Use ProSST Mutation Effect Prediction

Overview

ProSST is a protein language model that integrates sequence and structural information for enhanced protein function prediction. It employs a structure quantization module to convert 3D protein structures into discrete tokens using a GVP encoder and k-means clustering, coupled with a Transformer architecture featuring sequence-structure disentangled attention. Pre-trained on 18.8 million protein structures, ProSST achieves state-of-the-art performance in zero-shot mutation effect prediction (e.g., Spearman correlation of 0.504 on ProteinGYM) and supervised tasks like protein localization (94.32% accuracy on DeepLoc). The model explicitly learns residue-structure relationships, enabling robust predictions even for stability, binding, and expression-related mutations.

Neurosnap Overview

The ProSST Mutation Effect Prediction online webserver allows anybody with a Neurosnap account to run and access ProSST Mutation Effect Prediction, no downloads required. Information submitted through this webserver is kept confidential and never sold to third parties as detailed by our strong terms of service and privacy policy.