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SaProt: Protein Language Modeling with Structure-Aware Vocabulary
SaProt (Structure-aware Protein language model) a powerful general-purpose language model that integrates protein sequence and structure information into a novel "structure-aware vocabulary". By combining residue tokens with 3D structure tokens from a tool called Foldseek, SaProt overcomes the limitations of traditional protein language models (pLMs) that lack explicit structural awareness. This approach enables SaProt to achieve state-of-the-art performance on 10 diverse downstream tasks, including clinical disease variant prediction, fitness landscape prediction, and protein-protein interaction prediction.
How SaProt Works
SaProt's core innovation is its ability to represent a protein's primary and tertiary structures as a single sequence of "structure-aware" tokens. The model uses a standard transformer encoder, similar to ESM-2, but with an expanded vocabulary to handle this new token type.
Structure-Aware Vocabulary: The model's vocabulary combines residue tokens (the amino acids) with 3D tokens from Foldseek that encode the geometric conformation of each residue in its local spatial environment. This allows the model to learn from both sequence and structural data simultaneously.
Unsupervised Training: SaProt is trained in an unsupervised fashion on a massive dataset of approximately 40 million protein sequences and their corresponding structures from AlphaFoldDB. This large-scale training allows it to capture a deeper understanding of protein representations.
Superior Predictions: Through this structure-aware approach, SaProt consistently outperforms other leading pLMs, including the ESM family of models, on various tasks. In one example, it showed remarkable superiority over ESM-2 in contact map prediction, demonstrating that it has learned more accurate structural information.
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 SaProt on Tamarind Bio
Using SaProt on a platform like Tamarind would empower researchers to accelerate protein engineering and discovery by providing a powerful, structure-aware tool that is easy to use.
Enhanced Prediction for Mutational Effects: SaProt's high accuracy in predicting mutational effects on a zero-shot basis would enable researchers to screen large libraries of protein variants to identify those with desirable properties, such as improved stability or function.
Structural and Functional Insights: The model's ability to learn from both sequence and structure could be used to predict a protein's contact map and other structural properties, providing a deeper understanding of the relationship between a protein's sequence and its function.
Accessible and Scalable Workflow: The integration of SaProt into a no-code platform would make advanced, structure-aware protein modeling accessible to a broader community. The computational resources required for training and inference would be handled by the platform, allowing researchers to focus on their biological questions.
How to Use SaProt on Tamarind Bio
To leverage SaProt'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 SaProt: From the list of available computational models, choose the SaProt tool.
Input a Protein Sequence: Provide the amino acid sequence of the protein you want to analyze.
Generate Structure-Aware Tokens: The platform would use an internal tool like Foldseek to encode the protein's 3D structure into "structure-aware" tokens.
Run SaProt: The platform would run the SaProt model on this new sequence representation to predict various properties, such as mutational effects or subcellular location.
Analyze and Visualize: The results would provide a detailed analysis of the protein, and the platform could use visualizations like t-SNE plots to help you understand the structural and functional relationships learned by the model.