ProteinMPNN
ProteinMPNN is a neural network model that predicts protein sequences based on desired structures. It is a GNN-based AI method for structure-based design, also known as inverse folding. By using a message-passing architecture, it captures the relationships between sequences and their structural features. This tool is valuable for protein engineering, enabling the design of novel proteins, enhancing enzyme efficiency, and improving binding properties, thus advancing biotechnology and drug discovery.
Inversefold P00698
Inputs:
Target Protein Structure in PDB Format
A 3D protein structure, typically provided in PDB format, serves as the foundation for effective sequence design. This structural data offers crucial spatial information that guides the optimization of amino acid sequences, enhancing the accuracy of designs and improving the chances of successful experimental outcomes.
Key Structural Features for Sequence Design
This input includes detailed information on structural elements like secondary structure annotations, binding sites, and conserved motifs that influence the sequence design process. Understanding these features is essential for predicting how sequence changes affect protein folding, stability, and interactions, leading to more functional protein designs.
Outputs:
Predicted Amino Acid Sequence
The predicted amino acid sequence corresponds to the given protein structure, capturing the design goals established during input. This sequence reflects modifications aimed at enhancing function, stability, or interaction properties, making it a crucial output for further research and application.
Reliability Metrics for Protein Predictions
Confidence scores provide essential metrics that indicate the reliability of the predicted sequences. These scores help researchers assess the potential effectiveness of the designed protein, guiding decisions on which sequences warrant further experimental validation and investigation.
Binding Affinity for Designed Proteins
Binding affinity estimates offer predictions related to the interaction properties of the designed protein. These insights are critical for understanding how well the protein may interact with ligands or other biological targets, informing downstream applications in drug discovery and protein engineering.
Examples of ProteinMPNN Applications
Inversefold P60568 and show 3 results
