F. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=PRODRG+a+tool+for+high-throughput+crystallography+of+protein-ligand+complexes.">PRODRG a tool for high-throughput crystallography of protein-ligand complexes.</a> Acta Crystallographica Sect D Biol Crystallography. 60 (8), 1355-1363 (2004).</li><li>Harikishore, A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Adamantyl+derivative+as+a+potent+inhibitor+of+Plasmodium+FK506+binding+protein+35.">Adamantyl derivative as a potent inhibitor of Plasmodium FK506 binding protein 35.</a> ACS Med Chem Lett. 4 (11), 1097-1101 (2013).</li><li>Sousa Da Silva, A. W., Vranken, W. 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The authors have nothing to disclose.

Automating processes is not easy, but it is also less difficult than reprogramming a system from scratch. Gromacs is currently the most popular molecular simulation software, and it is constantly updated. The Department of Biophysical Chemistry at Groningen University initially developed it, and it is now maintained by the Life Sciences Laboratory at the University of Stockholm43.
For any new user, learning simulation techniques is a lengthy journey. VD emerges as an al...

According to the IUPAC definition, MD is the simulation procedure that consists of computing the motion of atoms in a molecule or of individual atoms or molecules in solids, liquids, and gases, according to Newton&#39;s laws of motion. The forces acting on atoms, necessary to simulate their motion, are commonly calculated using force fields from molecular mechanics1. It can be applied to any phenomenon that seeks to extract information at a molecular and often atomic level2.
MD is one of the techniques incorporated into bioinformatics, specifically structural bioinformatics. With it, it is possible to obtain kinetic and thermodynamic characteristics of biomolecular structures. For example, macromolecular stability, identification of allosteric sites, elucidation of mechanisms of enzymatic activity, molecular recognition and properties of complexes with small molecules, association between proteins, protein folding, and its hydration3. Furthermore, MD enables a wide range of studies, including molecular design (widely used in drug design), in determining the structure and its refinement (X-ray, NMR, and protein modeling)3. The results obtained at the end of an MD are the richest and most complete in terms of non-quantum simulation4.Classical MD is much more efficient than might be expected from a full consideration of the physics of biomolecular systems due to the number of substantial approximations. Notably, quantum dynamical effects are usually ignored3. However, implementing an MD experiment is not trivial5. It requires knowledge of computing, especially the Linux Terminal, as most structural bioinformatics software is made for it. Even with that knowledge, learning Gromacs commands and parametrization is another steep learning curve.
Since its first application to biology in 19776, much has evolved due to increased computational processing and improved coding. More than two decades ago, the first MD software intended for biological problems was launched, namely Gromacs7, AMBER8, and NAMD9.
Since their first versions, these software still remain the most used and cited. However, they continue with the same common implementation difficulties that plague researchers who are not computer specialists5.&#160;Some have complex installation and configuration steps, sometimes requiring extensive knowledge about the hardware it will run on to get the most out of it and highly computer-centric technical documentation. An easier way to interface with them, aside from the command line and infinite parameters, is needed.
An interface acts as an intermediary between the logical process to be performed and the human10. The paradigm of how software is executed has evolved as computing resources have improved. The first digital paradigm was the command line interfaces (CLI) followed by the evolution to the known graphical user interfaces (GUI)11. Following the evolutionary cycle, the interface produced by the World Wide Web (or simply WEB) is considered an evolution of GUIs11. These three paradigms currently co-exist depending on developers. CLI applications use textual commands on the operating system console. GUI applications, also called graphical desktops, use a graphical interface made up of windows, buttons, and other components. It is specific and pre-programmed for an operating system. The main difference from the CLI is the use of the mouse as an additional element in human-machine interaction12. WEB applications, despite being confused with a GUI, are more complex to develop but are more versatile and by far the most agile in operation. Furthermore, they only depend on an interpreter software called a browser, which makes it possible for the client application to communicate with the server through a network independent of the operating system13.
Structural bioinformatics software most commonly use CLI and GUI paradigms. Some examples of classic software that use CLI are Modeller14 for similarity modeling, Autodock15 for molecular docking, and Gromacs16 for molecular dynamics. Examples of software that adopt the GUI type are SwissPDBviewer17, Pymol18, VMD19, UCSF Chimera20, Autodock tools15, PyRx21, Biovia22, Maestro23, and Moe24, among others.
With the emergence of Hypertext Markup Language version 5 (HTML5)25, Cascading Style Sheets (CSS)26, and Javascript27 technologies, among others, many structural bioinformatics applications could be brought to the WEB, thus becoming more accessible. Examples of similarity modeling WEB servers are MODWEB28, which uses Modeller14 as a back-end and Swissmodel29. Examples of web application servers for molecular docking are Haddock30, Swissdock31, Cluspro32, Dockthor33, and others.
While structural analysis, modeling, and docking methodologies evolved from CLI paradigms to GUI and finally to WEB, MD continues to be mostly supported by command line execution (CLI type). Some good initiatives have emerged to improve this panorama. Examples of these initiatives are the implementation of plugins in existing software, such as QwikMD plugin to VMD34, GROMACS Plugin to PyMOL, and the Molecular Dynamics Simulation option in UCSF Chimera20, some new and easier CLI applications, such as ASGARD35, Gmx_qk36, and CHAPERONg37, and a robust web platform, BioBB-Wfs38.&#160;Although the use of these plugins and applications is an advance, their implementation is still a challenge for most unskilled researchers. Common difficulties include problems installing and configuring the MD software, which often compromise the full execution of the simulation5.
In 2022, the Visual Dynamics software for web-based computational simulation was made available by the Laborat&#243;rio de Bioinform&#225;tica e Qu&#237;mica Medicinal at Fiocruz Rond&#244;nia39. Its initial version was built in Python and Flask, allowing simulations of systems with free proteins (apoenzymes) for only 2 ns. Subsequently, it was enhanced to include an automated simulation version with ligands prepared using PRODRG40.
VD was built to assist all researchers in the field of structural biophysics, biotechnology, and related areas who have limitations in computational knowledge; the tool allows these researchers to test their hypotheses involving MD simulations from any operational system and without access to a high-performance computer (HPC). The purpose of this work is to present the new features of Visual Dynamics version 3.0. Additionally, it aims to introduce an updated usage protocol for the tool and highlight the limitations to be addressed in the future, along with usage statistics up to the present moment (Figure 1).

