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 A systematic analysis of atomic protein–ligand interactions in the PDB

MedChemComm ◽  
2017 ◽  
Vol 8 (10) ◽  
pp. 1970-1981 ◽  
Author(s):  
Renato Ferreira de Freitas ◽  
Matthieu Schapira
Keyword(s):  
Small Molecule ◽  
Interaction Type ◽  
Systematic Analysis ◽  
Protein Ligand Interactions ◽  
Atom Pairs ◽  
Atomic Interactions ◽  
Small Molecule Ligands ◽  
Ligand Interactions ◽  
The Impact ◽  
Protein Atom

We compiled a list of 11 016 unique structures of small-molecule ligands bound to proteins representing 750 873 protein–ligand atomic interactions, and analyzed the frequency, geometry and the impact of each interaction type. The most frequent ligand–protein atom pairs can be clustered into seven interaction types.

scholarly journals Probing molecular specificity with deep sequencing and biophysically interpretable machine learning

2021 ◽  
Author(s):  
H. Tomas Rube ◽  
Chaitanya Rastogi ◽  
Siqian Feng ◽  
Judith Franziska Kribelbauer ◽  
Allyson Li ◽  
...  
Keyword(s):  
Machine Learning ◽  
Biological Networks ◽  
Specific Protein ◽  
Biophysical Parameters ◽  
Kinase Substrate ◽  
Protein Ligand Interactions ◽  
Sequence Recognition ◽  
Ligand Interactions ◽  
The Impact

Quantifying sequence-specific protein-ligand interactions is critical for understanding and exploiting numerous cellular processes, including gene regulation and signal transduction. Next-generation sequencing (NGS) based assays are increasingly being used to profile these interactions with high-throughput. However, these assays do not provide the biophysical parameters that have long been used to uncover the quantitative rules underlying sequence recognition. We developed a highly flexible machine learning framework, called ProBound, to define sequence recognition in terms of biophysical parameters based on NGS data. ProBound quantifies transcription factor (TF) behavior with models that accurately predict binding affinity over a range exceeding that of previous resources, captures the impact of DNA modifications and conformational flexibility of multi-TF complexes, and infers specificity directly from in vivo data such as ChIP-seq without peak calling. When coupled with a new assay called Kd-seq, it determines the absolute affinity of protein-ligand interactions. It can also profile the kinetics of kinase-substrate interactions. By constructing a biophysically robust foundation for profiling sequence recognition, ProBound opens up new avenues for decoding biological networks and rationally engineering protein-ligand interactions.

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 Processing binding data using an open-source workflow

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Errol L. G. Samuel ◽  
Secondra L. Holmes ◽  
Damian W. Young
Keyword(s):  
Open Source ◽  
Protein Ligand Interactions ◽  
Open Source Data ◽  
Binding Data ◽  
Wide Range ◽  
Ligand Discovery ◽  
Ligand Interactions ◽  
Analysis Workflow ◽  
Set Up ◽  
Programming Knowledge

AbstractThe thermal shift assay (TSA)—also known as differential scanning fluorimetry (DSF), thermofluor, and Tm shift—is one of the most popular biophysical screening techniques used in fragment-based ligand discovery (FBLD) to detect protein–ligand interactions. By comparing the thermal stability of a target protein in the presence and absence of a ligand, potential binders can be identified. The technique is easy to set up, has low protein consumption, and can be run on most real-time polymerase chain reaction (PCR) instruments. While data analysis is straightforward in principle, it becomes cumbersome and time-consuming when the screens involve multiple 96- or 384-well plates. There are several approaches that aim to streamline this process, but most involve proprietary software, programming knowledge, or are designed for specific instrument output files. We therefore developed an analysis workflow implemented in the Konstanz Information Miner (KNIME), a free and open-source data analytics platform, which greatly streamlined our data processing timeline for 384-well plates. The implementation is code-free and freely available to the community for improvement and customization to accommodate a wide range of instrument input files and workflows. Graphical Abstract

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.

Atomic resolution structure of biotin-free Tyr43Phe streptavidin: what is in the binding site?

