Improved protein binder design using β-pairing targeted RFdiffusion

Domain: physics
Published: 2026-01-10
Editorial outcome: major_revision

Abstract
Designing proteins that bind with high affinity to hydrophilic protein target sites remains a challenging problem. Here we show that RFdiffusion can be conditioned to generate protein scaffolds that form geometrically matched extended β-sheets with target protein edge β-strands in which polar groups on the target are complemented with hydrogen bonding groups on the design. We use this approach to design binders against edge-strand target sites on KIT, PDGFRɑ, ALK-2, ALK-3, FCRL5, NRP1, and α-CTX, and obtain higher (pM to mid nM) affinities and success rates than unconditioned RFdiffusion. Despite sharing β-strand interactions, designs have high specificity, reflecting the precise customization of interacting β-strand geometry and additional designed binder-target interactions. A binder-KIT co-crystal structure is nearly identical to the design model, confirming the accuracy of the design approach. The ability to robustly generate binders to the hydrophilic interaction surfaces of exposed β-strands considerably increases the range of computational binder design.

Introduction
There has been considerable recent progress in de novo protein binder design1–6. Both energy based1 and deep learning methods, such as RFdiffusion2, now enable design of protein binders given only the structure of the target of interest and (optionally) the specification of region of the target surface to bind3. Despite this progress, design of high affinity binders to hydrophilic regions of a target surface remains challenging since the exposed hydrogen bond donors and acceptors must be complemented with precisely positioned acceptors and donors on the designed binder to compensate for the loss of interactions with water. While RFdiffusion excels at generating backbones that are shape complementary to the targeted region of a protein surface, these solutions do not always provide detailed complementarity of hydrogen bonding donors and acceptors7. Many therapeutically relevant target proteins have β-sheets with unpaired and exposed β-strands; these often have non-canonical structures to reduce the tendency for aggregation8,9. β-strand targeted binders have been generated using pre-deep learning Rosetta methodology10, but this approach has limited ability to match the diversity of edge β-strand geometries. A generalizable deep learning method for designing β-strand pairing based binders to complement arbitrary target β-strand twists11, bends12, bulges13, and other irregular features14,15 could improve the design of binders to polar target surfaces.

Here, we demonstrate that RFdiffusion generation of binder backbone mediated hydrogen bonding interfaces with the target can yield designs with polar groups nearly perfectly complementing those on the target. We develop a general approach for guiding RFdiffusion denoising-diffusion trajectories towards such β-strand centric interfaces, and we experimentally validate this binder design approach.

Results
β-strand interface conditioning improves binder designs
During training of RFdiffusion, a subset of training examples was provided with secondary structure and secondary structure block adjacency (SS/ADJ) information; this was found to enable the conditioning of the model towards user defined protein monomer folds (as previously described by Watson et. al.)2. We explored whether providing interface conditioning information indicating a binder β-strand pairing with a target edge β-strand at the point of inference could yield designable strand pairing complexes. RFdiffusion takes as input desired residue secondary structures as well as an N by N block adjacency matrix (N is the length of the designed protein), which specifies desired adjacencies between secondary structure blocks. The conditioning tensor can specify binder residues having desired secondary structure identities (helix, strand, loop, or masked) as well as the binder residue interaction identities (interacting, non-interacting, masked) with other binder residues (used for fold conditioning), or with target residues (used here for interface conditioning). Given the length of the designed interacting β-strand (L) and the identity of the target β-strand to be bound (T), RFdiffusion generates binders assigning a random set of L consecutive binder residues to form a β-strand pair with the target strand T, while the remainder of the binder output residues are not explicitly assigned a secondary structure or target interaction. We found that outputs from conditioned β-strand pairing runs indeed contained the user specified β-strand interfaces (Fig. 1a-b, Supplementary Fig. 1).

We supplied RFdiffusion with β-strand interface conditioning tensors for larger scale binder design campaigns against protein targets containing edge β-strands sufficiently exposed for binder access.
To evaluate the generality of the approach, we selected targets that span a range of edge β-strand geometries. The seven selected targets also have considerable therapeutic relevance. Activin receptor-like kinases 2 and 3 (ALK-2 and ALK-3) are both Type I bone morphogenetic protein receptors that regulate growth of bone, vasculature, hair follicles, enamel as well as wound healing and tumor suppression in various soft tissues16–20. Platelet-derived growth factor receptor ɑ (PDGFRɑ) and Mast/stem-cell factor receptor (also known as KIT, SCFR, CD117) are both type III receptor tyrosine kinases that play roles in cardiomyocyte proliferation and heart tissue regeneration after myocardial infarction21–29. FCRL5 functions in critical signaling pathways for B-cell activation30. NRP1 is a coreceptor for various growth factor signaling pathways (TGF-1, EGF, VEGF, PI3K, HGF, and PDGF)31, a viral entry factor32, and plays a role in the of RAS/MAPK signaling in various cancers33,34. α-Cobratoxin (α-CTX), the lone non-receptor target, is a prominent toxin derived from elapid snakes35 that acts by blocking muscle and nerve acetylcholine signaling. All except α-CTX are therapeutic targets for different cancers36–48. Designed protein binders against these targets could be useful for antagonizing native signaling, targeting drug conjugates and other therapeutics to tumors, designing novel agonists49–51 and targeted receptor degradation52, inhibitors1, triggering cargo endocytosis52, or target therapeutics toward particular cell types53.

These include single β-sheet targets with highly exposed edge β-strands (α-CTX, FCRL5, NRP1) and somewhat occluded edge β-strands (ALK-2 and ALK-3), as well as immunoglobulin (Ig) fold β-sandwich domains (FCRL5, KIT, and PDGFRɑ). These were found to be challenging for binder design using standard hotspot-only conditioned RFdiffusion–either in silico design calculations had failed to generate designs predicted with high confidence to bind to the target, or experimental testing had failed to yield binders (ALK-3, KIT, see Data Availability). Because no experimentally determined structure exists for FCRL5, we designed binders against the AlphaFold2 model54–56. To provide a stringent test of design specificity, the set includes some structurally related targets: ALK-2 and ALK-3 are members of the TGF-β superfamily57, and KIT and PDGFRɑ are type-III receptor-tyrosine kinases58 (Supplementary Fig. 2).

In a first in silico benchmark experiment, we compared the diversity and quality of binders designed using our interface conditioning method to designs generated using the standard RFdiffusion target site “hotspot” directed approach. Following the generation of 500 scaffolds using each diffusion approach, ten candidate binder sequences were generated for each scaffold by ProteinMPNN59 and each binder candidate complex was predicted with AlphaFold254. Across all eight target sites across the seven target proteins (two sites were targeted for PDGFRɑ due to predicted structural variability), β-strand interface conditioning yielded design models with improved in silico binding metrics6 AlphaFold 2 interface predicted aligned error (pAE) and Rosetta ΔΔG (Fig. 1b), with over 9.2% of β-strand interface conditioned designs meeting reasonable quality metrics, in contrast to 0.98% success rates by RFdiffusion conditioned on target hotspots alone. β-strand interface conditioning yielded in silico design successes for all targets, whereas hotspot conditioning alone did not yield any in silico successes for two targets (ALK-3 and FCRL5). β-strand interface conditioning also yielded more globular binder designs with 88.7% of output scaffolds meeting radius of gyration criteria and only 25.5% of designs from the other methods meeting the same criteria (Supplementary Fig. 3). β-strand interface conditioning yielded outputs that covered a distinct protein structure space compared to other methods, as quantified by all-by-all pairwise template modeling (TM) scores60 between aligned binder scaffolds (Fig. 1c), with as expected a higher fraction of β-sheet containing binders and a decrease in ɑ-helical bundle outputs (Fig. 1d).

A second, much larger campaign was carried out for each target to generate designs for experimental testing. The sets were generated using β-strand targeted RFdiffusion and with standard hotspot conditioned RFdiffusion, followed by ProteinMPNN, and selection based on AlphaFold 2 and Rosetta metrics; partial diffusion scaffold optimization4,61 and additional sequence sampling was carried out to generate designs with improved metrics in several cases. β-strand pairing designs greatly outperformed other scaffold types in this in silico filtering stage for most targets with the exception of the PDGFRɑ target site, which was likely more amenable to helical bundle binder design success due to its flat, hydrophobic surface (Supplementary Fig. 2b). Structurally diverse designs with high AlphaFold 2 confidence (interface pAE < 8 and pLDDT > 85) designs were selected for experimental screening by yeast surface display. While we ordered libraries including thousands of designs for some targets (KIT, PDGFRɑ, FCRL5, ALK-2, and ALK-3), binder success rates in our screens suggested that it would be possible to design strand pairing binders at smaller scales (Supplementary Table 1). Indeed, design sets for NRP1 and α-CTX yielded binders at 96 well scales. The α-CTX binder originally introduced by Vazques-Torres et. al., referred to in that work as LNG, will be referenced as α-CTX binding protein (α-CTXbp) in this work. α-CTXbp had been designed with the interface-conditioning method introduced here, but limited details on the method were revealed previously62. For PDGFRɑ, where both helical bundle designs and β-strand pairing designs passed in silico filters, significantly more binders were obtained with the strand directed approach than with standard RFdiffusion at this selection stage (Supplementary Fig. 4). The most enriched designs for binding affinity and expression from this assay were cloned, expressed in E. coli, and purified (Supplementary Fig. 5). SPR revealed mid- to sub-nanomolar affinity binders for our targets (Fig. 2a-c). In the following sections, we describe the active designs for each target in turn.

TGF-β superfamily targets: ALK-2 and ALK-3
ALK-2 and ALK-3 are two of four known Type I Bone Morphogenetic Protein Receptors (BMPRI). Targeting BMPR therapeutics towards specific tissues and pathologies could have considerable utility, but achieving binding specificity for ALK-2 versus ALK-3 presents a difficult challenge due to their structural homology. The ALK-2 and ALK-3 ectodomains have 30% sequence identity and high structural similarity (Supplementary Fig. 2). On both targets, there is an edge β-strand with five consecutive residues with backbone atoms in very similar atomic positioning (RMSD 0.07), and each strand is partially occluded by an N-terminal coil. Prior to this study, multiple design campaigns—including campaigns utilizing RFdiffusion, ProteinMPNN, and AlphaFold2—had failed to yield binders for ALK-3 (see Data Availability). The difficulty of designing binders to these targets is demonstrated in their lower success rates during our yeast surface display assay, with less than 1% of designs showing strong binding and enrichment (Supplementary Table 1).

We conditioned RFdiffusion on generating β-strand pairings to these five edge β-strand residues that were not occluded by the N-terminal coil. We identified binding proteins, ALK-2bp and ALK-3bp, with Kd values of 96 nM and 528 pM, respectively (Fig. 2c) by surface plasmon resonance (SPR). While both binders were intended to strand pair with the homologous target edge β-strand, the designed interaction footprints of their validated were quite distinct, with ALK-2bp forming a parallel β-strand interaction with the target β-strand and ALK-3bp pairing to the same strand in an antiparallel manner. ALK-3bp makes additional strand-pairing like contacts with the C-terminal coil of ALK-3 in a non-canonical hydrogen bonding pattern, with the binder β-strand curvature nearly perfectly complementing a bulge in the coil motif; these additional interactions may account for the four orders of magnitude greater binding affinity. This additional hydrogen bonding network with the C-terminal coil was not prespecified; RFdiffusion simply generated the secondary binder β-strand given the context of the initial β-strand motif specified by interface conditioning.

Single exposed edge β-strand: α-CTX, FCRL5, and NRP1
Three of the targets have more exposed edge β-strands. The acetylcholine receptor antagonist α-CTX is a three-finger toxin that consists of a single β-sheet with extended loops that bind a hydrophobic receptor pocket. We aimed to design a β-strand pairing binder that would sterically interfere with this interaction. FCRL5 weakly binds IgG to modulate B-cell activation. While the exact IgG binding site is unknown63, the receptor consists of several Ig domains. In the AlphaFold 2 model, the N-terminal Ig domain contains one three stranded sheet and one five stranded sheet, leaving an exposed β-strand available to target during design. Finally, we targeted the discoidin domain of NRP1, which contains a highly twisted β-sheet with a potential β-strand binding site where the edge β-strand twists about 90 degrees with exposed backbone polar atoms before entering into the protein core. While quite exposed, these target edge strands have distinctive structures that we hypothesized could allow for specific β-strand complementarity.

β-strand interface conditioned RFdiffusion resulted in a 5-fold improved in silico success rate for these targets compared to hotspot directed RFdiffusion which, even without conditioning, generated β-strand pairing binder designs with up to 20% of outputs (β-strand pairing designs were generated <5% of the time for the less exposed edge strand containing targets). Binders for α-CTX (originally introduced by Vazques-Torres, et. al.) and NRP1 (1.9 nM and 101 nM, respectively) were obtained by testing the 96 designs based solely on in silico metrics. The best binder to FCRL5, with a 82 nM Kd, was obtained from a yeast display library of 4841 designs. The FCLR5 and NRP1 target β-strands both had similar conformational twists, but FCLR5bp and NRP1bp utilize quite distinct antiparallel and parallel hydrogen bonding binding modes, respectively (Fig. 2a, right). Similar to FCLR5bp, α-CTXbp forms a mostly canonical antiparallel strand pair with a slight irregularity as the binder β-strand adapts to complement a small target strand bulge.

Type III receptor tyrosine kinases: KIT and PDGFRɑ
The type III RTK family receptors KIT and PDGFRɑ play roles in angiogenesis, tissue regeneration, and aberrant cancer signaling. Both receptor ectodomains are comprised of five Ig-like folds, with native ligands—stem cell factor and platelet derived growth factor—activating cellular signaling pathways by binding Ig domain 2 of KIT and PDGFRɑ, respectively, to induce receptor dimerization and intracellular cross-phosphorylation. Binders designed to occupy the ligand-binding pocket could act as antagonists to prevent aberrant signaling, and when oligomerized50, high-affinity binders could function as strong signaling agonists for tissue repair therapeutics. As such, we targeted the ligand binding sites on domain 2 of each receptor. As in the case of ALK-3, previous attempts to de novo design binders against KIT had failed (see Data Availability).

For KIT, a 65 nM binder (KITbp) was identified from a yeast surface library screening of 1298 designs. KITbp was designed to bind domain 3 strand on KIT which is part of the stem cell factor ligand binding site; the binding interface features an extensive canonical antiparallel hydrogen bond network, with 8 consecutive hydrogen bonds contributing a β-strand pair of 18 total binder and target residues (Fig. 2a, left). For PDGFRɑ, two binders were identified—PDGFRɑbp-7LBF (Kd = 137 pM) was designed to bind the cryo-EM structure (PDB accession code: 7LBF), while PDGFRɑbp-AF2model (Kd, 4 nM) was designed to bind the AlphaFold 2 model. They were enriched from 5427 and 189 member design libraries via yeast display, without any further experimental sequence optimization. Despite significant disagreement between the cryo-EM structure and the AlphaFold 2 model regarding the conformation of the target domain 2 Ig fold, design against both conformations yielded high-affinity binders. The binders may selectively induce the cognate PDGFRɑ conformation upon binding, as their design model conformations are not cross-compatible (Supplementary Fig. 7). PDGFRɑbp-7LBF forms a complex h-bond network in which the binder strand forms both parallel and antiparallel interactions with two different target strands of the target domain 2 Ig fold. This precisely complementary strand pairing h-bond network highlights the power of β-pairing conditioned RFdiffusion to design complex β-strand architectures to perfectly complement idiosyncratic target topologies.

