TopologyNet: Prediction of mutation impact on soluble protein stability
Predictor for protein folding free energy change upon single point mutation for soluble proteins using persistent homology and deep convolutional neural networks.
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User Guide
Input
- Input Protein: The protein file in PDB format either by uploading or by PDBID. The residue id should be in ascending order in the chain of interest and contain no non integer characters.
- Chain ID: The chain ID for the mutation site.
- Residue ID: The residue ID of the mutation site.
- Wild Name: The one letter code of the residue type of the mutation site in wild protein.
- Mutant Name: The one letter code of the residue type for the mutation site to be mutated to.
- Experimental pH: The pH at which the experiments are done.
- ddG: Negative/positive value means destabilizing/stabilizing mutation.
- Through Email: Once the job is done, an email with a download link will be sent.
Reference
[1] Cang Z, Wei G (2017) TopologyNet: Topology based deep convolutional and multi-task neural networks for biomolecular property predictions. PLOS Computational Biology 13(7): e1005690. https://doi.org/10.1371/journal.pcbi.1005690
[2] Zixuan Cang, Guowei Wei; Analysis and prediction of protein folding energy changes upon mutation by element specific persistent homology. Bioinformatics 2017 btx460. doi: 10.1093/bioinformatics/btx460