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 PackIO and EphysViewer: software tools for acquisition and analysis of neuroscience data

2016 ◽  
Author(s):  
Brendon O. Watson ◽  
Rafael Yuste ◽  
Adam M. Packer
Keyword(s):  
Open Source ◽  
General Purpose ◽  
Analysis Tool ◽  
Temporal Precision ◽  
Open Source Data ◽  
Source Data ◽  
Timing Data ◽  
Multiple Devices ◽  
Event Times ◽  
Set Up

AbstractWe present an open-source synchronization software package, PackIO, that can record and generate voltage signals to enable complex experimental paradigms across multiple devices. This general purpose package is built on National Instruments data acquisition and generation hardware and has temporal precision up to the limit of the hardware. PackIO acts as a flexibly programmable master clock that can record experimental data (e.g. voltage traces), timing data (e.g. event times such as imaging frame times) while generating stimuli (e.g. voltage waveforms, voltage triggers to drive other devices, etc.). PackIO is particularly useful to record from and synchronize multiple devices, for example when simultaneously acquiring electrophysiology while generating and recording imaging timing data. Experimental control is easily enabled by an intuitive graphical user interface. We also release an open-source data visualisation and analysis tool, EphysViewer, written in MATLAB, as well as a module to import data into Python. These flexible and programmable tools allow experimenters to configure and set up customised input and output protocols in a synchronized fashion for controlling, recording, and analysing experiments.

scholarly journals Indian Premier League Dataset Analytics using Hadoop-Hive

2019 ◽  
Vol 9 (2) ◽  
pp. 3999-4004
Keyword(s):  
Big Data ◽  
Open Source ◽  
Database Systems ◽  
Huge Amount ◽  
Premier League ◽  
Open Source Data ◽  
Open Source Framework ◽  
Source Data ◽  
Set Up ◽  
Processing Techniques

Big Data is a term used to represent huge volume of both unstructured and structured data which cannot be processed by the traditional data processing techniques. This data is too huge, grows exponentially and doesn't fit into the structure of the traditional database systems. Analyzing Big Data is a very challenging task since it involves the processing of huge amount of data. As the industry or its business grows, the data related to the industries also tend to grow on a larger scale. Prominent data analysis tools are required to analyze the data in order to gain value out of it. Hadoop is a sought-after open source framework that uses MapReduce techniques to store and process huge datasets. However, the programs written using MapReduce techniques are not flexible and also require maintenance. This problem is overcome by making use of HiveQL. In order to execute queries in HiveQL, the platform required is Hive. It is an open-source data warehousing set-up built on Hadoop. HiveQL queries are compiled into MapReduce jobs that are executed utilizing Hadoop. In this paper we have analyzed the Indian Premier League dataset using HiveQL and compared its execution time with that of traditional SQL queries. It was found that the HiveQL provided better performance with larger dataset while SQL performed better with smaller datasets

scholarly journals Molecular basis for high affinity agonist binding in GPCRs

2018 ◽  
Author(s):  
Tony Warne ◽  
Patricia C. Edwards ◽  
Andrew S. Doré ◽  
Andrew G. W. Leslie ◽  
Christopher G. Tate
Keyword(s):  
Hydrogen Bonds ◽  
Binding Site ◽  
G Protein ◽  
Molecular Basis ◽  
Agonist Binding ◽  
High Affinity ◽  
Protein Ligand Interactions ◽  
Wide Range ◽  
Ligand Interactions ◽  
G Protein Coupled

AbstractA characteristic of GPCRs in the G protein-coupled state is that the affinity of the agonist often increases significantly, but the molecular basis for this is unclear. We have determined six active-state structures of the β1-adrenoceptor (β1AR) bound to conformation-specific nanobodies in the presence of agonists of varying efficacy. A direct comparison with structures of β1AR in inactive states bound to the identical ligands showed a 24-42% reduction in the volume of the orthosteric binding site. Potential hydrogen bonds were also shorter, and there was up to a 30% increase in the number of atomic contacts between the receptor and ligand. GPCRs are highly conserved, so these factors will likely be essential in increasing the affinity of a wide range of structurally distinct agonists.One Sentence SummaryHigh affinity agonist binding to G protein-coupled GPCRs results from an increase in the number and strength of protein-ligand interactions.

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 Brain Predictability toolbox: a Python library for neuroimaging-based machine learning

2020 ◽  
Author(s):  
Sage Hahn ◽  
De Kang Yuan ◽  
Wesley K Thompson ◽  
Max Owens ◽  
Nicholas Allgaier ◽  
...  
Keyword(s):  
Machine Learning ◽  
Open Source ◽  
Unified Framework ◽  
Brain Volumes ◽  
Specific Data ◽  
Physiological Variables ◽  
Wide Range ◽  
Set Up ◽  
Tabulated Data

Abstract Summary Brain Predictability toolbox (BPt) represents a unified framework of machine learning (ML) tools designed to work with both tabulated data (e.g. brain derived, psychiatric, behavioral and physiological variables) and neuroimaging specific data (e.g. brain volumes and surfaces). This package is suitable for investigating a wide range of different neuroimaging-based ML questions, in particular, those queried from large human datasets. Availability and implementation BPt has been developed as an open-source Python 3.6+ package hosted at https://github.com/sahahn/BPt under MIT License, with documentation provided at https://bpt.readthedocs.io/en/latest/, and continues to be actively developed. The project can be downloaded through the github link provided. A web GUI interface based on the same code is currently under development and can be set up through docker with instructions at https://github.com/sahahn/BPt_app.

