Science TLDR

Monday Immune Engager — our weekly pick from the latest immune-engager digest.

Discovery and engineering of bispecific TCR-mimic antibodies targeting peptide–HLA complex

mAbs, 2026

DOI: 10.1080/19420862.2026.2731282

Question: Can building proteome-wide off-target counter-screening into TCR-mimic antibody discovery yield more specific WT1-targeting bispecifics than published comparators?

Answer: Their lead antibody showed tighter peptide-contact footprints and fewer predicted proteome off-targets than benchmarks ESK1/11D06, with clean bispecific killing in WT1+/A2+ tumor lines. But the comparators may have been disadvantaged by the assay format, and a WT1-negative control line was not tested.

Three takeaways

Top Trending Friday — our weekly pick of the top-trending paper on r/science this week.

Substantial under-detection of human rabies cases in the United States

Communications Health, 2026

DOI: 10.1038/s44528-026-00031-4

Question: Using rare transplant-transmitted rabies events as a proxy sample of undiagnosed deaths, how many US rabies deaths does official surveillance actually miss?

Answer: Scaling 3 undiagnosed rabies deaths among 239,251 deceased organ donors (2000–2024) to all US deaths implies about 34 rabies deaths a year (856 over 25 years; 95% CI 213–2220) against 2.4 confirmed a year — roughly 7% detection, assuming donors carry average population rabies risk.

Three takeaways

Monday Immune Engager — our weekly pick from the latest immune-engager digest.

Paper: A blinded, prospective benchmark of in silico antibody discovery anchored to experimental affinity and developability

Authors: M. Frank Erasmus, Daniel Bedinger, Elizabeth Hopkins, Ginger Ferguson, et al.

Journal: Nature Biotechnology, 2026

Why it matters: This CASP-style blinded benchmark—testing 511 AI-designed antibodies from 29 organizations against hard experimental data—reveals where computational antibody discovery genuinely adds value and where it still fails to beat random chance.

Summary

The AIntibody challenge was designed to do for antibody discovery what CASP did for protein structure prediction: create a rigorous, prospective, blinded test anchored to real biology. The organizers targeted the SARS-CoV-2 receptor-binding domain (RBD)—the most data-rich antigen available—precisely to evaluate AI under best-case conditions. Participating groups received deep sequencing data from early selection campaigns and were evaluated across three tasks: in silico affinity maturation (optimizing a known parental antibody while holding the HCDR3 fixed), affinity ranking within heterogeneous HCDR3 clusters from a selection output, and out-of-library CDR design. Binding affinity was validated by surface plasmon resonance (SPR) and KinExA, and all candidates had to pass a five-assay developability panel covering aggregation, polyspecificity, thermal stability, and hydrophobic interactions.

The headline results are sharply task-dependent. In Challenge 1, the top submission from Arika achieved 95 pM affinity—a ~2,000-fold improvement over the parental antibody—while maintaining a clean developability profile. Strikingly, the third-place finisher, Probiogen, used no machine learning at all: a pure positional consensus derived from sequencing abundance beat nearly every sophisticated neural network, illustrating that many AI models are likely overfitting to noise when the data is already rich. Challenge 2 exposed a more serious failure: except for one group (Washington University), AI-guided clone selection from heterogeneous HCDR3 clusters performed worse than random picking (9.8–13.8% success versus a ~39% random baseline), with cluster 28F proving especially damaging for developability. Challenge 3's apparent winner—Zincor's FTESM protein language model at 2.9 pM—failed the hydrophobic interaction chromatography (HIC) assay outright, having exploited the composite scoring function rather than producing a genuinely developable molecule. Deeper inspection also revealed that "out-of-library" winners largely copy-pasted known HCDR3 sequences from training data, making conservative one-to-five residue edits in the surrounding loops rather than exploring genuinely novel sequence space.

A key limitation the authors acknowledge: participants were given far richer sequencing data than a typical early-stage discovery program would possess, so performance here likely represents an upper bound on current capabilities rather than a realistic field benchmark.

Three takeaways

Read the source: https://doi.org/10.1038/s41587-026-03238-6

Top Trending Friday — our weekly pick of the top-trending paper on r/science this week.

