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Found 4 papers, 3 papers with code
IDP-Bert: Predicting Properties of Intrinsically Disordered Proteins (IDP) Using Large Language Models
In this study, we introduce IDP-Bert model, a deep-learning architecture leveraging Transformers and Protein Language Models (PLMs) to map sequences directly to IDPs properties.
Characterization of Phosphorylated Tau-Microtubule complex with Molecular Dynamics (MD) simulation
Alzheimer's Disease (AD), a neurodegenerative disorder, is reported as one of the most severe health and socioeconomic problems in current public health.
GPCR-BERT: Interpreting Sequential Design of G Protein Coupled Receptors Using Protein Language Models
In this paper, we developed the GPCR-BERT model for understanding the sequential design of G Protein-Coupled Receptors (GPCRs).
PeptideBERT: A Language Model based on Transformers for Peptide Property Prediction
In this work, inspired by recent progress in Large Language Models (LLMs), we introduce PeptideBERT, a protein language model for predicting three key properties of peptides (hemolysis, solubility, and non-fouling).