1999 ◽  
Vol 55 (6) ◽  
pp. 1118-1126 ◽  
Author(s):  
Stefanie Freitag ◽  
Isolde Le Trong ◽  
Lisa A. Klumb ◽  
Patrick S. Stayton ◽  
Ronald E. Stenkamp
Keyword(s):  
Binding Pocket ◽  
Crystal Form ◽  
Atomic Resolution ◽  
Side Chain ◽  
High Resolution Data ◽  
Protein Ligand Interactions ◽  
Ligand Interactions ◽  
Biotin Binding ◽  
The Impact ◽  
First Time

The streptavidin–biotin system is an example of a high-affinity protein–ligand pair (Ka ≃ 1013 mol−1). The thermodynamic and structural properties have been extensively studied as a model system for protein–ligand interactions. Here, the X-ray crystal structure of a streptavidin mutant of a residue hydrogen bonding to biotin [Tyr43Phe (Y43F)] is reported at atomic resolution (1.14 Å). The biotin-free structure was refined with anisotropic displacement parameters (using the SHELXL97 program package). The high-resolution data also allowed interpretation of side-chain and residue disorder in 41 residues where alternate conformations were refined. The Y43F mutation is unambiguously observed in difference maps, although only a single O atom per monomer is altered. The atomic resolution enabled the identification of 2-methyl-2,4-pentanediol (MPD) molecules in the biotin-binding pocket for the first time. Electron density for MPD was observed in all four subunit binding sites of the tetrameric protein. This was not possible with data at lower resolution (1.8–2.3 Å) for wild-type streptavidin or mutants in the same crystal form using MPD in the crystallization. The impact of MPD binding on these studies is discussed.

INDONESIA LANGUAGE INTERFERENCE FORM IN BASIC PPL LEARNING THAILAND UNHASY COLLEGE SYUDENTS

2020 ◽  
Vol 8 (2) ◽  
pp. 38
Author(s):  
Siti Mariana Ulfa
Keyword(s):  
Qualitative Research ◽  
Data Analysis ◽  
Data Collection ◽  
Teaching Practice ◽  
Data Presentation ◽  
Analysis Techniques ◽  
Thai Students ◽  
The Impact ◽  
Collection Methods ◽  
Recording Techniques

AbstractHumans on earth need social interaction with others. Humans can use more than one language in communication. Thus, the impact that arises when the use of one or more languages is the contact between languages. One obvious form of contact between languages is interference. Interference can occur at all levels of life. As in this study, namely Indonesian Language Interference in Learning PPL Basic Thailand Unhasy Students. This study contains the form of interference that occurs in Thai students who are conducting teaching practices in the classroom. This type of research is descriptive qualitative research that seeks to describe any interference that occurs in the speech of Thai students when teaching practice. Data collection methods in this study are (1) observation techniques, (2) audio-visual recording techniques using CCTV and (3) recording techniques, by recording all data that has been obtained. Whereas the data wetness uses, (1) data triangulation, (2) improvement in perseverance and (3) peer review through discussion. Data analysis techniques in this study are (1) data collection, (2) data reduction, (3) data presentation and (4) conclusions. It can be seen that the interference that occurs includes (1) interference in phonological systems, (2) interference in morphological systems and (3) interference in syntactic systems. 

19F-NMR in Target-based Drug Discovery

2019 ◽  
Vol 26 (26) ◽  
pp. 4964-4983 ◽  
Author(s):  
CongBao Kang
Keyword(s):  
Drug Discovery ◽  
Conformational Changes ◽  
Protein Structures ◽  
19F Nmr ◽  
Binding Affinities ◽  
Protein Ligand Interactions ◽  
Compound Libraries ◽  
Ligand Interactions ◽  
Reference Ligand ◽  
Hit Identification

Solution NMR spectroscopy plays important roles in understanding protein structures, dynamics and protein-protein/ligand interactions. In a target-based drug discovery project, NMR can serve an important function in hit identification and lead optimization. Fluorine is a valuable probe for evaluating protein conformational changes and protein-ligand interactions. Accumulated studies demonstrate that 19F-NMR can play important roles in fragment- based drug discovery (FBDD) and probing protein-ligand interactions. This review summarizes the application of 19F-NMR in understanding protein-ligand interactions and drug discovery. Several examples are included to show the roles of 19F-NMR in confirming identified hits/leads in the drug discovery process. In addition to identifying hits from fluorinecontaining compound libraries, 19F-NMR will play an important role in drug discovery by providing a fast and robust way in novel hit identification. This technique can be used for ranking compounds with different binding affinities and is particularly useful for screening competitive compounds when a reference ligand is available.

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