Using machine-learning-driven approaches to boost hot-spot’s knowledge

This review aims to present the current knowledge on PPIs, providing a detailed understanding of the microspecifications of the residues involved in those interactions and the characteristics of those defined as HS through a thorough assessment of related field-specific methodologies.

2022-11-03T09:58:05+01:00February 18, 2022|Publications|Comments Off on Using machine-learning-driven approaches to boost hot-spot’s knowledge

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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