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GFP sequence

Predict protein thermostability changes (ΔΔG) for point mutations using a graph neural network. Enables computational saturation mutagenesis screening to identify stabilizing mutations.

Predict protein stability using validated BioPython methods: Instability Index, Aliphatic Index, GRAVY, flexibility analysis, and charge distribution

Predict protein aggregation nucleation propensity from amino acid sequences using the Lehner Lab CANYA neural network.

Faithful static-mode Aggrescan3D tool for per-residue aggregation propensity analysis from a single protein structure.

Predict pKa values of ionizable groups in proteins and protein-ligand complexes from 3D structure. PROPKA calculates environment-driven pKa shifts for standard ionizable residues, terminal groups, and supported ligand atom types.

Predict protein solubility from amino acid sequence using the University of Manchester Protein-Sol method.

Predict metal and water binding sites in protein structures using 3D convolutional neural networks (AllMetal3D + Water3D).

Predict protein hydration sites from a structure using a diffusion model with ESM features and a confidence-filtering head.

Compute 200+ RDKit molecular descriptors, drug-likeness rule violations, and structural fingerprints for QSAR, virtual screening, and ML workflows

Plot net charge vs pH for protein sequences. Visualize how protein charge changes across pH 0-14 and identify the isoelectric point (pI) where the net charge crosses zero.