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 A SURVEY PAPER ON ELASTIC SEARCH SIMILARITY ALGORITHM

2017 ◽  
Vol 10 (13) ◽  
pp. 361
Author(s):  
Nilanjana Dev Nath ◽  
Shreekant Jha ◽  
Janki Meena M ◽  
Syedibrahim S.p
Keyword(s):  
Open Source ◽  
Web Search ◽  
Data Retrieval ◽  
Survey Paper ◽  
Programming Library ◽  
Open Source Data ◽  
Wide Range ◽  
Similarity Algorithm ◽  
Search Tool ◽  
The Right

Elasticsearch is a web search tool in view of Lucene. Apache Lucene is a free and open-source data retrieval programming library. Versatile Search gives a conveyed, multitenant-fit full-content web search tool with a HTTP web interface and pattern free JSON archives. It is created in Java and has been released as open source under the terms of the Apache License. Elasticsearch can be utilized to pursuit a wide range of records. It gives adaptable hunt, has close continuous pursuit, and backings multitenancy. It is appropriated, which implies that records can be partitioned into shards and every shard can have zero or more duplicates. Every hub has one or more shards, and goes about as a facilitator to delegate operations to the right shard(s). Elasticsearch is like a wrapper on top of Lucene. In this paper a detailed description of how lucene’s scoring algorithm works and how elasticsearch uses it as “similarity algorithm”

scholarly journals Efficient multi-task chemogenomics for drug specificity prediction

2017 ◽  
Author(s):  
Benoit Playe ◽  
Chloé-Agathe Azencott ◽  
Véronique Stoven
Keyword(s):  
Side Effects ◽  
State Of The Art ◽  
Support Vector ◽  
Task Support ◽  
Protein Ligand Interactions ◽  
Test Protein ◽  
Wide Range ◽  
Ligand Interactions ◽  
Prediction Test ◽  
Benchmark Datasets

AbstractAdverse drug reactions, also called side effects, range from mild to fatal clinical events and significantly affect the quality of care. Among other causes, side effects occur when drugs bind to proteins other than their intended target. As experimentally testing drug specificity against the entire proteome is out of reach, we investigate the application of chemogenomics approaches. We formulate the study of drug specificity as a problem of predicting interactions between drugs and proteins at the proteome scale. We build several benchmark datasets, and propose NN-MT, a multi-task Support Vector Machine (SVM) algorithm that is trained on a limited number of data points, in order to solve the computational issues or proteome-wide SVM for chemogenomics. We compare NN-MT to different state-of-the-art methods, and show that its prediction performances are similar or better, at an efficient calculation cost. Compared to its competitors, the proposed method is particularly efficient to predict (protein, ligand) interactions in the difficult double-orphan case, i.e. when no interactions are previously known for the protein nor for the ligand. The NN-MT algorithm appears to be a good default method providing state-of-the-art or better performances, in a wide range of prediction scenarii that are considered in the present study: proteome-wide prediction, protein family prediction, test (protein, ligand) pairs dissimilar to pairs in the train set, and orphan cases.

Facilitating reproducible science: a workflow for setting up SUMMA simulations anywhere on the globe

2021 ◽  
Author(s):  
Wouter Knoben ◽  
Shervan Gharari ◽  
Martyn Clark
Keyword(s):  
Open Source ◽  
Source Model ◽  
Climatic Forcing ◽  
Open Source Data ◽  
Model Configuration ◽  
Source Data ◽  
Scientific Outputs ◽  
Global Coverage ◽  
Set Up

<p>Setting up earth system models can be cumbersome and time-consuming. Model-agnostic tasks are typically the same regardless of model used and include definition and delineation of the modeling domain and preprocessing of forcing data and parameter fields. Model-specific tasks include conversion of preprocessed data into model-specific formats and generation of model inputs and run scripts. We present a workflow that includes both model-agnostic and model-specific steps needed to set up the Structure for Unifying Multiple Modeling Alternatives (SUMMA) anywhere on the planet, with the goal of providing a baseline SUMMA set up that can easily be adapted for specific study purposes. The workflow therefore uses open source data with global coverage to derive basin delineations, climatic forcing, and geophysical inputs such as topography, soil and land use parameters. The use of open source data, an open source model and an open source workflow that relies on established software packages results in transparent and reproducible scientific outputs, open to verification and adaptation by the community. The workflow substantially reduces model configuration time for new studies and paves the way for more and stronger scientific contributions in the long term, as it lets the modeler focus on science instead of set up.</p>

Settlers or Movers? The Temporality of Past Migrations, Political Inaction and its Consequences, 1945–1985

2018 ◽  
Author(s):  
Jozefien De Bock
Keyword(s):  
Temporary Workers ◽  
Guest Workers ◽  
Temporary Migration ◽  
Wide Range ◽  
Settler Societies ◽  
Future Policy ◽  
Set Up ◽  
Legal Frameworks ◽  
Temporary Migrants ◽  
Multicultural Policies

Historically, those societies that have the longest tradition in multicultural policies are settler societies. The question of how to deal with temporary migrants has only recently aroused their interest. In Europe, temporary migration programmes have a much longer history. In the period after WWII, a wide range of legal frameworks were set up to import temporary workers, who came to be known as guest workers. In the end, many of these ‘guests’ settled in Europe permanently. Their presence lay at the basis of European multicultural policies. However, when these policies were drafted, the former mobility of guest workers had been forgotten. This chapter will focus on this mobility of initially temporary workers, comparing the period of economic growth 1945-1974 with the years after the 1974 economic crisis. Further, it will look at the kind of policies that were developed towards guest workers in the era before multiculturalism. This way, it shows how their consideration as temporary residents had far-reaching consequences for the immigrants, their descendants and the receiving societies involved. The chapter will finish by suggesting a number of lessons from the past. If the mobility-gap between guest workers and present-day migrants is not as big as generally assumed, then the consequences of previous neglect should serve as a warning for future policy making.

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