23 citations · 26 across the 5 of their papers we have counts for
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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…
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…
ANTIPASTI: interpretable prediction of antibody binding affinity exploiting Normal Modes and Deep Learning
Kevin Michalewicz, Mauricio Barahona, Barbara Bravi
The high binding affinity of antibodies towards their cognate targets is key to eliciting effective immune responses, as well as to the use of antibodies as research and therapeuti…
Direct Coupling Analysis of Epistasis in Allosteric Materials
Barbara Bravi, Riccardo Ravasio, Carolina Brito +1
In allosteric proteins, the binding of a ligand modifies function at a distant active site. Such allosteric pathways can be used as target for drug design, generating considerable…