Software & Data
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- De novo protein design via learning evolutionary signatures.
- Web-server is available to non-commercial use.
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- Predict disease variants.
- Web-server is available to non-commercial use.
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- Protein design using graph neural networks.
- Web-server is available to non-commercial use.
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- Accurate protein function annotation.
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Web-server is available to non-commercial use.
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- Explore sequence space for a given protein family.
- Designed sequences that are generated from a physics-informed machine.
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- Directly predict coordinates of atoms using deep generative adversarial networks.
- Ramachandran basin in 5x5-bin that is generated from the RCSB proteins.
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A web-server is here to learn protein folding pathways and predict its tertiray structures.
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- Package contains the trained models of the ESIDEN and the datasets to reproduce the results.
The features inlcude position-specific scoring matrix (PSSM), relative entropy (RE), degree of conservation (DC), position-specific substitution probabilities (PSSP), Ramachandran basin potential (RBP, φ & ψ) from protein chains.
- Reference: Y.-C. Xu, et al.. Accurate prediction of protein torsion angles using evolutionary signatures and recurrent neural network, Scientific Reports 11, 21033 (2021). [Datasets]
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A general framework for deep neural network, and it includes the protocols to generate the best-so-far network for a given dataset without manually tuning the parameters.
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Protein folding using Modyfing from an amino acid sequence.
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Generation of Ramachandran basins is provided HERE. Ramachandran basin (bin of 5x5) derived from protein torsional angles (Φ and Ψ)
- Ramachandran basin in 5x5-bin for the CATH_S40 domains
- Ramachandran basin in 5x5-bin for the SPOT-1D domains
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A data-driven predictor, in which all parameters of a neural network are optimized by a swarm of particles.
© AmoAi. All rights reserved.
Nov 20, 2021
To install Amo on your machine, please choose a package from the following list and download it.
Run the shell script in the terminal (on Unix-like system) if you download the DEB package. Copy the files to /usr/local or /usr/ if you download the pre-built tar ball. Please install the required dependencies in advance following the tutorials here.