Biology subjects

Liu, p.

Publications and source records attributed to Liu, p..

2 recordsLinked to original sources

bioRxiv2026

ProLoc: Text-guided Localization of Protein Functional Regions

MotivationProtein function is often mediated by specific sequence regions, such as domains, motifs and functional sites. Identifying these regions is important for understanding protein mechanisms, annotating newly sequenced proteins and prioritizing residues for experimental validation. However, existing protein function prediction and protein-text models mainly capture global protein-level associations, making it difficult to determine which residues support a given textual functional description. This limits their use for mechanistic interpretation and residue-level experimental prioritization. ResultsWe introduce text-guided protein functional region localization, a span-level grounding task that identifies residue regions corresponding to natural-language functional descriptions. We construct an InterPro-derived localization benchmark of explicit protein-text-region examples, covering both domain-level and functional-site annotations with sequence-similarity-aware splits and a unified span-level evaluation protocol. We further propose ProLoc, a text-conditioned localization model built on raw ESM2-650M and PubMedBERT with direct residue-level localization and anchor-free span proposal generation. On the held-out test set, ProLoc substantially outperforms window-based adaptations of representative protein and protein-text models. Its direct output achieves the strongest single-region localization performance, reaching 0.7730 IoU@1, while its anchor-free proposal output improves visible multi-site recovery, reaching 0.9671 VM R@10 IoU50 and 0.9489 VM All-Hit@50. Availability and ImplementationSource code and evaluation scripts are available at https://github.com/ShiDeng7rz/Proloc. The processed benchmark and data splits are archived at Zenodo: https://doi.org/10.5281/zenodo.20729714. Contactliupeishuo@nju.edu.cn

bioinformatics↗

bioRxiv2026

Robust Conditional Diffusion with Noisy Templates for Antibody Sequence-Structure Design

Antibodies specifically recognize antigens and play a central role in therapeutic discovery. Designing antibodies for a given antigen remains challenging because antigen-antibody complex data are limited, whereas the sequence and conformational spaces of complementarity-determining regions (CDRs) are large. Retrieved CDR templates from databases or candidate libraries can narrow the design space and improve controllability, but retrieval for novel antigens is often sparse and imperfect; treating retrieved templates as hard conditions can bias the denoising process and cause negative transfer. To address this problem, we propose Robust Conditional Diffusion with Noisy Templates for antibody sequence-structure design (NT-ABDiff), a joint diffusion framework that treats candidate CDR-only templates as optional and potentially unreliable conditions. NT-ABDiff uses reliability-aware template modulation to estimate the context-conditioned usefulness of each candidate and to adaptively reweight and fuse multiple templates during conditioning. We further train the model with mixed-quality and corrupted templates as conditional perturbation regularization, encouraging the denoiser to exploit informative templates while remaining stable when templates are uninformative. Experiments under controlled template shifts and a train-set retrieval evaluation show that NT-ABDiff improves CDR-H3 sequence recovery and structural accuracy over strong baselines, while retaining robustness to missing, mismatched, and corrupted templates. Under a stringent random-template CDR-H3 evaluation, NT-ABDiff improves amino-acid recovery (AAR) from 30.03% to 39.47% and reduces RMSD from 3.160 to 2.915 [A]; with train-set retrieval candidates, it achieves 39.50% AAR and 2.76 [A] RMSD. Code, processed splits, configuration files, and evaluation scripts are available at https://github.com/ShiDeng7rz/NT-ABDiff.

bioinformatics↗