Quantum mechanics simulation of protein dynamics on long timescale

Abstract

Protein structure and dynamics are the keys to a wide range of problems in biology. In principle, both can be fully understood by using quantum mechanics as the ultimate tool to unveil the molecular interactions involved. Indeed, quantum mechanics of atoms and molecules have come to play a central role in chemistry and physics. In practice, however, direct application of quantum mechanics to protein systems has been prohibited by the large molecular size of proteins. As a consequence, there is no general quantum mechanical treatment that not only exceeds the accuracy of state-of-the-art empirical models for proteins but also maintains the efficiency needed for extensive sampling in the conformational space, a requirement mandated by the complexity of protein systems. Here we show that, given recent developments in methods, a general quantum mechanical-based treatment can be constructed. We report a molecular dynamics simulation of a protein, crambin, in solution for 350 ps in which we combine a semiempirical quantum-mechanical description of the entire protein with a description of the surrounding solvent, and solvent-protein interactions based on a molecular mechanics force field. Comparison with a recent very high-resolution crystal structure of crambin (Jelsch et al., Proc Natl Acad Sci USA 2000;102:2246-2251) shows that geometrical detail is better reproduced in this simulation than when several alternate molecular mechanics force fields are used to describe the entire system of protein and solvent, even though the structure is no less flexible. Individual atomic charges deviate in both directions from "canonical" values, and some charge transfer is found between the N and C-termini. The capability of simulating protein dynamics on and beyond the few hundred ps timescale with a demonstrably accurate quantum mechanical model will bring new opportunities to extend our understanding of a range of basic processes in biology such as molecular recognition and enzyme catalysis.

Origin of intrinsic 310‐helix versus strand stability in homopolypeptides and its implications for the accuracy of the Amber force field

“…1, Table 1). In agreement with others, 13,18,20,22 helices in the gas phase adopt a 3 10 -helix conformation (which we call ''helical'' for simplicity) rather than an -helix, which, for shorter peptides (up to *20 residues), 18 is higher in energy and is not stable for lengths shorter than eight residues. 18 This is due to the formation of an additional hydrogen bond in a 3 10 -helix compared with a canonical -helix at the cost of breaking the parallel alignment of the hydrogen bonds.…”

“…Previous studies analyzed the relative helix versus -strand stability based on polyglycine 13 or pAla [18][19][20] peptides. The influence of single amino acid replacements in glycine pentapeptides 21 on helix stability were also studied, using mixed QM/semiempirical calculations (DFT/AM1).…”

Unraveling the Stability of Polypeptide Helices: Critical Role of van der Waals Interactions

“…3 10 helices is increased from À0:6 kcal=mol per residue in PBE calculations to À2:1 kcal=mol in PBE þ vdW. Previous tight-binding calculations arrived at the same conclusion for polyalanine helices up to 8 residues when vdW interactions were accounted for by using an empirical potential [18]. Since the helix has the most compact structure, the vdW interaction is the largest in this case.…”

“…We choose polyalanine as a target for our study due to its high propensity to form helical structures [11], and its widespread use as a benchmark system for peptide stability in experiment [12][13][14][15] and in theory (see, e.g., Refs. [16][17][18][19][20][21][22][23][24] and references therein). As detailed below, we find that vdW interactions stabilize native gas-phase helical forms of alanine polypeptide by a factor of 2 in relative energy over the fully extended structure on top of the widely used and established Perdew-Burke-Ernzerhof (PBE) [25] density functional.…”

Empirical force fields for biological macromolecules: Overview and issues

“…3, lower panels) compared to results from previously published QM/MM calculations. 175,176 Subsequently, additional empirical optimization of the alanine dipeptide conformational energies, empirical optimization of the glycine dipeptide surface and inclusion of a proline dipeptide surface yielded a CHARMM22/CMAP force field included in version 31 of CHARMM. 30 Although additional tests verifying the accuracy of this approach are ongoing (Rich Pastor, personal communication; Peter Steinback, personal communcation; Matthias Buck, personal communication), the inclusion of the 2D Figure 3.…”