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Generative Capacity of Probabilistic Protein Sequence Models
[post]
2021
unpublished
Potts models and variational autoencoders (VAEs) have recently gained popularity as generative protein sequence models (GPSMs) to explore fitness landscapes and predict the effect of mutations. Despite encouraging results, quantitative characterization and comparison of GPSM-generated probability distributions is still lacking. It is currently unclear whether GPSMs can faithfully reproduce the complex multi-residue mutation patterns observed in natural sequences arising due to epistasis. We
doi:10.21203/rs.3.rs-145189/v1
fatcat:tjofoinxcbf3bmqtllnmvo4kha