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Ligand Binding Sites

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Proteins are dynamic macromolecules that carry out a wide variety of essential processes; however, the activities of most proteins depend on their interactions with other molecules or ions, known as ligands.
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Many proteins’ biological role depends on their interactions with their ligands, small molecules that bind to specific locations on the protein known as ligand-binding sites. Ligand-binding sites are often conserved among homologous proteins as these sites are critical for protein function.
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Author Spotlight: Streamlining Protein Target Prediction and Validation via Molecular Docking and CETSA

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Protein embeddings and deep learning predict binding residues for various ligand classes.

Maria Littmann¹, Michael Heinzinger^2,3, Christian Dallago^2,3

¹Department of Informatics, Bioinformatics and Computational Biology, I12, TUM (Technical University of Munich), Boltzmannstr. 3, 85748, Garching/Munich, Germany. littmann@rostlab.org.

Scientific Reports

|December 14, 2021

Summary

A new Artificial Intelligence (AI) method, bindEmbed21, predicts protein binding sites for metal ions, nucleic acids, and small molecules using protein Language Model (pLM) embeddings. This approach surpasses traditional methods, offering a fast and broadly applicable tool for identifying protein-ligand interactions.

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A Protocol for Computer-Based Protein Structure and Function Prediction

A Protocol for Computer-Based Protein Structure and Function Prediction

Published on: November 3, 2011

Area of Science:

Biochemistry
Computational Biology
Bioinformatics

Background:

Protein-ligand binding is crucial for protein function.
Experimental methods for identifying binding sites are limited and many remain undiscovered.

Purpose of the Study:

To develop a novel Artificial Intelligence (AI)-based method, bindEmbed21, for predicting protein residue binding to metal ions, nucleic acids, or small molecules.
To evaluate the performance of bindEmbed21 against existing methods.

Main Methods:

bindEmbed21 utilizes embeddings from the Transformer-based protein Language Model (pLM) ProtT5 as input.
The method exclusively uses single protein sequences, avoiding multiple sequence alignments (MSAs).
Performance was further enhanced by combining with homology-based inference.

Main Results:

bindEmbed21DL, using only single sequences, outperformed MSA-based predictions.
The combined method achieved F1 score of 48±3% and MCC of 0.46±0.04 for all ligand classes.
For the top 25% predicted binding residues, accuracy reached at least 73%, even accounting for missing experimental data.
The method identified binding residues in over 42% of human proteins not previously associated with binding.

Conclusions:

bindEmbed21 is a fast, simple, and broadly applicable method for predicting protein-ligand binding sites without requiring protein structure or MSAs.
This AI-driven approach can significantly aid in discovering novel binding sites and understanding protein function.