Use AMBER Relaxation
Official Neurosnap webserver for accessing AMBER Relaxation online.
Overview
Relax a protein structure using an AlphaFold2 and OpenMM implementation of an AMBER forcefield.
Neurosnap Overview
The AMBER Relaxation online webserver allows anybody with a Neurosnap account to run and access AMBER Relaxation, no downloads required. Information submitted through this webserver is kept confidential and never sold to third parties as detailed by our strong Terms of Use and Privacy Policy.
Features
- Uses OpenMM for molecular dynamics-based energy minimization
- Implements Amber14 force field with TIP3P water model
- Removes water molecules and non-biopolymers before minimization
- Fixes missing residues, atoms, and hydrogen placements using PDBFixer
- Clamps energy minimization tolerance between 0.1 and 10.0 Kcal/mol
- Converts tolerance from Kcal/mol to kJ/mol for OpenMM compatibility
- Uses Langevin integrator with a 2 fs time step at 300K
- Computes initial and final potential energy values in kJ/mol
- Computes RMSD between original and minimized structure in Ångströms
- Saves the minimized structure in PDB format
- Logs energy minimization progress and outputs results in JSON format
- Allows customization of maximum iterations and minimization tolerance
- Uses OpenMM’s NoCutoff method for non-bonded interactions
- Applies hydrogen bond constraints for improved stability
- Perfect for quickly fixing side chains and other minor stereochemical issues from models like AlphaFold
Statistics
Neurosnap periodically calculates runtime statistics based on job execution data. These estimates provide a general guideline for how long your job may take, but actual runtimes can vary significantly depending on factors like input size or settings used.
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API Request
Access AMBER Relaxation using the Neurosnap API by sending a request using any programming language with HTTP support. To safely generate an API key, visit the API tab of your overview page.
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Citations
Please cite the original work when using AMBER Relaxation in publications or research outputs.
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Article Source: OpenMM 7: Rapid development of high performance algorithms for molecular dynamics Eastman P, Swails J, Chodera JD, McGibbon RT, Zhao Y, et al. (2017) OpenMM 7: Rapid development of high performance algorithms for molecular dynamics. PLOS Computational Biology 13(7): e1005659. https://doi.org/10.1371/journal.pcbi.1005659 |
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ff14SB: Improving the Accuracy of Protein Side Chain and Backbone Parameters from ff99SB James A. Maier, Carmenza Martinez, Koushik Kasavajhala, Lauren Wickstrom, Kevin E. Hauser, and Carlos Simmerling Journal of Chemical Theory and Computation 2015 11 (8), 3696-3713 DOI: 10.1021/acs.jctc.5b00255 |
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Jorgensen, W. L., Chandrasekhar, J., Madura, J. D., Impey, R. W., & Klein, M. L. (1983). Comparison of simple potential functions for simulating liquid water. The Journal of Chemical Physics, 79(2), 926–935. https://doi.org/10.1063/1.445869 |
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Neurosnap Inc. (2022). Neurosnap: An online platform for computational biology and chemistry. Available at: https://neurosnap.ai/ |
Job Note
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Inputs & Configuration
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