HOPMA

HOPMA predicts protein conformational states by combining modified elastic network models and Non-Linear rigid Block Normal Mode Analysis (NOLB) informed by colored contact maps.

Key Features:

• Elastic Network Model Modification: Uses a modified elastic network model that derives contact maps from protein 3D structures and excludes disconnected patches from the network to improve modeling of dynamics.
• Non-Linear Normal Mode Analysis (NOLB): Integrates Non-Linear rigid Block Normal Mode Analysis (NOLB) to enable computationally efficient nonlinear normal mode exploration of conformational transitions.
• Conformational Space Exploration: Combines the modified elastic network and NOLB to enhance sampling of protein conformational space and identify functional conformations not captured by static experimental structures.
• Application to Large Datasets: Demonstrated on a dataset comprising over 400 transitions to assess robustness and scalability in predicting protein dynamics.

Scientific Applications:

• Structural biology: Predicting functional conformations and conformational changes from known protein 3D structures.
• Computational biochemistry: Modeling protein flexibility and dynamics to elucidate mechanisms of action.
• Protein function and interaction analysis: Generating candidate conformations to inform hypotheses about protein interactions and function under physiological conditions.

Methodology:

Take a protein 3D structure, generate a colored contact map, analyze contact patterns to identify and exclude specific patches from the elastic network model, and apply the resulting modified network with NOLB nonlinear normal mode analysis to explore potential conformational states.