Metric definitions, color schemes, and references
.cxc script and structure file, place them in the same folder, then open the script in ChimeraX or PyMOLCDK2, TP53)famdb_details_ortho URL directly in the Paste URL tabP69905), a model entity ID (e.g. AF-0000000066214167), or a full AlphaFold DB URLP31749) — autocomplete suggestions appear as you typeAll tools include adjustable LIR display settings that affect the visualization script and 3D viewer:
Click Apply after changing values to update the visualization script and 3D viewer.
All four tools include a sequence viewer that displays the actual amino acid sequence (one-letter codes) for each chain or domain. Residues are color-coded based on their interaction status:
Residue numbering is shown at intervals. The viewer scrolls horizontally for long sequences.
All tools use consistent, fixed colormaps for maps and matrices. These are not affected by the color presets.
| Map | Colormap | Scale |
|---|---|---|
| PAE map | blue – white – red (bwr) | 0 Å (confident) → 30 Å (uncertain) |
| LIS map | matplotlib Blues | 0 → 1 (higher = stronger interaction) |
| cLIS map | matplotlib Greens | 0 → 1 (higher = stronger contact-filtered interaction) |
| Score matrix (iLIS) | matplotlib Oranges | Lower-left triangle |
| Score matrix (cLIR / ipTM) | white → green (or Purples for ipTM) | Upper-right triangle |
The visualization script (.cxc for ChimeraX, .pml for PyMOL) uses chain colors that you can customize via presets. The default is Teal/Coral gradient.
The Monomer tool provides additional coloring presets beyond the default domain-colored view:
Color presets and custom colors update the visualization script (ChimeraX/PyMOL) automatically when you select a preset. The PAE/LIS/cLIS maps use fixed colorscales and are not affected by color choices.
| Metric | Full Name | Definition |
|---|---|---|
| iLIS | integrated LIS | √(LIS × cLIS) — geometric mean of LIS and cLIS; balanced score combining confident domain and confident contact |
| LIS | Local Interaction Score | Average of inversely scaled PAE (0–1, higher is better) within LIA |
| cLIS | contact-filtered LIS | Average of inversely scaled PAE within cLIA (contact-filtered) |
| LIR | Local Interaction Residues | Residues in the confident interaction region (PAE ≤ 12 Å) |
| cLIR | contact-filtered LIR | Residues in direct physical contact (PAE ≤ 12 Å & Cβ ≤ 8 Å) |
| LIA | Local Interaction Area | Confident interaction area (PAE ≤ 12 Å) |
| cLIA | contact-filtered LIA | Confident interaction area within contact distance (PAE ≤ 12 Å & Cβ ≤ 8 Å) |
| iLIA | integrated LIA | √(LIA × cLIA) — geometric mean of confident interface area and contact-supported interface area; reflects interface size weighted by contact density |
| iLISA | integrated LIS-area | iLIS × iLIA — composite score combining interface confidence (iLIS) with interface size (iLIA); useful for ranking large vs. small interfaces of comparable confidence |
Default cutoffs: PAE ≤ 12 Å (confident interaction) and Cβ ≤ 8 Å (direct contact). These can be adjusted before processing in the Universal and Dimer tools.
PAE transformation: For each inter-chain residue pair (i, j), the PAE value is converted to a confidence score: confidence = 1 − (PAE / cutoff) if PAE ≤ cutoff, otherwise 0. This linearly maps PAE = 0 Å to confidence = 1.0 (highest) and PAE = cutoff to 0. The PAE cutoff of 12 Å was determined by ROC analysis to maximize AUC (Kim et al., 2024).
LIS calculation: LIA (Local Interaction Area) is the count of inter-chain residue pairs with PAE ≤ cutoff. LIS is the mean confidence score across all LIA pairs. cLIA further restricts to pairs also in physical contact (Cβ ≤ 8 Å), and cLIS is the mean confidence within cLIA. The inter-chain PAE block is symmetrized by averaging (A→B) and (B→A) directions. iLIS = √(LIS × cLIS), combining interface confidence with direct contact evidence into a single robust metric (Kim et al., 2026). Two derived size-weighted metrics are also reported for completeness: iLIA = √(LIA × cLIA) summarizes interface size, and iLISA = iLIS × iLIA combines interface confidence with size. These secondary metrics may help differentiate predictions with similar iLIS but different interface sizes.
Additional confidence metrics: ipSAE (interaction prediction Score from Aligned Errors; Dunbrack, 2025) uses a PTM-like scoring function on PAE values at contact residues. actifpTM (actual interface pTM; Varga et al., 2025).
Contact map: Built from Cβ atom distances (Cα for glycine). Two residues are in contact if their Cβ–Cβ distance ≤ 8 Å. For nucleic acids, phosphorus (P) atoms are used with a 4 Å distance adjustment.
Symmetrization: Results for chain pairs (A→B) and (B→A) are averaged. LIR/cLIR residue sets are the union across all models.
Suggested threshold: iLIS ≥ 0.223 was established at 10% FPR using large-scale Y2H reference datasets in yeast (Yu et al., 2008), fly (Tang et al., 2023), and human (Braun et al., 2009). This threshold was determined using AlphaFold-Multimer predictions (via ColabFold). See Kim et al., 2026, for details.
For large-scale batch analysis of many predictions, use lis.py from the AFM-LIS repository. It supports all the same platforms (AlphaFold3, ColabFold, Boltz, Chai-1, OpenFold3), auto-detects the prediction format, and outputs CSV.
Features: .gz/.xz decompression, incremental CSV output (safe to interrupt and resume), progress bar with ETA.
AFM-LIS
Structure Prediction
Confidence Metrics
Y2H Reference Datasets (iLIS Benchmarks)
Visualization
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If you use LIVIA in your research, please cite:
If you use the LIS or iLIS metric, please also cite: