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StaB-ddG: Predict Mutational Effects on Protein Binding from Folding Energy
Accurate, state-of-the-art ΔΔG predictions at 1,000x the speed of traditional force fields.
What is StaB-ddG?
StaB-ddG (Stability-to-Binding delta delta G) is a groundbreaking deep learning predictor designed to accurately estimate the effects of mutations on protein-protein binding free energies (ΔΔGbind).
Historically, deep learning models have underperformed traditional empirical force-field methods like FoldX and Flex ddG due to a lack of experimental binding data. StaB-ddG solves this data scarcity problem using a novel transfer-learning approach. By leveraging the fundamental laws of thermodynamics, StaB-ddG parameterizes binding energy as the difference between the folding energy of the complete protein complex and the sum of the folding energies of its individual binding partners.
By utilizing a pre-trained inverse-folding model (ProteinMPNN) and a sequential fine-tuning strategy on massive datasets, StaB-ddG delivers state-of-the-art accuracy that matches empirical force fields while offering an over 1,000x computational speed-up.
Core Features & Advantages
Thermodynamic Validity by Design: Unlike other deep learning models, StaB-ddG satisfies strict physical constraints by construction:
Antisymmetry: Predicting a mutation from state A to B yields the exact negative value of B to A.
Mutational Path Independence: The energy change between multiple mutation pathways remains mathematically consistent.
Massive Transfer Learning: Capitalizes on large-scale datasets by pre-training on structural data from the PDB, sequential fine-tuning on the Megascale folding stability dataset (776,298 measurements), and finishing with targeted binding affinity data (SKEMPIv2.0).
Advanced Variance Reduction: Integrates Monte Carlo ensembling and antithetic variates (fixing decoding orders and backbone noise across wildtype and mutant systems) to dramatically eliminate prediction error.
Multi-Chain & Complex Mutation Support: Built natively on ProteinMPNN, allowing the tool to flawlessly evaluate complex interfaces and multiple simultaneous mutations.
Blazing Fast Performance: Processes complex batched mutations in a fraction of a second, substituting hours of CPU-intensive force-field modeling with efficient GPU parallelism.
Performance Benchmarks
StaB-ddG sets a new paradigm for machine learning in structural biology. Evaluated on rigorous, homology-filtered test splits, it significantly outperforms previous deep learning architectures and balances accuracy with extreme computational thrift.
Accuracy vs. Baseline Methods (SKEMPIv2.0 Test Split)
On the standard benchmark test split of SKEMPIv2.0, StaB-ddG achieves a higher per-interface Spearman correlation than all prior deep learning methods:
Predictor Category | Method | Per Interface Spearman (±1SEM) | Prediction Time Per Mutation |
Our Method | StaB-ddG | 0.45 +/- 0.04 | 0.2 seconds (GPU) |
State-of-the-Art Ensemble | StaB-ddG + FoldX | 0.53 +/- 0.03 | Dynamic |
Force-Field Methods | FoldX | 0.48 +/- 0.03 | ~210 seconds (CPU) |
Force-Field Methods | Flex ddG | 0.42 +/- 0.04 | ~15 hours (CPU) |
Deep Learning | Surface-VQMAE | 0.33 +/- 0.05 | — |
Deep Learning | RDE-Net | 0.28 +/- 0.05 | — |
Deep Learning | Prompt-DDG | 0.27 +/- 0.04 | — |
Deep Learning | PPIformer | 0.20 +/- 0.04 | — |
Real-world Generalization
Yeast Surface Display Case Study: Validated across 28,293 single mutants on Rosetta-designed small protein binders, confirming that sequential stability and binding fine-tuning sequentially increase real-world predictive power.
Rigid Interfaces: Demonstrates superior performance on smaller, highly rigid complexes, achieving a Root Mean Squared Error (RMSE) of 0.94 kcal/mol on complexes under 150 residues.
What is Tamarind Bio?
Tamarind Bio is a premier computational biology platform designed to democratize access to state-of-the-art structural biology and protein engineering web tools. By wrapping advanced command-line machine learning models into intuitive, web-based interfaces, Tamarind Bio allows researchers, therapeutics designers, and biochemists to execute complex in silico designs seamlessly without managing deep learning dependencies, GPU environments, or complex codebases.
How to Use StaB-ddG on Tamarind Bio
Running your mutational binding calculations takes just a few clicks through the Tamarind Bio interface:
Upload Input Structure: Upload your target protein-protein complex in standard PDB format.
Define Sequences: Input your reference (Wildtype) amino acid sequence alongside your engineered (Mutant) sequence.
Configure Mutations: Select single or multiple amino acid substitutions across the interaction interface.
Run Prediction: Click "Run". Tamarind Bio handles the batched calculations across optimized GPU nodes, implementing automated Monte Carlo averaging and antithetic variates for maximum accuracy.
Download Results: Within seconds, receive your predicted ΔΔGbind values, complete with interface metrics to rank your candidate sequence variants for easy experimental screening.