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Biophysics-based protein language models for protein engineering

Nature Methods(2025)10.1038/s41592-025-02776-2Source: DataRank Database

Biophysics-based protein language models for protein engineering is a research paper published in Nature Methods (2025). On theSindex it has a DataRank of 0.682. It has been cited 36 times, with 36 citing works in its 1-hop citation network.

N/A

0.682DataRank · unranked

Scored on demand from live citation data

Open Access36 citations · 36 citing works in its network

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Linked data & code

pdb doi

DataRank reads this dataset's downstream impact straight off the citation graph — no black box, no proprietary weighting. How is this computed?

›Methodology & internals

datarank_citation_only_1hop_v6· scope data_onlyMethodology

Pipeline:MetadataData-paper checkEnrichmentCitation networkScoring

Enrichment:Funding (8 grants)Impact (FWCI 15.71)OA: hybridTopics (28)Linked Datasets (12)DataCite Reuse (12)IDs (PubMed, PMC, OpenAlex)Annotations (2)

FAIR Checklist

Context only (not used in score)

Findable (2/2)

Has DOI
Indexed in repositories

Accessible (1/2)

Open Access

Interoperable (2/2)

DataCite relations
Linked datasets

Reusable (0/3)

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DataRank Breakdown

Base Score 79%Citation Network 21%

Base Score Contribution

0.542

From this paper's citation signal

Citation Network Contribution

0.141

From 10 citing papers with measurable signal

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Top 5 citers driving the network score

Ranked by each citer's contribution to N(p) — log1p(C_q) divided by its reference count — out of 36 citers.

Protein language models trained on biophysical dynamics inform mutation effects
Proceedings of the National Academy of Sciences20265 citations49 referencesContributes 0.037
Beyond additivity: zero-shot methods cannot predict impact of epistasis on protein properties and function
bioRxiv (Cold Spring Harbor Laboratory)20262 citations37 referencesContributes 0.030
Evaluation of machine learning-assisted directed evolution across diverse combinatorial landscapes
Cell Systems20257 citations93 referencesContributes 0.022
Machine Learning-Driven Enzyme Mining: Opportunities, Challenges, and Future Perspectives
ACS Catalysis202521 citations180 referencesContributes 0.017
Advances and critical evaluation of autonomous protein engineering: towards transparent, accessible, and reproducible platforms
Current Opinion in Biotechnology20251 citations51 referencesContributes 0.014

Authors (8)

Bryce Johnson ORCID,Chase R. Freschlin ORCID,Arnav Sharma ORCID,Sameer D’Costa ORCID,John Peters ORCID

Live enrichment

PubMed:40935922 PMC:PMC12446067 OpenAlex:https://openalex.org/W4414131808

U.S. Department of Health & Human Services | National Institutes of Health

Grant: R01GM135631

U.S. Department of Health & Human Services | National Institutes of Health

Grant: R35GM119854

National Science Foundation

Grant: 2226383

Collaborative Research: MFB: Integrating Deep Learning and High-throughput Experimentation to Rapidly Navigate Protein Fitness Landscapes for Non-native Enzyme Catalysis

National Science Foundation

Grant: 2030508

Partnership to Advance Throughput Computing (PATh)

National Institutes of Health

Grant: 5R35GM119854-02

Data-driven analysis of protein structure, function, and regulation

National Science Foundation

Grant: 1836650

S2I2: Institute for Research and Innovation in Software for High Energy Physics (IRIS-HEP)

National Institutes of Health

Grant: 5R01GM135631-03

A Machine Learning Platform for Adaptive Chemical Screening

National Science Foundation

Grant: 2226451

Collaborative Research: MFB: Integrating Deep Learning and High-throughput Experimentation to Rapidly Navigate Protein Fitness Landscapes for Non-native Enzyme Catalysis

FWCI

15.71

Citation Percentile

1.0%

Citation Trend

2025

2026

hybridLicense: cc-by

journalhttps://www.nature.com/articles/s41592-025-02776-2.pdf

publisherhttps://www.nature.com/articles/s41592-025-02776-2.pdf

publisherhttps://www.nature.com/articles/s41592-025-02776-2

journalhttps://doi.org/10.1038/s41592-025-02776-2

repositoryhttps://pubmed.ncbi.nlm.nih.gov/40935922

Fields of Study

Machine Learning in BioinformaticsGenetics, Bioinformatics, and Biomedical ResearchMicrobial Metabolic Engineering and Bioproduction0206 medical engineering02 engineering and technology

MeSH Terms

Machine LearningBiophysicsModels, MolecularMutationProteinsProtein EngineeringNeural Networks, ComputerGreen Fluorescent Proteins

Keywords

Protein engineeringArtificial neural networkProtein structure predictionProtein structureProtein–protein interactionLanguage modelData modelingMachine LearningModels, MolecularGreen Fluorescent ProteinsMutationBiophysicsProteinsNeural Networks, ComputerArticle

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