<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>ACPYPE Server</td>
			<td>Bio2Byte</td>
			<td></td>
			<td>Available at https://www.bio2byte.be/acpype/</td>
		</tr>
		<tr>
			<td>GRACE software</td>
			<td>Plasma Laboratory at the Weizmann Institute of Science</td>
			<td></td>
			<td>Available at https://plasma-gate.weizmann.ac.il/Grace/</td>
		</tr>
		<tr>
			<td>GROMACS software</td>
			<td>GROMACS Team</td>
			<td></td>
			<td>Installation instructions at https://manual.gromacs.org/current/install-guide/index.html</td>
		</tr>
		<tr>
			<td>The structure of the FK506-binding protein 
			From Plasmodium vivax complexed with the 
			inhibitor D5</td>
			<td>RCSB Protein Data Bank</td>
			<td></td>
			<td>Available at https://www.rcsb.org/structure/4mgv 
			Already contains the ligand complexed to the macromolecule.</td>
		</tr>
	</tbody>
</table>

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.

VD provides a fully autonomous simulation execution that does not require user intervention or user-provided computational resources. After submitting a simulation to execution, the user can leave it, turn off their machines, and the simulation will continue running. It also allows users to access the results from any device, be it a laptop or mobile device.
As an example of using VD in automated mode through the WEB, the test was made for a protein-ligand complex prepared in ACPYPE using the ...

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.

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 ...

