How to Evaluate AI-Predicted Structures
AI-based protein structure predictions have become invaluable tools in structural biology research, offering rapid access to high-quality models. However, their effective use requires critical evaluation to avoid misinterpretation and ensure biological relevance. This guide provides a practical checklist and key DOs and DON’Ts to help researchers rigorously assess AI-generated models before integrating them into experimental design or drug discovery workflows.
Understanding the Output
Confidence Scores (e.g., pLDDT): Predicted Local Distance Difference Test (pLDDT) scores range from 0 to 100 and estimate the reliability of atomic positions per residue [Jumper et al., 2021]. Scores above ~90 generally indicate high confidence comparable to experimental accuracy, 70–90 moderate confidence, and below 50 suggest uncertain or disordered regions. Interpretation requires context: low scores may reflect intrinsic disorder rather than prediction failure [Uversky, 2019].
Predicted Aligned Error (PAE): PAE provides a matrix estimating positional errors between residue pairs, useful for assessing uncertainties in domain orientation or multimer interfaces [Evans et al., 2022]. High PAE values between domains signal caution in interpreting relative positions.
When Low-Confidence Regions Matter:
Regions critical to function (e.g., active sites, interfaces) with low confidence warrant experimental validation.
Conversely, low confidence in flexible loops or intrinsically disordered segments is expected and should not be overinterpreted.
Checklist Before Using an AI Model
Before incorporating an AI-predicted structure into your research, verify the following:
Coverage: Does the model encompass all biologically relevant domains or motifs? Missing segments may indicate gaps in prediction or sequence coverage.
Plausibility: Are predicted conformations consistent with available experimental data (e.g., partial crystal structures, cross-linking, mutagenesis)?
Disorder Correspondence: Do low-confidence regions align with known intrinsically disordered regions or experimentally validated flexibility?
Functional Sites: Are predicted active sites, ligand-binding pockets, and protein-protein interfaces structurally reasonable based on biochemical knowledge?
Multimeric States: If relevant, does the model accurately reflect quaternary assembly, or is additional modeling required (e.g., AlphaFold-Multimer)?
Common Mistakes to Avoid
Blind Trust in Drug Design: Never rely solely on AI predictions for structure-based drug design without experimental validation of binding sites and conformations.
Ignoring Low Confidence: Disregarding low-confidence regions can lead to overlooking flexible or functionally important disorder.
Assuming Static = Biological Reality: Proteins are dynamic; a static predicted structure represents only one conformation and may not capture relevant states.
Overlooking Training Data Bias: Be aware that AI models are trained predominantly on certain protein families, potentially limiting accuracy for novel folds or underrepresented classes.
Misinterpreting PAE: Confusing high PAE values as failure rather than uncertainty requiring cautious interpretation.
Examples
Reliable Model with Experimental Correlation
A well-predicted globular enzyme domain with high pLDDT scores (>90), low PAE values, and structural alignment to known crystal structures. Functional sites predicted accurately, facilitating successful mutagenesis validation [Jumper et al., 2021].
Over-Interpreted Model That Failed in Practice
A predicted membrane protein complex where AI assigned high confidence scores to interfaces despite lack of experimental support, leading to erroneous functional hypotheses and failed drug screening attempts [Callaway, 2022]. Highlighting the need for orthogonal validation.
