MMseqs2 app to run on your workstation or servers
Protein 3D structure prediction pipeline
Local Interaction Score (LIS) for structure prediction analysis
Design data and process for the AdaptyvBio protein design competition
Stand-alone server for structural proteome curation
Predict protein folding structures using ColabFold. Gain a deeper understanding of protein folding prediction with AlphaFold2 and MMseqs2. Run the Jupyter notebook on UCloud, learn to interpret results, predict protein structures of interest. Technical requirements provided. Enhance your knowledge of protein folding and AlphaFold2's principles. Fam
MSAffect is a computational pipeline to evaluate the robustness of AlphaFold2 protein structure predictions under adversarial MSA perturbations to identify structural sensitivity and confidence shifts in neural-network-based folding.
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.
🧬 Companion repository for the Methods in Molecular Biology protocol 📓 combining AlphaFold confidence metrics with pyDock energy scoring⚡ for protein–protein complex modeling.
Learn how AlphaFold2 works by seeing it and by running real LocalColabFold inference on your own GPU. Interactive, visual companion to the AlphaFold2 paper.
AlphaFold2 prediction for Human Insulin P01308 using Google Colab + T4 GPU
Predict 3D protein structures using AlphaFold2 via ColabFold, with visualizations of per-residue confidence and alignment error
A measured benchmark of structure prediction confidence, reporting pLDDT calibration against lDDT, the physical validity of predicted structures, and what docking into a predicted receptor costs against docking into the crystal structure it was built from.
End-to-end bioinformatics AI project: AlphaFold/ColabFold protein structure prediction with CASP-style RMSD benchmarking, plus a Nextflow-orchestrated ML pipeline for cancer diagnosis (dataset retrieval → preprocessing → tuning → SHAP explainability → FastAPI deployment).
Predict 3D protein structures using AlphaFold2 via ColabFold, with visualizations of per-residue confidence and alignment error.
MHC Atlas OS — runtime-agnostic, multi-agent system for explainable, structure-guided mutation prioritization.
🧬 Lectures for course ML-protein-design
MSA MCP server for generating multiple sequence alignments using ColabFold
生信模式 (Bioinformatics Mode) — research-grade protein structure & interaction agent preset for DeepSeek Harness: AF2-Multimer, ESMFold, AutoDock Vina, MM-GBSA/MD, structure QA, virtual screening.
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