ArticleProceedings of the National Academy of Sciences of the United States of America2025
A protein dynamics-based deep learning model enhances predictions of fitness and epistasis.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 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
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.
Who cites it
3 citing papers in PubMed.
- Artificial intelligence and automation in enzyme engineering: evolution, advances, and future perspectives.Bioresources and bioprocessing · 2026Review
- Deep learning insights into β-lactamase dynamics and resistance evolution.The Biochemical journal · 2026Review
- A protein dynamics-based deep learning model enhances predictions of fitness and epistasis.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
Deep learning has advanced our ability to assess the effects that individual mutations have on protein function; however, predicting the complex interplay between two or more mutations remains challenging. Here, we seek to address this challenge by building a deep learning framework that incorporates information related to protein dynamics. Namely, we build a neural network architecture using a physics-based metric called the Asymmetric Dynamic Coupling Index (DCI
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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.