OpenMole

OpenMole orchestrates distributed execution, exploration, diagnosis, and optimization of numerical models for scientific studies and integrates with tools such as MOLE 2.0 for analysis of molecular channels and pores in proteins and nucleic acids.

Key Features:

• Distributed model execution: Executes numerical models across distributed computing environments to scale computations.
• Model exploration, diagnosis, and optimization: Provides facilities for exploring parameter spaces, diagnosing model behavior, and optimizing model outputs.
• Language and binary interoperability: Supports models implemented in Java, Binary exe, NetLogo, R, SciLab, Python, and C++.
• Integration with MOLE 2.0: Integrates with MOLE 2.0 for detection and analysis of molecular channels and pores in biomacromolecules.
• Physicochemical property estimation: Uses MOLE 2.0 to estimate channel properties including hydropathy, hydrophobicity, polarity, charge, and mutability.
• Performance characteristics of MOLE 2.0: Employs MOLE 2.0, which is reported to provide speed, robustness, and versatility for channel analysis.
• Benchmark validation: Supports benchmark analyses such as tests on eighty X-ray structures from the cytochrome P450 superfamily.

Scientific Applications:

• Structural biology: Analysis of molecular channels and pores in proteins and nucleic acids using integrated MOLE 2.0 functionality.
• Bioinformatics of molecular recognition: Characterization of channel physicochemical properties to inform molecular recognition and enzyme substrate specificity.
• Modeling and optimization studies: Exploration and optimization of numerical models across scientific disciplines using distributed computing.
• Benchmarking channel predictions: Validation of channel property estimates against X-ray structures from the cytochrome P450 superfamily.

Methodology:

OpenMole executes, explores, diagnoses, and optimizes numerical models on distributed computing environments, supports models in Java, Binary exe, NetLogo, R, SciLab, Python, and C++, integrates with MOLE 2.0 for channel detection and physicochemical property estimation, and has been used in benchmark analyses of eighty X-ray cytochrome P450 structures.