Stability of binders with exposed edge β-strand interfaces
No obvious trends were observed between the size of the edge β-strand interface and aggregation propensity of the binders. All binders could be purified at high yields, and analysis of size exclusion chromatography curves reflects elution patterns consistent with monomeric binders being the most prominent purified species (Supplementary Fig. 5). Circular dichroism thermal melts (Fig. 2b) obtained for each binder indicate that the binder folds remain intact at high temperatures, even those with significant edge β-strand content. ALK-3bp was stable and monomeric up to 95°C during a circular dichroism thermal melt experiment despite having four exposed edge β-strands. The binder with the second most β-strand content, ALK-2bp, was also thermostable with a measured TM of 81.2°C. Binders with majority ɑ-helical support for their β-strand interfaces (KITbp, α-CTXbp, and PDGFRɑbp-AF2model) did not seem to be intrinsically more thermostable with measured TMs of 63.2°C, 95°C, and 95°C, respectively. The binders with ɑ/β secondary structure content (2-3 β-strand sheets with 2 buttressing ɑ-helices; FCRL5bp, NRP1bp, and PDGFRɑbp-7LBF) had TMs of >95°C, 77.2°C, and 71.1°C, respectively. The excellent solution behavior of the designs despite having edge β-strands clearly available for intermolecular interactions suggests that the same idiosyncratic features that enable high affinity and specificity (see below) target binding disfavors self-self interactions9.

β-strand pairing binder interfaces are target specific
We next investigated whether the identified binders were specific for their designed target and did not form off-target high affinity β-strand pairing interactions. To test this, we performed an all-by-all SPR experiment where each binder was tested for binding affinity against each of the receptor targets in the test set. At 1.5 μM and 200 nM concentrations, all of the binders showed strong SPR response for their intended target receptor compared to off-target receptors (Fig. 3a). There was no evidence for off-target binding of the ALK-2, ALK-3, KIT, and PDGFRɑ binding proteins to related family members (e.g., ALK-2bp did not bind strongly to ALK-3). This high specificity may arise because the binders for each structurally similar target pair (i.e., ALK-2 and ALK-3, KIT and PDGFRɑ, FCRL5 and NRP1), were designed with different β-strand pairing hydrogen bonding arrays, and each binder makes additional non-strand-pairing contacts with the intended target. With the exception of ALK-3bp which forms exclusively β-strand contacts with ALK-3, all designed binders form additional ɑ-helical contacts that are complementary to neighboring target surface topologies. Overall, the interface side chain contacts resemble natural protein interfaces, with binder side chains making electrostatic and shape complementarity interactions with target side chains. The polar interactions made by strand-pairing lead to lower overall hydrophobicity, as measured by Spatial Aggregation Propensity (Supplementary Fig. 6b).

KITbp: KIT complex structure confirms design model accuracy
To assess the accuracy of our design method, we solved the structure of KITbp in complex with domains 1–3 of KIT at 2.8Å resolution (Fig. 4a, Supplementary Table 2). The crystal structure has near exact agreement with the computational design model with 1.9Å all-atom RMSD between the design model and the structure. Over the designed binder alone, the RMSD overall atoms between the crystal and design is 2.0Å, and over the backbone, 0.98Å RMSD (Fig. 4b). The design model and crystal structure align with atomic level accuracy over nearly all interfacial side chain residue atoms. The KITbp binding site on KIT overlap with that of the KIT native ligand stem cell factor (SCF), as all the binder designs targeted this site. Consistent with this, saturating concentrations of SCF (60 μM) reduced KIT library binding sort counts by 99% in a yeast surface display binding assay (Supplementary Table 2).

The 29 residue binder solvent exposed surface area (SASA) of the binder interface (1164 Å2) consists of 8 strand-pairing binder residues (from Gly21 to Asp28, 291 Å2) and 21 additional interface residues (873 Å2). By heavy atom SASA, the polar atoms comprise 65% of the strand-pairing interface, a somewhat higher proportion compared to the rest of the interface surface, which was only 45% polar. There are 21 side chain-side chain and backbone-backbone hydrogen bonds between KITbp and KIT (calculated by HBPLUS64), many more than in previous designed binders (Supplementary Fig. 6) and in most native protein-protein interfaces with similar interface sizes65 (Fig. 4c). The eight strand pairing binder residues participate in nearly half (9) out of the total (21) hydrogen-bonds in the 29 residue binder interface. The majority of these hydrogen bonds were predicted accurately in our design model (16 out of 21). At the center of the interface, a network of four binder and KIT tyrosine residues (Y38, Y39, Y259, Y269) form a highly complementary interface between the binder interfacial helix and core-boundary of the KIT domain 2 Ig fold. Lining the perimeter of the interface are numerous polar interactions, including the designed β-pairing interface as well as several polar side-chain interactions.

KITbp contains an unpaired β-strand that pairs with the target, with three buttressing ɑ-helices that tether the β-strand in place. Foldseek66 alignment of KITbp against the PDB did not identify significant matches to known protein structures. We were unable to find examples of lone β-strands in natural proteins, except those found in protein-protein complexes where a loop in one partner forms a β-strand conformation that extends the β-sheet of the other partner67. A similar induction of β strand formation upon binding may occur with KITbp; Alphafold 2 prediction of the monomeric KITbp without KIT target places the β-strand pairing residues in a coil conformation that better shields the binder’s hydrophobic core residues and hydrogen bonding atoms of the β-strand interface (Supplementary Fig. 8); such KIT-dependent conformational switching may contribute to the observed binding specificity.

Biological functionality of designed binders
We next sought to assess the biological functionality of the designs. FCRL5 is internalized and transits through the endocytic degradation pathway upon binding to antibodies63, and we investigated whether FCRL5bp could be similarly internalized. We incubated cells expressing FCRL5 with FCRL5bp tagged with pHrodo Deep Red, a pH sensitive (~5 pK) dye that emits 655 nm fluorescence at late endocytic vesicle pH, and observed binder internalization that correlated with FCRL5 expression levels, reaching a steady state in this expression system (Fig. 5a). We hypothesized that FCRL5bp could thus be useful as an Endotag52 for targeted protein degradation, as recently demonstrated for other designed proteins. We fused FCRL5bp to EGFRbp (EGFRn from Cao et al.) in both terminal orientations and compared the ability of this molecule to degrade EGFR by endocytic lysosomal trafficking induced by FCRL5 binding. After treating cells with 50 nM of either EGF, the binder fusions, or each binder alone, EGFR was robustly degraded in a manner dependent on doxycycline induced expression of FCRL5.

PDGFRɑbp-7LBF was designed to sterically occlude the binding site of the native ligand PDGF-AA, and indeed PDGFRɑbp-7LBF blocked activation of signaling by PDGF-AA through the Akt and Erk pathways with nanomolar inhibition of ERK phosphorylation (IC50 = 19.6 nM) and AKT phosphorylation (IC50 = 38.25 nM) in a CHO cell line engineered to stably express PDGFRɑ (Fig. 5d-e). ALK-2bp, ALK-3bp, and NRP1bp could not be tested for functional agonism in a straightforward manner, as the native ligands for ALK-2 and ALK-3 promiscuously activate signaling with other BMP receptors and NRP1 acts as a cofactor in signaling for various RTKs. In a companion study, α-CTXbp was found to potently neutralize α-CTX, protecting mice from a lethal neurotoxin challenge62.

Methods
In silico comparison of RFdiffusion-generated scaffolds
Binder scaffolds were generated with RFdiffusion conditioned with hotspots alone or with β-strand interface conditioning against the target edge β strand sites indicated throughout this work (Fig. 2, Supplementary Fig. 2). Interface conditioning tensors for this were pre-made with the script (see Data Availability) to ask RFdiffusion to create binder interface strands of equal length to the target edge β-strand. Scaffolds were compared by calculating the TM-score60 of each binder scaffold against all other binders scaffolds against all targets in the set. This data was transformed into a 2D-array with t-SNE transformation function in scikit-learn (version 0.24.2) to produce the graph in Fig. 2. After generation of ten sequences per scaffold with ProteinMPNN, sequence qualities were assessed with AlphaFold 2 initial guess6 and Rosetta Fast Relax1.

Production scale computational protein design workflow
Target structures used as inputs for binder design were obtained from the Protein Data Bank for KIT (PDB: 2E9W)68, NRP1 (PDB: 2QQI)69, and PDGFRɑ (PDB: 7LBF)70. Publicly available AlphaFold 2 models were used for the design of binders against ALK-2 (Uniprot: Q04771, Accessed October 2022), ALK-3 (Uniprot: P36894, Accessed October 2023), FCRL5 (Uniprot: Q96RD9, Accessed February 2023), and PDGFRɑ (Uniprot: P16234 Accessed April 2023). Target surfaces were assessed for surface hydrophobics and edge strands. Five to ten thousand binder backbone scaffolds complementing these sites were generated by standard RFDiffusion design with hotspot conditioning as well as β-strand interface conditioning. To condition RFdiffusion towards generating β-strand interfaces, the script introduced in this work (Data Availability) was used to create conditioning tensors that guide RFdiffusion toward generating interface strands of equal length to the target edge β-strand. ProteinMPNN sequences were generated for the output backbones and subject to in silico screening based on AlphaFold 2 initial guess (pAE interaction and binder pLDDT)6, AlphaFold 2 monomer pLDDT54, Rosetta ΔΔG1, radius of gyration (ROG), and spatial aggregation propensity (SAP)71. The cutoff values for these metrics varied somewhat for each target protein, but obtained minimum values of pAE interaction < 10, pLDDT > 85, ΔΔG < −30, a variable ROG filter adjusted for binder size length/4.5 = ROG, and SAP < 45 (more details can be found in Supplementary Table 1). In some cases, in silico optimization was carried out by iterative partial RFdiffusion and ProteinMPNN cycling72. These filters were used for in silico selection of top binder designs from hundreds of thousands of sequences, resulting in libraries ranging in size from 96 to 5000 that were screened experimentally. In silico and experimental library sizes depended on the in silico filter success rate, and the perceived difficulty of designing a binder interface based on target surface concavity, target surface hydrophobicity measured by SAP, and known previous experimental challenges (as for ALK-2, ALK-3, KIT, unpublished data). To ensure our binder design libraries sampled diverse sequences and structures, we clustered designs by sequence similarity or RMSD and selected a subset of designs such that each cluster was equally represented in the final library for experimental characterization.

DNA library preparation
For ALK-2, ALK-3, KIT, FCRL5, and PDGFRɑ, 104 scale DNA libraries were generated by using DNAworks 2.0 reverse translating designed amino acid sequences that optimally reflected the Saccharomyces cerevisiae codon frequency table. Additional 5‘ and 3‘ adapters were included to enable PCR amplification of libraries by single sets of primers. All libraries were amplified using Kapa HiFi polymerase (Kapa Biosystems) with a qPCR machine (Bio-Rad, CFX96). In detail, the libraries were first amplified in a 25 μl reaction, and the PCR reaction was terminated when the reaction reached half maximum yield to avoid over-amplification. The PCR product was loaded onto a DNA agarose gel. The band with the expected size was cut out, and DNA fragments were extracted using QIAquick kits (Qiagen). Then, the DNA product was re-amplified as before to generate enough DNA for yeast transformation. The final PCR product was cleaned up with a QIAquick Cleanup kit (Qiagen). For the yeast transformation step, 2–3 μg of linearized modified pETcon vector (pETcon3) and 6 μg of insert were transformed into the EBY100 yeast strain using the lithium-acetate/PEG protocol of ref. 73.

DNA libraries for deep sequencing were prepared using the same PCR protocol, except the first step started from yeast plasmid prepared from 5×107 to 1×108 cells by Zymoprep (Zymo Research). Illumina adapters and 6-bp pool-specific barcodes were added in the second qPCR step. Gel extraction was used to obtain the final DNA product for sequencing. All the different sorting pools were sequenced using Illumina NextSeq sequencing.

Target protein preparation
Biotinylated target protein was commercially available for KIT (Acro Biosystems, CD7H5255), FCRL5 (Acro Biosystems, FC5-H82E3), NRP1 (Acro Biosystems, NR1-H82E3), α-CTX (Latoxan, L8114), and PDGFRɑ (Sino Biological, 10556-H27H-B). For ALK-2, ALK-3 biotinylated receptor was expressed as avi-tagged ectodomain constructs in E. coli, purified, and biotinylated as described by Tao Huang and Andrew P. Hinck74.

Yeast display
Saccharomyces cerevisiae EBY100 strain cultures were grown in C-Trp-Ura medium supplemented with 2% (w/v) glucose. For induction of expression, yeast cells were centrifuged at 4500 × g for 5 min and resuspended in SGCAA medium supplemented with 0.2% (w/v) glucose at the cell density of 1×107 cells per ml and induced at 30°C for 16–22 h. Cells were washed with PBSF (PBS with 1% (w/v) BSA) and labeled with biotinylated targets using two labelling methods: with-avidity and without-avidity labeling. For the with-avidity method, the cells were incubated with biotinylated target, together with anti-c-Myc fluorescein isothiocyanate (FITC, Miltenyi Biotech) and streptavidin–phycoerythrin (SAPE, ThermoFisher). The concentration of SAPE in the with-avidity method was used at one-quarter of the concentration of the biotinylated targets. For the without-avidity method, the cells were first incubated with biotinylated targets, washed and secondarily labeled with SAPE and FITC. All the original libraries of de novo designs were sorted using the with-avidity method for the first few rounds of screening to exclude weak binder candidates, followed by several without-avidity sorts with different concentrations of biotinylated targets. Binder affinities were estimated from this titration data using the software provided (Data Availability) to estimate SC50, originally introduced by Cao et. al.

Protein expression and purification
Protein binder designs were ordered as synthetic genes (eBlocks, Integrated DNA Technologies) and cloned via BsaI overhangs into the target cloning vector, LM062775 for Golden Gate assembly. Golden Gate reaction mixtures were transformed into a chemically competent expression strain (BL21 (DE3)), and overnight outgrowth cultures were used to seed 500 mL protein expression cultures in auto-induction media (autoclaved TBII media supplemented with Kanamycin, 2 mM MgSO4, 1× 5052) and to propagate expression plasmid for isolation (QIAprep Spin Miniprep Kit) and sanger sequencing services performed by GENEWIZ (Azenta Life Sciences). The following day (20-24 hrs later), cells were harvested and lysed by sonication (QSonica Q500 Sonicator), and clarified lysates were purified by immobilized metal affinity chromatography using Ni-NTA agarose resin (Qiagen). Samples were eluted in a Tris elution buffer containing 300 mM imidazole, sterile filtered with 0.22 μm Polyvinylidene Fluoride syringe filter prior to size exclusion chromatography. Protein designs were then screened via SEC using an AKTA FPLC outfitted with an autosampler capable of running samples from a 96-well source plate. The protein binders were run on a Superdex 75 Increase 5/150 GL column (Cytiva 29148722) at room temperature.