Research

JoVE Journal

Biochemistry

736 Views.  Oswaldo Cruz Rondônia Foundation. 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.

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

Time-resolved ElectroSpray Ionization Hydrogen-deuterium Exchange Mass Spectrometry for Studying Protein Structure and Dynamics

Intrinsically disordered proteins (IDPs) have long been a challenge to structural biologists due to their lack of stable secondary structure elements. Hydrogen-Deuterium Exchange (HDX) measured at rapid time scales is uniquely suited to detect structures and hydrogen bonding networks that are briefly populated, allowing for the characterization of transient conformers in native ensembles. Coupling of HDX to mass spectrometry offers several key advantages, including high sensitivity, low sample consumption and no restriction on protein size. This technique has advanced greatly in the last several decades, including the ability to monitor HDX labeling times on the millisecond time scale. In addition, by incorporating the HDX workflow onto a microfluidic platform housing an acidic protease microreactor, we are able to localize dynamic properties at the peptide level. In this study, Time-Resolved ElectroSpray Ionization Mass Spectrometry (TRESI-MS) coupled to HDX was used to provide a detailed picture of residual structure in the tau protein, as well as the conformational shifts induced upon hyperphosphorylation.

Conformational flexibility plays a critical role in protein function. Herein, we describe the use of time-resolved electrospray ionization mass spectrometry coupled to hydrogen-deuterium exchange for probing the rapid structural changes that drive function in ordered and disordered proteins.

Intrinsically disordered proteins (IDPs) have long been a challenge to structural biologists due to their lack of stable secondary structure elements. ...

Investigating Protein Sequence-structure-dynamics Relationships with Bio3D-web

We demonstrate the usage of Bio3D-web for the interactive analysis of biomolecular structure data. The Bio3D-web application provides online functionality for: (1) The identification of related protein structure sets to user specified thresholds of similarity; (2) Their multiple alignment and structure superposition; (3) Sequence and structure conservation analysis; (4) Inter-conformer relationship mapping with principal component analysis, and (5) comparison of predicted internal dynamics via ensemble normal mode analysis. This integrated functionality provides a complete online workflow for investigating sequence-structure-dynamic relationships within protein families and superfamilies.

A protocol for the online investigation of protein sequence-structure-dynamics relationships using Bio3D-web is presented.

We demonstrate the usage of Bio3D-web for the interactive analysis of biomolecular structure data. The Bio3D-web application provides online functionality ...

Nitrogen Cavitation and Differential Centrifugation Allows for Monitoring the Distribution of Peripheral Membrane Proteins in Cultured Cells

Cultured cells are useful for studying the subcellular distribution of proteins, including peripheral membrane proteins. Genetically encoded fluorescently tagged proteins have revolutionized the study of subcellular protein distribution. However, it is difficult to quantify the distribution with fluorescent microscopy, especially when proteins are partially cytosolic. Moreover, it is often important to study endogenous proteins. Biochemical assays such as immunoblots remain the gold standard for quantification of protein distribution after subcellular fractionation. Although there are commercial kits that aim to isolate cytosolic or certain membrane fractions, most of these kits are based on extraction with detergents, which may be unsuitable for studying peripheral membrane proteins that are easily extracted from membranes. Here we present a detergent-free protocol for cellular homogenization by nitrogen cavitation and subsequent separation of cytosolic and membrane-bound proteins by ultracentrifugation. We confirm the separation of subcellular organelles in soluble and pellet fractions across different cell types, and compare protein extraction among several common non-detergent-based mechanical homogenization methods. Among several advantages of nitrogen cavitation is the superior efficiency of cellular disruption with minimal physical and chemical damage to delicate organelles. Combined with ultracentrifugation, nitrogen cavitation is an excellent method to examine the shift of peripheral membrane proteins between cytosolic and membrane fractions.

Here we present protocols for detergent-free homogenization of cultured mammalian cells based on nitrogen cavitation and subsequent separation of cytosolic and membrane-bound proteins by ultracentrifugation. This method is ideal for monitoring the partitioning of peripheral membrane proteins between soluble and membrane fractions.

Cultured cells are useful for studying the subcellular distribution of proteins, including peripheral membrane proteins. Genetically encoded fluorescently ...

