Article in Science (New York, N.Y.), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
2 · The registry
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
5 · Who and what money
Authors and funding
13 authors.
Aerin Yang *Department of Molecular and Cellular Physiology, Stanford University School of Medicine, Stanford, CA, USA.ORCID 0000-0002-8704-0288
Hanlun Jiang *Department of Electrical Engineering and Computer Science, University of California, Berkeley, Berkeley, CA, USA.ORCID 0000-0002-4649-1584
Kevin M Jude *Department of Molecular and Cellular Physiology, Stanford University School of Medicine, Stanford, CA, USA.ORCID 0000-0002-3675-5136
Deniz AkpinarogluThe UC Berkeley-UCSF Graduate Program in Bioengineering, University of California, San Francisco, San Francisco, CA, USA.
Stephan Allenspach *Department of Electrical Engineering and Computer Science, University of California, Berkeley, Berkeley, CA, USA.ORCID 0000-0002-6772-6792
Alex Jie LiThe UC Berkeley-UCSF Graduate Program in Bioengineering, University of California, San Francisco, San Francisco, CA, USA.ORCID 0000-0003-0226-562X
James BowdenDepartment of Electrical Engineering and Computer Science, University of California, Berkeley, Berkeley, CA, USA.
Carla Patricia PerezBiophysics Program, Stanford University, Stanford, CA, USA.ORCID 0000-0002-9154-2360
Liu LiuDepartment of Molecular and Cellular Physiology, Stanford University School of Medicine, Stanford, CA, USA.ORCID 0009-0005-2939-6787
Po-Ssu HuangDepartment of Bioengineering, Stanford University, Stanford, CA, USA.ORCID 0000-0002-7948-2895
Tanja KortemmeThe UC Berkeley-UCSF Graduate Program in Bioengineering, University of California, San Francisco, San Francisco, CA, USA.ORCID 0000-0002-8494-680X
Jennifer ListgartenDepartment of Electrical Engineering and Computer Science, University of California, Berkeley, Berkeley, CA, USA.ORCID 0000-0002-6600-1431
K Christopher GarciaDepartment of Molecular and Cellular Physiology, Stanford University School of Medicine, Stanford, CA, USA.ORCID 0000-0001-9273-0278
Funding
A Synchrotron Radiation Structural Biology ResourcesP30GM133894 · NIGMS · STANFORD UNIVERSITY · PI Aina E. Cohen, KEITH O HODGSON · 2020 to 2026
$43.3M
X-ray Scattering Technology CoreP30GM133893 · NIGMS · BROOKHAVEN SCIENCE ASSOC-BROOKHAVEN LAB · PI Vivian Stojanoff · 2019 to 2026
$38.6M
User Training and OutreachP30GM124169 · NIGMS · UNIVERSITY OF CALIF-LAWRENC BERKELEY LAB · PI Jay C. Nix · 2017 to 2026
$28.6M
A Global Map of Interactions Among Human Cell Surface Proteins and Secreted LigandsR01GM150125 · NIGMS · CALIFORNIA INSTITUTE OF TECHNOLOGY · PI GARCIA, KENAN CHRISTOPHER, THOMSON, MATTHEW W. · 2022 to 2025
$11.3M
Structural correlates of T cell receptor signalingR01AI103867 · NIAID · STANFORD UNIVERSITY · PI Kenan Christopher GARCIA · 2014 to 2026
$5.3M
Equipment Supplement - Computational design of proteins and protein functionsR35GM145236 · NIGMS · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI KORTEMME, TANJA · 2022 to 2025
$2.4M
Generative neural networks for structure-based antibody designR01GM147893 · NIGMS · STANFORD UNIVERSITY · PI Possu Huang · 2022 to 2026
$1.9M
MATCHMAKERS: SOLVING TCR RECOGNITION AND DESIGN VIA INTEGRATED HIGH-THROUGHPUT SCREENING, STRUCTURAL, FUNCTIONAL, AND COMPUTATIONAL APPROACHESOT2CA297242 · NCI · STANFORD UNIVERSITY · PI Kenan Christopher GARCIA · 2024 to 2026
$1.1M
Pilatus 6mS10OD021832 · OD · UNIVERSITY OF CALIFORNIA BERKELEY · PI ADAMS, PAUL DAVID · 2016 to 2016
Understanding how protein binding sites evolve interactions with other proteins could hold clues to targeting "undruggable" surfaces. We used synthetic coevolution to engineer new interactions between naïve surfaces, simulating the de novo formation of protein complexes. We isolated seven distinct structural families of protein Z-domain complexes and found that synthetic complexes explore multiple shallow energy wells through ratchet-like docking modes, whereas complexes formed by natural binding sites converged in a deep energy well with a relatively fixed geometry. Epistasis analysis of a machine learning-estimated fitness landscape revealed "seed" contacts between binding partners that anchored the earliest stages of encounter complex formation. Our results suggest that "silent" surfaces have a shallower energy landscape than natural binding sites, disfavoring tight binding, likely owing to evolutionary counterselection.
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.
Structural ontogeny of protein-protein interactions. · full record | OpenQuestion