Prediction Of The Impact Of Genetic Variability On Drug Sensitivity For Clinically Relevant EGFR Mutations
Mutations in the kinase domain of the Epidermal Growth Factor [...]
Differential interactions of resting, activated, and desensitized states of the α7 nicotinic acetylcholine receptor with lipidic modulators
The α7 nicotinic acetylcholine receptor is a pentameric ligand-gated ion [...]
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).
Enabling the execution of large scale workflows for molecular dynamics simulations
In this paper we present an approach to support biomolecular researchers in the development of complex workflows. Our approach is based on the orchestration of computational building blocks for Molecular Dynamics simulations through an efficient workflow management system that has already been adopted in many scientific fields to run applications on multitudes of computing backends.
Implementing FAIR Digital Objects in the EOSC-Life Workflow Collaboratory
To serve the needs of this established and diverse community, EOSC-Life has developed WorkflowHub as an inclusive workflow registry, agnostic to any Workflow Management System (WfMS). WorkflowHub aims to incorporate their workflows in partnership with the WfMS, to embed the registration of workflows in the community processes, e.g. based on pre-existing workflow repositories.
