A Nextflow pipeline for protein binder design
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Updated
May 28, 2026 - Python
A Nextflow pipeline for protein binder design
A modular, extensible peptide design pipeline with target preparation, backbone generation, sequence design, scoring, and ranking. Full local CPU pipeline, and backend hooks for RFpeptides, ProteinMPNN/LigandMPNN, and ColabFold.
Open hotspot-guided de novo protein binder design pipeline integrating OpenMM, BindCraft, ProteinMPNN, and AlphaFold2.
Protein binder design GUI
Unified Python library and CLI for protein structure prediction and inverse folding.
Retraining of ProteinMPNN model specifically with acid-stable structures and sequences
LigandMPNN but with dynamic constraints. This allows the biases applied during inference to adjust to a desired goal during the generation process. Current implementations are for pI targeting and surface patch generation
Protein binder mutagenesis GUI
RNA-seq counts to ranked de novo protein binder candidates, with full provenance back to the patient cohort.
Optimized ProteinMPNN for Apple Silicon: 15× speedup with 0% accuracy loss through architecture pruning, batching, and ANE acceleration. Comprehensive benchmarking study of speed-accuracy trade-offs.
De novo protein binder design pipeline: motif-scaffolded RFdiffusion + ProteinMPNN + AlphaFold2 self-consistency validation. PD-L1 reference example.
Personalized lymphoma therapy design with Gemma 4, AlphaFold, RFdiffusion. Kaggle Gemma 4 Good Hackathon submission.
Manage protein design processes
PEN-ASSEMBLE: Automated Strategy and Scoring Engine for Molecular Bridge-recombinase Library Engineering | IS110-family design pipeline, 1,029 candidates, 5/5 pre-registered predictions | Part of PEN-STACK
Computational pipeline for measuring protein interior reprogrammability. Identifies chassis candidates where exterior fold is preserved while interior chemistry varies.
Molecular dynamic simulation of RFdiffusion/ProteinMPNN designed HMERF mutated titin Ig152/Fn3-119 domain protein binder
🧬 Lectures for course ML-protein-design
🧬 Experiments and setup of RFdiffusion/ProteinMPNN running on macOS Apple Silicon (CPU).
Universal Peptide Drug Discovery — AI-driven cyclic peptide design pipeline with ncAA integration
Empirical dual-use risk assessment of protein language models (ESM-2) and structure-based design tools (ProteinMPNN)
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