Cyclization and Docking Protocol for Cyclic Peptide–Protein Modeling Using HADDOCK2.4

This study presents a step-by-step protocol for generating cyclic peptide conformations and docking them to their protein target using HADDOCK2.4. A dataset of 30 cyclic peptide-protein complexes was used to optimize both cyclisation and docking protocols. It supports peptides cyclized via an N- and C-terminus peptide bond and/or a disulfide bond.

2022-11-03T09:58:03+01:00June 2, 2022|Publications|Comments Off on Cyclization and Docking Protocol for Cyclic Peptide–Protein Modeling Using HADDOCK2.4

MetaScore: A novel machine-learning based approach to improve traditional scoring functions for scoring protein-protein docking conformations

We present here MetaScore, a new machine-learning based approach to improve the scoring of docked conformations. MetaScore utilizes a random forest (RF) classifier trained to distinguish near-native from non-native conformations using a rich set of features extracted from the respective protein-protein interfaces. These include physico-chemical properties, energy terms, interaction propensity-based features, geometric properties, interface topology features, evolutionary conservation and also scores produced by traditional scoring functions (SFs).

2022-11-03T09:58:05+01:00October 9, 2021|Publications|Comments Off on MetaScore: A novel machine-learning based approach to improve traditional scoring functions for scoring protein-protein docking conformations
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