One Structure Is Not Enough: Conformational Ensemble Representations Improve In-Frame Indel Effect Prediction Across Protein Language Models

Predicting the functional effect of in-frame insertions and deletions (indels) is essential in genome interpretation for clinical diagnosis, genomic research and drug development. Structural information is one of the main sources for variant effect prediction, as variant effects are often mediated by protein structure alteration. A predicted static wild-type structure has enabled structure-informed protein language models (PLMs) to achieve high accuracy in genomic variant effect prediction, especially for missense variants. However, current computational tools show relatively lower prediction performance for in-frame indels, which are also difficult for experts to interpret manually. This limitation may partly stem from relying on a single static structure, since (i) AlphaFold’s reliability for structure-disrupting variants remains uncertain, and (ii) a static structure cannot fully capture the protein dynamics linked to indel tolerance, such as local flexibility. Here, we systematically evaluate conformational ensembles generated from indel variant sequences as structural input to six structure-informed PLMs used as frozen feature extractors. Across 4,270 in-frame indels from 1,418 proteins, the PLMs’ pathogenicity prediction improved significantly with the ensembles over single static structure inputs in most comparisons, especially for ESM3, whose AUROC rose from 0.8794 to 0.9359. Most PLMs also produced more variant-specific representations, and the improvement was concentrated in regions with low pLDDT, unstructured regions, and long benign variants. Consistent with recent findings on the relationship between intrinsic structural dynamics and protein tolerance to indels, these results suggest a way to incorporate such biological knowledge into PLMs at the representation level.