Structural Determinants of Transmembrane β-Barrels

Area of Science:

• Structural biology
• Computational biophysics
• Membrane protein research

Background:

• Recognizing beta-barrel membrane proteins from sequence is challenging.
• Understanding physical determinants of transmembrane beta-barrel structure is crucial for accurate prediction.
• Existing models require enhancement for proteins with internal aqueous pores.

Purpose of the Study:

• To extend the IMM1 implicit membrane model to accommodate membrane proteins with internal aqueous pores (IMM1-pore).
• To evaluate the IMM1-pore model's ability to generate stable trajectories and discriminate correct folds for beta-barrel proteins.
• To investigate the sequence-structure relationship and energy contributions for transmembrane beta-barrel folding using OmpA and retinol binding protein.

Main Methods:

• Extension of the IMM1 implicit membrane model to create the IMM1-pore model.
• Molecular dynamics simulations of three beta-barrel membrane proteins using IMM1-pore.
• Fold recognition and energy evaluation by threading sequences onto known structures (OmpA and retinol binding protein).
• Decomposition of energy contributions by residue to identify stability drivers.

Main Results:

• The IMM1-pore model produced stable molecular dynamics trajectories for beta-barrel proteins.
• The model demonstrated the ability to discriminate correct folds for 10- and 12-stranded transmembrane beta-barrels.
• Energy analysis revealed that interior polar residue interactions stabilize OmpA, while exterior polar/charged residues and less favorable interior interactions hinder retinol binding protein folding.
• The energy function successfully distinguished native structures from decoys.

Conclusions:

• The enhanced IMM1-pore model is effective for simulating and predicting beta-barrel membrane protein structures.
• Specific residue interactions, particularly interior polar contacts, are key determinants of beta-barrel stability.
• These findings provide insights for designing improved scoring functions for beta-barrel protein structure prediction and fold recognition.