Identification of Cyclin-dependent Kinase 1 Specific Phosphorylation Sites by an In Vitro Kinase Assay

Cyclin-dependent kinase 1 (Cdk1) is a master controller for the cell cycle in all eukaryotes and phosphorylates an estimated 8 - 13% of the proteome; however, the number of identified targets for Cdk1, particularly in human cells is still low. The identification of Cdk1-specific phosphorylation sites is important, as they provide mechanistic insights into how Cdk1 controls the cell cycle. Cell cycle regulation is critical for faithful chromosome segregation, and defects in this complicated process lead to chromosomal aberrations and cancer.
Here, we describe an in vitro kinase assay that is used to identify Cdk1-specific phosphorylation sites. In this assay, a purified protein is phosphorylated in vitro by commercially available human Cdk1/cyclin B. Successful phosphorylation is confirmed by SDS-PAGE, and phosphorylation sites are subsequently identified by mass spectrometry. We also describe purification protocols that yield highly pure and homogeneous protein preparations suitable for the kinase assay, and a binding assay for the functional verification of the identified phosphorylation sites, which probes the interaction between a classical nuclear localization signal (cNLS) and its nuclear transport receptor karyopherin &#945;. To aid with experimental design, we review approaches for the prediction of Cdk1-specific phosphorylation sites from protein sequences. Together these protocols present a very powerful approach that yields Cdk1-specific phosphorylation sites and enables mechanistic studies into how Cdk1 controls the cell cycle. Since this method relies on purified proteins, it can be applied to any model organism and yields reliable results, especially when combined with cell functional studies.

Identification of Cyclin-dependent Kinase 1 Specific Phosphorylation Sites by an In Vitro Kinase Assay

Cyclin-dependent kinase 1 (Cdk1) is activated in the G2 phase of the cell cycle and regulates many cellular pathways. Here, we present a protocol for an in vitro kinase assay with Cdk1, which allows the identification of Cdk1-specific phosphorylation sites for establishing cellular targets of this important kinase.

Cyclin-dependent kinase 1 (Cdk1) is a master controller for the cell cycle in all eukaryotes and phosphorylates an estimated 8 - 13% of the proteome; ...

Dissipative Microgravimetry to Study the Binding Dynamics of the Phospholipid Binding Protein Annexin A2 to Solid-supported Lipid Bilayers Using a Quartz Resonator

The dissipative quartz crystal microbalance technique is a simple and label-free approach to measure simultaneously the mass uptake and viscoelastic properties of the absorbed/immobilized mass on sensor surfaces, allowing the measurements of the interaction of proteins with solid-supported surfaces, such as lipid bilayers, in real-time and with a high sensitivity. Annexins are a highly conserved group of phospholipid-binding proteins that interact reversibly with the negatively charged headgroups via the coordination of calcium ions. Here, we describe a protocol that was employed to quantitatively analyze the binding of annexin A2 (AnxA2) to planar lipid bilayers prepared on the surface of a quartz sensor. This protocol is optimized to obtain robust and reproducible data and includes a detailed step-by-step description. The method can be applied to other membrane-binding proteins and bilayer compositions.

Here, we present an experimental protocol that can be employed to determine the binding affinities and mode of interaction of label-free phospholipid-binding protein annexin A2 with immobilized solid-supported bilayers (SLB) by simultaneously measuring the mass uptake and the viscoelastic properties of the protein annexin A2.

The dissipative quartz crystal microbalance technique is a simple and label-free approach to measure simultaneously the mass uptake and viscoelastic ...

Proteome-wide Quantification of Labeling Homogeneity at the Single Molecule Level

Cell proteomes are often characterized using electrophoresis assays, where all species of proteins in the cells are non-specifically labeled with a fluorescent dye and are spotted by a photodetector following their separation. Single molecule fluorescence imaging can provide ultrasensitive protein detection with its ability for visualizing individual fluorescent molecules. However, the application of this powerful imaging method to electrophoresis assays is hampered by the lack of ways to characterize the homogeneity of fluorescent labeling of each protein species across the proteome. Here, we developed a method to evaluate the labeling homogeneity across the proteome based on a single molecule fluorescence imaging assay. In our measurement using a HeLa cell sample, the proportion of proteins labeled with at least one dye, which we termed &#8216;labeling occupancy&#8217; (LO), was determined to range from 50% to 90%, supporting the high potential of the application of single molecule imaging to sensitive and precise proteome analysis.

Here, we present a protocol to assess the labeling homogeneity for each protein species in a complex protein sample at the single molecule level.

