ArticlePLoS computational biology2022
Interpretable pairwise distillations for generative protein sequence models.
Article in PLoS computational biology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 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.
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Who cites it
5 citing papers in PubMed, 7 citations in OpenAlex.
- Gaugefixer: overcoming parameter non-identifiability in models of sequence-function relationships.BMC bioinformatics · 2026Article
- GaugeFixer: overcoming parameter non-identifiability in models of sequence-function relationships.bioRxiv : the preprint server for biology · 2026Article
- Gauge fixing for sequence-function relationships.PLoS computational biology · 2025Article
- Gauge fixing for sequence-function relationships.bioRxiv : the preprint server for biology · 2024Article
- Symmetry, gauge freedoms, and the interpretability of sequence-function relationships.Physical review researchArticle
Corrections and comments
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Authors and funding
3 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Many different types of generative models for protein sequences have been proposed in literature. Their uses include the prediction of mutational effects, protein design and the prediction of structural properties. Neural network (NN) architectures have shown great performances, commonly attributed to the capacity to extract non-trivial higher-order interactions from the data. In this work, we analyze two different NN models and assess how close they are to simple pairwise distributions, which have been used in the past for similar problems. We present an approach for extracting pairwise models from more complex ones using an energy-based modeling framework. We show that for the tested models the extracted pairwise models can replicate the energies of the original models and are also close in performance in tasks like mutational effect prediction. In addition, we show that even simpler, factorized models often come close in performance to the original models.
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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.