bioRxiv Science⌕ Search

bioRxiv · 10.64898/2026.10.15.759522

Sequence-only prediction of VHH nanobody epitopes and paratopes on HER2 with a frozen protein language model ( ESM2) and a structure-derived residue prior

Abstract

Mapping which antigen residues an antibody contacts (the epitope) and which antibody residues do the contacting (the paratope) is central to therapeutic antibody engineering, yet most accurate methods require a solved or predicted three dimensional complex. We address a sequence only formulation: predict the HER2 epitope and the single domain antibody (VHH, or nanobody) paratope from the two sequences alone, with no three dimensional structure prediction at any stage. We pair a frozen ESM2 650M protein language model, used strictly as a feature extractor, with a lightweight trainable module per chain encoders, a shared cross attention block, and epitope, paratope and binder heads (12.2M trainable parameters) trained jointly on three public sources: HER2 binding and blocking competition assays ("AVIDa hHER2" to be published by organizers of SEPIQ 2026 The authors have no right to share the dataset to public ), residue level antibody antigen contacts (SAbDab2), and two reference antibody crystal structures. The blocking competition signal is de confounded into a trastuzumab minus pertuzumab contrast whose sign encodes the HER2 domain and which is used only as a training target, never as a model input. Under a three way, sequence cluster level split with a test set opened exactly once, the binder head reaches PR AUC 0.999 and the paratope head 0.659, while residue level epitope prediction on novel antigens remains near baseline (0.070) a limitation the sealed split exposes rather than hides. Sequence alone recovers the competition derived HER2 domain (the competitor class) for 87.7% of held out clones, a leakage free result. For HER2 specifically, a residue prior built from 25 mined HER2 antibody complexes and validated by leave one out reaches 0.26 to 0.29 residue PR AUC once the domain is correct, roughly 3.6 times the language model head alone. We release and report two submission variants per test clone, a fully sequence only prediction and a competition informed one ,and document which data were used where. Keywords: epitope prediction; paratope prediction; nanobody; VHH; HER2; protein language model; ESM2; cross attention; antibody antigen interface; leakage safe evaluation.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Babu, D., Pandurangan, S., Agilisium Consulting,. 2026-10-21. Sequence-only prediction of VHH nanobody epitopes and paratopes on HER2 with a frozen protein language model ( ESM2) and a structure-derived residue prior. https://doi.org/10.64898/2026.10.15.759522

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

Accelerating discovery and integration from RNA to Protein with R2T2P

Integrating transcriptomics and proteomics data remains an unmet challenge in the post-genomics era, discouraging researchers from pursuing integrative approaches to molecular biology research. Leveraging the power of translation profiling, we present RNA to Translation to Protein (R2T2P), a computational platform integrating genome-guided transcriptome assembly with identification of translation and proteins, including a tailored differential analysis framework and support for multiple reference transcriptomes. We applied R2T2P to investigate a cellular model of facioscapulohumeral muscular dystrophy (FSHD), where ectopic expression of the DUX4 retrogene in skeletal muscle triggers significant alterations in transcription and RNA metabolism. We uncovered hundreds of uncharacterized, highly overexpressed RNAs, including antisense and alternative transcripts, and quantified their contribution to translatome and proteome. Re-analysis of expression datasets from patient-derived myotubes and muscle biopsies validated this alternative transcriptome in FSHD patients, adding a reservoir of bona-fide novel molecular actors in a rare disease. Available at https://github.com/nfdata-omics/R2T2P/, R2T2P is a flexible and powerful workflow in modern integrative biology.

bioinformatics↗

MorphoANOVA: Dissecting Histology-Gene Expression Relationships in Spatial Transcriptomics

Spatial transcriptomics (ST) measures the expression of hundreds to thousands of genes within tissue architecture, often accompanied by histology images. However, it remains challenging to systematically characterize which genes exhibit associations with tissue morphology, whether these associations vary spatially, and which genes show additional spatial expression patterns beyond those captured by morphology. Here we introduce MorphoANOVA, a gene-wise statistical framework for characterizing morphology--expression relationships across genes measured by ST. Using nested spatially varying coefficient models and sequential ANOVA, MorphoANOVA assesses slide-wide associations with an H\&E-derived morphology feature, location-specific changes in these associations, and additional spatial variation after accounting for the selected feature. The resulting statistics enable gene prioritization and pathway enrichment analysis, while spatial component maps support the interpretation of individual genes. Simulations demonstrate MorphoANOVA's ability to distinguish the modeled components and recover their spatial patterns. Applications to human cortex and breast cancer identify biological programs associated with different morphology--expression patterns and reveal distinct relationships with cell composition. Across three HER2-positive breast cancer patients, invasive-cancer marker genes consistently exhibit stronger evidence of location-specific morphology associations. MorphoANOVA thus provides a framework for systematically interpreting spatial gene-expression patterns in relation to tissue morphology.

bioinformatics↗

Evaluation effects can rival reported concordance gains in cross-cancer survival prediction

Studies of cross-cancer learning for survival prediction from tumour omics have reported concordance gains of 0.020 and 0.045, but how such gains compare with evaluation effects remains unclear. We examined these effects across 13 TCGA cancers (5,761 patients, 1,526 disease-specific deaths), using five repeats of five-fold cross-validation and paired bootstrap intervals. When model selection did not use test data, our borrowing model gained 0.009 (95% interval 0.001 to 0.016) over its target-fitted reference when cancers received equal weight (post hoc). Random-effects pooling gave a gain of 0.002 (-0.002 to 0.006), with most weight assigned to cancers where the model made little use of the pretrained score. Choosing the fusion weight on test splits raised the apparent equal-weight gain to 0.017. We reimplemented a published method, VAECox, which showed no pretraining gain over random initialisation at the last or validation-selected training step. Choosing each network's best test step, as the published code does, raised concordance by 0.03 to 0.05 across the 13-cancer and ten-cancer overall-survival settings, but barely changed the difference between networks. In a constructed post hoc experiment, choosing the best test step only for the pretrained network changed the equal-weight mean difference from -0.004 to +0.045 (+0.035 to +0.054). Across seven multi-omics comparisons, estimates from single five-fold runs differed from their cancer's five-run mean by at least 0.02 in 11% to 42% of runs. Concordance was about 0.02 lower at held-out tissue source sites. Interpreting a reported gain therefore requires each model's selection rule, variation across repeated partitions, the weighting of cancers and the evaluation population.

bioinformatics↗