How to Use ProteinMPNN Online
Try ProteinMPNN
Commercially Available Online Web Server
ProteinMPNN: A Powerful Tool for Protein Design
Scientists have developed ProteinMPNN, a deep learning-based method for protein sequence design that has demonstrated exceptional performance in both computational and experimental tests. While previous protein design efforts have largely relied on physically based approaches, ProteinMPNN leverages the power of deep learning to design new amino acid sequences for a given protein structure.
How ProteinMPNN Works
ProteinMPNN is an "inverse folding" model that takes a protein's 3D structure as input and generates a sequence that is likely to fold into that specific shape. This is a crucial advancement because it allows for the design of sequences for a wide range of protein design challenges, including single or multiple protein chains.
Sequence Recovery: On native protein backbones, ProteinMPNN achieves a high sequence recovery rate of 52.4%, significantly outperforming the 32.9% recovery rate of traditional methods like Rosetta.
Noise and Robustness: The model is trained with added noise, which enhances its ability to design sequences for protein structures that are not perfectly native. This makes the sequences more robust and more likely to fold correctly, even when the input structure is a computational model rather than an experimental one.
Wide Applicability: ProteinMPNN's ability to handle coupled amino acid sequences across multiple chains makes it useful for designing a variety of complex structures, such as cyclic homo-oligomers and tetrahedral nanoparticles.
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 ProteinMPNN on Tamarind Bio
Using ProteinMPNN on a platform like Tamarind can significantly accelerate protein engineering efforts by providing a powerful, user-friendly, and efficient workflow.
Streamlined Design: Researchers can upload their desired protein structure—whether it's a monomer, a multi-chain complex, or a de novo design—and use the platform to generate a high-quality sequence in a fraction of the time required by previous methods.
Rescuing Failed Designs: ProteinMPNN has demonstrated its ability to rescue previously failed designs created by other methods, including Rosetta and AlphaFold, by providing a new sequence that is more likely to fold and function correctly.
High-Throughput and Automation: The efficiency of ProteinMPNN makes it ideal for high-throughput screening and optimization. On Tamarind, researchers could quickly generate and test thousands of sequence variants for a single structure, rapidly identifying the most promising candidates for experimental validation.
How to Use ProteinMPNN on Tamarind Bio
To leverage ProteinMPNN's power, a researcher could follow this streamlined workflow:
Access the Platform: Log in to the tamarind.bio website.
Select ProteinMPNN: From the list of available computational models, choose the ProteinMPNN tool.
Provide a 3D Structure: Upload a PDB file of a protein structure or complex.
Select ProteinMPNN: Choose the ProteinMPNN tool from the platform's menu.
Generate Sequence: The platform handles the deep learning inference and provides a set of optimized amino acid sequences designed to fold into the provided structure.
Analyze and Validate: Researchers can then use the platform's other tools, such as structure prediction algorithms, to further validate the generated sequences and select the best candidates for experimental testing.