A generative model for programmable protein design

TCRconv is a deep learning model for predicting recognition between T cell receptors and epitopes. It uses protBERT embeddings for the TCRs and convolutional neural networks for the prediction.

TCRGP, a novel Gaussian process method that can predict if TCRs recognize certain epitopes. This method can utilize different CDR sequences from both TCRα and TCRβ chains from single-cell data and …

Code for "T Cell Receptor Specificity Prediction with Bimodal Attention Networks" (https://doi.org/10.1093/bioinformatics/btab294, ISMB 2021)

🗂️ [vdjdb.cdr3.net is up and running] Git-based TCR database storage & management. Submissions welcome!

flexible CDR based distance metrics

T/B cell receptor sequencing analysis notes

📚 Tools and databases for analyzing HLA and VDJ genes.

Deep Learning Methods for Parsing T-Cell Receptor Sequencing (TCRSeq) Data

NetTCR-2.0. Sequence-based prediction of peptide-TCR binding

EigenFold: Generative Protein Structure Prediction with Diffusion Models

Trainable, memory-efficient, and GPU-friendly PyTorch reproduction of AlphaFold 2

Python code for fine-tuning AlphaFold to perform protein-peptide binding predictions

TEIM: TCR-Epitope Interaction Modeling

OmegaFold Release Code

Code for running RFdiffusion

EquiDock: geometric deep learning for fast rigid 3D protein-protein docking

python tools for TCR:peptide-MHC modeling and analysis

Implementation of Chroma, generative models of protein using DDPM and GNNs, in Pytorch

Official repository for the paper "Tranception: Protein Fitness Prediction with Autoregressive Transformers and Inference-time Retrieval"

Source code for fitting thermodynamic models (MoCHI), downstream analyses and to reproduce all figures in the following publication: Mapping the energetic and allosteric landscapes of protein bindi…

Python package for graph statistics

A comprehensive library for computational molecular biology