scholarly journals SimpleDSFviewer: a tool to analyse and view differential scanning fluorimetry data for characterising protein thermal stability and interactions

2015 ◽  
Author(s):  
Changye Sun ◽  
Yong Li ◽  
Edwin A Yates ◽  
David G Fernig
Keyword(s):  
Data Analysis ◽  
Fibroblast Growth Factors ◽  
Fibroblast Growth ◽  
Protein Ligand Interactions ◽  
Melting Curves ◽  
Differential Scanning Fluorimetry ◽  
Ligand Interactions ◽  
Protein Thermal Stability ◽  
User Friendly ◽  
Thermal Shift

Differential scanning fluorimetry (DSF) is used widely as a thermal shift assay to study protein stability and protein-ligand interactions. The benefit of DSF is that it is simple, cheap and can generate melting curves in 96-well plates providing good throughput. However, data analysis remains a challenge, and requires different methods to optimise and analyse the collected raw data. Here, the program SimpleDSFviewer is introduced to help view and analyse DSF data in an efficient way and with a user-friendly interface. The data analysis, optimisation and view methods provided by the program are described, using sample melting curves of fibroblast growth factors.

scholarly journals SimpleDSFviewer: a tool to analyse and view differential scanning fluorimetry data for characterising protein thermal stability and interactions

2015 ◽  
Author(s):  
Changye Sun ◽  
Yong Li ◽  
Edwin A Yates ◽  
David G Fernig
Keyword(s):  
Data Analysis ◽  
Fibroblast Growth Factors ◽  
Fibroblast Growth ◽  
Protein Ligand Interactions ◽  
Melting Curves ◽  
Differential Scanning Fluorimetry ◽  
Ligand Interactions ◽  
Protein Thermal Stability ◽  
User Friendly ◽  
Thermal Shift

Differential scanning fluorimetry (DSF) is used widely as a thermal shift assay to study protein stability and protein-ligand interactions. The benefit of DSF is that it is simple, cheap and can generate melting curves in 96-well plates providing good throughput. However, data analysis remains a challenge, and requires different methods to optimise and analyse the collected raw data. Here, the program SimpleDSFviewer is introduced to help view and analyse DSF data in an efficient way and with a user-friendly interface. The data analysis, optimisation and view methods provided by the program are described, using sample melting curves of fibroblast growth factors.

scholarly journals Enlighten2: molecular dynamics simulations of protein–ligand systems made accessible

2020 ◽  
Vol 36 (20) ◽  
pp. 5104-5106
Author(s):  
Kirill Zinovjev ◽  
Marc W van der Kamp
Keyword(s):  
Molecular Dynamics ◽  
Expert Knowledge ◽  
Structural Data ◽  
Md Simulations ◽  
Enzyme Engineering ◽  
Protein Ligand Interactions ◽  
Ligand Interactions ◽  
Dynamics Simulations ◽  
User Friendly ◽  
Dynamical Information

Abstract Motivation Experimental structural data can allow detailed insight into protein structure and protein–ligand interactions, which is crucial for many areas of bioscience, including drug design and enzyme engineering. Typically, however, little more than a static picture of protein–ligand interactions is obtained, whereas dynamical information is often required for deeper understanding and to assess the effect of mutations. Molecular dynamics (MD) simulations can provide such information, but setting up and running these simulations is not straightforward and requires expert knowledge. There is thus a need for a tool that makes protein–ligand simulation easily accessible to non-expert users. Results We present Enlighten2: efficient simulation protocols for protein–ligand systems alongside a user-friendly plugin to the popular visualization program PyMOL. With Enlighten2, non-expert users can straightforwardly run and visualize MD simulations on protein–ligand models of interest. There is no need to learn new programs and all underlying tools are free and open source. Availability and implementation The Enlighten2 Python package and PyMOL plugin are free to use under the GPL3.0 licence and can be found at https://enlighten2.github.io. We also provide a lightweight Docker image via DockerHub that includes Enlighten2 with all the required utilities.

scholarly journals MolADI: A Web Server for Automatic Analysis of Protein–Small Molecule Dynamic Interactions

Molecules ◽  
2021 ◽  
Vol 26 (15) ◽  
pp. 4625
Author(s):  
Bing Bai ◽  
Rongfeng Zou ◽  
H. C. Stephen Chan ◽  
Hongchun Li ◽  
Shuguang Yuan
Keyword(s):  
Protein Design ◽  
Web Application ◽  
Interaction Analysis ◽  
Complex Structure ◽  
Dynamic Interactions ◽  
Ligand Interaction ◽  
Protein Ligand Interactions ◽  
Ligand Interactions ◽  
Single Structure ◽  
User Friendly

