Inverse Conformational Selection in Lipid–Protein Binding

Abstract

Interest in lipid interactions with proteins and other biomolecules is emerging not only in fundamental biochemistry but also in the field of nanobiotechnology where lipids are commonly used, for example, in carriers of mRNA vaccines. The outward-facing components of cellular membranes and lipid nanoparticles, the lipid headgroups, regulate membrane interactions with approaching substances, such as proteins, drugs, RNA, or viruses. Because lipid headgroup conformational ensembles have not been experimentally determined in physiologically relevant conditions, an essential question about their interactions with other biomolecules remains unanswered: Do headgroups exchange between a few rigid structures, or fluctuate freely across a practically continuous spectrum of conformations? Here, we combine solid-state NMR experiments and molecular dynamics simulations from the NMRlipids Project to resolve the conformational ensembles of headgroups of four key lipid types in various biologically relevant conditions. We find that lipid headgroups sample a wide range of overlapping conformations in both neutral and charged cellular membranes, and that differences in the headgroup chemistry manifest only in probability distributions of conformations. Furthermore, the analysis of 894 protein-bound lipid structures from the Protein Data Bank suggests that lipids can bind to proteins in a wide range of conformations, which are not limited by the headgroup chemistry. We propose that lipids can select a suitable headgroup conformation from the wide range available to them to fit the various binding sites in proteins. The proposed inverse conformational selection model will extend also to lipid binding to targets other than proteins, such as drugs, RNA, and viruses.

Specificity of Loxosceles α clade phospholipase D enzymes for choline-containing lipids: Role of a conserved aromatic cage

“…Interestingly, in our simulations, the orientation of the bound PC lipids headgroups do not significantly differ from that of PC groups in the bilayer (Table A in S4 Text ) but without a thorough evaluation of the CHARMM-WYF force field for caged POPC lipids, we cannot rule out the inverse conformational selection model proposed by Bacle et al . [ 65 ].…”

Structure of POPC lipid bilayers in OPLS3e force field

“…Previous studies comparing experimental data to simulations show that in general, acyl chains of lipids are usually quite well described in simulations, and agreement of the structure and behavior of this region between the simulation and the experimental data is quite good (3,(13)(14)(15). However, correct description of headgroups and glycerol backbone have proven to be more challenging, and large variation in performance with different force field occurs (16)(17)(18)(19).…”

“…Response to lowering hydration level is qualitatively correctly produced by several current force fields; but large variation occurs in description of cation binding which is typically highly overestimated (16,23). There are challenges in the correct description of Na + binding, but especially in the correct description of multivalent ions: Ca 2+ over-accumulates at the membranewater interface in most of the currently used force fields (18,19,23).…”

buildH: Build hydrogen atoms from united-atom molecular dynamics of lipids and calculate the order parameters

“…It can be easily extended or customized if needed. buildH is currently widely used in the NMRlipids project IVb dealing with the conformational plasticity of lipid headgroups in cellular membranes and protein-lipid complexes (Bacle et al, 2021). In addition, it is planned to use buildH in the next NMRlipids project dealing with a databank containing MD trajectories of lipids .…”

“…There are two main strategies to compute S CH from UA simulations (Piggot et al, 2017): i) expressing S CH as a function of the coordinates of other atoms (Douliez et al, 1995), ii) reconstructing hydrogen coordinates and calculating S CH as in AA simulations. The trend in the recent years has been towards strategy ii), such as in the NMRlipids project (Antila et al, 2019;Bacle et al, 2021;Botan et al, 2015;Catte et al, 2016). NMRlipids is an open science project which uses MD simulations and experimental S CH with the goal of improving lipid force fields or conducting fundamental research on the structure and dynamics of lipid membranes.…”