Thomas A. Hopf

The paper introduces a hybrid framework to isolate stability-independent activity effects in enzymes using dual-graph deconvolution, validated with experimental data.

evedesign provides a unified framework for conditional biosequence design, enabling multiobjective workflows and experimental integration across various applications.

This paper introduces popEVE, a deep generative model for proteome-wide disease variant prediction, identifying 442 genes in a developmental disorder cohort, including 123 novel candidates.

The paper quantifies protein-to-mRNA (PTR) ratios across 29 human tissues and identifies sequence determinants influencing these ratios, finding many new post-transcriptional regulatory elements.

The paper creates a proteome and transcriptome atlas of 29 human tissues, revealing differences in mRNA-protein abundance and challenges in proteogenomics.

The paper introduces EVcouplings, a Python framework for coevolutionary sequence analysis that enables structure prediction and mutation effect assessment.

This paper uses sequence co-evolution to predict 3D contacts and structures of protein complexes, achieving accurate results on known structures and predicting contacts for unknown ones.