DIPEND

DIPEND generates ensembles of intrinsically disordered protein segments through neighbor-dependent conformational sampling to model flexible regions and produce structural pools for ensemble selection.

Key Features:

• Residue-Specific Sampling: Utilizes neighbor-dependent Ramachandran (backbone) distributions to sample residue-specific backbone conformations.
• Flexible and Extendable Framework: Implemented in Python and integrates with ChimeraX, Scwrl4, and Gromacs for structure handling and downstream processing.
• Bias Towards Known Conformations: Supports biasing of sampling towards specified conformations for selected residues to incorporate preformed structural elements.

Scientific Applications:

• Initial Pool Generation: Generates conformational pools for flexible segments and intrinsically disordered proteins to support ensemble modeling.
• Ensemble Selection and Fitting: Conformational pools can be used with selection-based tools such as CoNSEnsX+ to select ensembles that match experimental observables.
• Case Studies: Applied to an intrinsically disordered segment of Cd3ϵ and to a single-alpha helical (SAH) region to produce pools for ensemble selection.

Methodology:

Conformational sampling uses neighbor-dependent backbone (Ramachandran) distributions (Dunbrack lab) with optional biasing towards specified residue conformations; the implementation is in Python with integration points for ChimeraX, Scwrl4, and Gromacs.