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Inserting Small Molecules across Membrane Mixtures: Insight from the Potential of Mean Force.

ABSTRACT: Small solutes have been shown to alter the lateral organization of cell membranes and reconstituted phospholipid bilayers; however, the mechanisms by which these changes happen are still largely unknown. Traditionally, both experiment and simulation studies have been restricted to testing only a few compounds at a time, failing to identify general molecular descriptors or chemical properties that would allow extrapolating beyond the subset of considered solutes. In this work, we probe the competing energetics of inserting a solute in different membrane environments by means of the potential of mean force. We show that these calculations can be used as a computationally efficient proxy to establish whether a solute will stabilize or destabilize domain phase separation. Combined with umbrella-sampling simulations and coarse-grained molecular dynamics simulations, we are able to screen solutes across a wide range of chemistries and polarities. Our results indicate that for the system under consideration, preferential partitioning and therefore effectiveness in altering membrane phase separation are strictly linked to the location of insertion in the bilayer (i.e., midplane or interface). Our approach represents a fast and simple tool for obtaining structural and thermodynamic insight into the partitioning of small molecules between lipid domains and its relation to phase separation, ultimately providing a platform for identifying the key determinants of this process.

SUBMITTER: Centi A

PROVIDER: S-EPMC7091563 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundThe development and testing of functions for the modeling of protein energetics is an important part of current research aimed at understanding protein structure and function. Knowledge-based mean force potentials are derived from statistical analyses of interacting groups in experimentally determined protein structures. Current knowledge-based mean force potentials are developed at the atom or amino acid level. The evolutionary information contained in the profiles is not investigated. Based on these observations, a class of novel knowledge-based mean force potentials at the profile level has been presented, which uses the evolutionary information of profiles for developing more powerful statistical potentials.ResultsThe frequency profiles are directly calculated from the multiple sequence alignments outputted by PSI-BLAST and converted into binary profiles with a probability threshold. As a result, the protein sequences are represented as sequences of binary profiles rather than sequences of amino acids. Similar to the knowledge-based potentials at the residue level, a class of novel potentials at the profile level is introduced. We develop four types of profile-level statistical potentials including distance-dependent, contact, Phi/Psi dihedral angle and accessible surface statistical potentials. These potentials are first evaluated by the fold assessment between the correct and incorrect models generated by comparative modeling from our own and other groups. They are then used to recognize the native structures from well-constructed decoy sets. Experimental results show that all the knowledge-base mean force potentials at the profile level outperform those at the residue level. Significant improvements are obtained for the distance-dependent and accessible surface potentials (5-6%). The contact and Phi/Psi dihedral angle potential only get a slight improvement (1-2%). Decoy set evaluation results show that the distance-dependent profile-level potentials even outperform other atom-level potentials. We also demonstrate that profile-level statistical potentials can improve the performance of threading.ConclusionThe knowledge-base mean force potentials at the profile level can provide better discriminatory ability than those at the residue level, so they will be useful for protein structure prediction and model refinement.

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Inserting Small Molecules across Membrane Mixtures: Insight from the Potential of Mean Force.

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