Circular dichroism
Far-ultraviolet circular dichroism measurements were carried out with a JASCO-1500 instrument equipped with a temperature-controlled multi-cell holder. Wavelength scans were measured from 260 to 190 nm at 25 and 95°C. Temperature melts monitored the dichroism signal at 217 nm in steps of 2°C/min with 30 s of equilibration time. Wavelength scans and temperature melts were performed using 0.3 mg/ml protein in PBS buffer (20 mM NaPO4, 150 mM NaCl, pH 7.4) with a 1 mm path-length cuvette. Melting temperatures were determined by fitting the data with a sigmoid curve equation. For designs retained more than half of the mean residue ellipticity values, Tm values are reported as greater than 95°C. Tm values of the other designs were determined as the inflection point of the fitted function.

Surface plasmon resonance measurement of binding kinetics
Binding kinetics were analyzed via Surface Plasmon Resonance (SPR) on a Biacore 8K (Cytiva) operated at 25°C with a data collection rate of 10 Hz. Receptor binding was measured by capturing biotinylated receptor ectodomains on a streptavidin-coated CAPture chip using the Biotin CAPture Kit (Cytiva #28920234). Biotinylated receptors (0.125 μg/mL) were injected for 100 s at a flow rate of 10 μL/min in HBS-EP⁺ buffer (0.01 M HEPES pH 7.4, 0.15 M NaCl, 3 mM EDTA, 0.005% v/v Surfactant P20; Cytiva #BR100669) to achieve capture levels of approximately 250 RU. Analytes were prepared by serial 1:7 dilution from 10 μM stocks in HBS-EP⁺ and injected at a flow rate of 30 μL/min to monitor association, followed by dissociation in running buffer (HBS-EP⁺) at the same flow rate. Single-cycle kinetics were performed by injecting increasing analyte concentrations sequentially over the same captured ligand surface (six concentrations per cycle, 100 s association, 60 s dissociation between each cycle, and 750 s final dissociation). Regeneration was achieved by two 60 s pulses of regeneration buffer at 10 μL/min. Blank-injection (buffer-only) subtraction was applied for all analyte injections, double referencing (reference-surface+blank) was performed. Sensorgrams were processed and fit in the Biacore Insight Evaluation Software using a 1:1 Langmuir binding model. An exception was made to use a heterogenous kinetic fit model for α-CTXbp as this was found in ref. 62 to better model the partially biphasic binding properties for the complex. Global fit was applied for kₐ, kd, Rmax, and tc parameters with default initial values 10e5, 10–e, Y, 10e8 initial values. Drift was fitted locally (and is reported in the kinetic parameters in the Source Data file), and RI was set to 0 as a constant. Fitting drift was capped at a maximum contribution 15%, where the drift contribution over the 750 s fit could not exceed 15% of the Rmax parameter (capping only performed for KITbp and FCRL5bp for optimal fitting values χ2 and SE, while other binders were affected). 10 μM data was excluded from the fit due to non-ideal solution behaviour, observable in the overlaid fits in Fig. 2.

Surface plasmon resonance to measure target specificity
All by all binder-target interactions were measured according to SPR protocol described above. Identical binder titres and receptor loading protocols were used to maintain as much signal consistency as possible, with binder titre values ranging in seven fold dilutions from 10 μM to 600 pM to capture non specific binding across a large concentration spectrum. Non-specific binding responses were evaluated by averaging response values across the association phase for a given titre, normalizing responses such that all values were positive, and taking each response as a fraction of the maximum observed response (for each binder the maximum response was observed against the target protein for which it was designed).

Recombinant production of KIT D1-D3 for X-ray crystallography
KIT123, i.e., extracellular domains D1–3 of the tyrosine kinase receptor KIT, was recombinantly produced via transient expression of suspension-adapted HEK293 cells. Cells were grown and maintained in a 1:1 mixture of the Freestyle (Gibco) and the Ex-Cell (Merck) medium. Before the transfection, cells at the density of 1.5× 106 cells.mL–1 were centrifuged at 250 × g for 6 min and resuspended in the pre-warmed FreeStyle medium only to reach the density of 3.0×106 cells.mL–1 and incubated at 37°C, 130 rpm, 70% humidity, and 8.0% CO2 for 15 min. The cells were subsequently added 450 μg of the plasmid DNA carrying the target construct per 100 mL of the medium. After 5 min, the cells were added 900 μg of linear polyethylenimine 25 kDa (Polysciences) and 3 μmol of kifunensine per 100 mL of the medium and continued incubation. After additional 5 h, an equal volume of the Ex-Cell medium to the FreeStyle medium was added to the cultures to return back to the density of ~1.5× 106 cells.mL–1. 24 hours post-transfection, the cells were added D-glucose and valproic acid to the resulting concentration of 55 mM and 3.5 mM, respectively. 96 hours post-transfection, the cells were harvested by centrifugation at 500 × g and 4°C for 10 min and the conditioned medium (supernatant), carrying the recombinantly produced protein of interest, was collected, added 10,000 U of EndoHf (NEB) per 100 mL of the medium, and incubated for 4 h at room temperature to remove heterogenous N-linked glycans and facilitate the subsequent crystallization attempts. After filtering the incubated medium through a 0.22 μm filter, the clarified sample was loaded to a 1 mL Ni-NTA HisTrap HP column (Cytiva) equilibrated with HEPES-buffered saline (HBS; 20 mM HEPES, 150 mM NaCl, pH 7.4). The column was washed with 10 mM imidazole in HBS and the His-tagged protein of interest was eluted using 150 mM imidazole in HBS. The eluted protein fraction was subsequently loaded to a Superdex 75 Increase 10/300 GL column (Cytiva) to simultaneously remove aggregates and remaining impurities and to exchange buffer to HBS with no imidazole. The fractions corresponding to the protein of interest were pooled together, their purity was analyzed by SDS-PAGE (Bio-Rad), and the concentration was determined using the NanoDrop Spectrophotometer (Thermo Fisher Scientific).

Crystal structure of KIT123 in complex with KITmb
The KIT123–KITbp complex was formed by adding a 3-fold molar excess of the purified KITbp to the recombinantly produced, EndoH-treated KIT123 receptor (domains D1–3 of the ectodomain). The complex was isolated using size-exclusion chromatography equipped with a Superdex 75 Increase 10/300 GL column (Cytiva) equilibrated with HEPES-buffered saline (HBS; 20 mM HEPES, 150 mM NaCl, pH 7.4). Fractions corresponding to the KIT123–KITbp complex were pooled and concentrated by centrifugal ultrafiltration to the concentration of 6.1 mg.ml–1. Sparse-matrix crystallization screens were carried out in 96-well 3-drop plates (Molecular Dimensions) using the BCS-Screen (Molecular Dimensions) at 293 K and the sitting-drop method. The vapour-diffusion geometry was used to setup sitting drops consisting of 100 nL of a protein solution and 100 nL of each reservoir solution using a Mosquito nanolitre crystallization robot (SPT Labtech). The protein complex crystallized in the condition G11 (0.2 M sodium/potassium phosphate pH 7.5, 0.1 M HEPES pH 7.5, 22.5% PEG Smear Medium, 10 % glycerol). Crystals were cryo-protected with mother liquor supplemented with 25% v/v glycerol and subsequently flash-cooled by direct plunging into liquid nitrogen. X-ray diffraction data of protein crystals were collected at the P13 beamline (PETRA III, EMBL Hamburg). Obtained data were processed using XDS76 and severe data pathologies, including strong anisotropy and translational non-crystallographic symmetry, were revealed, yielding similar characteristics as reported previously77. Based on these findings, the data were elliptically-truncated and corrected using the STARANISO78,79 server and accordingly treated during the following steps. Initial phases were determined by maximum-likelihood molecular replacement in Phaser80 using the domains D1–3 part of the KIT-SCF structure (PDB ID: 2E9W)68 as a search model. Model (re)building was performed in Coot81, and coordinate and ADP refinement was performed in PHENIX82. Model and map validation tools in Coot, the PHENIX suite, and the PDB_REDO server83 were used to validate the quality of crystallographic models. Atomic coordinates and structure factors of the protein-protein complex were deposited in the Protein Data Bank under the PDB code 9H71.

In silico binder interface characterization
SAP and SASA metrics were calculated using PyRosetta and PyMOL software using the calculate_sap and get_area functions, respectively. ΔSAP and ΔSASA were obtained by calculating each metric for the binder before and after removing target proteins from complex models. To calculate the polar interface surface, the ΔSASA was calculated for each binder atom. The sum of oxygen and nitrogen ΔSASA values yielded the polar interface values, while all carbon residues were considered to be non-polar. Using the DSSP algorithm, binder-target strand pairing interactions were identified by counting β-strand conformation binder and target residues with backbone interactions less than 3.5Å in distance. Strand pairing orientation (parallel or anti-parallel) was determined by calculating the average position of the N-terminal and C-terminal halves of the binder and target strand-pairing residues. If the distance between the binder N-terminal strand contacts and the target N-terminal strand contacts was less than that of the distance to the target C-terminal strand contacts, the strand-pair was determined to be parallel. Otherwise, it was determined to be antiparallel. The software used to determine these metrics is provided in the Data Availability section.

PDGFRɑ antagonism assay
Heparan-deficient Chinese hamster ovary cells stably overexpressing PDGFRα (CHO-PDGFRα) were grown to 70-90% confluency in CHO growth medium (Kaighn’s Modification of Ham’s F12 (F12K) medium (ATCC# 30-2004)+10% Fetal Bovine Serum (FBS) (Biowest, #S1620)+4% Penicillin-Streptomycin (P/S) (Gibco, #15140122) with 10 μg/mL puromycin (Gibco, #A11138-03). The cells were starved for 4 h in serum free F12K media and treated with synthetic and/or native ligand for 15 min at 37 degrees Celsius. Cells were subsequently washed with PBS and lysed with buffer containing 20 mM Tris–HCl (Sigma-Aldrich, 1185-53-1) (pH 7.5), 150 mM NaCl, 15% glycerol (Sigma-Aldrich, G5516), 1% triton (Sigma-Aldrich, 9002-93-1), 3% SDS (Sigma-Aldrich, 151-21-3), 25 mM β-glycerophosphate (Sigma-Aldrich, 50020-100G), 50 mM NaF (Sigma-Aldrich, 7681-49-4), 10 mM sodium pyrophosphate (Sigma-Aldrich, 13472-36-1), 0.5% orthovanadate (Sigma-Aldrich, 13721-39-6), 1% PMSF (Roche Life Sciences, 329-98-6), 25 U benzonase nuclease (EMD, 70664-10KUN), protease inhibitor cocktail (PierceTM Protease Inhibitor Mini Tablets, Thermo Scientific, A32963), and phosphatase inhibitor cocktail 2 (Sigma-Aldrich, P5726). Lysates were collected, mixed with 4x Laemmli buffer (BioRad, #161-0747), and boiled at 95 Celsius for 10 min before 10 uL were loaded onto 4–10% SDS-PAGE gels and run for 30 min at 250 Volts.

Western blotting
The PDGFRɑ antagonism assay was analyzed using two different western blot techniques. One repeat of each was analyzed via traditional techniques which is as follows: after separation, proteins were transferred onto a nitrocellulose membrane (12 min, semi-dry transfer) and blocked for one hour in 5% bovine serum albumin. Membranes were probed with the following primary antibodies: phospho-PDGFRα (Tyr762) (Cell Signaling Technology, #24188) 1:1000 dilution, Phospho-Akt (Ser473) (Cell Signaling Technology, #9271) 1:1000 dilution, Phospho-p44/42 MAPK (Erk1/2) (Thr202/Tyr204) (Cell Signaling Technology, #9101) 1:10,000 dilution, and either S6 (Cell Signaling Technology, #2117) at 1:1000 dilution or H3 (Cell Signaling Technology, #9715) at 1:5000 dilution as a loading control. After overnight incubation on a rocker, the membrane was probed with HRP-conjugated secondary antibody, washed 3 times, and imaged with a Bio-Rad ChemiDoc Imager. The blot image was quantified using ImageJ peak band intensity. In addition, samples were analyzed using a Biotechne Jess Automated Western Blot Machine (Biotechne, #004-650). Samples were diluted 1:3 before being prepared as per the kit instructions for 12-230 kDA separation modules (Biotechne, #SM-W001). Samples were probed using the same primary antibodies as above via a RePlex assay (Biotechne, #RP-001) that was run using the default settings for anti-rabbit chemiluminescence (Biotechne, #DM-001). Signal was quantified using area under the curve for each protein of interest. All data points from each assay were normalized to a housekeeping gene in the same lane and then normalized to the 1 nM PDGF-AA condition within each experiment.

Vectors and constructs/lentiviral generation and infection
Lentiviral particles of the pHAGE-PDGFRɑ plasmid (Addgene, 116769)84 were generated by transfecting 60-80% confluent HEK293FT cells, maintained in 10 mL HEK growth medium (Gibco™ DMEM, high glucose (Gibco, 11965092) + 10% FBS + 1% P/S) on a 100 mm TC-treated culture dish, with 20 μg pHAGE-PDGFRɑ, 15 μg psPAX2 (Addgene, 12260), and 5 μg pMD2.g (Addgene, 12259) combined with 1.8 mL Opti-MEM medium (Gibco, 31985070) and 60 μg linear polyethylenimine. Transfected HEK293FT cells were replenished with fresh HEK growth medium 24 h after the transfection. The supernatant containing lentivirus was collected 48 and 72 h post-transfection. The collected supernatant was filtered through a 0.45 μM PES syringe filter. CHO-PDGFRα cells were generated by infecting 60–80% confluent heparan-deficient Chinese Hamster Ovary cells (CHO) cells (pgsD-677 cells) (# CRL-2244) with the filtered lentivirus-containing supernatant. Infected CHO cells were supplemented with fresh CHO growth medium 24 h after the lentiviral infection. 48 h after the infection, CHO cells were selected in the CHO selection medium (CHO growth medium containing 10 μg/mL puromycin). CHO selection medium was replaced every 48 h for 7 days. CHO-PDGFRα cells post-selection were maintained in the selection medium.

FCRL5 cell line preparation
Doxycycline-inducible expression of Flag-FCRL5-P2A-T2A-EGFP was generated by first seeding 1×106 WT HeLa cells in 10 cm dish (Genesee 25-202) in Dulbecco’s Modified Eagle Medium (DMEM, Gibco 11995073) supplemented with 10% heat-inactivated fetal bovine serum (HI FBS, Gibco A5256801) and 1% penicillin-streptomycin (PS, Gibco 15140122). The next day, cells were transfected using TransIT-LT1 Transfection reagent (Mirus Bio MIR2300) according to manufacturer protocol with 2:1 donor:sleeping beauty transposase plasmids (Addgene Plasmid #34879)85. After 72 h, cells were covered with selection media comprised of DMEM + 10% HI FBS + 1% PS and 2 μg/mL puromycin (Invivogen 58-58-2). Selection media was replaced every 48 h until control WT cells were completely dead. Cells were maintained in selection media.