Cell proteomes are often characterized using electrophoresis assays, where all species of proteins in the cells are non-specifically labeled with a ...

Quantification of Protein Interaction Network Dynamics using Multiplexed Co-Immunoprecipitation

Dynamic protein-protein interactions control cellular behavior, from motility to DNA replication to signal transduction. However, monitoring dynamic interactions among multiple proteins in a protein interaction network is technically difficult. Here, we present a protocol for Quantitative Multiplex Immunoprecipitation (QMI), which allows quantitative assessment of fold changes in protein interactions based on relative fluorescence measurements of Proteins in Shared Complexes detected by Exposed Surface epitopes (PiSCES). In QMI, protein complexes from cell lysates are immunoprecipitated onto microspheres, and then probed with a labeled antibody for a different protein in order to quantify the abundance of PiSCES. Immunoprecipitation antibodies are conjugated to different MagBead spectral regions, which allows a flow cytometer to differentiate multiple parallel immunoprecipitations and simultaneously quantify the amount of probe antibody associated with each. QMI does not require genetic tagging and can be performed using minimal biomaterial compared to other immunoprecipitation methods. QMI can be adapted for any defined group of interacting proteins, and has thus far been used to characterize signaling networks in T cells and neuronal glutamate synapses. Results have led to new hypothesis generation with potential diagnostic and therapeutic applications. This protocol includes instructions to perform QMI, from the initial antibody panel selection through to running assays and analyzing data. The initial assembly of a QMI assay involves screening antibodies to generate a panel, and empirically determining an appropriate lysis buffer. The subsequent reagent preparation includes covalently coupling immunoprecipitation antibodies to MagBeads, and biotinylating probe antibodies so they can be labeled by a streptavidin-conjugated fluorophore. To run the assay, lysate is mixed with MagBeads overnight, and then beads are divided and incubated with different probe antibodies, and then a fluorophore label, and read by flow cytometry. Two statistical tests are performed to identify PiSCES that differ significantly between experimental conditions, and results are visualized using heatmaps or node-edge diagrams.

Quantitative Multiplex Immunoprecipitation (QMI) uses flow cytometry for sensitive detection of differences in the abundance of targeted protein-protein interactions between two samples. QMI can be performed using a small amount of biomaterial, does not require genetically engineered tags, and can be adapted for any previously defined protein interaction network.

Dynamic protein-protein interactions control cellular behavior, from motility to DNA replication to signal transduction. However, monitoring dynamic ...

Detection of Total Reactive Oxygen Species in Adherent Cells by 2',7'-Dichlorodihydrofluorescein Diacetate Staining

Oxidative stress is an important event under both physiological and pathological conditions. In this study, we demonstrate how to quantify oxidative stress by measuring total reactive oxygen species (ROS) using 2&#39;,7&#39;-dichlorodihydrofluorescein diacetate (DCFH-DA) staining in colorectal cancer cell lines as an example. This protocol describes detailed steps including preparation of DCFH-DA solution, incubation of cells with DCFH-DA solution, and measurement of normalized intensity. DCFH-DA staining is a simple and cost-effective way to detect ROS in cells. It can be used to measure ROS generation after chemical treatment or genetic modifications. Therefore, it is useful for determining cellular oxidative stress upon environment stress, providing clues to mechanistic studies.

Here, we present a protocol to detect total cellular reactive oxygen species (ROS) using 2&#39;,7&#39;-dichlorodihydrofluorescein diacetate (DCFH-DA). This method can visualize cellular ROS localization in adherent cells with a fluorescence microscope and quantify ROS intensity with a fluorescence plate reader. This protocol is simple, efficient and cost-effective.

Oxidative stress is an important event under both physiological and pathological conditions. In this study, we demonstrate how to quantify oxidative ...

