# Overview

This guide provides usage examples for data cleaning modules organized by dataset source:

- [**Human Domainome Dataset**](supported_datasets.md#human-domainome-dataset): Site-saturation mutagenesis of 500 human protein domains.
- [**ProteinGym DMS Substitutions Dataset**](supported_datasets.md#proteingym-dms-substitutions-dataset): Large-scale benchmarks for protein design and fitness prediction.
- [**cDNA Proteolysis Dataset**](supported_datasets.md#cdna-proteolysis-dataset): Mega-scale experimental analysis of protein folding stability in biology and design.
- [**ddG-dTm Dataset**](supported_datasets.md#ddg-dtm-dataset): A collection of datasets providing single- and multiple-mutant measurements, labeled by the thermodynamic parameter (ΔΔG or ΔTm).
- [**ArchStabMS1E10 Epistasis Dataset**](supported_datasets.md#archstabms1e10-epistasis-dataset): High-order multi-mutant libraries (“1e10”) measuring protein stability for GRB2-SH3 and SRC.
- [**Antitoxin ParD3 Epistasis Dataset**](supported_datasets.md#antitoxin-pard3-epistasis-dataset): The antitoxin ParD3 3-position library is a combinatorially exhaustive dataset of 8,000 variants demonstrating that simple, independent per-residue mutation preferences are sufficient to almost perfectly predict combinatorial protein fitness.
- [**TrpB Epistasis Dataset**](supported_datasets.md#trpb-epistasis-dataset): A combinatorially complete sequence-fitness landscape comprising 160,000 variants across four active-site residues of the enzyme tryptophan synthase, capturing significant epistatic interactions to serve as a benchmark for model-guided enzyme engineering.
- [**Human Myoglobin Epistasis Dataset**](supported_datasets.md#human-myoglobin-epistasis-dataset): A deep mutational scanning library detailing the expression fitness scores for near-comprehensive single-codon mutations and a small fraction of double-codon mutations in yeast surface-displayed human myoglobin, which was used to train machine learning models for predicting epistatic effects and discovering stability-enhancing variants.
- [**CTXM Epistasis Dataset**](supported_datasets.md#ctxm-epistasis-dataset): A large-scale pairwise deep mutational scanning dataset of the CTX-M-14 β-lactamase active site, covering 49,096 double mutants across 17 active-site residues. Fitness measurements were obtained from functional selection under ampicillin and cefotaxime, providing substrate-dependent fitness landscapes for studying epistasis, compensatory mutations, and antibiotic resistance prediction.
- [**RBD-ACE2 Dataset**](supported_datasets.md#rbd-ace2-dataset): SARS-CoV-2 RBD sequences with ACE2 binding affinity scores, labeled by `log10Ka` where higher values indicate stronger ACE2 binding affinity.
- [**RBD-Antibody Dataset**](supported_datasets.md#rbd-antibody-dataset): SARS-CoV-2 RBD antibody escape data with mutation-level antibody escape scores.
- [**Chitosanase dTm Dataset**](supported_datasets.md#chitosanase-dtm-dataset): Wet-lab generated chitosanase mutation dataset containing wild-type protein sequences, amino acid mutation annotations, and experimentally measured melting temperatures (Tm). The Tm value serves as the label for protein thermostability prediction and mutation effect analysis.
- [**MGnify ddG Dataset**](supported_datasets.md#mgnify-ddg-dataset):A computationally derived thermodynamic dataset constructed from MGnify absolute free energy (dG) data. By applying specific sequence-length constraints and clustering operations, variants are systematically paired to establish wild-type and mutant relationships, yielding relative stability (ΔΔG) labels for protein engineering models.