Paper: Educational expansion and the hidden reversal of the Flynn effect—Differential trends across socioeconomic strata

Authors: Bernt Bratsberg, Anders M. Fjell, Ole J. Rogeberg, Kristine B. Walhovd

Journal: Proceedings of the National Academy of Sciences, 2025

Why it matters: The apparent plateau and reversal of rising IQ scores in wealthy nations may be a statistical artifact of averaging across socioeconomic groups whose cognitive trajectories diverged sharply — with educational expansion temporarily masking an earlier-onset, broader decline.

Summary

The Flynn effect — the well-documented rise of roughly three IQ points per decade throughout the 20th century — has recently stalled or reversed in several high-income countries. This paper asks whether that reversal is actually a uniform population trend, or whether it looks uniform only because researchers have been averaging across socioeconomic groups with very different trajectories. Using compulsory military conscription cognitive tests linked to administrative records for 579,379 Norwegian men born across 25 cohorts (1967–1991), the authors stratified results by paternal income rank — chosen over combined household income because rapid growth in women's labor force participation made maternal earnings an unstable proxy across those specific decades.

The stratified picture dismantles the single-trend narrative. Sons of fathers in the top income percentiles peaked in cognitive scores in the late 1960s birth cohorts and declined from there, while sons of low-income fathers kept gaining and didn't peak until the mid-1970s cohorts — nearly a decade later. During the intervening years, the rising scores at the bottom of the income distribution mathematically cancelled the falling scores at the top, producing a deceptively flat national average. The mechanism the authors identify is educational expansion: using a generalized additive change-on-change model controlling for birth weight, gestational age, height at conscription, and parental education, each additional year of schooling at conscription age correlated with approximately 4.2 IQ points — consistent with quasi-experimental estimates from other literature. Once Norway's 1994 high school reform and earlier expansions effectively saturated secondary school attendance across all income groups, this compensatory effect disappeared, and a broad underlying decline became visible everywhere.

A key limitation is that cognitive data were only available at conscription age (~18–19), not in early childhood, meaning reverse causality — that higher-ability children simply stayed in school longer — cannot be entirely ruled out, a point the authors openly acknowledge.

Three takeaways

Read the source: https://doi.org/10.1073/pnas.2536089123

Top Trending Friday — our weekly pick of the top-trending paper on r/science this week.

Paper: Probability of a timely vocal response in mother-infant interaction and later psychiatric diagnosis: A case-control study

Authors: Bethany Stanley, Clare S. Allely, Jenna Charlton, Christopher Gillberg, et al.

Journal: PLOS One, 2024

Why it matters: A single timing metric — whether a mother vocalizes back within one second — independently predicts her child's risk of ADHD or disruptive behavior disorder at age seven, suggesting that the rhythmic architecture of early caregiver–infant interaction may be a useful, objective clinical marker.

Summary

This case-control study asks whether the timing of vocal exchanges between mothers and 12-month-old infants — measured as the probability of responding within a defined window — predicts later psychiatric diagnosis. The researchers drew on the Avon Longitudinal Study of Parents and Children (ALSPAC), a UK population cohort enrolling pregnant women in the early 1990s, and analyzed VHS video recordings of 158 mother-infant dyads interacting over a picture book in a clinic setting. At age seven, those same children were assessed using the Development and Wellbeing Assessment (DAWBA), a structured tool that synthesizes parent and teacher reports to generate DSM-IV psychiatric classifications. The final sample comprised 103 neurotypical controls and 55 cases spanning disruptive behavior disorders (DBD), ADHD, autism spectrum (pervasive developmental disorder), and emotional disorders.

To identify optimal response thresholds, the team used survival analysis — specifically Kaplan-Meier curves — to map the distribution of silence duration following each vocalization across a sliding 0.5–10 second window. The most statistically stable thresholds emerged at one second for maternal responses and eight seconds for infant responses, reflecting the large gap in cognitive and motor processing speed between a fully developed adult and a one-year-old. Predictive modeling then used Firth's penalized logistic regression, a method that corrects for bias from small subgroup sizes, to test whether these timing probabilities forecast diagnosis six-and-a-half years later.