Monitoring Protein-RNA Interaction Dynamics In Vivo at High Temporal Resolution Using &#967;CRAC

The interaction between RNA-binding proteins (RBPs) and their RNA substrates exhibits fluidity and complexity. Within its lifespan, a single RNA can be bound by many different RBPs that will regulate its production, stability, activity, and degradation. As such, much has been done to understand the dynamics that exist between these two types of molecules. A particularly important breakthrough came with the emergence of &#8216;cross-linking and immunoprecipitation&#8217; (CLIP). This technique allowed stringent investigation into which RNAs are bound by a particular RBP. In short, the protein of interest is UV cross-linked to its RNA substrates in vivo, purified under highly stringent conditions, and then the RNAs covalently cross-linked to the protein are converted into cDNA libraries and sequenced. Since its conception, many derivative techniques have been developed in order to make CLIP amenable to particular fields of study. However, cross-linking using ultraviolet light is notoriously inefficient. This results in extended exposure times that make the temporal study of RBP-RNA interactions impossible. To overcome this issue, we recently designed and built much-improved UV irradiation and cell harvesting devices. Using these new tools, we developed a protocol for time-resolved analyses of RBP-RNA interactions in living cells at high temporal resolution: Kinetic CRoss-linking and Analysis of cDNAs (&#967;CRAC). We recently used this technique to study the role of yeast RBPs in nutrient stress adaptation. This manuscript provides a detailed overview of the &#967;CRAC method and presents recent results obtained with the Nrd1 RBP.

Kinetic cross-linking and analysis of cDNA is a method that allows investigation of the dynamics of protein-RNA interactions in living cells at high temporal resolution. Here the protocol is described in detail, including the growth of yeast cells, UV cross-linking, harvesting, protein purification, and next generation sequencing library preparation steps.

The interaction between RNA-binding proteins (RBPs) and their RNA substrates exhibits fluidity and complexity. Within its lifespan, a single RNA can be ...

CD Spectroscopy to Study DNA-Protein Interactions

Circular dichroism (CD) spectroscopy is a simple and convenient method to investigate the secondary structure and interactions of biomolecules. Recent advancements in CD spectroscopy have enabled the study of DNA-protein interactions and conformational dynamics of DNA in different microenvironments in detail for a better understanding of transcriptional regulation in vivo. The area around a potential transcription zone needs to be unwound for transcription to occur. This is a complex process requiring the coordination of histone modifications, binding of the transcription factor to DNA, and other chromatin remodeling activities. Using CD spectroscopy, it is possible to study conformational changes in the promoter region caused by regulatory proteins, such as ATP-dependent chromatin remodelers, to promote transcription. The conformational changes occurring in the protein can also be monitored. In addition, queries regarding the affinity of the protein towards its target DNA and sequence specificity can be addressed by incorporating mutations in the target DNA. In short, the unique understanding of this sensitive and inexpensive method can predict changes in chromatin dynamics, thereby improving the understanding of transcriptional regulation.

The interaction of an ATP-dependent chromatin remodeler with a DNA ligand is described using CD spectroscopy. The induced conformational changes on a gene promoter analyzed by the peaks generated can be used to understand the mechanism of transcriptional regulation.

Circular dichroism (CD) spectroscopy is a simple and convenient method to investigate the secondary structure and interactions of biomolecules. Recent ...

New Features in Visual Dynamics 3.0

Summary

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Chapters in this video

Time-resolved ElectroSpray Ionization Hydrogen-deuterium Exchange Mass Spectrometry for Studying Protein Structure and Dynamics

Investigating Protein Sequence-structure-dynamics Relationships with Bio3D-web

Nitrogen Cavitation and Differential Centrifugation Allows for Monitoring the Distribution of Peripheral Membrane Proteins in Cultured Cells

Identification of Cyclin-dependent Kinase 1 Specific Phosphorylation Sites by an In Vitro Kinase Assay

Dissipative Microgravimetry to Study the Binding Dynamics of the Phospholipid Binding Protein Annexin A2 to Solid-supported Lipid Bilayers Using a Quartz Resonator

Proteome-wide Quantification of Labeling Homogeneity at the Single Molecule Level

Quantification of Protein Interaction Network Dynamics using Multiplexed Co-Immunoprecipitation

Detection of Total Reactive Oxygen Species in Adherent Cells by 2',7'-Dichlorodihydrofluorescein Diacetate Staining

Monitoring Protein-RNA Interaction Dynamics In Vivo at High Temporal Resolution Using χCRAC

CD Spectroscopy to Study DNA-Protein Interactions