AI-Guided De Novo Protein Design with AlphaFold 3 and RFdiffusion

To provide participants with practical knowledge and hands-on experience in designing novel protein binders against biologically relevant targets using generative AI, inverse-folding models and protein-structure prediction tools.

The workshop aims to help participants move from a target protein structure to a computationally generated, evaluated and prioritised binder candidate suitable for further research, optimisation and experimental validation.

By the end of the workshop, participants will be able to:

• • Understand generative AI, diffusion models and inverse folding in protein engineering.
  • Prepare target protein structures and identify binding sites, interface residues and hotspots.
  • Generate and filter diverse protein-binder backbones using RFdiffusion.
  • Design compatible amino-acid sequences using ProteinMPNN.
  • Assess sequence quality, solubility, hydrophobicity, charge and aggregation risks.
  • Predict designed protein structures and target–binder complexes using AlphaFold 3-class models.
  • Interpret pLDDT, PAE, pTM and ipTM scores and analyse interface contacts and structural clashes.
  • Rank promising binder candidates and understand the need for experimental validation.

📅 Day 1: Target Structural Backbone Generation with RFdiffusion

Core Objective: Master score-based diffusion models to generate novel protein backbones, binder scaffolds, and target-conditioned functional sites.

• Principles of generative denoising diffusion probabilistic models (DDPMs) in 3D protein structure space
• De novo scaffold generation and motif scaffolding for functional site presentation
• Target-conditioned diffusion: setting hotspot residues, binding pockets, and symmetry constraints
• Generating diverse candidates and evaluating structural backbone quality

🛠️ Hands-on Lab: Setup and execute an RFdiffusion pipeline to generate a library of novel binder backbones targeted to a designated protein receptor pocket using Google Colab.

🧰 Tools Covered: RFdiffusion, PyMOL, Biopython, Google Colab

📅 Day 2: Sequence Generation & Inverse Folding with ProteinMPNN

Core Objective: Translate target structural backbones into physical amino acid sequences engineered for high-affinity target binding.

• Fundamentals of message-passing graph neural networks (GNNs) in sequence inverse folding
• Fixed-backbone sequence design and residue-level conditioning (temperature, fixed positions)
• Optimizing amino acid sequence recovery, solubility, and aggregation propensity
• Automating multi-sequence sampling per generated backbone scaffold

🛠️ Hands-on Lab: Pass generated RFdiffusion backbones into ProteinMPNN to sample candidate sequences, apply sequence-level filters, and prepare FASTA inputs using Google Colab.

🧰 Tools Covered: ProteinMPNN, ANARCI, FastA Utilities, Google Colab

📅 Day 3: Biomolecular Complex Validation with AlphaFold 3 & Boltz-1

Core Objective: Predict complex interactions, score binding interfaces, and perform final candidate selection using next-generation AI architectures.

• Next-generation structure prediction: from AlphaFold 2 Evoformer to AlphaFold 3 Pairformer & diffusion modules
• Modeling biomolecular complexes: protein-protein interactions, nucleic acids, and small-molecule ligands
• Evaluating interface confidence metrics: pLDDT, PAE (Predicted Aligned Error), and ipTM scores
• Lead candidate ranking and automated export for wet-lab gene synthesis and expression

🛠️ Hands-on Lab: Predict target-binder complex structures using open-source AlphaFold 3 implementations (Boltz-1), filter leads by ipTM/pLDDT thresholds, and export validated designs using Google Colab.

🧰 Tools Covered: AlphaFold 3 Server, Boltz-1, ESMFold, Google Colab

• Protein engineering and computational protein-design researchers
• Structural bioinformatics and computational biology researchers
• Protein–protein interaction and molecular-modelling researchers
• Drug-discovery, biologics and therapeutic-protein researchers
• Antibody, peptide and enzyme-engineering researchers
• Synthetic biology, cancer biology and immunotherapy researchers
• PhD scholars, postdoctoral fellows and faculty members
• Biotechnology, bioinformatics and pharmaceutical professionals

After completing the workshop, participants will be able to execute a guided target-to-binder computational design workflow and generate:

• Novel protein-binder backbones
• Designed amino-acid sequences
• Predicted protein structures
• Target–binder complex models
• Structural-confidence analysis
• Protein-interface assessment
• Sequence-quality results
• A ranked list of computational binder candidates
• A reusable Google Colab workflow
• A roadmap for experimental validation