Biology subjects

Brasas, V.

Publications and source records attributed to Brasas, V..

3 recordsLinked to original sources

bioRxiv2026

Specificity-driven protein binder design with Odin-Multi

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.

bioengineering↗

bioRxiv2026

Hybrid quantum-classical de novo design of MHC-binding peptides

Deep generative models have become a leading approach for designing therapeutic molecules, yet efficiently exploring vast biomolecular sequence spaces remains difficult, particularly for targets with limited training data. The prior distribution that seeds a generative model shapes which regions of sequence space it explores, and recent work suggests that non-classical distributions sampled from quantum processors can serve as a structured alternative to the factorised Gaussian priors used by default. Whether such priors help on complex biological design tasks has been largely untested. Here we present what is, to our knowledge, the first end-to-end hybrid quantum-classical pipeline for de novo design of MHC class I-binding peptides, coupling a generative adversarial network (GAN) to latent vectors sampled from a real photonic quantum processor. Tested in silico across 131 HLA alleles, quantum-derived priors increased the yield of predicted strong binders, with the largest relative gains for understudied alleles where classical baselines perform worst. We selected three understudied alleles for further evaluation, finding that large gains coincided with broader sequence exploration at non-anchor positions while anchor specificity was preserved. On these three alleles, we validated the designs in vitro using peptide-MHC stability ELISAs, confirming that quantum-designed peptides are potent stabilisers of peptide-MHC class I complexes. These results establish structured, hardware-realisable non-classical priors as a useful inductive bias for generative peptide design, with direct relevance to personalised immunotherapies and vaccines.

biochemistry↗

bioRxiv2025

Predicting Experimental Success in De Novo Binder Design: A Meta-Analysis of 3,766 Experimentally Characterised Binders

Designing high-affinity de novo protein binders has become increasingly tractable, yet in vitro prioritisation continues to depend on heuristics in the absence of systematic analysis. Here, we present a large-scale meta-analysis of 3,766 experimentally tested binders across 15 structurally diverse targets. Using a unified, high-throughput pipeline that re-predicts each binder-target complex with AF2 (initial guess and ColabFold), AF3 and Boltz-1, we extract over 200 structural, energetic and confidence features per design. We show that interface-focused metrics, most notably the AF3-derived interaction prediction Score from Aligned Errors (ipSAE) outperform commonly used scores such as ipAE and ipTM, with a significant 1.4-fold increase in average precision compared to ipAE. We further show that combining these metrics with orthogonal physicochemical interface descriptors, including Rosetta {Delta}G/{Delta}SASA and interface shape complementarity, improves predictive performance. While overall per-formance varies by target, simple linear models trained on a small number of AF3-derived features generalize well across datasets. We propose interpretable, target-agnostic filtering strategies, such as combining AF3 ipSAE_min rankings with structural filters, to improve precision in selecting binders for testing. Finally, we release the complete dataset establishing a community resource to benchmark and accelerate de novo binder discovery.

bioinformatics↗