How to Use AF Cluster Online
Try AF Cluster
Commercially Available Online Web Server
AF-Cluster: Predicting Multiple Protein Conformations
AF-Cluster, a method that combines multiple-sequence alignment (MSA) clustering with AlphaFold2 to predict alternative conformations of proteins. While AlphaFold2 is a groundbreaking tool for predicting a single dominant protein structure, it often fails to predict the alternative, experimentally observed structures of "fold-switching" proteins. AF-Cluster addresses this by steering AlphaFold2 to sample these alternative states with high confidence, providing a valuable tool for understanding protein function and dynamics.
How AF-Cluster Works
AF-Cluster's core insight is that conflicting evolutionary signals within an MSA can be "deconvolved" by clustering sequences based on similarity and using these clusters as separate inputs for AlphaFold2.
Sequence Clustering: The method takes a protein's MSA and clusters the aligned sequences by their sequence similarity.
Targeted Prediction: AlphaFold2 then runs on each of these sequence clusters, effectively sampling distinct conformational states.
High Confidence: The method successfully generates high-confidence predictions for alternative conformations of known fold-switching proteins like KaiB, Mad2, and RfaH, which have been experimentally verified.
Mutation Prediction: The paper also demonstrates that AF-Cluster is sensitive to point mutations, successfully predicting a set of three mutations that flipped the equilibrium of a KaiB variant from one conformational state to another, which was confirmed with NMR spectroscopy.
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.
Accelerating Discovery with AF-Cluster on Tamarind Bio
Using AF-Cluster on a platform like Tamarind could accelerate research in structural biology and protein engineering by providing a streamlined, high-throughput workflow for conformational modeling.
Mapping Conformational Landscapes: Researchers can use the platform to generate a diverse set of predicted structures that represent a protein's conformational landscape, providing a more complete picture of its function beyond a single static model.
Predicting Mutation Effects: The model's ability to predict how point mutations can alter a protein's conformational state is crucial for rational protein engineering and for designing drugs that target specific protein states.
Accessible Workflow: By integrating AF-Cluster into a no-code platform, Tamarind would make advanced conformational modeling accessible to a wider range of researchers, democratizing access to cutting-edge tools and accelerating scientific progress.
How to Use AF-Cluster on Tamarind Bio
To leverage AF-Cluster's power, a researcher could follow this streamlined workflow on the Tamarind platform:
Access the Platform: Begin by logging in to the tamarind.bio website.
Select AF-Cluster: From the list of available computational models, choose the AF-Cluster tool.
Input a Protein Sequence: Provide the protein sequence you wish to analyze. The platform would automatically generate a multiple-sequence alignment (MSA) for you.
Cluster the MSA: The platform would cluster the MSA based on sequence similarity to prepare the data for the next step.
Run AF-Cluster: The platform would run AlphaFold2 on the generated sequence clusters to predict a diverse set of protein conformations.
Analyze and Validate: The output provides a distribution of predicted structures with confidence scores. You can use this information to identify and analyze the different conformational states of your protein, guiding your future experimental work.