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

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