FCRL5bp internalization assay
Flag-FCRL5 HeLa cells were counted and seeded at 50,000 per well in a 24-well plate (Genesee 25-107) in DMEM + 10% HI FBS + 1% PS. The next day, cells were treated with either 0, 100, or 1,000 ng/mL doxycycline (Fisher Scientific BP26531), and incubated for 48 h at 37°C 5% CO2. Cells were then lifted, counted using Trypan Blue, and seeded at 12,500 cells per well in a 96-well plate (Corning 3595) in phenol red-free DMEM (Gibco 31053028) supplemented with 10% HI FBS and 0, 100, or 1000 ng/mL. Cells were incubated at 37°C 5% CO2 for 8 h. Then, AviTagged-FCRL5bp was diluted in phenol red-free DMEM + 10% HI FBS and respective doxycycline concentrations. Then, FCRL5bp was complexed 1:1 with TFP ester-pHrodo Deep Red (Invitrogen P35358) labeled NeutrAvidin (Thermo Scientific 31000) for 15 min at 37°C covered from light. Media was then replaced with respective treatments, and cells were monitored with a live-cell imaging incubator (Sartorius Incucyte S3) and internalization was quantified as the overlap of red and green fluorescent area divided by the total phase area in a well.

FCRL5bp-EGFRn bifunctional degradation assay
Flag-FCRL5 HeLa cells were counted and seeded at 300,000 per well in a 6-well plate (Genesee 25-105) in DMEM + 10% HI FBS + 1% PS and incubated overnight at 37°C, 5% CO2. Then, cells were treated with either 0 or 1000 ng/mL doxycycline (Fisher Scientific BP26531) and incubated for 48 h at 37°C, 5% CO2. Media was then replaced with treatments of 100 ng/mL EGF (Gibco AF10015) or 50 nM of respective FCRL5bp, EGFRn, or bifunctional treatment were prepared in media with and without 1,000 ng/mL doxycycline. Cells were incubated with treatments for 48 h at 37°C, 5% CO2, then cells were lifted in 600 uL total volume. One-sixth of the total volume of cells was reserved for live-cell immunostaining and flow cytometry. Cells were washed three times by centrifuging for 5 min at 4°C, 500 g, discarding supernatants, and resuspending in ice-cold PBS + 1% BSA (Sigma-Aldrich A3608). After the final wash, cells were resuspended in PBS + 1% BSA + 4 ug/mL anti-EGFR (Thermo Scientific MA513319) and incubated on ice for 30 min before repeating washing three times in PBS + 1% BSA. Cells were then resuspended in PBS + 1% BSA + 4 ug/mL Alexa Fluor 647 AffiniPure Goat Anti- Mouse IgG (H+L) (Jackson ImmunoResearch Laboratories 115-605-003) and incubated on ice for 30 min covered from light. Cells were then washed three times and quantified on BD Accuri C6 Plus flow cytometer, and data were processed using FlowJo v10 Software. The remaining cells were washed three times in ice-cold PBS before resuspending in RIPA lysis buffer (Thermo Scientific 89900) + protease inhibitor cocktail tablet (Thermo Scientific A32955) and lysing cells on ice for 15 min. Lysates were then centrifuged at 4°C, 18,000 g for 15 min. The protein concentrations were quantified with a bicinchoninic acid assay kit (Thermo Scientific 23227) according to manufacturer’s protocols. Samples were prepared at equal protein with NuPage 4x lithium dodecyl sulfate buffer (Invitrogen NP0007) + 0.1 M DL-dithiothreitol (Thermo Scientific R0861), and boiled at 95°C for 10 min before spinning down and loading on a 4–12% Bis-Tris protein gel (Bio-Rad 3450124). The gel was loaded and ran at 200 V for 1 h in 1x XT MES running buffer (Bio-Rad 1610789). The gel was then released from its cassette and transferred to a nitrocellulose membrane using a Trans-Blot Turbo Transfer Kit (Bio-Rad 1704271) for 15 min at 25 A and 2.5 V. The membrane was then trimmed and blocked for 1 h at RT in Intercept (PBS) blocking buffer (LI-COR 927-70001). Then, the membrane was covered with blocking buffer + D38B1 anti-EGFR antibody (Cell Signaling Technology 4267) and incubated at 4°C overnight on a rocker. The membrane was then washed three times for 5 min in PBS + 0.1% Tween-20 (Thermo Scientific J20605.AP), and incubated for 1 h rocking at RT in goat anti-rabbit IgG (H+L) antibody IRDye 800CW (LI-COR 926-32211) in blocking buffer. The membrane washing procedure was repeated, then rinsed three times in PBS before imaging on an Odyssey CLx Imaging System. The membrane was then incubated overnight at 4°C in blocking buffer + 7F9 anti-Vinculin antibody (Santa Cruz Biotech sc-73614). The washing procedure was repeated and the membrane was incubated for 1 h at RT in goat anti-mouse IgG (H+L) antibody IRDye 680RD (LI-COR 926-68070) in blocking buffer. The washing, rinsing, and imaging procedure was repeated. EGFR staining was quantified relative to vinculin staining using Image Studio software, and reported as a percentage relative to respective no doxycycline or +doxycycline control.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "The main achievement of the paper is the development and application of beta-strand interface-conditioned RFdiffusion. This computational approach generates protein binders specifically tailored to target polar protein surfaces with exposed beta-strands. The method overcomes challenges in designing high-affinity binders for polar protein regions by ensuring precise hydrogen bonding complementarity between the binder and the target's beta-strands. The authors claim that the designed beta sheets show no aggregation due to their customized target recognition. The off-target binding was also tested on the panel of test proteins, and a reasonable level of specificity was found. The work is technically sound, and the results support the conclusions. The method enhances the scope of computational protein design in developing a useful binder with free beta sheet edges. Although the presented data on aggregation-free behavior and specificity is promising, developing a therapeutic protein with a free beta edge would still be risky. Despite my doubts about the possible future applications, I recommend publication in Nat Comms because of the scientific advancement in approaching these challenging targets.\nMinor points to address:\n1. The CD and size exclusion chromatography data indicate sufficient protein stability. Did the authors use the same protein concentrations in the two types of experiments? Please indicate the concentrations for the size exclusion data. What was the temperature? Cold denaturation can prevent hydrophobic aggregation for beta-sheet structures.\n2. For therapeutic applications, it is essential that the designer proteins have long-term stability and do not refold to an amyloid state. Is it possible to run a forced stability test for amyloid formation?\n3. Neither the abstract nor the discussion indicates that the methodology relies on yeast display selection as a crucial final step. This can be misleading. It should be pointed out that the conditioned RFDiffusion results in substantial in silico enrichment before the yeast display selection.\n4. Please resolve the abbreviations in Fig.1 (BFF4, t-SNE).", "reviewer_2": "Review of \"Improved protein binder design using beta-pairing targeted RFdiffusion\"\nGeneral Assessment:\nThe manuscript titled \"Improved protein binder design using beta-pairing targeted RFdiffusion\" presents an advancement in the field of computational protein design. The authors introduced a simple yet effective approach based on previously reported RFDiffusion to designing protein binders that target interfaces with exposed beta-strands with high affinity and specificity. This work holds promise for therapeutic applications, particularly in the development of binders for challenging targets that are often involved in critical biological pathways and diseases. The manuscript is well-structured, with a clear and logical flow of information, and the experimental design is robust and well-reasoned. The use of surface plasmon resonance (SPR) and co-crystal structure analysis to validate the computational models adds a layer of rigor to the study.\nOverall, the manuscript is of high quality but would benefit from minor revisions.\nMinor comments and suggestions:\n1. In the abstract and introduction, the authors describe the problem to be solved as designing binders for \"polar protein targets.\" However, the actual focus of the manuscript is the design of binding proteins for target proteins with exposed beta-strands. While there is some overlap between \"polar protein targets\" and \"exposed beta-strands,\" these concepts are not synonymous. Exposed beta-strands do not inherently imply that the interfaces are polar. The polarity of the interface is influenced by the side chains of the beta-strands as well. If the target interfaces discussed in the manuscript are indeed polar, it would be helpful for the authors to use figures to better demonstrate this characteristic.\n2. The methods section lacks a detailed description of the computational approaches used. Terms such as “tensors” are mentioned throughout the manuscript but are not clearly defined.\n3. Section: Results\nSubsection: Single Exposed Edge beta-strand: ɑ-CTX, FCRL5, and NRP1\nContext: \"Binders for ɑ-CTX and NRP1 (1.9 nM and 84 nM respectively) were obtained by testing the 96 designs based solely on in silico metrics.\"\nComment: A more detailed discussion on why some targets can be addressed through computational metrics alone, while others require experimental library screening, would be beneficial.\n4. Section: Results\nSubsection: Structural characterization of a KITbp:KIT complex confirms design model accuracy\nContext: \"Alphafold 2 prediction of the monomeric KITbp without KIT target places the beta-strand pairing residues in a coil conformation that better shields the binder’s hydrophobic core residues and hydrogen bonding atoms of the beta-strand interface (Extended data figure 7);\"\nComment: Here the author use “Alphafold” rather than “AlphaFold”, the capitalization of \"AlphaFold 2\" should be consistent with the rest of the manuscript.\n5. Section: Main Figure 1\nSub-figure: 1c\nContext: \"(c) Binder design success rates.\"\nComment: The acronym \"BFF4\" appears in Figure 1c without prior explanation in the main text. This may cause confusion for readers who are not familiar with the term. To enhance clarity, it would be beneficial to introduce the full term or provide a brief explanation in the main text, even though it is explained in the figure legend.\n6. Section: Main Figure 1\nSub-figure: 1d\nContext: \"(d) Structural clustering of RFdiffusion output binder scaffolds using hotspot conditioning (left) or beta-strand interface conditioning (right) t-SNE transforms of all-by-all pairwise RMSD data among binder scaffolds across all targets are plotted, with close proximity of points representing structural similarity.\"\nComment: The transformation from RMSD data to t-SNE plots, as shown in Figure 1d, is a critical step in understanding the structural clustering of the binder scaffolds. It would be beneficial to provide a detailed description of this process in the Methods section or the Supplementary Methods. This should include how the all-by-all pairwise RMSD data were calculated and how these distances were transformed into t-SNE coordinates. Such information is essential for readers to fully understand and reproduce the structural analysis presented in the study.\n7. Section: Main Figure 2\nSub-figure: 2a\nContext: \"(a) Design models of binding complexes for each target. Left: Entire complex design models with binders indicated in green/gold and targets in teal/cyan. Beta-strand interface conditioned scaffold design yielded binders with interfacial beta-strands (gold) forming strand-pairing hydrogen bonding interactions with target edge beta-strands (cyan). Middle: Close up view of design model backbone hydrogen-bond interactions with putative hydrogen bonds shown in white. Right: schematic representation of strand pairing interactions showcase the diversity of sequence-independent beta-strand pairing interactions.\"\nComment: Figure 2a provide detailed views of the hydrogen-bond interactions and schematic representations of the beta-strand pairing interactions, respectively. To enhance the clarity and interpretability of these panels, it would be beneficial to include residue numbers or specific labels to indicate the positions of the target residues involved in the interactions. This would allow readers to more easily identify the key residues contributing to the binding.\n8. Section: Main Figure 2\nSub-figure: 2b\nContext: \"(b) Circular dichroism thermal melts. Full spectrum analyses (left) performed at 25C (cyan), 75C (green), and 95C (gold) assess the overall binder fold at these three temperatures, while single wavelength thermal melts (right) were measured at 217 nM to calculate binder Tm values.\"\nComment: The notation for temperature in Figure 2b uses \"C\" (e.g., 25C, 75C, 95C), while the main text uses \"℃\" (e.g., 25 ℃). For consistency, it is recommended to use \"℃\" throughout the manuscript.", "reviewer_3": "In this study, the authors developed a workflow for de novo design et subsequent testing of protein binders targeting polar regions of exposed beta-sheets structures. The authors first generated 500 protein scaffolds by conditioning an RFdiffusion model to specifically target exposed beta-strands of seven proteins. These targets were selected to offer a range of different context and accessibility for the binding partner. For comparison, the same number of scaffolds were generated using a standard RFdiffusion approach. In silico, analysis showed significant improvement in the numbers of candidates meeting quality criteria.\nThe second part of the paper experimentally evaluated candidate binders for each protein target.\nThis paper successfully shows the design of binders with high affinity and specificity for each of the protein target investigated. In the experimental section, the text lacks clarity and cohesion, making it difficult to follow exactly what has been tested for each protein target. Although the authors seem to have performed extensive experimental testing of their yeast screen and the successful binders, some of the data is often mentioned with little to no representation to support it. I have detailed my comments below.\nMain comments:\n1) The number of candidate binders that were experimentally tested and how they were selected is difficult to follow.\nAs far as I understand, 5,000 candidate binders were tested in silico: ten candidate binder sequences were generated for each of 500 scaffolds, bringing the total to 5,000 potential binders per approach for each target. Subsets were determined to be in silico successes according to AlphaFold 2 and Rosetta metrics, as represented in Figure 1, panel c. Additional criteria (“partial diffusion scaffold optimization and additional sequence sampling”) are mentioned in the last paragraph of p.3, and slightly more detailed in the methods section, “computation protein design workflow”. However, it is unclear which parameters caused such differences in library size, as high in silico success rates do not seem to be synonymous with larger experimental libraries (e.g. 2.8% of in silico successes and 4,846 library variants for FCRL5, or 52.6% of in silico successes and 1,230 library variants for KIT). In the last paragraph of p.3, 100 to 10^5 variants are said to have been experimentally tested, in the previous paragraph 5,000 variants are said to have been tested in silico whereas the methods section mentions a 10^5 scale. Are the 10^5 variants per target the sum of the variants generated with the two diffusion models?\nThe number of candidates is not stated for ALK-2 and ALK-3. For α-CTX and NRP1, were 96 designs tested for each in total (i.e. 48 each)?\nAdditional details, for instance a table summarising the cutoffs and resulting library sizes, would make it easier for the reader to understand the selection process.\n2) In the last paragraph of p.3, designs are described as being “first screened using yeast surface display”. No yeast display results are mentioned for four of the targets (ALK-2, ALK-3, α-CTX and NRP1), although the method section describe the generation of yeast libraries for ALK-2 and ALK-3. Could the authors clarify this point and detail if and why they took the decision to bypass yeast display screening for some targets?\nThe same paragraph in p.3 suggests that libraries were generated for each targets using both RF diffusion approaches, however data is only shown for one of them in Extended data Figure 4. If this data exists, adding it to the paper would better showcase the reproducibility of these results.\n3) The numbers of scaffolds and protein designed for each target and approach, as well as the number of targets are not consistent:\n- 6 targets cited in the abstract – although the\n- 5,000 candidate binders (p.3) for seven targets\n- 40,000 designs for 8 targets (extended data figure 3), with seven targets shown in the figure\n4) Throughout the paper, references are made to previous unsuccessful studies which failed to yield an acceptable binder to specific targets. Could the authors clarify if they are discussing unpublished data or specific publications?\n- p.3, l.12-16\n- p.5, l.4\n- p.7, l.35-36\n5) This paper clearly demonstrates that the present interface conditioning method can generate high affinity binders to beta-strands regions of proteins. It also shows that a higher number of promising candidates are generated compared to a more common “hotspot” approach. This is demonstrated both in silico (Figure 1) and experimentally (Extended data Figure 4). However, there are no direct comparison about the quality of the binders generated by each approach. The fold-enrichment described in Extended data Figure 4 is dependent on the activity of other library members and could be influenced by protein expression levels. Direct comparison of binding affinity for the most enriched clone(s) from the two RF diffusion approaches would strongly strengthen the claim of this article.