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Predicting Ion Mobility Collision Cross Sections using Projection Approximation with ROSIE-PARCS Webserver

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Ion mobility (IM) coupled to mass spectrometry informs on the shape and size of protein structures in the form of a collision cross section (CCSIM). While there are several computational methods for predicting CCSIM based on protein structures, including our previously developed PARCS, the process usually requires prior experience with the command-line interface (CLI). To overcome this challenge, here we present a web application on the ROSIE webserver to predict CCSIM from protein structure using projection approximation with PARCS. In this web interface, the user is only required to provide one or more PDB files as input. Results from our case studies suggest that CCSIM predictions (with ROSIE-PARCS) are highly accurate with an average error of 6.12%. Furthermore, the absolute difference between CCSIM and CCSPARCS can help in distinguishing accurate from inaccurate AlphaFold2 protein structure predictions. ROSIE-PARCS is designed with a user-friendly interface, is available publicly, and is free to use. The ROSIE-PARCS web interface is supported by all major web browsers and can be accessed via this link (https://rosie.graylab.jhu.edu).
Title: Predicting Ion Mobility Collision Cross Sections using Projection Approximation with ROSIE-PARCS Webserver
Description:
Ion mobility (IM) coupled to mass spectrometry informs on the shape and size of protein structures in the form of a collision cross section (CCSIM).
While there are several computational methods for predicting CCSIM based on protein structures, including our previously developed PARCS, the process usually requires prior experience with the command-line interface (CLI).
To overcome this challenge, here we present a web application on the ROSIE webserver to predict CCSIM from protein structure using projection approximation with PARCS.
In this web interface, the user is only required to provide one or more PDB files as input.
Results from our case studies suggest that CCSIM predictions (with ROSIE-PARCS) are highly accurate with an average error of 6.
12%.
Furthermore, the absolute difference between CCSIM and CCSPARCS can help in distinguishing accurate from inaccurate AlphaFold2 protein structure predictions.
ROSIE-PARCS is designed with a user-friendly interface, is available publicly, and is free to use.
The ROSIE-PARCS web interface is supported by all major web browsers and can be accessed via this link (https://rosie.
graylab.
jhu.
edu).

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