use the ESM3 model to predict protein structures
Create interactive 3D scenes with torus knots
Generate protein structures from specified lengths and seeds
Generate a 3D model and video from images
Create 3D mesh by chatting.
Generate dynamic 3D torus knots with random materials
Generate random 3D torus knots
Explore maps in a virtual reality scene
View 3D models in AR/VR using your phone
Gradio demo of CharacterGen (SIGGRAPH 2024)
Generate a dynamic 3D scene with random shapes and lights
Generate a 3D mesh model from an image
Generate 3D models and videos from images
Conformity Protein Dynamics is a cutting-edge 3D modeling tool designed to predict and analyze protein structures using advanced AI technology. Leveraging the ESM3 model, it enables researchers to accurately predict protein conformations and visualize their dynamic behavior. This tool is particularly useful for understanding how proteins fold and interact, making it a valuable asset in structural biology and drug discovery.
What kind of proteins can I model with Conformity Protein Dynamics?
You can model any protein for which you have a known or predicted amino acid sequence. The ESM3 model is particularly effective for predicting structures of proteins with unknown or variable folds.
Can I refine my predictions using experimental data?
Yes, you can incorporate experimental data such as NMR or X-ray crystallography constraints to refine your predictions and improve accuracy.
What is the difference between noise handling and MD frames?
Noise handling introduces random variations to simulate natural protein fluctuations, while MD frames provide a step-by-step visualization of protein movements over time. Together, they offer a comprehensive view of protein dynamics.