Protein–ligand interaction analysis is important for drug discovery and rational protein design. The existing online tools adopt only a single conformation of the complex structure for calculating and displaying the interactions, whereas both protein residues and ligand molecules are flexible to some extent. The interactions evolved with time in the trajectories are of greater interest. MolADI is a user-friendly online tool which analyzes the protein–ligand interactions in detail for either a single structure or a trajectory. Interactions can be viewed easily with both 2D graphs and 3D representations. MolADI is available as a web application.

scholarly journals Inflect: Optimizing Computational Workflows for Thermal Proteome Profiling Data Analysis

2020 ◽  
Author(s):  
Neil A. McCracken ◽  
Sarah A. Peck Justice ◽  
Aruna B. Wijeratne ◽  
Amber L. Mosley
Keyword(s):  
Data Analysis ◽  
Proteome Profiling ◽  
Protein Ligand Interactions ◽  
Calculation Step ◽  
Ligand Interactions ◽  
Analysis Workflow ◽  
Cellular Context ◽  
Essential Resource ◽  
Key Steps ◽  
The Impact

ABSTRACTThe use of CETSA and Thermal Proteome Profiling (TPP) analytical methods are invaluable for the study of protein-ligand interactions and protein stability in a cellular context. These tools have increasingly been leveraged in work ranging from understanding signaling paradigms to drug discovery. Consequently, there is an important need to optimize the data analysis pipeline that is used to calculate protein melt temperatures (Tm) and relative melt shifts from proteomics abundance data. Here we report a user-friendly analysis of the melt shift calculation workflow where we describe the impact of each individual calculation step on the final output list of stabilized and destabilized proteins. This report also includes a description of how key steps in the analysis workflow quantitatively impacts the list of stabilized/destabilized proteins from an experiment. We applied our findings to develop a more optimized analysis workflow that illustrates the dramatic sensitivity of chosen calculation steps on the final list of reported proteins of interest in a study and will make the R based program Inflect available for research community use. Overall, this work provides an essential resource for scientists as they analyze data from TPP and CETSA experiments and implement their own analysis pipelines geared towards specific applications.

scholarly journals Enlighten2: Molecular Dynamics Simulations of Protein-Ligand Systems Made Accessible

2020 ◽  
Author(s):  
Kirill Zinovjev ◽  
Marc W. van der Kamp
Keyword(s):  
Molecular Dynamics ◽  
Expert Knowledge ◽  
Md Simulations ◽  
Enzyme Engineering ◽  
X Ray Crystallography ◽  
Protein Ligand Interactions ◽  
Ligand Interactions ◽  
Dynamics Simulations ◽  
User Friendly ◽  
Dynamical Information

X-ray crystallography allows detailed insight into protein structure and protein-ligand interactions, which is crucial for many areas of bioscience, including drug design and enzyme engineering. However, crystal structures provide little more than a static picture of protein-ligand interactions, whereas dynamical information is often required for deeper understanding and to assess the effect of mutations. Molecular dynamics (MD) simulations can provide such information but setting up and running these simulations is not straightforward and requires expert knowledge. There is thus a need for a tool that makes proteinligand simulation easily accessible to non-expert users. We present Enlighten2: efficient simulation protocols for protein-ligand systems alongside a user-friendly plugin to the popular visualization program PyMOL. With Enlighten2, non-expert users can straightforwardly run and visualize MD simulations on protein-ligand models of interest. There is no need to learn new programs and all underlying tools are free and open source. The Enlighten2 python package and PyMOL plugin are free to use under the GPL3.0 licence and can be found at enlighten2.github.io. We also provide a lightweight Docker image via DockerHub that includes Enlighten2 with all the required utilities. <br>

scholarly journals Protein–ligand interactions investigated by thermal shift assays (TSA) and dual polarization interferometry (DPI)

2015 ◽  
Vol 71 (1) ◽  
pp. 36-44 ◽  
Author(s):  
Morten K. Grøftehauge ◽  
Nelly R. Hajizadeh ◽  
Marcus J. Swann ◽  
Ehmke Pohl
Keyword(s):  
Spatial Information ◽  
Thermodynamic Description ◽  
Titration Calorimetry ◽  
Dual Polarization ◽  
Polarization Interferometry ◽  
Protein Ligand Interactions ◽  
Wide Range ◽  
Dual Polarization Interferometry ◽  
Ligand Interactions ◽  
Thermal Shift

Over the last decades, a wide range of biophysical techniques investigating protein–ligand interactions have become indispensable tools to complement high-resolution crystal structure determinations. Current approaches in solution range from high-throughput-capable methods such as thermal shift assays (TSA) to highly accurate techniques including microscale thermophoresis (MST) and isothermal titration calorimetry (ITC) that can provide a full thermodynamic description of binding events. Surface-based methods such as surface plasmon resonance (SPR) and dual polarization interferometry (DPI) allow real-time measurements and can provide kinetic parameters as well as binding constants. DPI provides additional spatial information about the binding event. Here, an account is presented of new developments and recent applications of TSA and DPI connected to crystallography.

