Nicolas Jonsson

The paper identifies proteins where variant effect predictors disagree, suggesting these are prioritized for experimental validation to improve variant interpretation.

This study maps degrons in human transcription factors, identifies new degrons, and links degron potency to variant effects and transcriptional activation.

The paper systematically identifies degrons in human cytosolic proteins and links them to disease mechanisms through machine learning and structural analysis.

The paper uses machine learning to predict how loss-of-function variants affect protein stability and function across the human proteome, finding that half of such variants disrupt protein stability.

This paper introduces SSEmb, a model that integrates protein sequence and structure to improve variant effect predictions and downstream tasks like binding site prediction.