Role of main-chain electrostatics, hydrophobic effect and side-chain conformational entropy in determining the secondary structure of proteins

Abstract

The physiochemical bases of amino acid preferences for α-helical, β-strand, and other main-chain conformational states in proteins is controversial. Hydrophobic effect, side-chain conformational entropy, steric factors, and main-chain electrostatic interactions have all been advanced as the dominant physical factors which determine these preferences. Many attempts to resolve the controversy have focused on small model systems. The disadvantage of such systems is that the amino acids in small molecules are largerly exposed to the solvent. In proteins, however, the amino acids are in contact with the solvent to a different degree, causing a large variability of strengths of all interactions. The estimates of mean strengths of interactions in the actual protein environment are therefore essential to resolve the controversy. In this work the experimental protein structures are used to estimate the mean strengths of various interactions in proteins. The free energy contributions of the interactions are implemented into the Lifson-Roig theory to calculate the helix and strand free energy profiles. From the profiles the secondary structures of proteins and peptides are predicted using simple rules. The role of hydrophobic effect, side-chain conformational entropy, and main-chain electrostatic interactions in determining the secondary structure of proteins is assessed from the abilities of different models, describing stability of secondary structures, to correctly predict α-helices, β-strands and coil in 130 proteins. The three-state accuracy of the model, which contains only the free energy terms due to the main-chain electrostatics with 40 coefficients, is 68.7%. This accuracy is approaching to the accuracy of currently the best secondary structure prediction algorithm based on neural networks (72%); however, many thousands of parameters have to be optimized during the training of the neural networks to reach this level of accuracy. The correlation coefficient between the calculated and the experimental helix contents of 37 alanine based peptides is 0.91. If the hydrophobic and the side-chain conformational entropy terms are included into the helix-coil transition parameters, the accuracy of the algorithm does not improve significantly. However, if the main-chain electrostatic interactions are excluded from the helix-coil and strand-coil transition parameters, the accuracy of the algorithm reaches only 59.5%. These results support the dominant role of the short-range main-chain electrostatics in determining the secondary structure of proteins and peptides. The role of the hydrophobic effect and the side-chain conformational entropy is small.