dockECR

dockECR implements an open consensus docking and ranking pipeline that integrates multiple molecular docking programs to prioritize small-molecule candidates in virtual screening of protein targets.

Key Features:

• Consensus docking integration: Integrates AutoDock Vina, Smina, LeDock, and rDock to cross-validate docking results across multiple algorithms.
• Exponential consensus ranking: Aggregates docking scores from each program using an exponential consensus ranking method to prioritize molecular candidates.
• RMSD-based scoring: Computes the average Root Mean Square Deviation (RMSD) between best poses from each docking program as a secondary metric to assess pose similarity.
• Multi-conformation screening: Supports screening against multiple receptor conformations, including different crystal structures and molecular dynamics simulation frames.

Scientific Applications:

• Virtual screening campaigns: Prioritizes potential small-molecule actives in structure-based virtual screening of protein targets.
• Emerging disease drug discovery: Enables rapid ranking of large small-molecule libraries to support discovery efforts against emerging pathogens, exemplified by application to SARS-CoV-2 main protease.
• Candidate prioritization and interaction analysis: Produces ranked candidates and complementary pose-similarity information to inform evaluation of interactions with key residues.

Methodology:

Docking performed with AutoDock Vina, Smina, LeDock, and rDock; scores aggregated by an exponential consensus ranking and complemented by average RMSD of best poses; benchmarked on 15 protein targets with known actives and decoys; practical screening applied to 79 molecules derived from co-crystallized fragments against the SARS-CoV-2 main protease using multiple crystal structures and molecular dynamics frames.