AlphaFold-Multimer Local Interaction Score (AFM-LIS, now with iLIS)

This repository contains a computational framework to analyze protein-protein interactions (PPIs) from AlphaFold-Multimer predictions. It is designed to accurately identify transient and flexible interactions (e.g., involving SLiMs or IDRs) that global confidence metrics like ipTM often miss.

The framework includes two validated approaches:

• iLIS (integrated Local Interaction Score): Our newest, robust single metric that combines interface confidence with a direct physical contact filter. This is the recommended score for most analyses. (Kim et al., 2025)
• LIS + LIA (Local Interaction Score + Area): Our original dual-metric system that evaluates interactions by assessing both the score (LIS) and the area (LIA) of the predicted interface. (Kim et al., 2024)

How iLIS Works: The Core Concept

The iLIS framework identifies the high-confidence local interface, filters it for direct physical contacts, and combines these scores into a single, robust metric.

Schematic of the iLIS calculation and benchmark data.

Figure 1A-D from Kim et al., 2025. iLIS outperforms existing metrics for global structure evaluation particularly for flexible protein complexes (low pLDDT groups).

The Evolution from LIS to iLIS

1. The Original Framework: LIS + LIA

Standard AlphaFold metrics (like ipTM) often fail on flexible interactions because non-interacting disordered regions can "dilute" the global score. Our original Local Interaction Score (LIS) was developed to solve this by focusing only on the confidently predicted interface, defined as all residue pairs with a Predicted Aligned Error (PAE) ≤ 12 Å.

However, LIS by itself had a potential failure mode: it could assign a high score to a few residue pairs that were confidently predicted but physically distant and not in actual contact. Our original (2024) framework addressed this by using LIS in combination with a second metric, the Local Interaction Area (LIA), which measures the area in the confident interface (PAE ≤ 12 Å). In the failure case, the LIA score would be tiny, successfully filtering out the false positive. This dual-metric LIS + LIA system remains a valid approach.

Examples of positive PPIs with flexible regions (decent LIS & LIA & low ipTM). All PPI examples were experimentally proven previously.

2. The New Metric: iLIS (integrated LIS)

The new iLIS metric (2025) streamlines this process into a single, more robust score. It was designed to solve the same problem by directly integrating a physical contact filter.

iLIS is calculated as the geometric mean of two components:

• LIS (Local Interaction Score): The same as the original metric; measures the average confidence of the broad LIA (PAE ≤ 12 Å).
• cLIS (contact-filtered LIS): A stricter metric that measures the average confidence only for residue pairs that are in direct physical contact (PAE ≤ 12 Å AND Cβ–Cβ distance ≤ 8 Å).

The final score is:

iLIS = sqrt(LIS * cLIS)

This use of a geometric mean is critical. If no direct physical contacts are confidently predicted (cLIS = 0), the final iLIS score is forced to zero, robustly eliminating this class of false positives. It now allows single metrics to distinguish positive and negative predictions.

Examples of positive PPIs with flexible regions (decent iLIS & low ipTM). All PPI examples were experimentally proven previously.

Interpreting Results & Thresholds

We provide two validated frameworks for analysis. iLIS is the recommended single-metric approach.

1. Recommended: iLIS Framework (Kim et al., 2025)

A high-confidence interaction is suggested if:

• iLIS ≥ 0.223

This threshold was established using large-scale Y2H reference sets used in yeast (Yu et al., 2008), fly (Tang et al., 2023), and human (Braun et al., 2009). Please see the details in the supplementary text in Kim et al., 2025.

2. Original: LIS + LIA Framework (Kim et al., 2024)

A positive PPI is suggested if either of the following conditions is met:

• Best LIS ≥ 0.203 AND Best LIA ≥ 3432, or

• Average LIS ≥ 0.073 AND Average LIA ≥ 1610.

These cutoffs were derived from specific fly (Tang et al., 2023) and human (Braun et al., 2009) reference sets. As always, optimal thresholds may vary by dataset, and experimental validation is highly recommended.

Usage

To calculate scores, use the provided Jupyter notebooks:

For AlphaFold 3:

• use Jupyter Notebook in your local computer or
• use Google Colab

For AlphaFold-Multimer (ColabFold output):

• use lis_analysis_colabfold.py or
• use integrated_local_interaction_score_20250514.ipynb

Requirements

• Python (3.6.7)
• NumPy (1.19.4)
• Pandas (1.1.4)
• Biopython (1.78)
• openpyxl (3.0.5)
• Multiprocessing support

How to cite

If you use this work, please cite the relevant paper:

• For the iLIS metric (Recommended): Kim et al (2025). A Structure-Guided Kinase-Transcription Factor Interactome Atlas Reveals Docking Landscapes of the Kinome. bioRxiv (link)

• For the original LIS + LIA framework: Kim et al (2024). Enhanced Protein-Protein Interaction Discovery via AlphaFold-Multimer. bioRxiv (link)

FlyPredictome

The Fly PPI database, which uses this framework, is available at:

• www.flyrnai.org/tools/fly_predictome/web/
• Now covers ~800,000 PPI predictions.

Declaration of generative AI usage

This project utilized OpenAI's ChatGPT and Google's Gemini to assist in generating Python code, documentation, or other textual content.