Biology subjects

Jacobsen, C. P.

Publications and source records attributed to Jacobsen, C. P..

4 recordsLinked to original sources

bioRxiv2026

pH-sensitive binder design with Proton-PottsMPNN

Physiological compartments span a wide pH range, from blood plasma near pH 7.4 through the mildly acidic tumor microenvironment to the acidified endosome and lysosome, and binders whose affinity responds to these differences underpin recycling antibodies, catalytic extracellular degraders and tumor-conditional therapeutics. Such binders are still obtained mainly by combinatorial histidine scanning and display selection, which is laborious and yields few transferable design rules. Recent computational approaches have enabled the design of pH-sensitive binders that engage at physiological pH and release under acidic conditions, by introducing histidines at key positions so that protonation generates electrostatic repulsion, evaluated through physics-based calculations. However, because the design model has no explicit representation of protonation state, it cannot shape the residues around a titratable site to favor one state over the other, leaving pH sensitivity to be screened for rather than specified. Here we present Proton-PottsMPNN, a sequence-design model that explicitly represents the protonated and deprotonated states of histidine, aspartate and glutamate as distinct sequence tokens. Because protonation state is specified rather than inferred, the model redesigns the microenvironment around a titratable residue so that the chosen protonation state is energetically favored over its alternative. Using Proton-PottsMPNN we designed a library of 8,407 de novo PD-L1 candidate binders in which sequence-structure compatibility and protonation state preference were designed jointly under a single energy function, with no structure-prediction filtering applied. The full library was synthesized and displayed on yeast, then screened against PD-L1 across a series of pH values by fluorescence- and magnetic-activated cell sorting, recovering 237 and 288 unique pH-dependent designs respectively. Transition pH values spanned 4.0 to 5.8, and switches were centered on protonated aspartate and glutamate as well as histidine. Recovered designs showed a stronger model preference for their designed protonation state than designs from the same backbone that were not recovered, and this preference was strongest in the designs that required the most acidic conditions to bind. Proton-PottsMPNN therefore designs binders that engage PD-L1 under acidic conditions fully de novo, directly from a specified protonation state.

biophysics↗

bioRxiv2026

De novo designed single-domain antibodies protect against lethal cobra venom neurotoxicity in vivo

Generative protein design can now rapidly produce de novo binders with high affinity and functional activity against a wide range of targets, including lethal snake venom toxins. However, so far most reported successes rely on new-to-nature scaffolds with limited therapeutic precedent. Single-domain antibodies (VHHs) offer a clinically validated alternative scaffold that can bind and neutralize long-chain -neurotoxins, which are some of the most lethal components in snake venoms. Here we compare three recently established de novo design models with VHH-design capabilities (Germinal, RFantibody, and BoltzGen) for their ability to generate VHHs against the neurotoxin -cobratoxin from the monocled cobra (Naja kaouthia). Using standardized model inputs and evaluation criteria based on AlphaFold3 interface confidence (ipTM) and RMSD self-consistency, we find that Germinal was the only method to generate designs passing stringent in silico criteria for experimental testing. We therefore performed a larger Germinal design campaign employing three different VHH frameworks and experimentally validated 46 designs in vitro. Of these, 42 expressed as soluble proteins and we identified four binding hits derived from two of the three tested frameworks. Of the four binders, two lead candidates were further characterized and demonstrated high affinity (KDs of 4.1 nM and 10.8 nM), monomeric behavior and low polyreactivity, indicating favorable biophysical and developability properties, as well as functional toxin neutralization in vitro. To assess their therapeutic potential we investigated their ability to protect against -cobratoxin toxicity in vivo. Both candidates fully protected mice after -cobratoxin challenge, with 100% survival compared to a lethal control. One candidate also retained notable neutralization capacity against whole venom of Naja kaouthia with a survival of 56%, while the other protected 22% when tested in a rescue setting. Together, we demonstrate that de novo VHH design can generate high affinity single-domain antibodies with in vivo protection against lethal cobra venom neurotoxicity, and provide practical insights into method- and framework-dependent performance.

bioengineering↗

bioRxiv2025

De novo designed protein enables precise epitope-level control of Gremlin-1 antagonism

Growth-factor antagonists regulate key developmental and pathological processes, yet the molecular principles governing their control remain poorly understood. Gremlin-1 (GREM1) is a secreted antagonist of bone morphogenetic proteins (BMPs) whose dysregulation is implicated in fibrosis and cancer. Here, we report a de novo designed protein that binds GREM1 with sub-nanomolar affinity and selectively releases BMPs for downstream signalling. By integrating generative deep-learning-based protein design with molecular-dynamics-derived flexibility descriptors, we identify a predictive relationship between interface rigidity, desolvation energy, and binding success. The resulting binder, RF1-2, reproduces the native BMP-binding epitope on GREM1 at near-atomic precision, as confirmed by cryo-electron microscopy, and competitively blocks BMP-2 and BMP-4 association. These results establish interface rigidity as a key physical determinant of antagonist inhibition and demonstrate how AI-guided protein design can uncover molecular principles underlying extracellular signalling control.

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↗