Evidence map›Paper›PMID 40742329›Full record

ArticleBioinformatics (Oxford, England)2025

AF3Complex yields improved structural predictions of protein complexes.

Jonathan Feldman, Jeffrey Skolnick

Abstract read
In one paragraph

Article in Bioinformatics (Oxford, England), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing 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

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3 · Its place in the literature

Who cites it

13 citing papers in PubMed.

  1. Article
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  3. Toward mechanistic virtual immune cells.Nature biotechnology · 2026
    Article
  4. Article
  5. Proteome-wide prediction of interactions between structured domains and peptide motifs reveals functionally coherent subnetworks.Proceedings of the National Academy of Sciences of the United States of America · 2026
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  8. Review
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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

2 authors.

Jonathan FeldmanCenter for the Study of Systems Biology/School of Biological Sciences, Georgia Institute of Technology, Atlanta, GA 30332, United States.ORCID 0000-0003-4130-6447
Jeffrey SkolnickCenter for the Study of Systems Biology/School of Biological Sciences, Georgia Institute of Technology, Atlanta, GA 30332, United States.ORCID 0000-0002-1877-4958

Funding

Purchase of a GPU cluster for deep learning applications in protein-protein interaction and supercomplex prediction and biochemical literature annotation.R35GM118039 · NIGMS · GEORGIA INSTITUTE OF TECHNOLOGY · PI JEFFREY SKOLNICK · 2016 to 2026
$5.8M
Division of General Medical SciencesNIGMS NIH HHS R35 GM118039NIH HHSOvarian Cancer Institute
6 · The paper itself

Abstract

motivationAccurate structures of protein complexes are essential for understanding biological pathway function. A previous study showed how downstream modifications to AlphaFold 2 could yield AF2Complex, a model better suited for protein complexes. Here, we introduce AF3Complex, a model equipped with both similar and novel improvements, built on AlphaFold 3.

resultsBenchmarking AF3Complex and AlphaFold 3 on a large dataset of protein complexes, it was shown that AF3Complex outperforms AlphaFold 3. Moreover, by evaluating the structures generated by AF3Complex on datasets of protein-peptide complexes and antibody-antigen complexes, it was established that AF3Complex could create high-fidelity structures for these challenging complex types. Additionally, when deployed to generate structural predictions for protein complexes used in the recent CASP16 competition, AF3Complex yielded structures that would have placed it among the top models in the competition. AVAILABILITY AND IMPLEMENTATION: The AF3Complex code is freely available at https://github.com/Jfeldman34/AF3Complex.git.

Indexed as

Computational BiologyMultiprotein ComplexesProteinsSoftwareAlgorithmsDatabases, ProteinModels, MolecularProtein ConformationMultiprotein ComplexesProteins

Identifiers

PMID40742329
PMCPMC12349917

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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.