ArticleJournal of enzyme inhibition and medicinal chemistry2025
Enzyme sequence optimisation via Gromov-Wasserstein Autoencoders integrating MSA techniques.
Article in Journal of enzyme inhibition and medicinal chemistry, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Enzyme sequence design has always been a challenging task, particularly in optimising key properties such as enzyme solubility, stability, and activity. This study proposes an innovative approach by utilising a variational autoencoder (VAE) model integrated with the Gromov-Wasserstein (GW) distance for enzyme sequence optimisation. The GWAE model improves representation learning by using the GW distance, thereby generating functional variants with desired characteristics. We also introduce an innovative enzyme dataset construction method that incorporates multiple sequence alignment (MSA) techniques to address sequence length discrepancies, enhancing the accuracy of the optimisation process. Experimental results show that the GWAE model outperforms the traditional VAE on multiple metrics. The generated enzyme sequences demonstrate superior solubility, stability, and hydrophobicity. Additionally, by integrating AlphaFold3 for structural prediction, we verify the structural stability of the generated sequences, further enhancing their practical applicability.
Indexed as
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.