Marco Giulini

The paper characterizes two anti-HPA-1a antibodies from a severe FNAIT case, showing functional differences in platelet activation through FcγRIIa engagement.

This study evaluates machine learning methods for nanobody structure prediction and benchmarks HADDOCK workflows to model nanobody-antigen interactions using AlphaFold2 and ImmuneBuilder predictions.

HADDOCK3 is a new modular platform for integrative biomolecular complex modelling, offering enhanced flexibility and integration with third-party tools to complement machine learning predictions.

This study identifies the mumps virus SH protein as a pentameric viroporin and shows it functions as a chloride channel inhibited by BIT225.

The paper improves antibody structure prediction by combining AlphaFlow and HADDOCK to generate diverse CDR-H3 loop conformations, enhancing docking accuracy for antibody-antigen complexes.

The paper summarizes HADDOCK's performance in CAPRI rounds 47-55, highlighting the effectiveness of integrating HADDOCK with AI-driven methods like AlphaFold2 for protein complex structure prediction.

The paper evaluates how machine learning-generated antibody and antigen structures can improve antibody-antigen complex modelling using HADDOCK, showing improved accuracy over ZDOCK.

This study demonstrates the use of HADDOCK for predicting protein-glycan complexes, achieving 70% and 40% top 5 success rates on bound and unbound datasets, respectively.

ARCTIC-3D retrieves and clusters protein-protein interface data from structural information to classify interfaces and model biomolecular assemblies.