Elucidating the folding problem of α-helices: local motifs, long-range electrostatics, ionic-strength dependence and prediction of NMR parameters¹

Abstract

The information about the conformational behavior of monomeric helical peptides in solution, as well as the α-helix stability in proteins, has been previously utilized to derive a database with the energy contributions for various interactions taking place in an α-helix: intrinsic helical propensities, side-chain-side-chain interactions, main-chain-main-chain hydrogen bonds, and capping effects. This database was implemented in an algorithm based on the helix/coil transition theory (AGADIR). Here, we have modified this algorithm to include previously described local motifs: hydrophobic staple, Schellman motif and Pro-capping motif, new variants of these, and newly described side-chain-side-chain interactions. Based on recent experimental data we have introduced a position dependence of the helical propensities for some of the 20 amino acid residues. A new electrostatic model that takes into consideration all electrostatic interactions up to 12 residues in distance in the helix and random-coil conformations, as well as the effect of ionic strength, has been implemented. We have synthesized and analyzed several peptides, and used data from peptides already analysed by other groups, to test the validity of our electrostatic model. The modified algorithm predicts, with an overall standard deviation value of 6.6 (maximum helix is 100%), the helical, content of 778 peptides of which 223 correspond to wild-type and modified protein fragments. To improve the prediction potential of the algorithm and to have a direct comparison with nuclear magnetic resonance data, the algorithm now predicts the conformational shift of the C^αH protons,¹³C^α and ³J^αN values. We have found that for those peptides correctly predicted from the point of view of circular dichroism, the prediction of the NMR parameters is very good.

Introduction

The energetics of systems formed by short polypeptide chains has been described for α-helices. The helix/coil transition theory integrates that description within the framework of statistical mechanics, and lays the foundation for algorithms that quantify the helical tendency of amino acid sequences. Its simplest version, postulated by Zimm & Bragg (1959), used equilibrium constants characteristic of each amino acid to stand for the nucleation and elongation of helical segments. Later versions of the helix/coil transition theory and algorithms include detailed interaction terms such as capping interactions, side-chain-side-chain interactions, i, i + 3 and i,i + 4 electrostatic effects, and interaction of charged groups with the helix dipole (reviewed by Munoz and Serrano 1995a, Chakrabartty and Baldwin 1995). These terms were introduced either to follow experimental data Munoz and Serrano 1994, Munoz and Serrano 1995b, Chakrabartty and Baldwin 1995, Lifson and Roig 1961, Lomize and Mosberg 1997, Andersen and Tong 1998 or to correspond to the statistical analysis of the protein database (Misra & Wong, 1997).

Our helix/coil transition program, AGADIR, predicted the global helical behavior of peptides in solution. A calculation performed with 423 peptides gave results comparable to the data obtained by circular dichroism. AGADIR also roughly described the helicity at a residue level, and reproduced the tendencies obtained from nuclear magnetic resonance (NMR) data Munoz and Serrano 1995a, Munoz and Serrano 1995b. This algorithm was used to design mutations in protein α-helices and successful improvements of the helical tendencies of both the isolated peptides and the proteins were achieved Villegas et al 1996, Munoz et al 1996, Viguera et al 1996. The engineered proteins showed higher resistance towards chemical and thermal denaturations.

Two new approaches to model the formation of helical segments, the multiple sequence approximation (AGADIRms) and the one-sequence approximation (AGADIR1s) were extensively compared with the original (Muñoz & Serrano, 1997). While AGADIR1s and AGADIRms are virtually identical (for polyalanine peptide sequences of less than 50 residues), the new formalisms are more precise than the residue-partition function of AGADIR. They also perform equally to the Lifson-Roig formalism, despite the differences in the treatment of helix/coil cooperativity, when an equivalent set of parameters is used (Muñoz & Serrano, 1997).

However, details of the energetics are not yet properly implemented in helix/coil transition algorithms. First, electrostatic interactions are partially treated, i.e. only i, i + 1, i, i + 2,i, i + 3 and i, i + 4 interactions are considered, while charges separated by more than 20 Å in a helical peptide contribute to helix stability (Sitkof et al., 1994). Second, the ionic strength is not taken into consideration, although experiments established its strong effect on helix stability, even in neutral peptides (Scholtz et al., 1991). Third, the helical propensity of the 20 amino acid residues depends on their position in the α-helix (Petukhov et al., 1998), and helix/coil transition algorithms use, in contrast, a single set of parameters for all the positions of the helix (except the capping residues). Finally, recently identified sequence motifs stabilize the helical conformation, because contacts between helical and non-helical residues are formed (the “hydrophobic staple”; Munoz et al 1995, Munoz and Serrano 1995a, Creamer and Rose 1995; the “Schellman motif”; Viguera & Serrano, 1995a; and the “Pro-capping motif”; Prieto & Serrano, 1997). Since a residue either before the N-cap, or after the C-cap, is involved, classical helix/coil transition algorithms do not deal with these motifs.

We introduced ionic-strength dependence, long-range electrostatics and local motifs into our helix/coil transition algorithm, AGADIR1s. This results in a slight modification of the values for some of the parameters previously described (Muñoz & Serrano, 1995b), and improves the predictive power of the algorithm. The new version, AGADIR1s-2, predicts the helical behavior of peptides, in aqueous solution, for any pH, temperature and ionic-strength conditions. AGADIR1s-2 also predicts the conformational shifts of the individual C^α protons, and other NMR parameters, offering predictions at higher resolution. The quality of the prediction can be put to use for global (CD) and residue-level (NMR) estimations.