\n6) Could the authors clarify which yeast binding assay they used to measure binding inhibition of KITbp by SCF (p.9)? It would be helpful to add the data as an extended figure.\n7) In Figure 5, panel a, at which timepoint were the photos taken? Showing two timepoints (early and late) would help the reader better visualise the results.\n8) In Figure 5, panel b, the plotting of western blot analysis allows an easier comparison of results, and the calculation of the IC50. However, as western blot analysis is highly dependent on the selection of a region of interest, the addition of representative blot images in the extended data would be welcome.\n9) Extended data figure 8 is missing (p.11, l.4).\n10) On p.14, l.3, the reference for the “previously described protocol’ is missing.\nMinor points:\n- p.7, l.42: “to bind the a cryo-EM structure”\n- p.8, l.9: is the reference meant to be extended data figure 5 and not 4?\n- p.11, l.5: “reaching a steady state at in this expression system”", "reviewer_4": "In this manuscript, Sappington et al. show that RFdiffusion can be conditioned to generate designs that bind to a surface exposed beta strand in target protein via a beta strand interface. Success in this area has been challenging for computational binder design protocols, which is frustrating given that many protein targets, particularly cell surface receptors, have surface exposed beta strands.\nThe authors report details of the development of the approach and show its utility by applying the conditioned RFdiffusion to 8 medically relevant protein targets. They report that in each case, preconditioning RFdiffusion for beta strand interfaces results in favourable designs. They experimentally characterise the designs in a variety of ways, ranging from screening relatively large numbers of candidate designs using yeast display to characterisation or binders using SPR to crystal structures of binder-target complexes to functional assays.\nOverall, this is an interesting study that merits publication if the authors address the points below. Additionally, the text was very concise which meant that explanations of methodology, description of results, and the closing discussion felt briefer than they needed to be.\n### Major points:\n- The major concern is that the authors do not clearly differentiate aspects of this study from a previous report by Torres et al., 2025 (ref 64 in the current manuscript; https://doi.org/10.1038/s41586-024-08393-x).\n- It seems that in ref 64 the authors report SS/ADJ as a tool to better direct RFdiffusion towards exposed beta-strand interfaces, but in ref 64 SS/ADJ is used at the point of inference (ie on a model that hasn’t been conditioned for SS/ADJ) whereas in the current manuscript SS/ADJ is used to train the model prior to use. This difference should be made much clearer in the text, including early discussion and citation of ref 64, clearer descriptions of both approaches, and better distinction between the two. Moreover, given that SS/ADJ as a concept is not being introduced here (that was done in ref 64), a comparison of the two should be include using one or ideally more of the targets to show benefits, if any, of conditioning the model.\n- Page 11: The authors say “In a companion study, ɑ-CTXbp was found to potently neutralize ɑ-CTX, protecting mice from a lethal neurotoxin challenge.” This sentence was surprising as it suggests that the same design has been previously reported in Torres et al., 2025 (https://doi.org/10.1038/s41586-024-08393-x) although it isn’t clear which design is common to both papers. However, the methodology used to design binders in ref 64 and in the current manuscript appear to be different and so it is therefore not really clear whether ɑ-CTXbp was characterized in ref 64 or whether this sentence relates to another design that targets ɑ-CTX. This should be clarified.\n### Minor points:\n- Page 4: ALK-2 and ALK-3 were chosen as targets as they have similar structures which presents a design challenge. However, they only show 30% sequence identity (presumably this value is global), which is low. Extended data figure 2 suggests that, while the beta strands targeted in each protein have some structural similarity with respect to the overall fold, they have very different sequences. Is this really as difficult a challenge as the authors claim?\n- Figure 2: The ALK-2bp interacts ALK-2 via parallel beta strands while ALK-3bp interacts with ALK-3 via antiparallel beta strands. This raises a question about the favorability of one configuration vs the other. Was there a preference for anti-parallel or parallel or no preference across the 8 targets? In the case of ALK-2, did all designs interact via a parallel beta strand interface or were both modes sampled and if both modes were present what was the distribution overall and with respect to the scoring metrics (ie did higher scoring designs all interact with the same mode?)\n- Figure 2: The terms BFF4 and BFF9 (SFig 3) are not explained\n- Page 5: Authors state that previous attempts to make binders to ALK-2, ALK-3 or KIT had failed. Has this been reported anywhere?\n- Page 5: The authors say “... the actual binder interaction footprints …” I think the use of “actual” isn’t appropriate here as they don’t resolve an experimental structure of either complex. They are basically saying that their approach returned different designs.\n- Page 7: the authors report that 20% of designs obtained using non conditioned RFdiffusion interacted with the target via a beta-strand interface. How did these designs compare to those generated using the conditioned RFdiffusion protocol? Did they have the same/different interface? Was this parallel or antiparallel? Etc. More details would be helpful.\n- Page 8 cites “extended data figure 4” but should this be 5?\n- Page 8 - the authors talk about the additional interface contacts involved in design/target binding. It would be useful to know more about the broader interfaces that are generated (e.g. total interface surface area, surface area not related to contacts involving the targeted beta strand, types of interaction involving residues not in the beta strand; are these exclusively hydrophobic?)\n- Figure 3: The all-against-all comparison appears to be of limited value given the differences between the structures and compositions of interfaces being targeted. Do the authors have any evidence from the large number of sequences they test that binders that score less well would show reduced specificity and would bind to one or more of the other targets used in the study?\n- Page 9: The authors say “The KITbp binding site on KIT overlaps with that of the KIT native ligand stem cell factor (SCF), and consistent with this, saturating concentrations of SCF (20(cid:1)M) reduced KITbp binding by >99% in a yeast surface binding assay.” Which figure does this observation relate to?\n- Page 9: The authors say “Lone beta-strands are rare in nature and are found primarily in protein-protein complexes, where a loop in one partner forms a beta-strand conformation that extends the beta-sheet of the other partner.” This statement should be supported by references.\n- Page 10: The authors say “such KIT-dependent conformational switching may contribute to the observed binding specificity.” NMR spectroscopy could be used to evaluate whether KIT exists in multiple conformations in the absence of KITbp and if yes whether any of these conformations preferentially interact with KITbp.\n- Figure 4: It would be useful to see density at interface. The FoFc map provided suggests some uncertainty with the side chain position of Trp262 in both copies in the asymmetric unit.\n- Page 13: The authors say “”We show that beta-strand interface conditioned RFdiffusion outperforms previous methods for the design of binders against edge beta-strand containing targets.” but they do not do direct experimental comparisons of binders generated with and without conditioning. See also the “major” points above.\n- Extended data figure 1: missing (a) and (b) labels\n- Extended data figure 2: Why not use standard AF pLDDT colour scheme?\n- Extended data figure 5: SEC traces are used to support the claim that designs are monodisperse and that “the lack of self association”, but no calibration of the columns are provided to show that elution volumes are consistent with monomeric molecular weights. Several traces show evidence of other species, both at shorter and longer elution times. The claim of lack of self association would be strengthened if each sample was run at several concentrations and the elution volume shown to be invariant of loading concentration.\n- Extended data figure 7: would help to show secondary structure of structures to help orient reader\n- Reference formatting was not appropriate\n- Though not my work, the system described in Schwarz-Linek et al (2003; https://doi.org/10.1038/nature01589) might be an interesting test case for the conditioned RFdiffusion approach described here."}, "author_response": "REVIEWER COMMENTS (Author responses bolded)\nReviewer #1 (Remarks to the Author):\nThe main achievement of the paper is the development and application of\nbeta-strand interface-conditioned RFdiffusion. This computational approach\ngenerates protein binders specifically tailored to target polar protein surfaces with\nexposed beta-strands. The method overcomes challenges in designing high-affinity\nbinders for polar protein regions by ensuring precise hydrogen bonding\ncomplementarity between the binder and the target's beta-strands. The authors\nclaim that the designed beta sheets show no aggregation due to their customized\ntarget recognition. The off-target binding was also tested on the panel of test\nproteins, and a reasonable level of specificity was found. The work is technically\nsound, and the results support the conclusions. The method enhances the scope of\ncomputational protein design in developing a useful binder with free beta sheet\nedges. Although the presented data on aggregation-free behavior and specificity is\npromising, developing a therapeutic protein with a free beta edge would still be\nrisky. Despite my doubts about the possible future applications, I recommend\npublication in Nat Comms because of the scientific advancement in approaching\nthese challenging targets. Minor points to address:\n1. The CD and size exclusion chromatography data indicate sufficient protein\nstability. Did the authors use the same protein concentrations in the two types of\nexperiments? Please indicate the concentrations for the size exclusion data. What\nwas the temperature? Cold denaturation can prevent hydrophobic aggregation for\nbeta-sheet structures.\nWe thank the reviewer for raising this point. The same binder samples were used\nfor all measurements in this study. We have now included the protein\nconcentrations observed during size exclusion chromatography (SEC) in the\nextended data figure description and clarified that all purifications were conducted\nat room temperature in the Methods section.\n2. For therapeutic applications, it is essential that the designer proteins have\nlong-term stability and do not refold to an amyloid state. Is it possible to run a forced\nstability test for amyloid formation?\nWe agree that long-term stability is an important consideration. While CD thermal\nmelts provide a standard assessment of protein stability, we note that for\ntherapeutic development, additional optimizations such as disulfide incorporation\nor edge-scaffold shielding could be implemented to prevent aggregation. We have\nincluded this clarification in the revised manuscript.\n3. Neither the abstract nor the discussion indicates that the methodology relies on\nyeast display selection as a crucial final step. This can be misleading. It should be\npointed out that the conditioned RFDiffusion results in substantial in silico\nenrichment before the yeast display selection.\nWe thank the reviewer for this suggestion. We have revised the main text to clarify\nthat the interface-conditioned RFdiffusion approach yielded substantial in silico\nenrichment, and that yeast surface display was used in some, but not all, cases to\nfurther improve binder affinity. For instance, binders such as NRP1 and α-CTX were\nidentified without high-throughput enrichment. Extended Data Table 1 was added to\ngive further detail on the role of yeast surface display in enrichment, where it can\nbe seen that our success rates should be high enough to yield binders in\nlower-throughput contexts, especially with more strict in silico filtering. In two\ncases (ALK-2 and ALK-3), we likely did need experimental enrichment to yield\nbinders. This lower success rate is acknowledged for these edge cases in the main\ntext.\n4. Please resolve the abbreviations in Fig.1 (BFF4, t-SNE).\nWe have updated the figure to replace the “BFF4” label with default (as this is the\ncolloquial name for the default binder design checkpoint, which is publicly\navailable on the RFDiffusion github) and replaced “t-SNE” with its full term,\n“t-distributed Stochastic Neighbor Embedding.”\nReviewer #2 (Remarks to the Author):\nReview of \"Improved protein binder design using beta-pairing targeted RFdiffusion\"\nGeneral Assessment:\nThe manuscript titled \"Improved protein binder design using beta-pairing targeted\nRFdiffusion\" presents an advancement in the field of computational protein design.\nThe authors introduced a simple yet effective approach based on previously\nreported RFDiffusion to designing protein binders that target interfaces with\nexposed beta-strands with high affinity and specificity. This work holds promise for\ntherapeutic applications, particularly in the development of binders for challenging\ntargets that are often involved in critical biological pathways and diseases. The\nmanuscript is well-structured, with a clear and logical flow of information, and the\nexperimental design is robust and well-reasoned. The use of surface plasmon\nresonance (SPR) and co-crystal structure analysis to validate the computational\nmodels adds a layer of rigor to the study.\nOverall, the manuscript is of high quality but would benefit from minor revisions.\nMinor comments and suggestions:\n1. In the abstract and introduction, the authors describe the problem to be solved as\ndesigning binders for \"polar protein targets.\" However, the actual focus of the\nmanuscript is the design of binding proteins for target proteins with exposed\nbeta-strands. While there is some overlap between \"polar protein targets\" and\n\"exposed beta-strands,\" these concepts are not synonymous. Exposed\nbeta-strands do not inherently imply that the interfaces are polar. The polarity of the\ninterface is influenced by the side chains of the beta-strands as well. If the target\ninterfaces discussed in the manuscript are indeed polar, it would be helpful for the\nauthors to use figures to better demonstrate this characteristic.\nWe appreciate this clarification. We have revised the text to emphasize that\nexposed beta-strand backbones often present polar atoms, making them a distinct\nclass of polar interfaces. To further support this, we have added a plot (Extended\nData Fig. 6) comparing deltaSAP and hydrogen bondering as polarity metrics\nacross our targets and other Baker lab designs. These figures illustrate the trend\nthat our beta-strand pairing binders represent several of the most polar de novo\ndesigned binder interfaces we have ever observed.\n2. The methods section lacks a detailed description of the computational\napproaches used. Terms such as “tensors” are mentioned throughout the\nmanuscript but are not clearly defined.\nWe have clarified the definition and role of interface conditioning tensors in the\nmain text. They are simply arrayed information labeling binder residue secondary\nstructure and target interactions for RFdiffusion.\n3. Section: Results\nSubsection: Single Exposed Edge beta-strand: ɑ-CTX, FCRL5, and NRP1\nContext: \"Binders for ɑ-CTX and NRP1 (1.9 nM and 84 nM respectively) were\nobtained by testing the 96 designs based solely on in silico metrics.\"\nComment: A more detailed discussion on why some targets can be addressed\nthrough computational metrics alone, while others require experimental library\nscreening, would be beneficial.\nWe have elaborated on the factors influencing experimental library size, noting that\ndesign difficulty, target flexibility, and project goals often determined the scale of\nlibrary testing. Here, we made a conscious choice to order large libraries, as we\nwere not sure of the feasibility of strand-pairing binders, expecting potential\ndifficulties with expression and specificity. As we obtained early results (KIT,\nPDGFRa, and FCRL5 YSD sorting data) indicating this would not be as much of an\nissue as expected, we followed up later studies (a-CTX and NRP1) with smaller\nscale orders to reflect this. However, tested larger libraries for ALK-2 and ALK-3\ndue to prior binder design campaigns indicating the exceptional challenge\npresented by this family of targets. We have also added yeast surface display\nsuccess rates to extended data table 1 and further clarified the library size rationale\nin the Methods under the subsection “Production scale computational protein\ndesign workflow”.\n4. Section: Results\nSubsection: Structural characterization of a KITbp:KIT complex confirms design\nmodel accuracy\nContext: \"Alphafold 2 prediction of the monomeric KITbp without KIT target places\nthe beta-strand pairing residues in a coil conformation that better shields the\nbinder’s hydrophobic core residues and hydrogen bonding atoms of the beta-strand\ninterface (Extended data figure 7);\"\nComment: Here the author use “Alphafold” rather than “AlphaFold”, the\ncapitalization of \"AlphaFold 2\" should be consistent with the rest of the manuscript.