How to Use LEGOLAS Online
Try LEGOLAS
Commercially Available
LEGOLAS: A Breakthrough in Protein NMR Analysis
Scientists have developed LEGOLAS (neuraL nEtwork enGine fOr caLculating chemicAl Shifts), an open-source machine learning model that makes it dramatically faster to predict protein NMR chemical shifts. This is a significant advance for structural biologists because NMR chemical shifts are crucial for determining protein structural features, such as secondary structure and conformational changes from folding or ligand binding. LEGOLAS is at least an order of magnitude faster than the widely used SHIFTX2 model while maintaining exceptional accuracy.
How LEGOLAS Works
LEGOLAS is a neural network model implemented using the PyTorch-based TorchANI framework. The model's efficiency comes from its unique approach to processing data:
Input Data: The model takes a protein structure from a Protein Data Bank (PDB) file as input. It then encodes the structure into local descriptors called AEVs (Atom-centered Symmetry Functions).
Local Descriptors: These AEVs incorporate detailed information about the interatomic distances and angles around each atom within specific cutoffs, without considering atoms beyond those limits.
Neural Network Prediction: The neural network uses this information, along with vector embeddings of amino acid types, to predict the chemical shifts for protein backbone atoms.
Accuracy Enhancement: To improve accuracy, the final chemical shifts are an average of the outputs from five independent models.
This method allows LEGOLAS to predict chemical shifts for a very large number of input structures, such as frames from a molecular dynamics (MD) trajectory, a feat that would be computationally prohibitive with older methods.
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 LEGOLAS on Tamarind Bio
Using LEGOLAS on a platform like Tamarind would revolutionize how researchers analyze protein dynamics and structure.
High-Throughput Analysis: The speed of LEGOLAS would allow researchers to analyze thousands of protein structures, such as those generated from MD simulations, in a fraction of the time. This would enable them to gain a deeper understanding of protein conformational changes and dynamics.
Protein Structure Determination: LEGOLAS can reliably identify the correct native structure of a protein from a large set of incorrectly folded "decoys". By running a LEGOLAS prediction on Tamarind, researchers could quickly validate computational models and identify the most likely correct structure from a pool of candidates.
How to Use LEGOLAS on Tamarind.bio
To leverage LEGOLAS's power, researchers could use this streamlined workflow:
Access the Platform: Log in to the tamarind.bio website.
Select LEGOLAS: From the list of available computational models, choose the LEGOLAS tool.
Upload Structures: Upload one or more protein structure files (PDBs) to the Tamarind platform.
Select LEGOLAS Model: Choose the LEGOLAS model from the list of available tools.
Run Analysis: The platform handles all the calculations, utilizing its powerful hardware to run the predictions quickly.
Analyze Results: Receive the predicted NMR chemical shifts, which can then be compared to experimental data or used to rank the quality of different protein models.