Protein Sequence Annotation And Function Prediction

via “masked-position-prediction-with-context”

fill-mask model by undefined. 17,90,395 downloads.

Unique: Leverages 33 transformer layers trained on 250M diverse protein sequences to capture multi-scale evolutionary and functional patterns — the model learns implicit structural constraints and functional requirements without explicit 3D structure input, enabling predictions that correlate with experimentally-validated amino acid substitutions better than simple conservation-based methods

vs others: More accurate than position-specific scoring matrices (PSSMs) or conservation-based methods for predicting functional amino acids, and faster than structure-based design tools like Rosetta while maintaining competitive performance on protein engineering benchmarks

via “structure-based functional annotation and motif detection”

* 📰 2022: [ChatGPT: Optimizing Language Models For Dialogue (ChatGPT)](https://openai.com/blog/chatgpt/)

Unique: Uses geometric hashing to detect structural motifs independent of sequence, enabling functional annotation of proteins with no sequence homologs. Combines spatial clustering with database matching to identify recurring 3D patterns at sub-domain resolution.

vs others: Complements sequence-based annotation (BLAST, Pfam) by identifying functional sites in proteins with low sequence identity but conserved structure; more sensitive to subtle structural similarities than RMSD-based methods.

via “protein-sequence-annotation-and-function-prediction”

A large language model for science. Can summarize academic literature, solve math problems, generate Wiki articles, write scientific code, annotate molecules and proteins, and more. [Model API](https://github.com/paperswithcode/galai).

via “biological-sequence-prediction”

via “protein-structure-prediction”

via “protein-structure-prediction”

via “protein activity and function prediction”