Efficient Quantification of Lipid Packing Defect Sensing by Amphipathic Peptides: Comparing Martini 2 and 3 with CHARMM36

Abstract

In biological systems, proteins can be attracted to curved or stretched regions of lipid bilayers by sensing hydrophobic defects in the lipid packing on the membrane surface. Here, we present an efficient end-state free energy calculation method to quantify such sensing in molecular dynamics simulations. We illustrate that lipid packing defect sensing can be defined as the difference in mechanical work required to stretch a membrane with and without a peptide bound to the surface. We also demonstrate that a peptide’s ability to concurrently induce excess leaflet area (tension) and elastic softening—a property we call the “characteristic area of sensing” (CHAOS)—and lipid packing sensing behavior are in fact two sides of the same coin. In essence, defect sensing displays a peptide’s propensity to generate tension. The here-proposed mechanical pathway is equally accurate yet, computationally, about 40 times less costly than the commonly used alchemical pathway (thermodynamic integration), allowing for more feasible free energy calculations in atomistic simulations. This enabled us to directly compare the Martini 2 and 3 coarse-grained and the CHARMM36 atomistic force fields in terms of relative binding free energies for six representative peptides including the curvature sensor ALPS and two antiviral amphipathic helices (AH). We observed that Martini 3 qualitatively reproduces experimental trends while producing substantially lower (relative) binding free energies and shallower membrane insertion depths compared to atomistic simulations. In contrast, Martini 2 tends to overestimate (relative) binding free energies. Finally, we offer a glimpse into how our end-state-based free energy method can enable the inverse design of optimal lipid packing defect sensing peptides when used in conjunction with our recently developed evolutionary molecular dynamics (Evo-MD) method. We argue that these optimized defect sensors—aside from their biomedical and biophysical relevance—can provide valuable targets for the development of lipid force fields.

Physics-based generative model of curvature sensing peptides; distinguishing sensors from binders

“…Furthermore, the bulkier the peptide is, and thus the larger its excluded volume and effective helical radius, the more pronounced the induced leaflet tension will be. [12]. B) Three independent replica Evo-MD runs show convergence within 25 iterations, as evident by the population best (solid lines) and population average (dashed lines).…”

“…Consequently, our search space contains 10 24 peptide sequences. [19]. B) Three independent replica Evo-MD runs show convergence within 25 iterations, as evident by the population best (solid lines) and population average (dashed lines).…”

“…To date, HCV AH is the only example of a clinically relevant curvature selective antiviral peptide [5]. When we plug in the previously calculated free energy value [19] for HCV AH, we find that this peptide falls into the 'binder' regime. This is consistent with evidence that the vesicle size specificity of this peptide is due to curvature specific pore formation and not to curvature specific binding (i.e.…”

“…For the optimisation of lipid packing defect sensing peptides, we used the aforementioned mechanical free-energy method [12] to quantify sensing. The fitness function is given by the resulting ΔΔ F value, scaled by a factor c that equals 1 when located on the membrane surface and goes to 0 for transmembrane or soluble configurations (see SI).…”

“…To reduce system size and, consequently, reduce the computational cost, curved membranes are often represented as stretched flat membranes in MD simulations [10, 11] (Fig. 1A), such that the lipid packing defects on the surface are similar and the consequent relative binding free energies correlate [12]. This approximation is valid since biologically relevant bilayer curvatures are negligible on the length scale of peptides (≈5 nm).…”