Paste your amino acid sequence in FASTA format >ExampleSeq MAGHLASDFAFSPPPGGGGDGPGGPEPGWVDPRTWLSFQGPPGGPGIGPGVGPGSEVWGI PPCPPPYEFCGGMAYCGPQVGVGLVPQGGLETSQPEGEAGVGVESNSDGASPEPCTVTPG AVKLEKEKLEQNPEESQDIKALQKELEQFAKLLKQKRITLGYTQADVGLTLGVLFGKVFS QTTICRFEALQLSFKNMCKLRPLLQKWVEEADNNENLQEICKAETLVQARKRKRTSIENR VRGNLENLFLQCPKPTLQQISHIAQQLGLEKDVVRVWFCNRRQKGKRSSSDYAQREDFEA AGSPFSGGPVSFPLAPGPHFGTPGYGSPHFTALYSSVPFPEGEAFPPVSVTTLGSPMHSN

Assistance
if you have used these tools or found them helpful, please cite the following manuscript:

Sun, J., Qu, J., Zhao, C. et al. Precise prediction of phase-separation key residues by machine learning. Nature Communications 15, 2662 (2024). https://doi.org/10.1038/s41467-024-46901-9 (IF: 16.6)

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PSPHunter, a machine learning method designed to predict phase-separating proteins and their corresponding driving residues. Using only the amino acid sequence information of a protein, we can determine its probability of phase separation, identify the driving residues, and assess the impact of various mutations on phase separation. With the application of PSPHunter, we have demonstrated that truncating just 6 driving residues in SOX2 and GATA3 significantly disrupts their phase separation properties.

Furthermore, we successfully predicted the driving residues of the core pluripotency factor OCT4 (truncated 3 residues, Cell Stem Cell, 2021) and the PcG family protein RYBP (truncated 21 residues, Cell Research, 2022).

Note:
● As for the prediction of phase-separating proteins, up to 5 sequences at a time.
● As for the prediction of driving region, 1 sequence at a time.
● Please use the standard FASTA format starting with '>' .
● The predicted probability is the result of the sub-model word2vec of PSPHunter. The phase separation probability of all-human reviewed proteins predicted by the full PSPHunter model can be found in the 'dataset' section of the website.