Gregory Butler

TooTranslator: Zero-Shot Classification of Specific Substrates for Transport Proteins by Language Embedding Alignment of Proteins and Chemicals

TooTranslator uses language embedding alignment to predict substrates of transport proteins, achieving 80% accuracy with top-100 predictions for unseen substrates.

TooT-PLM-P2S: Incorporating secondary structure information into protein language models

This paper introduces TooT-PLM-P2S, a protein language model incorporating secondary structure information, and evaluates its performance on eight downstream tasks.

Exploiting protein language models for the precise classification of ion channels and ion transporters

This paper introduces TooT-PLM-ionCT, a framework using six protein language models to classify ion channels and transporters from membrane proteins, achieving strong performance in distinguishing these classes.

A general prediction model for substrates of transport proteins

This paper introduces SPOT, a machine learning model that predicts substrates for transport proteins with high accuracy, outperforming existing methods.

Predicting the specific substrate for transmembrane transport proteins using BERT language model

The paper uses a fine-tuned BERT model to predict specific substrates of transmembrane transport proteins with high accuracy.

TooT-SC: Predicting Eleven Substrate Classes of Transmembrane Transport Proteins

TooT-SC predicts eleven substrate classes of transmembrane transport proteins using a combination of amino acid composition and evolutionary information, achieving high performance.

TranCEP: Predicting transmembrane transport proteins using composition, evolutionary, and positional information

TranCEP predicts transmembrane transport protein substrates by integrating amino acid composition, evolutionary information, and positional data, outperforming existing methods.