Prediction of side-chain conformations on protein surfaces

Area of Science:

• Structural biology
• Computational chemistry
• Biophysics

Background:

• Predicting protein side-chain conformations is crucial for understanding protein function and interactions.
• Existing methods often struggle with accuracy, especially for surface residues.

Purpose of the Study:

• To develop and validate a novel approach for improving the prediction accuracy of surface side-chain conformations in protein crystal structures.
• To assess the impact of 'colony energy' and other factors on prediction accuracy.

Main Methods:

• Utilized a 'colony energy' term to favor frequently sampled conformations, approximating entropic effects.
• Analyzed factors including conformational sampling, rotamer libraries, and crystallographic environment.
• Quantified prediction accuracy by comparing predicted dihedral angles to X-ray diffraction data.

Main Results:

• Colony energy improved chi(1) and chi(1+2) prediction accuracy from 65% to 74% and 40% to 59%, respectively.
• Hydrogen-bonded polar surface residues showed higher prediction accuracy (79% for chi(1), 63% for chi(1+2)).
• The most accurate predictions (82% for chi(1), 73% for chi(1+2)) were achieved using colony energy with an all-atom description including crystal packing.

Conclusions:

• Colony energy is an effective and computationally inexpensive method to enhance surface side-chain conformation predictions.
• Accounting for crystal packing and hydrogen-bonding interactions further refines prediction accuracy.
• This approach offers significant improvements for structural biology and drug design applications.