AF2BIND: Predicting Ligand-Binding Sites with AlphaFold's Deep Features

AF2BIND (AF2 Bait-Informed Neural Descriptor), a highly accurate model for predicting small-molecule-binding residues in proteins using only their sequence and structure. AF2BIND solves the significant challenge of de novo binding-site prediction—identifying pockets without relying on solved homologous structures or multiple sequence alignments (MSAs). By cleverly leveraging the internal pairwise representation of AlphaFold2 (AF2), AF2BIND achieves a new state-of-the-art performance in accurately identifying which residues are most likely to contact a small-molecule ligand.

How AF2BIND Works

AF2BIND is a logistic regression model trained on the deep, internal features of a modified AlphaFold2 pipeline. The workflow is designed to tease out binding signatures without explicitly knowing the small molecule's structure.

• Bait Amino Acids: The key is the introduction of 20 disconnected "bait" amino acids (one of each canonical type), which are appended to the target protein's sequence. These act as surrogates for a small-molecule ligand, allowing AF2 to "finish folding" frustrated regions of the target structure.

• Pairwise Feature Extraction: AF2BIND extracts the pairwise attention features generated between each of the 20 bait amino acids and every residue in the target protein. This AF2 pairwise representation has been found to outperform all other neural-network representations for this task.

• Interpretable Prediction: The output is a per-residue prediction, P(bind), indicating the probability of binding. The model is highly interpretable: the activation contributions of the bait amino acids directly correlate with the ligand's chemical properties (e.g., hydrophobicity).

What is Tamarind Bio?

Tamarind Bio is a pioneering no-code bioinformatics platform built to democratize access to powerful computational tools for life scientists and researchers. Recognizing that many cutting-edge machine learning models are often difficult to deploy and use, Tamarind provides an intuitive, web-based environment that completely abstracts away the complexities of high-performance computing, software dependencies, and command-line interfaces.

The platform is designed provide easy access to biologists, chemists, and other researchers who may not have a background in programming or cloud infrastructure but want to run experimental models with their data. Key features include a user-friendly graphical interface for setting up and launching experiments, a robust API for integration into existing research pipelines, and an automated system for managing and scaling computational resources. By handling the technical heavy lifting, Tamarind empowers researchers to concentrate on their scientific questions and accelerate the pace of discovery. The Tamarind team hold information/data security as a top priority, as detailed in our Trust Center & Terms of Service, ensuring your data is safe on the platform.

Accelerating Discovery with AF2BIND on Tamarind Bio

Using AF2BIND on a platform like Tamarind could drastically accelerate structure-based drug discovery and functional annotation by:

• De Novo Target Identification: Researchers can quickly screen large quantities of newly predicted protein structures (e.g., from AlphaFoldDB) to discover novel, unliganded binding sites.

• Rational Ligand Design: By analyzing the bait-residue activation map, researchers gain direct insight into the chemical nature of the optimal ligand for a predicted pocket, guiding the rational design of small molecules.

• High-Confidence Prioritization: The tool provides a hierarchy of residues most likely involved in binding, giving researchers a strong basis for prioritizing follow-up studies over simple pocket-finding results. The predictions are robust, even to slight backbone variations and template side-chain rotamers.

How to Use AF2BIND on Tamarind Bio

To leverage AF2BIND's power, a researcher could follow this streamlined workflow on Tamarind:

1. Access the Platform: Begin by logging in to the tamarind.bio website.

2. Select AF2BIND: From the list of available computational models, choose the AF2BIND tool.

3. Input Target Information: Provide the target protein's amino acid sequence and its backbone structure (e.g., a PDB file or an AlphaFold model).

4. Run AF2BIND: The platform automatically runs the AF2 model in a single pass, appending the 20 bait amino acids.

5. Analyze P(bind) and Activations: The output provides a P(bind) score for every residue and a bait-residue activation map. Use these to identify the binding site residues and infer the optimal chemical properties of the binding ligand.

Source