We present a physics-based machine learning approach to predict transcription factor binding affinities in vitro from structural and mechanical DNA properties directly derived from atomistic molecular dynamics simulations.
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.
Three-dimensional (3D) structures of protein complexes provide fundamental information to [...]
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).