ArticleDigital discovery2022
Rapid prediction of protein natural frequencies using graph neural networks.
Article in Digital discovery, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
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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.
The trial behind it
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Who cites it
9 citing papers in PubMed, 32 citations in OpenAlex.
- AI for bioactive materials: From material design to biological applications.Bioactive materials · 2026Review
- Dynamics-informed multigraph neural networks for protein thermostability prediction and residue-level interpretation.iScience · 2026Article
- Modeling protein motions through reinforcement learning.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Learning collective cell migratory dynamics from a static snapshot with graph neural networks.ArXiv · 2024Article
- Adaptive CVgen: Leveraging reinforcement learning for advanced sampling in protein folding and chemical reactions.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- ProtAgents: protein discoveryDigital discovery · 2024Article
- ForceGen: End-to-end de novo protein generation based on nonlinear mechanical unfolding responses using a language diffusion model.Science advances · 2024Article
- Dominance vs epistasis: the biophysical origins and plasticity of genetic interactions within and between alleles.Nature communications · 2023Article
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors at 2 institutions in 2 countries.
Funding
Abstract
Natural vibrational frequencies of proteins help to correlate functional shifts with sequence or geometric variations that lead to negligible changes in protein structures, such as point mutations related to disease lethality or medication effectiveness. Normal mode analysis is a well-known approach to accurately obtain protein natural frequencies. However, it is not feasible when high-resolution protein structures are not available or time consuming to obtain. Here we provide a machine learning model to directly predict protein frequencies from primary amino acid sequences and low-resolution structural features such as contact or distance maps. We utilize a graph neural network called principal neighborhood aggregation, trained with the structural graphs and normal mode frequencies of more than 34 000 proteins from the protein data bank. combining with existing contact/distance map prediction tools, this approach enables an end-to-end prediction of the frequency spectrum of a protein given its primary sequence.
Identifiers
What OpenQuestion holds
Registered trials
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.