Evidence map›Paper›PMID 38844754›Full record

ArticleScientific data2024

Folding the human proteome using BioNeMo: A fused dataset of structural models for machine learning purposes.

Michael Hetmann, Lena Parigger, Hassan Sirelkhatim, Abraham Stern, Andreas Krassnigg, Karl Gruber, Georg Steinkellner, David Ruau, Christian C Gruber

Abstract readDataset
In one paragraph

Article in Scientific data, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

6 citing papers in PubMed.

  1. Article
  2. Review
  3. Article
  4. Article
  5. Article
  6. Review
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

9 authors.

Michael Hetmann *Innophore, San Francisco, CA, USA.
Lena Parigger *Innophore, San Francisco, CA, USA.
Hassan SirelkhatimNVIDIA, Santa Clara, CA, USA.
Abraham SternNVIDIA, Santa Clara, CA, USA.
Andreas KrassniggInnophore, San Francisco, CA, USA.
Karl GruberInnophore, San Francisco, CA, USA.
Georg SteinkellnerInnophore, San Francisco, CA, USA.
David RuauNVIDIA, Santa Clara, CA, USA. druau@nvidia.com.
Christian C GruberInnophore, San Francisco, CA, USA. christian.gruber@innophore.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Human proteins are crucial players in both health and disease. Understanding their molecular landscape is a central topic in biological research. Here, we present an extensive dataset of predicted protein structures for 42,042 distinct human proteins, including splicing variants, derived from the UniProt reference proteome UP000005640. To ensure high quality and comparability, the dataset was generated by combining state-of-the-art modeling-tools AlphaFold 2, OpenFold, and ESMFold, provided within NVIDIA's BioNeMo platform, as well as homology modeling using Innophore's CavitomiX platform. Our dataset is offered in both unedited and edited formats for diverse research requirements. The unedited version contains structures as generated by the different prediction methods, whereas the edited version contains refinements, including a dataset of structures without low prediction-confidence regions and structures in complex with predicted ligands based on homologs in the PDB. We are confident that this dataset represents the most comprehensive collection of human protein structures available today, facilitating diverse applications such as structure-based drug design and the prediction of protein function and interactions.

Indexed as

Machine LearningProteomeDatabases, ProteinHumansModels, MolecularProtein ConformationProtein FoldingProteome

Identifiers

PMID38844754
PMCPMC11156891

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.