Evidence map›Paper›PMID 42821378›Full record

ArticleSTAR protocols2026

Protocol for tri-modal pretraining and downstream fine-tuning of protein representations using ProteinAligner.

Han Guo, Li Zhang, Leah Schaffer, Young Su Ko, Digvijay Singh, Hamid Rahmani, Danielle Grotjahn, Elizabeth Villa, Michael Gilson, Wei Wang and 3 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in STAR protocols, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

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.

Han GuoDepartment of Electrical and Computer Engineering, University of California San Diego, La Jolla, CA 92093, USA.
Li ZhangDepartment of Electrical and Computer Engineering, University of California San Diego, La Jolla, CA 92093, USA.
Leah SchafferDepartment of Medicine, University of California San Diego, La Jolla, CA 92093, USA.
Young Su KoDepartment of Chemistry and Biochemistry, University of California San Diego, La Jolla, CA 92093, USA.
Digvijay SinghSchool of Biological Sciences, University of California San Diego, La Jolla, CA 92093, USA.
Hamid RahmaniDepartment of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA 92037, USA.
Danielle GrotjahnDepartment of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA 92037, USA.
Elizabeth VillaSchool of Biological Sciences, University of California San Diego, La Jolla, CA 92093, USA; Howard Hughes Medical Institute, University of California San Diego, La Jolla, CA 92093, USA.
Michael GilsonSkaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA 92093, USA.
Wei WangDepartment of Chemistry and Biochemistry, University of California San Diego, La Jolla, CA 92093, USA; Department of Cellular and Molecular Medicine, University of California San Diego, La Jolla, CA 92093, USA.
Trey IdekerDepartment of Medicine, University of California San Diego, La Jolla, CA 92093, USA; Department of Bioengineering, University of California San Diego, La Jolla, CA 92093, USA.
Eric XingMachine Learning Department, Carnegie Mellon University, Pittsburgh, PA 15213, USA.
Pengtao XieDepartment of Electrical and Computer Engineering, University of California San Diego, La Jolla, CA 92093, USA; Department of Medicine, University of California San Diego, La Jolla, CA 92093, USA. Electronic address: p1xie@ucsd.edu.

Funding

Develop Multi-modal Foundation Models for Sepsis Early DetectionR35GM157217 · NIGMS · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI Pengtao Xie · 2025 to 2026
$655k
NIGMS NIH HHS R35 GM157217
6 · The paper itself

Abstract

ProteinAligner integrates protein sequence, three-dimensional structure, and functional annotations in a shared embedding space. Here, we present a protocol for tri-modal pretraining and downstream fine-tuning of protein representations using ProteinAligner. We describe steps for preparing both paired sequence-structure and sequence-text datasets from public protein resources and pretraining ProteinAligner with distributed multimodal learning. We then detail procedures for fine-tuning downstream tasks and evaluating performance. For complete details on the use and execution of this protocol, please refer to Zhang et al.

Indexed as

BioinformaticsBiotechnology and bioengineeringComputer sciences

Identifiers

PMID42821378
PMCPMC13659462

What OpenQuestion holds

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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.