Only the maternal measure proved predictive. After adjusting for maternal age, birth weight, maternal depression, and socioeconomic status, a 10% increase in the probability of the mother responding within one second was associated with a 17% decrease in the odds of any psychiatric diagnosis — an effect concentrated in ADHD and DBD. Neither timing metric predicted autism spectrum or emotional disorders, though the autism subgroup contained only six children, making the null result essentially uninterpretable due to insufficient statistical power. A key limitation across all findings is that degraded 1990s audio prevented any analysis of speech content, pitch, or motherese — so every vocalization was coded as a binary event regardless of its emotional quality.

Three takeaways

Read the source: https://doi.org/10.1371/journal.pone.0344552

Monday Immune Engager — our weekly pick from the latest immune-engager digest.

Paper: Covalently PD‐L1 Anchoring Drives Bispecific Nanostructure Assembly for Spatial Control of T Cell Recruitment and Activation

Authors: Fengzhen Zhang, Yuhan Dong, Kailu Liu, Hui Hu, et al.

Journal: Angewandte Chemie International Edition, 2025

Why it matters: Rather than passively blocking tumor immune checkpoints, this work shows it is possible to chemically hijack a tumor's own surface machinery to build synthetic nanostructures that simultaneously relieve immune suppression and actively recruit and activate T cells.

Summary

Current immune checkpoint therapies that block the PD-1/PD-L1 axis — the suppressive signaling pathway tumors use to exhaust T cells — benefit only around 20% of patients. Blocking the pathway removes an inhibitory signal but does not actively stimulate T cells to attack, a distinction the authors liken to releasing a parking brake without pressing the accelerator. This paper introduces a two-step "localized oxidation–covalent assembly" strategy designed to do both at once.

The first component, the P1 probe, is a conjugate of a PD-L1-targeting peptide (Kd ≈ 4.5 µM) and the enzyme galactose oxidase (GAO). Once the probe transiently binds PD-L1, GAO oxidizes the C6 hydroxyl groups on PD-L1's N-linked glycans, converting them to aldehydes. Specificity was confirmed using a glycosylation-deficient PD-L1 mutant (4NQ) and the inhibitor tunicamycin, both of which sharply reduced oxidation. These aldehyde groups serve as covalent anchors for the second component: a hydrazide-functionalized self-assembling peptide (HSP) incorporating an anti-CD3 sequence. Covalent attachment of HSP to the oxidized membrane dramatically lowers its critical aggregation concentration (CAC) from 38.26 µM to 2.18 µM, driving rapid β-sheet and π–π stacking assembly into stable artificial topological nanostructures (ATNs) directly on the tumor surface.

The resulting ATNs act as membrane-anchored bispecific T cell engagers (BiTEs): they block PD-1/PD-L1 interaction while projecting anti-CD3 sequences outward to recruit and activate passing T cells. In vivo in MC38 tumor-bearing mice, this achieved 62.6% tumor inhibition, with increased intratumoral PD-1⁺ T cells and interferon-γ secretion. Efficacy also translated to patient-derived lung cancer organoids. A key limitation the authors acknowledge is the potential for off-target glycan oxidation by the GAO enzyme in a systemic context, which will require tumor-microenvironment-responsive activation strategies in future iterations.

Three takeaways

Read the source: https://doi.org/10.1002/anie.5894316

Top Trending Friday — our weekly pick of the top-trending paper on r/science this week.

Paper: People prefer to negotiate with women, even when outcomes are identical and gender is unknown

Authors: Charlotte H. Townsend, Laura J. Kray, Solène Delecourt

Journal: Proceedings of the National Academy of Sciences, 2026

Why it matters: Decades of negotiation research have focused on who wins the most money, but this paper shows that the social and emotional quality of a negotiation — and its compounding career consequences — may matter just as much.

Summary

Negotiation research has long prioritized economic outcomes (who captured the most value) while largely ignoring subjective outcomes: trust built, perceived fairness, and the counterpart's desire to negotiate again. This paper, drawing on over 2,000 observations, finds that people consistently report higher subjective value when negotiating with women — and that this preference survives even complete anonymization. In a text-chat study using a fictitious resource-allocation task, gender was so effectively hidden that third-party observers reading the transcripts guessed the negotiator's gender correctly only 43% of the time — statistically worse than a coin flip — yet participants still preferred their women counterparts on subjective measures. Crucially, women achieved identical distributive and integrative economic outcomes to men across all conditions.

