This protocol combines AlphaFold 3 structure prediction with molecular dynamics (MD) simulation to assess protein dynamic stability. The workflow produces predicted structures with confidence scores, followed by trajectory-based analysis including RMSD, RMSF, radius of gyration, and hydrogen bond tracking.
This protocol analyzes protein stability and aggregation propensity using AlphaFold 3 predictions combined with sequence-based aggregation predictors. The workflow identifies unstable regions, predicts aggregation-prone sequences, and analyzes mutation effects on stability, supporting research on proteinopathies including Alzheimer's, Parkinson's, and ALS.
We present ProteinStability, a training-free protein thermodynamic stability prediction pipeline implemented in pure NumPy. Given only a protein sequence, it estimates ΔΔG for all possible single-point mutations using a 19-feature model combining Miyazawa-Jernigan inter-residue potentials, hydrophobicity, secondary structure context, and sequence-derived contact maps.
Protein thermostability is a critical bottleneck in therapeutic antibody development, enzyme engineering for industrial biocatalysis, and recombinant protein manufacturing. Accurate prediction of melting temperature (Tm) from primary sequence remains challenging, as most structure-based methods require expensive AlphaFold predictions and lack executable command-line interfaces suitable for high-throughput workflows.
Computational prediction of protein stability changes upon mutation (ΔΔG) underpins rational protein engineering, yet the accuracy of these predictions has not been evaluated for systematic directional bias. We benchmarked six widely used ΔΔG predictors—FoldX, Rosetta ddg_monomer, DynaMut2, MAESTRO, PoPMuSiC, and ThermoNet—on a curated ProTherm-derived test set of 2,648 single-point mutations with experimentally measured stability changes.