Specificity-driven protein binder design with Odin-Multi
Brasas, V.; Christensen, C. R.; Björnsson, K. H.; Moller, V. E.; Scapolo, B.; Benard-Valle, M.; Nygaard, M. M.; Nielsen, J. C.; Hadrup, S. R.; Johansen, K. H.; Jenkins, T. P.
Show abstract
A useful protein binder is defined as much by what it does not bind as by what it does. Some applications call for one binder to cover a family of related targets; others require it to distinguish a single member from near-identical relatives. Yet, widely used deep-learning-based de novo design methods typically optimise one interaction at a time, leaving cross-reactivity and specificity to emerge during downstream screening. Here we present Odin-Multi, a binder design framework that optimises a shared binder sequence against several complexes simultaneously, applying attractive objectives to on-targets and repulsive objectives to off-targets. We benchmarked Odin-Multi in silico across three systems representing distinct cross-reactivity and specificity challenges: class B1 G protein-coupled receptors (GPCRs), testing cross-reactivity across multiple therapeutically relevant receptors; short-chain three-finger toxins, testing cross-reactivity across homologous toxin family members; and peptide-MHC (pMHC) complexes, testing specificity between near-identical target and off-target surfaces. For pairs of related class B1 GPCRs, 83.5 to 96.8% of jointly optimised designs exceeded an interaction-confidence threshold for both targets, compared with 6.8 to 36.3% of designs from single-target campaigns. For two short-chain three-finger neurotoxins, 9.2% of jointly optimised designs exceeded the corresponding threshold for both targets, compared with 0.8% of designs optimised against one toxin alone. Finally, in a pMHC specificity benchmark where target and off-target differed only in a single peptide residue, counter-selection increased the fraction of designs satisfying both the target-confidence criterion and a target-to-off-target interaction-confidence ratio of 2.5 from 6.0% to 14.2%. Experimental screening produced leads consistent with both design regimes in the two systems tested in vitro. We identified a cross-reactive toxin minibinder showing apparent nanomolar binding to the neurotoxin Erabutoxin A and to a candidate NK-shNTx-containing fraction from Naja kaouthia venom (higher-affinity fitted components of 11.95 and 34.43 nM, respectively), and a pMHC minibinder with greater target-to-off-target discrimination than a previously reported design. By treating cross-reactivity and specificity as explicit design objectives rather than screening outcomes, Odin-Multi widens the range of binding behaviours accessible to computational design.
Matching journals
The top 8 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- ProT-Diff: A Modularized and Efficient Approach to De Novo Generation of Antimicrobial Peptide Sequences through Integration of Protein Language Model and Diffusion Model 92%
- A Multi-Property Optimizing Generative Adversarial Network for de novo Antimicrobial Peptide Design 91%
- A high-affinity calmodulin-binding site in the CyaA toxin translocation domain is essential for invasion into eukaryotic cells 91%
Similar papers in this journal
Similar papers in this journal
- DeepRank-Ab: a dedicated scoring function for antibody-antigen complexes based on geometric deep learning 93%
- Accurate Protein-Protein Interactions Modeling through Physics-informed Geometric Invariant Learning 92%
- Large Scale Cell Painting Guided Compound Selection Reveals Activity Cliffs and Functional Relationships 91%
Similar papers in this journal
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.