use the ESM3 model to predict protein structures
Create a dynamic torus knot scene with random properties
Create 3D models from images
Generate protein structures from specified lengths and seeds
Gradio Demo of DI-PCG
Create an interactive 3D sphere fountain that follows your mouse
Create 3D recursive polygons and mathematical functions in a virtual environment
Create 3D models from images
Create a 3D scene with random shapes and lights
Create 3D mesh by chatting.
text-to-3D & image-to-3D
Generate a dynamic 3D scene with rotating lights and knots
Generate a 3D scene with dynamic lights and torus knots
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.