Context-Independent, Temperature-Dependent Helical Propensities for Amino Acid Residues

Abstract

Assigned from data sets measured in water at 2, 25, and 60 °C containing 13C=O NMR chemical shifts and [θ]222 ellipticities, helical propensities are reported for the twenty genetically coded amino acids, as well as for norvaline and norleucine. These have been introduced by chemical synthesis at central sites within length-optimized, spaced, solubilized Ala19 hosts. The resulting polyalanine-derived, quantitative propensity sets express for each residue its temperature-dependent but context-independent tendency to forgo a coil state and join a preexisting helical conformation. At 2 °C their rank ordering is: P ⪡ G < H < C, T, N < S < Y, F, W < V, D < K < Q < I < R, M < L < E < A; at 60 °C the rank becomes: H, P < G < C < R, K < T, Y, F < N, V < S < Q < W, D < I, M < E < A < L. The ΔΔ G values, kcal/mol, relative to alanine, for the cluster T, N, S, Y, F, W, V, D, Q, imply that at 2 °C all are strong breakers: ΔΔ Gmean = +0.63 ± 0.11, but at 60 °C their breaking tendencies are dramatically attenuated and converge toward the mean: ΔΔ Gmean = +0.25 ± 0.07. Accurate modeling of helix-rich proteins found in thermophiles, mesophiles, and organisms that flourish near 0 °C thus requires appropriately matched propensity sets. Comparisons are offered between the temperature-dependent propensity assignments of this study and those previously assigned by the Scheraga group; the special problems that attend propensity assignments for charged residues are illustrated by lysine guest data; and comparisons of errors in helicity assignments from shifts and ellipticity data show that the former provide superior precision and accuracy.

Grid-Based Backbone Correction to the ff12SB Protein Force Field for Implicit-Solvent Simulations

“…W i can be understood as the equilibrium constant for a residue in coil conformation to extend an existing helical segment and is related to the free energy of helix extension by: Δ G ext = − k B Tln ( w i ). We can then compare the values of w i to experimental measurements 25 . Additionally, we can get estimates for the initial grid correction to apply by noting that $\mathrm{normal\Delta }G=k_{B}T\text{italicln}false(\frac{w_{\text{italicsim}}}{w_{\text{italicexp}}}false)$.…”

“…Helical propensities can be calculated for each amino acid from experiments 25 . These propensities can also be calculated from computations by following the approach outlined by Best and coworkers 26 .…”

Advances in free-energy-based simulations of protein folding and ligand binding

“…As they get better and faster, they are tested on larger databases and on systems that are bigger, more challenging, and more biological, which, in turn, leads to further improvements. Force fields advances are driven by increased computer power, particularly GPUs; the use of training against higher levels of QM; the inclusion of more conformational diversity in training sets; the use of bigger peptide-sized molecules, rather than just small organics; and the use of better databases for testing, such as of NMR scalar couplings [48] and amino-acid helical propensities [49,50]. In earlier protein-folding modeling, force fields were systematically unbalanced between different types of secondary structures.…”

Protonation‐dependent conformational variability of intrinsically disordered proteins

“…Data obtained on a large set of intrinsically disordered human proteins show that IDPs fold under acidic conditions into more compact structures with higher α‐helical content largely due to reduced electrostatic repulsion of negatively charged side chains. It was shown recently that the inherent context‐independent α‐helical propensities are significantly higher for Asn and Gln than for Asp and Glu, respectively . The meta‐structure derived increases in α‐helical propensities are not only related to individual (context‐independent) amino acid specific properties but also take into account context‐dependent (primary sequence) influences.…”