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quickARSC: standalone package and web interface for profiling elemental stoichiometry of proteomes.

Satoshi Nishino^1,2, Kento Tominaga³, Yuki Nishimura¹

¹Department of Integrated Biosciences, Graduate School of Frontier Sciences, The University of Tokyo, Kashiwa, Chiba, Japan.

Microbiology Resource Announcements

|March 30, 2026

Summary

We developed quickARSC, a tool that calculates elemental composition for amino acid residue side chains (ARSC). This provides easy access to ARSC data for over 143,000 prokaryotic species.

Keywords:

ARSC computational biology elemental stoichiometry python package web interface

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Area of Science:

Bioinformatics
Computational Biology
Biochemistry

Background:

Elemental composition analysis is crucial for understanding protein function and evolution.
Previous methods for calculating elemental composition per amino acid residue side chain (ARSC) were often complex or inaccessible.

Purpose of the Study:

To introduce quickARSC, a user-friendly command-line tool for computing ARSC from amino acid sequences.
To provide a web interface for accessing, filtering, visualizing, and downloading pre-computed ARSC data for a large prokaryotic dataset.

Main Methods:

Development of a command-line tool (quickARSC) for ARSC calculation.
Creation of a web interface linked to a database of pre-computed N-ARSC, C-ARSC, and S-ARSC values.
Curated data for 143,614 prokaryotic species.

Main Results:

quickARSC successfully computes elemental composition per amino acid residue side chain.
The web interface offers comprehensive data access and analysis functionalities.
Pre-computed ARSC data for a substantial number of prokaryotic species is now readily available.

Conclusions:

quickARSC simplifies and standardizes ARSC computation.
The associated web interface democratizes access to valuable biochemical data for prokaryotic species.
This resource will aid research in microbial genomics, proteomics, and evolutionary biology.