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Ligand Binding and Linkage

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Allosteric proteins have more than one ligand binding site; the binding of a ligand to any of these sites influences the binding of ligands to the other sites. When a protein is allosteric, its binding sites are called coupled or linked. In the case of enzymes, the site that binds to the substrate is known as the active site and the other site is known as the regulatory site. When a ligand binds to the regulatory site, this leads to conformational changes in the protein that can influence...

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Updated: Feb 25, 2026

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

MCPmed: a call for Model Context Protocol-enabled bioinformatics web services for LLM-driven discovery.

Matthias Flotho^1,2, Ian Ferenc Diks^1,2, Philipp Flotho¹

¹Chair for Clinical Bioinformatics, Center for Bioinformatics, Saarland University, Saarland University Campus, 66123 Saarland, Germany.

Briefings in Bioinformatics

|February 23, 2026

Summary

This summary is machine-generated.

Bioinformatics web servers are made machine-readable using the Model Context Protocol (MCP). This enhances large language model (LLM) exploration of biomedical data, improving research automation and reproducibility.

Keywords:

API GEO LLM MCP database web server

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An Integrated Approach for Microprotein Identification and Sequence Analysis

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Published on: July 12, 2022

Area of Science:

Bioinformatics
Computational Biology
Biomedical Research

Background:

Bioinformatics web servers are vital for data exploration in biomedical research.
Current human-centric designs limit machine readability for AI agents.
Lack of machine-actionable interfaces hinders advanced automation and data integration.

Purpose of the Study:

To enhance machine readability of bioinformatics web servers for large language models (LLMs) and research agents.
To develop a standardized, machine-actionable layer for bioinformatics web services.
To improve automation, reproducibility, and interoperability in biomedical data exploration.

Main Methods:

Adapted the Model Context Protocol (MCP) to bioinformatics web server backends.
Created a standardized, machine-actionable layer associating endpoints with scientific concepts and metadata.
Implemented MCP on widely used databases: GEO, STRING, and UCSC Cell Browser.

Main Results:

Demonstrated enhanced data exploration capabilities through MCP-enabled LLMs.
Successfully integrated MCP into established bioinformatics databases.
Showcased improved machine readability and interaction with bioinformatics resources.

Conclusions:

MCP provides a standardized approach to make bioinformatics web servers machine-actionable.
MCP facilitates enhanced exploration of biomedical data by LLMs and research agents.
MCPmed initiative aims to accelerate adoption and prepare services for future AI-driven research.