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Despite their biological importance in many regulatory processes, protein-peptide recognition mechanisms are diffi cult to study experimentally at the structural level because of the inherent fl exibility of peptides and the often transient interactions on which they rely. Complementary methods like biomolecular docking are therefore required. The prediction of the three-dimensional structure of protein-peptide complexes raises unique challenges for computational algorithms, as exemplifi ed by the recent introduction of proteinpeptide targets in the blind international experiment CAPRI (Critical Assessment of PRedicted Interactions). Conventional protein-protein docking approaches are often struggling with the high fl exibility of peptides whose short sizes impede protocols and scoring functions developed for larger interfaces. On the other side, protein-small ligand docking methods are unable to cope with the larger number of degrees of freedom in peptides compared to small molecules and the typically reduced available information to defi ne the binding site. In this chapter, we describe a protocol to model protein-peptide complexes using the HADDOCK web server, working through a test case to illustrate every steps. The fl exibility challenge that peptides represent is dealt with by combining elements of conformational selection and induced fi t molecular recognition theories. Key words Biomolecular interactions, Information-driven docking, Conformational changes, Flexibility, HADDOCK, Molecular modeling.

Despite their biological importance in many regulatory processes, protein-peptide recognition mechanisms are diffi cult to study experimentally at the structural level because of the inherent fl exibility of peptides and the often transient interactions on which they rely. Complementary methods like biomolecular docking are therefore required. The prediction of the three-dimensional structure of protein-peptide complexes raises unique challenges for computational algorithms, as exemplifi ed by the recent introduction of proteinpeptide targets in the blind international experiment CAPRI (Critical Assessment of PRedicted Interactions). Conventional protein-protein docking approaches are often struggling with the high fl exibility of peptides whose short sizes impede protocols and scoring functions developed for larger interfaces. On the other side, protein-small ligand docking methods are unable to cope with the larger number of degrees of freedom in peptides compared to small molecules and the typically reduced available information to defi ne the binding site. In this chapter, we describe a protocol to model protein-peptide complexes using the HADDOCK web server, working through a test case to illustrate every steps. The fl exibility challenge that peptides represent is dealt with by combining elements of conformational selection and induced fi t molecular recognition theories. Key words Biomolecular interactions, Information-driven docking, Conformational changes, Flexibility, HADDOCK, Molecular modeling.

Protein-protein docking aims at predicting the three-dimensional structure of a protein complex starting from the free forms of the individual partners. As assessed in the CAPRI community-wide experiment, the most successful docking algorithms combine pure laws of physics with information derived from various experimental or bioinformatics sources. Of these so-called "information-driven" approaches, HADDOCK stands out as one of the most successful representatives. In this chapter, we briefly summarize which experimental information can be used to drive the docking prediction in HADDOCK, and then focus on the docking protocol itself. We discuss and illustrate with a tutorial example a "classical" protein-protein docking prediction, as well as more recent developments for modelling multi-body systems and large conformational changes.

Despite their biological importance in many regulatory processes, protein-peptide recognition mechanisms are diffi cult to study experimentally at the structural level because of the inherent fl exibility of peptides and the often transient interactions on which they rely. Complementary methods like biomolecular docking are therefore required. The prediction of the three-dimensional structure of protein-peptide complexes raises unique challenges for computational algorithms, as exemplifi ed by the recent introduction of proteinpeptide targets in the blind international experiment CAPRI (Critical Assessment of PRedicted Interactions). Conventional protein-protein docking approaches are often struggling with the high fl exibility of peptides whose short sizes impede protocols and scoring functions developed for larger interfaces. On the other side, protein-small ligand docking methods are unable to cope with the larger number of degrees of freedom in peptides compared to small molecules and the typically reduced available information to defi ne the binding site. In this chapter, we describe a protocol to model protein-peptide complexes using the HADDOCK web server, working through a test case to illustrate every steps. The fl exibility challenge that peptides represent is dealt with by combining elements of conformational selection and induced fi t molecular recognition theories. Key words Biomolecular interactions, Information-driven docking, Conformational changes, Flexibility, HADDOCK, Molecular modeling.

Protein-protein docking aims at predicting the three-dimensional structure of a protein complex starting from the free forms of the individual partners. As assessed in the CAPRI community-wide experiment, the most successful docking algorithms combine pure laws of physics with information derived from various experimental or bioinformatics sources. Of these so-called "information-driven" approaches, HADDOCK stands out as one of the most successful representatives. In this chapter, we briefly summarize which experimental information can be used to drive the docking prediction in HADDOCK, and then focus on the docking protocol itself. We discuss and illustrate with a tutorial example a "classical" protein-protein docking prediction, as well as more recent developments for modelling multi-body systems and large conformational changes.