Prediction of Protein Loop Conformations using the AGBNP Implicit Solvent Model and Torsion Angle Sampling

Area of Science:

• Computational Biology
• Structural Bioinformatics
• Molecular Modeling

Background:

• Accurate prediction of protein loop conformations is crucial for understanding protein function and for applications like homology modeling.
• Existing computational methods face challenges in accurately modeling these flexible regions.

Purpose of the Study:

• To evaluate the effectiveness of the OPLS-AA force field and AGBNP implicit solvent model for predicting native protein loop conformations.
• To develop and test extended conformational search protocols for loop modeling.

Main Methods:

• Utilized the OPLS-AA all-atom force field and the Analytical Generalized Born plus Non-Polar (AGBNP) implicit solvent model.
• Employed torsion angle conformational search protocols based on the Protein Local Optimization Program (PLOP).
• Applied Replica Exchange Molecular Dynamics (T-REMD) for challenging sampling cases.

Main Results:

• Successfully predicted native conformations for 57 9-residue and 35 13-residue loops across diverse proteins.
• A novel nonpolar solvation free energy estimator in AGBNP, with ion pairing corrections, enhanced prediction accuracy.
• Developed PLOP-based schemes applicable to loop homology modeling independent of crystal environments.

Conclusions:

• The OPLS-AA/AGBNP potential is effective for high-resolution protein modeling, particularly in late stages of homology modeling and crystallographic refinement.
• Loop backbone conformation is generally not significantly influenced by crystal packing.
• The developed methods offer a robust approach for modeling protein loops.