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Author Spotlight: In Silico Creation and Impact of Carbonylated Amino Acids on Protein Structure and Function

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EMICSS: added-value annotations for EMDB entries.

Amudha Kumari Duraisamy¹, Neli Fonseca¹, Gerard J Kleywegt¹

¹Electron Microscopy Data Bank, European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Hinxton, Cambridgeshire CB10 1SD, United Kingdom.

Bioinformatics Advances

|September 26, 2025

Summary

This summary is machine-generated.

The Electron Microscopy Data Bank (EMDB) now features enhanced cross-references thanks to EMDB Integration with Complexes, Structures, and Sequences (EMICSS). This system automatically links 3DEM data with vital biological and structural resources.

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Area of Science:

Structural Biology
Bioinformatics
Data Science

Background:

The Electron Microscopy Data Bank (EMDB) is a primary repository for 3D electron microscopy (3DEM) data.
Current EMDB limitations include a lack of comprehensive annotations and connections to external biological, functional, and structural databases.
These limitations stem from optional data deposition and challenges in maintaining external references.

Purpose of the Study:

To develop an automated system for enhancing EMDB data integration and cross-referencing.
To improve the discoverability and utility of 3DEM data by linking it to related resources.
To address the challenges of depositor burden and maintaining up-to-date external references.

Main Methods:

Developed EMDB Integration with Complexes, Structures, and Sequences (EMICSS), an independent system.
Implemented automated cross-referencing with over 20 external resources (e.g., UniProt, AlphaFold DB, PubMed, Complex Portal, Gene Ontology).
Made EMICSS annotations accessible in multiple formats and programmatically via API.

Main Results:

EMICSS successfully automates and updates cross-references for EMDB entries.
Annotations generated by EMICSS are integrated into the EMDB website, enhancing entry pages, statistics, and search functionality.
The system provides access to linked resources and programmatic data retrieval.

Conclusions:

EMICSS significantly enhances the EMDB by providing comprehensive annotations and seamless integration with external biological databases.
The automated system improves data accessibility and utility for researchers in structural biology and related fields.
EMICSS is an open-source Python-based tool, promoting collaborative development and widespread adoption.