SSPSPredictor

A Sequence and Structure based Deep Learning Model for Predicting Phase-Separating Proteins

SSPSPredictor(Sequence and Structure based PSPs Predictor) takes sequence and structure as input. It utilizes the sequence embedding information from the large language model ESM-2, as well as a graph neural network to incorporate protein structure information based on the results of Alphafold 2.

SSPSPredictor is not just a binary classifier for phase-separating proteins. It goes beyond traditional prediction by providing:

1.Amino Acid-Level Analysis: Utilizing Attention scores, SSPSPredictor identifies key amino acids critical for driving phase separation.

2.Phase Separation Propensity: Quantitative insights into a protein's ability to undergo phase separation.

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For a protein sequence with a length of 700 residues, the calculation generally takes about 5 minutes to return the final result.
If users submit the corresponding UniProt ID directly, the result can be returned within approximately 10 seconds.

SSPSPredictor

A Sequence and Structure based Deep Learning Model for Predicting Phase-Separating Proteins

For a protein sequence with a length of 700 residues, the calculation generally takes about 5 minutes to return the final result. If users submit the corresponding UniProt ID directly, the result can be returned within approximately 10 seconds.

For a protein sequence with a length of 700 residues, the calculation generally takes about 5 minutes to return the final result.
If users submit the corresponding UniProt ID directly, the result can be returned within approximately 10 seconds.