4 papers
Machine learning approaches for interpretable antibody property prediction using structural data
Kevin Michalewicz, Mauricio Barahona, Barbara Bravi
Understanding the relationship between antibody sequence, structure and function is essential for the design of antibody-based therapeutics and research tools. Recently, machine le…
Protein generation with embedding learning for motif diversification
Kevin Michalewicz, Chen Jin, Philip Alexander Teare +4
A fundamental challenge in protein design is the trade-off between generating structural diversity while preserving motif biological function. Current state-of-the-art methods, suc…
MCbiF: Measuring Topological Autocorrelation in Multiscale Clusterings via 2-Parameter Persistent Homology
Juni Schindler, Mauricio Barahona
Datasets often possess an intrinsic multiscale structure with meaningful descriptions at different levels of coarseness. Such datasets are naturally described as multi-resolution c…
Integrating protein sequence embeddings with structure via graph-based deep learning for single-residue property prediction
Kevin Michalewicz, Mauricio Barahona, Barbara Bravi
Understanding the intertwined contributions of amino acid sequence and spatial structure is essential to explain protein behaviour. Here, we introduce INFUSSE (Integrated Network F…