\nCorrected to AlphaFold 2 throughout.\n5. Section: Main Figure 1\nSub-figure: 1c\nContext: \"(c) Binder design success rates.\"\nComment: The acronym \"BFF4\" appears in Figure 1c without prior explanation in\nthe main text. This may cause confusion for readers who are not familiar with the\nterm. To enhance clarity, it would be beneficial to introduce the full term or provide a\nbrief explanation in the main text, even though it is explained in the figure legend.\nReplaced with “Default.” See reviewer 1 comment 4.\n6. Section: Main Figure 1\nSub-figure: 1d\nContext: \"(d) Structural clustering of RFdiffusion output binder scaffolds using\nhotspot conditioning (left) or beta-strand interface conditioning (right) t-SNE\ntransforms of all-by-all pairwise RMSD data among binder scaffolds across all\ntargets are plotted, with close proximity of points representing structural similarity.\"\nComment: The transformation from RMSD data to t-SNE plots, as shown in Figure\n1d, is a critical step in understanding the structural clustering of the binder scaffolds.\nIt would be beneficial to provide a detailed description of this process in the\nMethods section or the Supplementary Methods. This should include how the\nall-by-all pairwise RMSD data were calculated and how these distances were\ntransformed into t-SNE coordinates. Such information is essential for readers to\nfully understand and reproduce the structural analysis presented in the study.\nWe have added a detailed description of how TMscore matrices were computed and\ntransformed into t-SNE plots in the Methods section under the subsection “In silico\ncomparison of RFdiffusion generated scaffolds”. Scripts to perform this\ntransformation are included in Additional Files.\n7. Section: Main Figure 2\nSub-figure: 2a\nContext: \"(a) Design models of binding complexes for each target. Left: Entire\ncomplex design models with binders indicated in green/gold and targets in\nteal/cyan. Beta-strand interface conditioned scaffold design yielded binders with\ninterfacial beta-strands (gold) forming strand-pairing hydrogen bonding interactions\nwith target edge beta-strands (cyan). Middle: Close up view of design model\nbackbone hydrogen-bond interactions with putative hydrogen bonds shown in\nwhite. Right: schematic representation of strand pairing interactions showcase the\ndiversity of sequence-independent beta-strand pairing interactions.\"\nComment: Figure 2a provide detailed views of the hydrogen-bond interactions and\nschematic representations of the beta-strand pairing interactions, respectively. To\nenhance the clarity and interpretability of these panels, it would be beneficial to\ninclude residue numbers or specific labels to indicate the positions of the target\nresidues involved in the interactions. This would allow readers to more easily\nidentify the key residues contributing to the binding.\nResidue numbers and identifiers have been added to the structural figures in 2a to\naid interpretation.\n8. Section: Main Figure 2\nSub-figure: 2b\nContext: \"(b) Circular dichroism thermal melts. Full spectrum analyses (left)\nperformed at 25C (cyan), 75C (green), and 95C (gold) assess the overall binder\nfold at these three temperatures, while single wavelength thermal melts (right) were\nmeasured at 217 nM to calculate binder Tm values.\"\nComment: The notation for temperature in Figure 2b uses \"C\" (e.g., 25C, 75C,\n95C), while the main text uses \"℃\" (e.g., 25 ℃). For consistency, it is\nrecommended to use \"℃\" throughout the manuscript.\nWe now use “°C” consistently throughout the manuscript.\nReviewer #2 (Remarks on code availability):\nA README file is provided with clear instructions on running the script used in this\nmanuscript.\nReviewer #3 (Remarks to the Author):\nIn this study, the authors developed a workflow for de novo design et subsequent\ntesting of protein binders targeting polar regions of exposed beta-sheets structures.\nThe authors first generated 500 protein scaffolds by conditioning an RFdiffusion\nmodel to specifically target exposed beta-strands of seven proteins. These targets\nwere selected to offer a range of different context and accessibility for the binding\npartner. For comparison, the same number of scaffolds were generated using a\nstandard RFdiffusion approach. In silico, analysis showed significant improvement\nin the numbers of candidates meeting quality criteria.\nThe second part of the paper experimentally evaluated candidate binders for each\nprotein target.\nThis paper successfully shows the design of binders with high affinity and specificity\nfor each of the protein target investigated. In the experimental section, the text lacks\nclarity and cohesion, making it difficult to follow exactly what has been tested for\neach protein target. Although the authors seem to have performed extensive\nexperimental testing of their yeast screen and the successful binders, some of the\ndata is often mentioned with little to no representation to support it. I have detailed\nmy comments below.\nMain comments:\n1) The number of candidate binders that were experimentally tested and how they\nwere selected is difficult to follow.\nAs far as I understand, 5,000 candidate binders were tested in silico: ten candidate\nbinder sequences were generated for each of 500 scaffolds, bringing the total to\n5,000 potential binders per approach for each target. Subsets were determined to\nbe in silico successes according to AlphaFold 2 and Rosetta metrics, as\nrepresented in Figure 1, panel c. Additional criteria (“partial diffusion scaffold\noptimization and additional sequence sampling”) are mentioned in the last\nparagraph of p.3, and slightly more detailed in the methods section, “computation\nprotein design workflow”. However, it is unclear which parameters caused such\ndifferences in library size, as high in silico success rates do not seem to be\nsynonymous with larger experimental libraries (e.g. 2.8% of in silico successes and\n4,846 library variants for FCRL5, or 52.6% of in silico successes and 1,230 library\nvariants for KIT). In the last paragraph of p.3, 100 to 10^5 variants are said to have\nbeen experimentally tested, in the previous paragraph 5,000 variants are said to\nhave been tested in silico whereas the methods section mentions a 10^5 scale. Are\nthe 10^5 variants per target the sum of the variants generated with the two diffusion\nmodels?\nThe number of candidates is not stated for ALK-2 and ALK-3. For α-CTX and\nNRP1, were 96 designs tested for each in total (i.e. 48 each)?\nAdditional details, for instance a table summarising the cutoffs and resulting library\nsizes, would make it easier for the reader to understand the selection process.\nAdditional clarification was provided throughout the manuscript to clearly define\nand differentiate our in silico experiments from our larger design campaigns for\ntesting in the lab. See reviewer 1 comment 3 and reveiwer 2 comment 3 for\nadditional comments on library sizes.\n2) In the last paragraph of p.3, designs are described as being “first screened using\nyeast surface display”. No yeast display results are mentioned for four of the targets\n(ALK-2, ALK-3, α-CTX and NRP1), although the method section describe the\ngeneration of yeast libraries for ALK-2 and ALK-3. Could the authors clarify this\npoint and detail if and why they took the decision to bypass yeast display screening\nfor some targets?\nThe same paragraph in p.3 suggests that libraries were generated for each targets\nusing both RF diffusion approaches, however data is only shown for one of them in\nExtended data Figure 4. If this data exists, adding it to the paper would better\nshowcase the reproducibility of these results.\nSee reviewer 1 comment 3 and reveiwer 2 comment 3 for additional comments on\nlibrary sizes. Please find Extended Data Table 1, which presents statistics on the\nsecondary structures represented in our YSD libraries along with their respective\nsuccess rates in our binding assay.\n3) The numbers of scaffolds and protein designed for each target and approach, as\nwell as the number of targets are not consistent:\n- 6 targets cited in the abstract – although the\n- 5,000 candidate binders (p.3) for seven targets\n- 40,000 designs for 8 targets (extended data figure 3), with seven targets shown in\nthe figure\nWe have ensured that references to the number of targets (six, seven, or eight) are\nnow consistent throughout the manuscript and figures. Note that there are eight\nbinder against seven targets, with two binders presented for PDGFRα. Thank you\nfor your attention to this detail.\n4) Throughout the paper, references are made to previous unsuccessful studies\nwhich failed to yield an acceptable binder to specific targets. Could the authors\nclarify if they are discussing unpublished data or specific publications?\n- p.3, l.12-16\n- p.5, l.4\n- p.7, l.35-36\nWe have unpublished data on failed binder design campaigns. Finding this data\ngenerated by lab alumni can be difficult, but we were able to recover sequences\nthat failed to bind for ALK-3 and KIT, which we have included in this submission in\nAdditional Files.\n5) This paper clearly demonstrates that the present interface conditioning method\ncan generate high affinity binders to beta-strands regions of proteins. It also shows\nthat a higher number of promising candidates are generated compared to a more\ncommon “hotspot” approach. This is demonstrated both in silico (Figure 1) and\nexperimentally (Extended data Figure 4). However, there are no direct comparison\nabout the quality of the binders generated by each approach. The fold-enrichment\ndescribed in Extended data Figure 4 is dependent on the activity of other library\nmembers and could be influenced by protein expression levels. Direct comparison\nof binding affinity for the most enriched clone(s) from the two RF diffusion\napproaches would strongly strengthen the claim of this article.\nThis is an important consideration. Although we attempted design of both helical\nand strand pairing binders for these targets, in each case strand pairing binders\nwere vastly overrepresented among our in silico successes. In some cases (e.g.,\nKIT, FCRL5, and NRP1), we were unable to generate entirely helical in silico\nsuccesses. When this was possible for other targets (ALK-2, ALK-3, PDGFRa),\nwe indeed ordered and tested the helical designs alongside the strand-pairing\nbinders. We only identified potential helical binders for PDGFRa that were tested\nby SPR alongside the strand pairing designs. We now include experimental data\ncomparing conditioned and unconditioned designs that were tested side-by-side by yeast\nsurface display and SPR (extended data figure 4 and extended data table 1).\n6) Could the authors clarify which yeast binding assay they used to measure\nbinding inhibition of KITbp by SCF (p.9)? It would be helpful to add the data as an\nextended figure.\nWe have added an extended data figure showing the KITbp–SCF competition assay.\nThe method is presented in the manuscript, and no changes were made for the\ncompetition assay besides adding ligand as described.\n7) In Figure 5, panel a, at which timepoint were the photos taken? Showing two\ntimepoints (early and late) would help the reader better visualise the results.\nWe include a mention of the timepoint for the photos in the figure legend.\n8) In Figure 5, panel b, the plotting of western blot analysis allows an easier\ncomparison of results, and the calculation of the IC50. However, as western blot\nanalysis is highly dependent on the selection of a region of interest, the addition of\nrepresentative blot images in the extended data would be welcome.\nRepresentative blots have been included in the Figure for visualization.\n9) Extended data figure 8 is missing (p.11, l.4).\nWe have corrected the figure reference.\n10) On p.14, l.3, the reference for the “previously described protocol’ is missing.\nCitation has been added.\nMinor points:\n● - p.7, l.42: “to bind the a cryo-EM structure”\n● - p.8, l.9: is the reference meant to be extended data figure 5 and not 4?\n● - p.11, l.5: “reaching a steady state at in this expression system”\nAll corrected.\nReviewer #4 (Remarks to the Author):\nIn this manuscript, Sappington et al. show that RFdiffusion can be conditioned to\ngenerate designs that bind to a surface exposed beta strand in target protein via a\nbeta strand interface. Success in this area has been challenging for computational\nbinder design protocols, which is frustrating given that many protein targets,\nparticularly cell surface receptors, have surface exposed beta strands.\nThe authors report details of the development of the approach and show its utility\nby applying the conditioned RFdiffusion to 8 medically relevant protein targets. They\nreport that in each case, preconditioning RFdiffusion for beta strand interfaces\nresults in favourable designs. They experimentally characterise the designs in a\nvariety of ways, ranging from screening relatively large numbers of candidate\ndesigns using yeast display to characterisation or binders using SPR to crystal\nstructures of binder-target complexes to functional assays.\nOverall, this is an interesting study that merits publication if the authors address the\npoints below. Additionally, the text was very concise which meant that explanations\nof methodology, description of results, and the closing discussion felt briefer than\nthey needed to be.\n### Major points:\n- The major concern is that the authors do not clearly differentiate aspects of this\nstudy from a previous report by Torres et al., 2025 (ref 64 in the current manuscript;\nhttps://doi.org/10.1038/s41586-024-08393-x).\nThe immediate impact of that binder rapid experimental progress resulted in that\nbeing published earlier than this. Here, we describe that method in more detail,\nprovide code for implementation, and demonstrate the generalizability of edge\nstrand pairing for binder design.\n- It seems that in ref 64 the authors report SS/ADJ as a tool to better direct\nRFdiffusion towards exposed beta-strand interfaces, but in ref 64 SS/ADJ is used at\nthe point of inference (ie on a model that hasn’t been conditioned for SS/ADJ)\nwhereas in the current manuscript SS/ADJ is used to train the model prior to use.\nThis difference should be made much clearer in the text, including early discussion\nand citation of ref 64, clearer descriptions of both approaches, and better\ndistinction between the two. Moreover, given that SS/ADJ as a concept is not being\nintroduced here (that was done in ref 64), a comparison of the two should be include\nusing one or ideally more of the targets to show benefits, if any, of conditioning the\nmodel.\nThis difference is incorrect. The binder presented here is the same as the binder\nintroduced by Susana’s paper, and designed using the same model and methods\ndescribed.\n- Page 11: The authors say “In a companion study, ɑ-CTXbp was found to potently\nneutralize ɑ-CTX, protecting mice from a lethal neurotoxin challenge.” This\nsentence was surprising as it suggests that the same design has been previously\nreported in Torres et al., 2025 (https://doi.org/10.1038/s41586-024-08393-x)\nalthough it isn’t clear which design is common to both papers. However, the\nmethodology used to design binders in ref 64 and in the current manuscript appear\nto be different and so it is therefore not really clear whether ɑ-CTXbp was\ncharacterized in ref 64 or whether this sentence relates to another design that\ntargets ɑ-CTX. This should be clarified.\nWe include a citation of Susana’s paper earlier in the text (as soon as ɑ-CTXbp is\nintroduced), as well as clarification that this is the same binder described there.\n### Minor points:\n- Page 4: ALK-2 and ALK-3 were chosen as targets as they have similar structures\nwhich presents a design challenge. However, they only show 30% sequence\nidentity (presumably this value is global), which is low. Extended data figure 2\nsuggests that, while the beta strands targeted in each protein have some structural\nsimilarity with respect to the overall fold, they have very different sequences. Is this\nreally as difficult a challenge as the authors claim?\nWe have toned down the claim regarding design difficulty and now emphasize\nbackbone similarity despite low sequence identity. Our presumption was that the\ninherently side-chain independent interactions of strand-pairs might lead to\npromiscuous interactions between proteins with similar edge strands. These\ntargets, having nearly identical edge strand backbones, provide at least one\nexample to test this hypothesis.\n- Figure 2: The ALK-2bp interacts ALK-2 via parallel beta strands while ALK-3bp\ninteracts with ALK-3 via antiparallel beta strands. This raises a question about the\nfavorability of one configuration vs the other. Was there a preference for\nanti-parallel or parallel or no preference across the 8 targets? In the case of ALK-2,\ndid all designs interact via a parallel beta strand interface or were both modes\nsampled and if both modes were present what was the distribution overall and with\nrespect to the scoring metrics (ie did higher scoring designs all interact with the\nsame mode?)\nExtended data table 1 includes data on the relative success rates of parallel and\nantiparallel designs, which seemed to be target dependent.\n- Figure 2: The terms BFF4 and BFF9 (SFig 3) are not explained\nRemoved from all figures. See reviewer 1 comment 4.\n- Page 5: Authors state that previous attempts to make binders to ALK-2, ALK-3 or\nKIT had failed. Has this been reported anywhere?