EdaFoldAA
EdaFoldAA predicts de novo protein structures by fragment assembly guided by an Estimation of Distribution Algorithm (EDA) to improve conformational sampling and all-atom model refinement.
Key Features:
- Fragment Assembly Approach: Builds initial coarse-grained models from pools of candidate fragments sourced from known protein structures and refines them to all-atom models.
- Stochastic Sampling Refinement: Uses stochastic sampling to explore complex energy landscapes, generating numerous independent models due to randomness in the search.
- Iterative Distribution Estimation (EDA): Iteratively refines a distribution over fragments based on low-energy all-atom models to bias subsequent fragment selection toward native-like regions.
- All-Atom Energy Function: Applies an all-atom energy function to evaluate and refine models at atomic resolution.
- Benchmark Performance: Demonstrated ability to reach lower energy levels and produce a higher percentage of near-native models in blind benchmark selections (reported relative to protocols such as AbInitioRelax).
Scientific Applications:
- De novo protein structure prediction: Predicts tertiary structures for proteins lacking homologous experimental structures.
- Structural biology: Supports modeling of protein conformations for studies of fold and stability when experimental data are limited.
- Drug discovery: Provides high-resolution models useful as starting points for structure-based ligand design and target characterization.
- Enzyme design: Supplies atomic-detail models to guide mutation design and catalytic site engineering.
- Protein function and interaction analysis: Generates structures that aid interpretation of molecular function and potential interaction interfaces.
Methodology:
Build initial models from a pool of candidate fragments, refine models via stochastic sampling to explore the energy landscape, iteratively estimate and refine fragment distributions using an EDA based on low-energy all-atom models, and evaluate/refine models with an all-atom energy function.
Topics
Details
- Tool Type:
- command-line tool
- Operating Systems:
- Linux
- Added:
- 12/18/2017
- Last Updated:
- 11/25/2024
Operations
Publications
Simoncini D, Zhang KYJ. Efficient Sampling in Fragment-Based Protein Structure Prediction Using an Estimation of Distribution Algorithm. PLoS ONE. 2013;8(7):e68954. doi:10.1371/journal.pone.0068954. PMID:23935913. PMCID:PMC3723781.