To uncover the behavioral mechanism, the researchers fed anonymous transcripts through a natural language processing model trained to classify negotiation utterances into 12 behavioral codes. Women were significantly more likely to make multi-issue offers and to accept offers — a pattern that might look like capitulation but functioned as strategic validation: yielding on lower-priority points while holding firm on overall economic value, leaving counterparts feeling heard without sacrificing outcomes. A Monte Carlo simulation then modeled what happens when a modest 2.5-percentage-point difference in willingness to re-engage compounds over a career: women accumulated roughly 44–56% more negotiation opportunities, translating a subjective edge into a substantial long-term earnings gap. One important caveat: all studies used low-ambiguity settings where negotiation was explicit; prior work suggests women can face economic and social penalties when the appropriateness of negotiating is itself unclear.

Three takeaways

Read the source: https://doi.org/10.1073/pnas.2523202123

Monday Immune Engager — our weekly pick from the latest immune-engager digest.

Paper: Combination therapy with a novel CD2-targeted costimulatory bispecific antibody overcomes limitations of CD3 T cell engager treatment for solid tumors

Authors: Welbeck Danquah, Mélanie Pichery, Thomas Eden, Aurelien Boyance, et al.

Journal: mAbs, 2026

Why it matters: CD3 T cell engagers have transformed blood cancer treatment but largely failed in solid tumors — this study proposes a modular two-drug strategy that separates T cell activation signals to rescue efficacy while dramatically reducing systemic toxicity.

Summary

CD3 T cell engagers (TCEs) work by bridging a T cell directly to a tumor cell, providing what immunologists call Signal 1. In blood cancers this is often sufficient, but solid tumors present an immunosuppressive microenvironment that demands a second costimulatory signal (Signal 2) for a sustained cytotoxic response. Pushing TCE doses higher to compensate triggers severe dose-limiting toxicities before the tumor is controlled — a bottleneck that has left tarlatamab as the only approved classical TCE for solid tumors. The Evotec team's solution is to keep the two signals as entirely separate drugs: a conventional EpCAM×CD3 TCE (solitomab) for Signal 1, and a novel HER2×CD2 bispecific for Signal 2.

The CD2 arm is built on a non-blocking antibody that acts as a positive allosteric modulator — rather than competing with CD58, the tumor's natural CD2 ligand, it increases CD2–CD58 binding by over twofold, amplifying the natural immune adhesion while simultaneously providing artificial costimulation. The Fc domain is silenced to prevent macrophage-mediated depletion of CD2-expressing T cells. In humanized xenograft mice bearing established HER2-positive breast tumors, the combination achieved complete tumor remission in 8 of 9 animals at a TCE dose that produced remission in only 1 of 9 when used alone.

Two additional findings underscore the approach's clinical relevance. First, when tumor cells were engineered via CRISPR-Cas9 to completely lack CD58 — mimicking a well-documented immune escape mechanism — the HER2×CD2 combination physically bridged the T cell to the tumor anyway, boosting TCE killing efficacy 25–35-fold over the TCE alone. Second, because CD28 expression is lost from a large proportion of CD8⁺ killer T cells with aging, CD28-targeted costimulatory strategies may be ineffective in the elderly patients who make up most solid tumor cases; CD2 expression remains robust on these cells. In a direct head-to-head comparison at matched killing efficacy, the CD2 bispecific induced only up to a 2-fold increase in IL-6, TNF, and IL-2, compared to up to a 200-fold increase from a comparable HER2×CD28 bispecific.

Three takeaways

Read the source: https://doi.org/10.1080/19420862.2026.2684378

Top Trending Friday — our weekly pick of the top-trending paper on r/science this week.

Paper: A 5.3-million-year-old deep-sea whale necropolis in the Diamantina Zone

Authors: Xiaotong Peng, Peng Zhou, Xikun Song, Giovanni Bianucci, et al.

Journal: Nature, 2026

Why it matters: The discovery of a 1,200-kilometer accumulation of whale skeletons in the southeastern Indian Ocean rewrites the known depth limits of complex ecosystems and establishes the deep seafloor as a multi-million-year fossil archive for cetacean evolution.

Summary

In 2023, researchers piloting the human-occupied submersible Fendouzhe mapped the Diamantina Zone, a V-shaped trench in the southeastern Indian Ocean plunging between 4,600 and 7,000 meters depth. They documented 485 whale sites across roughly 1,200 kilometers of seafloor — 476 fossilized and five actively decomposing — shattering the previous global record of a few dozen known whale-fall sites, nearly all at shallower depths. The deepest active community was observed at 6,789 meters, far exceeding the previous depth record of ~4,200 meters for any whale-fall ecosystem.

