Alex S. Holehouse

SHEPHARD is a software package designed to simplify the integration and analysis of large protein sequence annotations.

metapredict V2 claims better disorder-prediction accuracy than the legacy model, shipping as a web server, Python package and command-line tool, illustrated on human transcription factors.

The paper shows that the SWI/SNF complex uses a pH-sensitive low complexity sequence to sense carbon starvation in yeast.

The paper identifies three design principles of acidic transcriptional activation domains through experimental analysis and develops a predictor to identify these domains in the human proteome.

PARROT is a deep learning framework for analyzing large protein datasets, capable of classification and regression tasks using only raw protein sequences.

The paper introduces metapredict, a deep-learning-based tool for fast and accurate prediction of consensus disorder and structure in proteins, outperforming existing methods and offering user-friendly access.

The paper investigates how the charge patterning and sequence complexity of the FtsZ C-terminal tail influence Z-ring formation and bacterial cell division.

This study uses a deep mutational scan to analyze the sequence features of an acidic activation domain, finding that acidic residues help expose hydrophobic motifs and that activity increases under amino acid starvation.