scholarly journals Enlighten2: Molecular Dynamics Simulations of Protein-Ligand Systems Made Accessible

2020 ◽  
Author(s):  
Kirill Zinovjev ◽  
Marc W. van der Kamp
Keyword(s):  
Molecular Dynamics ◽  
Expert Knowledge ◽  
Structural Data ◽  
Md Simulations ◽  
Enzyme Engineering ◽  
Protein Ligand Interactions ◽  
Ligand Interactions ◽  
Dynamics Simulations ◽  
User Friendly ◽  
Dynamical Information

<div>Motivation: Experimental structural data can allow detailed insight into protein structure and protein-ligand interactions, which is crucial for many areas of bioscience, including drug design and enzyme engineering. Typically, however, little more than a static picture of protein-ligand interactions is obtained, whereas dynamical information is often required for deeper understanding and to assess the effect of mutations. Molecular dynamics (MD) simulations can provide such information, but setting up and running these simulations is not straightforward and requires expert knowledge. There is thus a need for a tool that makes protein-ligand simulation easily accessible to non-expert users.</div><div>Results: We present Enlighten2: efficient simulation protocols for protein-ligand systems alongside a user-friendly plugin to the popular visualization program PyMOL. With Enlighten2, non-expert users can straightforwardly run and visualize MD simulations on protein-ligand models of interest. There is no need to learn new programs and all underlying tools are free and open source.</div><div>Availability: The Enlighten2 Python package and PyMOL plugin are free to use under the GPL3.0 licence and can be found at https://enlighten2.github.io. We also provide a lightweight Docker image via DockerHub that includes Enlighten2 with all the required utilities.</div>

scholarly journals Enlighten2: Molecular Dynamics Simulations of Protein-Ligand Systems Made Accessible

2020 ◽  
Author(s):  
Kirill Zinovjev ◽  
Marc W. van der Kamp
Keyword(s):  
Molecular Dynamics ◽  
Expert Knowledge ◽  
Structural Data ◽  
Md Simulations ◽  
Enzyme Engineering ◽  
Protein Ligand Interactions ◽  
Ligand Interactions ◽  
Dynamics Simulations ◽  
User Friendly ◽  
Dynamical Information

<div>Motivation: Experimental structural data can allow detailed insight into protein structure and protein-ligand interactions, which is crucial for many areas of bioscience, including drug design and enzyme engineering. Typically, however, little more than a static picture of protein-ligand interactions is obtained, whereas dynamical information is often required for deeper understanding and to assess the effect of mutations. Molecular dynamics (MD) simulations can provide such information, but setting up and running these simulations is not straightforward and requires expert knowledge. There is thus a need for a tool that makes protein-ligand simulation easily accessible to non-expert users.</div><div>Results: We present Enlighten2: efficient simulation protocols for protein-ligand systems alongside a user-friendly plugin to the popular visualization program PyMOL. With Enlighten2, non-expert users can straightforwardly run and visualize MD simulations on protein-ligand models of interest. There is no need to learn new programs and all underlying tools are free and open source.</div><div>Availability: The Enlighten2 Python package and PyMOL plugin are free to use under the GPL3.0 licence and can be found at https://enlighten2.github.io. We also provide a lightweight Docker image via DockerHub that includes Enlighten2 with all the required utilities.</div>

Justifying the data analytical choice in single case research in relation to the expected data pattern

2019 ◽  
Author(s):  
Rumen Manolov
Keyword(s):  
Data Analysis ◽  
Visual Analysis ◽  
Multilevel Models ◽  
Single Case ◽  
Analytical Techniques ◽  
Real Data ◽  
Small Scale ◽  
Data User ◽  
Single Case Research ◽  
User Friendly

The lack of consensus regarding the most appropriate analytical techniques for single-case experimental designs data requires justifying the choice of any specific analytical option. The current text mentions some of the arguments, provided by methodologists and statisticians, in favor of several analytical techniques. Additionally, a small-scale literature review is performed in order to explore if and how applied researchers justify the analytical choices that they make. The review suggests that certain practices are not sufficiently explained. In order to improve the reporting regarding the data analytical decisions, it is proposed to choose and justify the data analytical approach prior to gathering the data. As a possible justification for data analysis plan, we propose using as a basis the expected the data pattern (specifically, the expectation about an improving baseline trend and about the immediate or progressive nature of the intervention effect). Although there are multiple alternatives for single-case data analysis, the current text focuses on visual analysis and multilevel models and illustrates an application of these analytical options with real data. User-friendly software is also developed.

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