Protein Language Models and Graph-Based Reranking for Toxin-Binder Screening in Antivenom Discovery
Identifying proteins that neutralize snake venom toxins is a critical bottleneck in antivenom development, constrained by the scarcity of experimentally resolved toxin-binder structures and the high cost of wet-lab screening. This paper presents a computational screening pipeline that prioritizes toxinbinder candidates for downstream experimental validation, addressing the challenge of candidate ranking when labeled data is limited and supervised models risk overfitting. A graph reranking algorithm, BinderGraph, is proposed: it propagates frozen ESM2 cosine similarity scores across a binder co-occurrence graph constructed from structural training data, without any learned parameters. Evaluated across 10 random toxin-level splits on 61 unique toxins, Frozen ESM-8M with BinderGraph achieves mean Recall@5 of $0.836 \pm 0.077$, outperforming a trained MLP DuaIEncoder (Recall@5 = 0.804 ± 0.099) and a larger ESM2-650M model. These results demonstrate that biologically informed post-processing of pretrained embeddings is more effective than additional parameters or model scale when training data is scarce. Intended as a first-stage filter for experimental followup such as surface plasmon resonance (SPR) and enzyme-linked immunosorbent assays (ELISA), all comparisons are subjected to multi-split statistical significance testing.