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Keyword: site variation ×Clear all

3 claims from 1 paper

1. Biochemistry, Genetics and Molecular Biology › Evolution and Genetic Dynamics

   Intrinsic dataset features drive mutational effect prediction by protein language models

   Vieira, Lin and Wilke · bioRxiv (Cold Spring Harbor Laboratory) · 2026

   Testing protein language models on 41 viral and 33 cellular mutational-scanning datasets, the authors report that dataset features such as site variability, more than model design, shape prediction success.

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   1. Unchecked“We observed consistently lower predictive performance on viral datasets compared to cellular datasets, independent of model architecture or transfer learning strategy.”
   2. UncheckedTwo measures of how fitness varies within and among protein sites are reported to limit model accuracy and largely explain why viral and cellular datasets differ.“Analysis of site variability using two metrics, relative variability of site means (RVSM) and fraction of highly variable sites (FHVS), revealed that patterns of fitness variation within and among sites constrain model performance and largely explain the observed differences between viral and cellu…”
   3. UncheckedWhen training and test data are split by protein site rather than pooled, supervised models often seem to rely on site effects, not broader mutational patterns.“Moreover, splitting training and test data by site, rather than pooling, revealed that supervised models often rely on site effects rather than capturing broader mutational patterns.”