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Updated: Jun 25, 2026

An Integrated Approach for Microprotein Identification and Sequence Analysis

An Integrated Approach for Microprotein Identification and Sequence Analysis

Published on: July 12, 2022

FeSseqdb: a curated sequence-level database and interpretable machine learning framework for identifying iron-sulfur

Jiyeon Min^1,2, Bernard R Brooks³, Muhamed Amin⁴

¹Laboratory of Computational Biology, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, 20892, USA. jmin7@umd.edu.

BMC Bioinformatics

|June 24, 2026

Summary

This summary is machine-generated.

A new database, FeSseqdb, and a machine learning model predict iron-sulfur (Fe-S) proteins using sequence data. This approach aids in discovering Fe-S proteins and understanding their functions across proteomes.

Keywords:

Explainable AI Iron-sulfur proteins Metalloproteins Protein Data Bank Sequence-based prediction

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Mass Spectrometry-Based Proteomics Analyses Using the OpenProt Database to Unveil Novel Proteins Translated from Non-Canonical Open Reading Frames

Published on: April 11, 2019

Area of Science:

Biochemistry
Bioinformatics
Computational Biology

Background:

Iron-sulfur (Fe-S) clusters are vital cofactors in metalloproteins, essential for biological processes like electron transfer and catalysis.
Identifying Fe-S proteins is challenging due to experimental limitations and database inconsistencies.
Accurate identification is crucial for understanding Fe-S protein functions and their roles in various biological pathways.

Purpose of the Study:

To develop a reliable resource for Fe-S protein research.
To create a machine learning framework for predicting Fe-S proteins using only sequence data.
To uncover sequence features critical for Fe-S protein identification and function.

Main Methods:

Creation of FeSseqdb, a curated database of Fe-S cluster-containing proteins from PDB, verified by atomic coordinates.
Development of a machine learning model using sequence-derived features (amino acid composition, cysteine metrics, length).
Application of explainable AI to identify key sequence characteristics driving Fe-S protein prediction.

Main Results:

FeSseqdb provides a standardized and reliable resource for Fe-S protein research.
A random forest model achieved high predictive performance for Fe-S proteins using sequence features.
Explainable AI identified cysteine frequency/distribution and proline content as primary predictors, with serine, glutamic acid, and arginine as secondary determinants.

Conclusions:

Sequence-based machine learning models can accurately predict Fe-S proteins.
These models offer insights into the biochemical principles governing Fe-S protein structure and function.
This approach facilitates large-scale, sequence-based discovery of Fe-S proteins and their functional diversity.