\nNo, but we have included the sequences of these failed designs in Additional Files.\nSee reviewer 3 comment 4.\n- Page 5: The authors say “... the actual binder interaction footprints …” I think the\nuse of “actual” isn’t appropriate here as they don’t resolve an experimental structure\nof either complex. They are basically saying that their approach returned different\ndesigns.\nAgreed it is not correct to say “actual”. Toned language appropriately.\n- Page 7: the authors report that 20% of designs obtained using non conditioned\nRFdiffusion interacted with the target via a beta-strand interface. How did these\ndesigns compare to those generated using the conditioned RFdiffusion protocol?\nDid they have the same/different interface? Was this parallel or antiparallel? Etc.\nMore details would be helpful.\nWe did not observe any statistical differences between strand pairing binders\ngenerated with or without conditioning. For NRP1, there was perhaps a slight\npreference for antiparallel strand pairs among unconditional designs (57 out of 112\nstrand pairing designs were antiparallel). This compares with (352 out of 482 for\nconditioned outputs). Overall, there seemed to be a preference for antiparallel\npairing (1640 out of 2179 strand pairs). While experimental data indicated that both\nparallel and antiparallel strand pairing can yield binders. See reviewer 3 comment\n5.\n- Page 8 cites “extended data figure 4” but should this be 5?\nFixed.\n- Page 8 - the authors talk about the additional interface contacts involved in\ndesign/target binding. It would be useful to know more about the broader interfaces\nthat are generated (e.g. total interface surface area, surface area not related to\ncontacts involving the targeted beta strand, types of interaction involving residues\nnot in the beta strand; are these exclusively hydrophobic?)\nWe added calculations of total interface surface area and composition, now\ndiscussed in the main text.\n- Figure 3: The all-against-all comparison appears to be of limited value given the\ndifferences between the structures and compositions of interfaces being targeted.\nDo the authors have any evidence from the large number of sequences they test\nthat binders that score less well would show reduced specificity and would bind to\none or more of the other targets used in the study?\nTypically, spatial aggregation propensity (SAP), determined by the degree of\nexposed hydrophobic surface area, is used to estimate a binder’s propensity for\naggregation and non-specific binding. These binders were filtered on this metric,\nbut the polar backbone groups of edge strand interfaces made us concerned that\nthe binders would nonspecifically bind other edge strands without exhibiting high\nSAP scores. This specificity experiment was performed to address this hypothesis.\nAdditional experimentation would be needed to fully characterize off-target binding\nagainst a proteome for a proper analysis, and we hope to do so in future\npublications that directly address this topic.\n- Page 9: The authors say “The KITbp binding site on KIT overlaps with that of the\nKIT native ligand stem cell factor (SCF), and consistent with this, saturating\nconcentrations of SCF (20(cid:1)M) reduced KITbp binding by >99% in a yeast surface\nbinding assay.” Which figure does this observation relate to?\nNow included in extended data figure 6a.\n- Page 9: The authors say “Lone beta-strands are rare in nature and are found\nprimarily in protein-protein complexes, where a loop in one partner forms a\nbeta-strand conformation that extends the beta-sheet of the other partner.” This\nstatement should be supported by references.\nWe revised the sentence and acknowledged the limited literature while clarifying\nour reasoning.\n- Page 10: The authors say “such KIT-dependent conformational switching may\ncontribute to the observed binding specificity.” NMR spectroscopy could be used to\nevaluate whether KIT exists in multiple conformations in the absence of KITbp and\nif yes whether any of these conformations preferentially interact with KITbp.\nWhile NMR spectroscopy would be the gold-standard for evaluation here, our\nprimary aim is to demonstrate the ability of beta-strand interface-conditioned\nRFdiffusion to design specific binders. The predicted flexibility of KITbp may reflect\na form of metastability not observed in previously designed binders, but this was\nan incidental finding rather than a predefined focus of our work. As such, we did\nnot pursue a detailed biophysical characterization, which we believe falls outside\nthe scope of this study.\n- Figure 4: It would be useful to see density at interface. The FoFc map provided\nsuggests some uncertainty with the side chain position of Trp262 in both copies in\nthe asymmetric unit.\nWe are not entirely clear on the suggested uncertainty at Trp262. The reviewer\nmight potentially refer to two unmodelled 'blobs' around this residue – see the\nscreenshot below where Fo-Fc is visualized by red and green colors (green means\nthere is some extra density in data which is not modelled while red means there is\nsome structure modelled in the place where there is no experimental density).\nHowever, these two green blobs are just some solvent molecules that are typical for\nmany of the deposited structures and are also present across our whole structure\nand not only here. We modelled some of these blobs (water molecules, co-solvent\nmolecules, etc.) but at this resolution, not everything can be reliably modelled to\nprevent data overfitting.\nWe are not entirely clear on the suggested uncertainty at Trp262. The reviewer might potentially\nrefer to two unmodelled 'blobs' around this residue – see the screenshot below where Fo-Fc is\nvisualized by red and green colors (green means there is some extra density in data which is\nnot modelled while red means there is some structure modelled in the place where there is no\nexperimental density). However, these two green blobs are just some solvent molecules that are\ntypical for many of the deposited structures and are also present across our whole structure and\nnot only here. We modelled some of these blobs (water molecules, co-solvent molecules, etc.)\nbut at this resolution, not everything can be reliably modelled to prevent data overfitting.\n- Page 13: The authors say “”We show that beta-strand interface conditioned\nRFdiffusion outperforms previous methods for the design of binders against edge\nbeta-strand containing targets.” but they do not do direct experimental comparisons\nof binders generated with and without conditioning. See also the “major” points\nabove.\nWe now qualify this claim and note that additional side-by-side experimental\ncomparisons are shown in the extended data figure 5 and table 1.\nExtended data figure 1: missing (a) and (b) labels\nCorrected figure labeling\n- Extended data figure 2: Why not use standard AF pLDDT colour scheme?\nWe were not aware of the extent that the blue, yellow, and orange pLDDT color\nscheme was standard at the time of making this figure. The depicted scale is how\nthe figure author visualizes pLDDT as it gives slightly more per residue granularity\nthan the 4-binned standard scheme.\n- Extended data figure 5: SEC traces are used to support the claim that designs are\nmonodisperse and that “the lack of self association”, but no calibration of the\ncolumns are provided to show that elution volumes are consistent with monomeric\nmolecular weights. Several traces show evidence of other species, both at shorter\nand longer elution times. The claim of lack of self association would be\nstrengthened if each sample was run at several concentrations and the elution\nvolume shown to be invariant of loading concentration.\nWe have adjusted the wording to clarify that SEC traces suggest monodispersity\nbut do not confirm monomeric status without additional validation. We instead\nstate that we did not not observe significant aggregation during any of our\nexperiments.\n- Extended data figure 7: would help to show secondary structure of structures to\nhelp orient reader\nSecondary structure representations were added to what is now extended data\nfigure 8.\n- Reference formatting was not appropriate\nAll references have been reformatted to comply with journal standards.\n- Though not my work, the system described in Schwarz-Linek et al (2003;\nhttps://doi.org/10.1038/nature01589) might be an interesting test case for the\nconditioned RFdiffusion approach described here.\nWe thank the reviewer for this suggestion and agree that this system could be a\ncompelling future test case for our approach.\nReviewer #4 (Remarks on code availability):\nWe looked at the code but did not have time to test it while reviewing the paper; we\nare planning to do so. We looked at the crystallographic model and density file and\nassociated statistics and these seemed fine though the Rfree is high for a structure\nof this resolution.\nPoint-by-Point Response to Reviewers\nReviewer #1:\n1. CD and SEC protein concentrations and temperatures\nWe thank the reviewer for raising this point. The same binder samples were used for all\nmeasurements in this study. We have now included the protein concentrations observed during\nsize exclusion chromatography (SEC) in the extended data figure description and clarified that\nall purifications were conducted at room temperature in the Methods section.\n2. Stability testing and amyloid aggregation\nWe agree that long-term stability is an important consideration. While CD thermal melts provide\na standard assessment of protein stability, we note that for therapeutic development, additional\noptimizations such as disulfide incorporation or edge-scaffold shielding could be implemented to\nprevent aggregation. We have included this clarification in the revised manuscript.\n3. Clarification of yeast display’s role in enrichment\nWe thank the reviewer for this suggestion. We have revised the main text to clarify that the\ninterface-conditioned RFdiffusion approach yielded substantial in silico enrichment, and that\nyeast surface display was used in some, but not all, cases to further improve binder affinity. For\ninstance, binders such as NRP1 and α-CTX were identified without high-throughput enrichment.\nExtended Data Table 1 was added to give further detail on the role of yeast surface display in\nenrichment, where it can be seen that our success rates should be high enough to yield binders\nin lower-throughput contexts, especially with more strict in silico filtering. In two cases (ALK-2\nand ALK-3), we likely did need experimental enrichment to yield binders. This lower success\nrate is acknowledged for these edge cases in the main text.\n4. Abbreviations in Fig. 1\nWe have updated the figure to replace the “BFF4” label with default (as this is the\ncolloquial name for the default binder design checkpoint, which is publicly available on the RFDiffusion\ngithub) and replaced “t-SNE” with its full term, “t-distributed Stochastic Neighbor Embedding.”\nReviewer #2:\n1. Clarification of “polar surfaces” vs. “exposed beta-strands”\nWe appreciate this clarification. We have revised the text to emphasize that exposed\nbeta-strand backbones often present polar atoms, making them a distinct class of polar\ninterfaces. To further support this, we have added a plot (Extended Data Fig. 6) comparing\ndeltaSAP and hydrogen bondering as polarity metrics across our targets and other Baker lab\ndesigns. These figures illustrate the trend that our beta-strand pairing binders represent several\nof the most polar de novo designed binder interfaces we have ever observed.\n2. Definition of computational terms (e.g., “tensor”)\nWe have clarified the definition and role of interface conditioning tensors in the Methods section.\n3. Variation in in silico vs. experimental success\nWe have elaborated on the factors influencing experimental library size, noting that design\ndifficulty, target flexibility, and project goals often determined the scale of library testing. Here,\nwe made a conscious choice to order large libraries, as we were not sure of the feasibility of\nstrand-pairing binders, expecting potential difficulties with expression and specificity. As we\nobtained early results (KIT, PDGFRa, and FCRL5 YSD sorting data) indicating this would not be\nas much of an issue as expected, we followed up later studies (a-CTX and NRP1) with smaller\nscale orders to reflect this. However, tested larger libraries for ALK-2 and ALK-3 due to prior\nbinder design campaigns indicating the exceptional challenge presented by this family of\ntargets. We have also added yeast surface display success rates to extended data table 1 and\nfurther clarified the library size rationale in the Methods under the subsection “Production scale\ncomputational protein design workflow”.\n4. Capitalization consistency of AlphaFold\nCorrected throughout.\n5. BFF4 label in Fig. 1c\nReplaced with “Default.” See reviewer 1 comment 4.\n6. Clarification of t-SNE methodology in Fig. 1d\nWe have added a detailed description of how TMscore matrices were computed and\ntransformed into t-SNE plots in the Methods section under the subsection “In silico comparison\nof RFdiffusion generated scaffolds”. Scripts to perform this transformation are included in\nAdditional Files.\n7. Residue labels in Fig. 2a\nResidue numbers and identifiers have been added to the structural figures to aid interpretation.\n8. Temperature notation consistency in Fig. 2b\nWe now use “°C” consistently throughout the manuscript.\nReviewer #3:\n1. Clarification of library sizes and selection criteria\nAdditional clarification was provided throughout the manuscript to clearly define and differentiate\nour in silico experiments from our larger design campaigns for testing in the lab. See reviewer 1\ncomment 3 and reveiwer 2 comment 3 for additional comments on library sizes.\n2. Explanation of yeast display use across targets\nSee reviewer 1 comment 3 and reveiwer 2 comment 3 for additional comments on library sizes.\nPlease find Extended Data Table 1, which presents statistics on the secondary structures\nrepresented in our YSD libraries along with their respective success rates in our binding assay.\n3. Target count consistency\nWe have ensured that references to the number of targets (six, seven, or eight) are now\nconsistent throughout the manuscript and figures. Note that there are eight binder against seven\ntargets, with two binders presented for PDGFRα. Thank you for your attention to this detail.\n4. Clarify whether prior failed binders were unpublished or published\nWe have unpublished data on failed binder design campaigns. Finding this data generated by\nlab alumni can be difficult, but we were able to recover sequences that failed to bind for ALK-3\nand KIT, which we have included in this submission in Additional Files.\n5. Direct comparison of conditioned vs unconditioned binder affinity\nThis is an important consideration. Although we attempted design of both helical and strand\npairing binders for these targets, in each case strand pairing binders were vastly\noverrepresented among our in silico successes. In some cases (e.g., KIT, FCRL5, and NRP1),\nwe were unable to generate entirely helical in silico successes. When this was possible for other\ntargets (ALK-2, ALK-3, PDGFRa), we indeed ordered and tested the helical designs alongside\nthe strand-pairing binders. We only identified potential helical binders for PDGFRa that were\ntested by SPR alongside the strand pairing designs. We now include experimental data\ncomparing conditioned and unconditioned designs that were tested side-by-side by yeast\nsurface display and SPR (extended data figure 4 and extended data table 1).\n6. Clarification of yeast assay and competition data\nWe have added an extended data figure showing the KITbp–SCF competition assay. The\nmethod is presented in the manuscript, and no changes were made for the competition assay\nbesides adding ligand as described.\n7. Timepoints in Figure 5a\nWe include a mention of the timepoint for the photos in the figure legend.\n8. Representative western blot images\nRepresentative blots have been included in the Figure for visualization.\n9. Missing Extended Data Fig. 8\nWe have corrected the figure reference.\n10. Missing citation on protocol\nCitation has been added.\nReviewer #4:\n1. Clarification of overlap with Torres et al. (2025)\nThis difference is incorrect. The binder presented here is the same as the binder introduced by\nSusana’s paper, and designed using the same model and methods described. The immediate\nimpact of that binder rapid experimental progress resulted in that being published earlier than\nthis. Here, we describe that method, demonstrate its generalizability, and make it more\navailable.\n2. Clarification of α-CTXbp overlap between studies\nWe include a citation of Susana’s paper earlier in the text, as well as clarification that this is the\nsame binder described there.\n3. Design challenge of ALK-2 and ALK-3\nWe have toned down the claim regarding design difficulty and now emphasize backbone\nsimilarity despite low sequence identity. Our presumption was that the inherently side-chain\nindependent interactions of strand-pairs might lead to promiscuous interactions between\nproteins with similar edge strands. These targets, having nearly identical edge strand\nbackbones, provide at least one example to test this hypothesis.\n4. Parallel vs antiparallel strand pairing preference\nExtended data table 1 includes data on the relative success rates of parallel and antiparallel\ndesigns, which seemed to be target dependent.\n5. Unexplained BFF4/BFF9 labels\nRemoved from all figures. See reviewer 1 comment 4.