The five active sites were all in the sulfur-philic stage, in which anaerobic bacteria metabolize lipids stored in whale bone marrow and produce hydrogen sulfide — a toxin to most life but the chemical foundation of a chemosynthesis-based food web (one that replaces sunlight with chemical energy). Organism densities reached 2,840 individuals per square meter, dominated by Osedax "zombie" worms (which bore into bone using secreted acid and digest lipids via symbiotic gut bacteria), chemosynthetic bivalves hosting sulfur-oxidizing bacteria in their gills, brittle stars, and — notably — Xyloplax sea daisies previously known only from sunken wood. The authors propose a "super corridor" hypothesis: with an estimated density of ~8 whale falls per square kilometer, the trench provides densely packed stepping stones allowing specialized larvae to disperse between falls, and onward to hydrothermal vents and cold seeps, across otherwise barren abyssal plains.

Strontium isotope dating (exploiting the fact that seawater's Sr-87/Sr-86 ratio shifts predictably over geologic time and becomes locked into bone mineral) of 33 fossil specimens yielded ages ranging from 120,000 years to 5.26 million years ago, with the oldest belonging to a newly described extinct beaked whale, Pterocetus diamantinae. Preservation over such timescales is attributed to an ultra-low sedimentation rate (<0.5 cm per 1,000 years), ferromanganese oxide coating that petrifies exposed surfaces, and the extraordinary bone density — pachyosteosclerosis — of beaked whale rostra, which resists both biological boring and chemical dissolution.

Three takeaways

Read the source: https://doi.org/10.1038/s41586-026-10546-z

Monday Immune Engager — our weekly pick from the latest immune-engager digest.

Paper: Discovery of TCR-like antibodies to the KRAS G12D neoantigen via in silico-in vitro workflow

Authors: SangPhil Ahn, Tae-Sung Oh, Seonghyuk Suh, Joon-Young Jeon, et al.

Journal: Molecular Therapy, 2026

Why it matters: A hybrid computational-experimental pipeline now offers a practical route to generate stable, high-affinity antibodies against intracellular oncoproteins — a class of targets that has long resisted conventional drug discovery.

Summary

KRAS G12D is one of the most prevalent oncogenic mutations in pancreatic, colorectal, and lung cancer, yet it sits inside the cell, beyond the reach of conventional antibody drugs. The cell's own antigen-presentation machinery offers a workaround: proteasomal degradation continuously loads short peptide fragments of intracellular proteins onto MHC class I complexes (here, HLA-C*08:02) for display at the surface. TCR-like antibodies — synthetic immunoglobulins engineered to recognize these peptide–MHC complexes — could exploit this window, but traditional screening tends to produce binders that grip the large, invariant HLA scaffold rather than the tiny, single-amino-acid-variant peptide, creating a catastrophic off-target risk.

To solve this, the authors built a two-stage pipeline. In the computational stage, the inverse-folding model ProteinMPNN was used to redesign only the complementarity-determining region (CDR) loop tips of human antibody variable fragments docked against the KRAS G12D–HLA-C*08:02 complex, leaving the backbone fixed to prevent misfolding. The nine top-scoring designs seeded a yeast surface-display library for iterative selection, including negative selection against cells pulsed with a SARS-CoV-2 peptide to eliminate cross-reactive binders. X-ray crystallography at 4.5 Å confirmed the final antibody contacts the mutant aspartate at position 3 and lysine at position 7 directly — notably, AlphaFold2 failed to predict this interface accurately, underscoring that physical validation remains essential.

Final affinity maturation and solubility engineering yielded variants with KD values as low as 2.38 nM and no detectable human off-target reactivity by phage display profiling. When the targeting head was reformatted as a CAR-T construct or a bispecific T-cell engager (SCDB), both formats selectively killed KRAS G12D-positive cells while sparing wild-type KRAS cells in vitro. The study's main limitation is that all cytotoxicity data are from cell-based assays; in vivo efficacy and safety remain to be demonstrated.

Three takeaways

Read the source: https://doi.org/10.1016/j.ymthe.2026.05.032