User friendly and accurate binder design pipeline
-
Updated
Sep 21, 2026 - Jupyter Notebook
User friendly and accurate binder design pipeline
Jupyter Notebooks for learning the PyRosetta platform for biomolecular structure prediction and design
Automated, modular pipeline for de novo protein binder design on SLURM clusters, with the BFmonitor dashboard
RosettaDesign using PyRosetta
Using Rotamer Interaction Fields from RIFGen/Dock in python
Protein Structure prediction using Hybrid Differential Evolution (HybridDE)
A Rosetta-based Python tool for constructing user-defined variant libraries with amino acid insertions, deletions, using loop closure algorithms and energy minimization.
Pre-registered self-consistency benchmark of ProteinMPNN VHH redesigns (PDB 8YVJ): ESMFold/AF2-multimer refolding + Rosetta interface cross-check, with shuffle/random/native controls
Does RFdiffusion hotspot specification change binder design outcomes? 50 epitope vs 50 non-epitope control designs, judged against pre-registered criteria. The generator's own backbones show no difference (0.93×); independent AF2 re-prediction shows 12.0×.
Method-independent benchmark of five de novo protein binder design tools on HPC
Structural bioinformatics pipeline for clustering antibody-antigen epitopes by geometric similarity and deriving consensus paratope design constraints for antibody engineering.
Computes biophysical metrics from monomeric PDB structures for use as features in machine learning models.
To associate your repository with the pyrosetta topic, visit your repo's landing page and select "manage topics."