\n6. Evidence for previous failed designs\nNo, but we have included the sequences of these failed designs in Additional Files. See\nreviewer 3 comment 4.\n7. Comparison of non-strand-pairing vs. strand-pairing RFdiffusion designs\nWe did not observe any statistical differences between strand pairing binders generated with or\nwithout conditioning. For NRP1, there was perhaps a slight preference for antiparallel strand\npairs among unconditional designs (57 out of 112 strand pairing designs were antiparallel). This\ncompares with (352 out of 482 for conditioned outputs). Overall, there seemed to be a\npreference for antiparallel pairing (1640 out of 2179 strand pairs). While experimental data\nindicated that both parallel and antiparallel strand pairing can yield binders. See reviewer 3\ncomment 5.\n8. Clarification of Figure reference (extended data fig. 5)\nFixed.\n9. Interface contact analysis and surface area\nWe added calculations of total interface surface area and composition, now discussed in the\nmain text.\n10. Specificity across targets\nTypically, spatial aggregation propensity (SAP) is used to estimate a binder’s propensity for\naggregation and non-specific binding. These binders were filtered on this metric, but the polar\nbackbone groups of edge strand interfaces made us concerned that the binders would\nnonspecifically bind other edge strands without exhibiting high SAP scores. This specificity\nexperiment was performed to address this hypothesis. Additional experimentation would be\nneeded to fully characterize off-target binding against a proteome for a proper analysis, and we\nhope to do so in future publications that directly address this topic.\n11. KITbp competition figure\nNow included in extended data figure 6a.\n12. Statement on lone beta-strands\nWe revised the sentence and acknowledged the limited literature while clarifying our reasoning.\n13. KIT conformational states\nWhile NMR spectroscopy would be the gold-standard for evaluation here, our primary aim is to\ndemonstrate the ability of beta-strand interface-conditioned RFdiffusion to design specific\nbinders. The predicted flexibility of KITbp may reflect a form of metastability not observed in\npreviously designed binders, but this was an incidental finding rather than a predefined focus of\nour work. As such, we did not pursue a detailed biophysical characterization, which we believe\nfalls outside the scope of this study.\n14. Density at interface (Figure 4)\nWe are not entirely clear on the suggested uncertainty at Trp262. The reviewer might potentially\nrefer to two unmodelled 'blobs' around this residue – see the screenshot below where Fo-Fc is\nvisualized by red and green colors (green means there is some extra density in data which is\nnot modelled while red means there is some structure modelled in the place where there is no\nexperimental density). However, these two green blobs are just some solvent molecules that are\ntypical for many of the deposited structures and are also present across our whole structure and\nnot only here. We modelled some of these blobs (water molecules, co-solvent molecules, etc.)\nbut at this resolution, not everything can be reliably modelled to prevent data overfitting.\n15. Claim of outperforming previous methods\nWe now qualify this claim and note that additional side-by-side experimental comparisons are\nshown in the extended data figure 5 and table 1.\n16. Extended Data figure formatting\nCorrected figure labeling\n17. SEC calibration and claims of monodispersity\nWe have adjusted the wording to clarify that SEC traces suggest monodispersity but do not\nconfirm monomeric status without additional validation. We instead state that we did not not\nobserve significant aggregation during any of our experiments.\n18. Extended Data Fig. 7 readability\nSecondary structure representations were added to what is now extended data figure 8.\n19. Reference formatting\nAll references have been reformatted to comply with journal standards.\n20. Schwarz-Linek et al. (2003) suggestion :\nWe thank the reviewer for this suggestion and agree that this system could be a compelling\nfuture test case for our approach.\nREVIEWER COMMENTS\nReviewer #1 (Remarks to the Author):\nMy concerns and suggestions have been addressed appropriately in the revision. I\nsuggest publication.\nReviewer #2 (Remarks to the Author):\nThe authors have adequately addressed all of my comments and concerns.\nPublication is recommended.\nReviewer #3 (Remarks to the Author):\nI am satisfied by the response from the authors. They have provided additional\ninformation regarding library design and size which clarified their approach and the\nwork described in the paper. The additional experimental data answered my\nconcerns regarding experimental bias and controls.\nI only have some minor comments left as detailed below.\n1) In extended data table 1, for PDGFRa multiple libraries are mentioned in the\npaper:\n- At p.7: libraries designed against the cryo-EM structure or the AlphaFold 2 model,\nmade of 3780 and 391 members\n- In extended data figure 4: “Two binder libraries, β-strand interface conditioned\n(N=5616) and hotspot only conditioned (N=5717)”\nAre those two different experiments and are the numbers listed in extended data\ntable 1 referring to one or both? Could the authors clarify in the table caption.\nThank you for your detailed attention to the library sizes, as they were\nindeed incongruent between the extended data and the main text by\nmistake. The correct library sizes were indicated in the extended data\ntable, but we neglected to correct the numbers in the text in our last\nsubmission. The library size errors result from typos, counting only\ndesigns detected sequencing counts from NGS (dropout can be a\nresult of some designs exhibiting poor expression and/or display on\nyeast cell surfaces), and mistakes made during counting of the library\nsizes (e.g., the hotspot only conditioned designs were indicated by\ndesigns with the patterns ‘normal’ (N=5727) and ‘bcov’ (N=4299), which\ncombined yielded the total hotspot-only conditioned library size,\nN=10026). Both libraries, β-strand interface conditioned (N=5616) and\nhotspot only conditioned (N=10026), were tested in the exact same\nexperiment. We chose to describe them as two different libraries\nbased entirely on the fact that different design methods were used, but\nlibraries were tested in the exact same experiment, simultaneously, as\nindicated in the extended data figure 4 description.\n2) Different library sizes are cited in the paper’s main text than listed in data table 1.\nMy understanding is that the numbers in extended data table 1 are before selection\ncriteria were applied. Could the authors explicitly state in the caption if that is the\ncase. While non-essential, adding a column in the table to summarise how many\ndesigns were experimentally tested (as stated in the main text for some of the\ntargets) would help with clarity and representation of the importance of the selection\ncriteria.\nEvery design in each library (all designs counted in extended data\ntable 1) was experimentally tested by yeast surface display. Some\nlibrary numbers indicated in the manuscript were incorrect due to\nmiscounting (some designs dropping out during initial YSD sorts and\ntherefore were not counted NGS). The library numbers are now\nconsistent, and all reflect the number of DNA sequences ordered and\nscreened. Additionally, we have added to the extended data table 1\ndescription to include the number of designs that were then purified\nand characterized for solubility and binding.\n3) p.4: the first part of the sentence “PDGFRɑ where both helical bundle designs\nand β-strand pairing designs passed in silico filters, significantly more binders were\nobtained with the strand directed approach than with standard RFdiffusion at this\nselection stage (Extended Data Figure 4).” seems incomplete.\nThank you for carefully reading the text. This was rephrased to “For\nPDGFRɑ, where both helical bundle designs and β-strand pairing\ndesigns passed in silico filters, significantly more binders were\nobtained with the strand directed approach than with standard\nRFdiffusion at this selection stage (Extended Data Figure 4).” for\nproper syntax.\n4) p.9, typo:\n“. the There are 21 sidechain-sidechain”\nFixed.\n5) p.12, figure 5\n“(b-c) FCRL5 is degraded HeLa-FCRL5 cells by de novo binders”\nDid the authors mean that \"EGFR\" is degraded in HeLa-FCRL5?\nYes, that is what we intended. Thank you!\nReviewer #4 (Remarks to the Author):\nLargely speaking, the authors have answered our original questions. Some minor\nremaining points:\nResponse 11 – should this be Ext Data Fig 4c?\nResponse 15 – should this be Ext Data Fig 6?\nYes, the figures were renumbered when writing the reviewer\nresponses, but were changed afterward for better organization.\nIn the section \"β-strand interface conditioning improves binder designs\", Sappington\net al. state that the authors of the original 2023 RFdiffusion paper tested training\nwith SS/ADJ. Sappington et al. now further supplement this by providing an\nadjacency matrix when running the RFdiffusion run_inference.py script. We think it\nwould be clearer if Sappington et al. more clearly state that in the text this step is\nperformed at inference, possibly in this sentence:\n“We explored whether providing interface conditioning information indicating a\nbinder β-strand pairing with a target edge β-strand at the point of inference could\nyield designable strand pairing complexes.”\nWe agree that this change will make the text more clear to readers. We\nreplaced the original sentence, “We explored whether providing\ninterface conditioning information indicating a binder β-strand pairing\nwith a target edge β-strand could yield designable strand pairing\ncomplexes.” with the suggestion above.\nReviewer #4 (Remarks on code availability):\nConda env installs fine and code works fine in our hands after the following change:\n1) Error in line 82 of make_interface_tensor.py\nIn our hands, the script creates an invalid path for the .txt file that is needed to feed\ninto the RFdiffusion run_inference.py script:\npython make_interface_tensor.py --input_pdb alk2_target.pdb --out_dir\n/local_home/####/NAT_REVIEW/software/ss_adjacency_tensors --binderlen 65 --\ntarget_adj B99-103 --binder_ss E --binder_ss_len 6\nError\nsh: 1: cannot create\n/local_home/####/NAT_REVIEW/software/ss_adjacency_tensors../software.txt:\nDirectory nonexistent\nWe were able to fix this by changing line 82 in make_interface_tensor.py from\nos.system(f'echo {name}_adj{\"-\".join([str(x[0]) for x in adj_set])}_{counter} >>\n{args.out_dir}../{args.out_dir.split(\"/\")[-2]}.txt')\nto\nparent_dir = os.path.dirname(os.path.normpath(args.out_dir))\nout_name = os.path.basename(os.path.normpath(args.out_dir)) + \".txt\"\noutfile = os.path.join(parent_dir, out_name)\nos.system(f'echo {name}_adj{\"-\".join([str(x[0]) for x in adj_set])}_{counter} >>\n{outfile}')\nThank you for taking the time to troubleshoot this on your system and\nfor providing the cleaner code block. We replaced line 82 in the script\nwith the suggested lines.\n2) Small change to README file\nNeed to add ‘python’ before the run_inference.py script commands in the third\nparagraph\nFixed.\nReviewer #5 (Remarks to the Author):\nI co-reviewed this manuscript with one of the reviewers who provided the listed\nreports. This is part of the Nature Communications initiative to facilitate training in\npeer review and to provide appropriate recognition for Early Career Researchers\nwho co-review manuscripts."}, {"round": 2, "reviewer_comments": {"reviewer_1": "My concerns and suggestions have been addressed appropriately in the revision. I suggest publication.", "reviewer_2": "The authors have adequately addressed all of my comments and concerns. Publication is recommended.", "reviewer_3": "I am satisfied by the response from the authors. They have provided additional information regarding library design and size which clarified their approach and the work described in the paper. The additional experimental data answered my concerns regarding experimental bias and controls.\nI only have some minor comments left as detailed below.\n1) In extended data table 1, for PDGFRa multiple libraries are mentioned in the paper:\n- At p.7: libraries designed against the cryo-EM structure or the AlphaFold 2 model, made of 3780 and 391 members\n- In extended data figure 4: “Two binder libraries, β-strand interface conditioned (N=5616) and hotspot only conditioned (N=5717)”\nAre those two different experiments and are the numbers listed in extended data table 1 referring to one or both? Could the authors clarify in the table caption.\n2) Different library sizes are cited in the paper’s main text than listed in data table 1. My understanding is that the numbers in extended data table 1 are before selection criteria were applied. Could the authors explicitly state in the caption if that is the case. While non-essential, adding a column in the table to summarise how many designs were experimentally tested (as stated in the main text for some of the targets) would help with clarity and representation of the importance of the selection criteria.\n3) p.4: the first part of the sentence “PDGFRɑ where both helical bundle designs and β-strand pairing designs passed in silico filters, significantly more binders were obtained with the strand directed approach than with standard RFdiffusion at this selection stage (Extended Data Figure 4).” seems incomplete.\n4) p.9, typo:\n“. the There are 21 sidechain-sidechain”\n5) p.12, figure 5\n“(b-c) FCRL5 is degraded HeLa-FCRL5 cells by de novo binders”\nDid the authors mean that \"EGFR\" is degraded in HeLa-FCRL5?", "reviewer_4": "Largely speaking, the authors have answered our original questions. Some minor remaining points:\nResponse 11 – should this be Ext Data Fig 4c?\nResponse 15 – should this be Ext Data Fig 6?\nIn the section \"β-strand interface conditioning improves binder designs\", Sappington et al. state that the authors of the original 2023 RFdiffusion paper tested training with SS/ADJ. Sappington et al. now further supplement this by providing an adjacency matrix when running the RFdiffusion run_inference.py script. We think it would be clearer if Sappington et al. more clearly state that in the text this step is performed at inference, possibly in this sentence:\n“We explored whether providing interface conditioning information indicating a binder β-strand pairing with a target edge β-strand at the point of inference could yield designable strand pairing complexes.”", "reviewer_5": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature Communications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career Researchers who co-review manuscripts."}, "author_response": "Thank you for your detailed attention to the library sizes, as they were\nindeed incongruent between the extended data and the main text by\nmistake. The correct library sizes were indicated in the extended data\ntable, but we neglected to correct the numbers in the text in our last\nsubmission. The library size errors result from typos, counting only\ndesigns detected sequencing counts from NGS (dropout can be a\nresult of some designs exhibiting poor expression and/or display on\nyeast cell surfaces), and mistakes made during counting of the library\nsizes (e.g., the hotspot only conditioned designs were indicated by\ndesigns with the patterns ‘normal’ (N=5727) and ‘bcov’ (N=4299), which\ncombined yielded the total hotspot-only conditioned library size,\nN=10026). Both libraries, β-strand interface conditioned (N=5616) and\nhotspot only conditioned (N=10026), were tested in the exact same\nexperiment. We chose to describe them as two different libraries\nbased entirely on the fact that different design methods were used, but\nlibraries were tested in the exact same experiment, simultaneously, as\nindicated in the extended data figure 4 description.\n\nEvery design in each library (all designs counted in extended data\ntable 1) was experimentally tested by yeast surface display. Some\nlibrary numbers indicated in the manuscript were incorrect due to\nmiscounting (some designs dropping out during initial YSD sorts and\ntherefore were not counted NGS). The library numbers are now\nconsistent, and all reflect the number of DNA sequences ordered and\nscreened. Additionally, we have added to the extended data table 1\ndescription to include the number of designs that were then purified\nand characterized for solubility and binding.\n\nThank you for carefully reading the text. This was rephrased to “For\nPDGFRɑ, where both helical bundle designs and β-strand pairing\ndesigns passed in silico filters, significantly more binders were\nobtained with the strand directed approach than with standard\nRFdiffusion at this selection stage (Extended Data Figure 4).” for\nproper syntax.\n\nFixed.\n\nYes, that is what we intended. Thank you!\n\nYes, the figures were renumbered when writing the reviewer\nresponses, but were changed afterward for better organization.\n\nWe agree that this change will make the text more clear to readers. We\nreplaced the original sentence, “We explored whether providing\ninterface conditioning information indicating a binder β-strand pairing\nwith a target edge β-strand could yield designable strand pairing\ncomplexes.” with the suggestion above.\n\nThank you for taking the time to troubleshoot this on your system and\nfor providing the cleaner code block. We replaced line 82 in the script\nwith the suggested lines.\n\nFixed."}, {"round": 3, "reviewer_comments": {"reviewer_3": "My comments have been addressed and the typos corrected. I recommend publication."}, "author_response": ""}]

Decision letter