title: LAMBADA and InflateGRO2: efficient membrane alignment and insertion of membrane proteins for molecular dynamics simulations.
abstract: At the beginning of each molecular dynamics membrane simulation stands the generation of a suitable starting structure which includes the working steps of aligning membrane and protein and seamlessly accommodating the protein in the membrane. Here we introduce two efficient and complementary methods based on pre-equilibrated membrane patches, automating these steps. Using a voxel-based cast of the coarse-grained protein, LAMBADA computes a hydrophilicity profile-derived scoring function based on which the optimal rotation and translation operations are determined to align protein and membrane. Employing an entirely geometrical approach, LAMBADA is independent from any precalculated data and aligns even large membrane proteins within minutes on a regular workstation. LAMBADA is the first tool performing the entire alignment process automatically while providing the user with the explicit 3D coordinates of the aligned protein and membrane. The second tool is an extension of the InflateGRO method addressing the shortcomings of its predecessor in a fully automated workflow. Determining the exact number of overlapping lipids based on the area occupied by the protein and restricting expansion, compression and energy minimization steps to a subset of relevant lipids through automatically calculated and system-optimized operation parameters, InflateGRO2 yields optimal lipid packing and reduces lipid vacuum exposure to a minimum preserving as much of the equilibrated membrane structure as possible. Applicable to atomistic and coarse grain structures in MARTINI format, InflateGRO2 offers high accuracy, fast performance, and increased application flexibility permitting the easy preparation of systems exhibiting heterogeneous lipid composition as well as embedding proteins into multiple membranes. Both tools can be used separately, in combination with other methods, or in tandem permitting a fully automated workflow while retaining a maximum level of usage control and flexibility. To assess the performance of both methods, we carried out test runs using 22 membrane proteins of different size and transmembrane structure.
authors.affiliation: Computational Structural Biology, Department of Life Science Informatics B-IT, Life & Medical Sciences (LIMES) Institute, University of Bonn, Dahlmannstr. 2, 53113 Bonn, Germany.
authors.forename: Thomas H
authors.initials: TH
authors.lastname: Schmidt
authors.forename: Christian
authors.initials: C
authors.lastname: Kandt
mesh_terms: D002462:Cell Membrane / Q000737:chemistry*; D004926:Escherichia coli; D029968:Escherichia coli Proteins / Q000737:chemistry*; D057927:Hydrophobic and Hydrophilic Interactions; D008051:Lipid Bilayers / Q000737:chemistry*; D008565:Membrane Proteins / Q000737:chemistry*; D056004:Molecular Dynamics Simulation; D010743:Phospholipids / Q000737:chemistry*; D017433:Protein Structure, Secondary; D012984:Software; D040681:Structural Homology, Protein; D013816:Thermodynamics; D014867:Water / Q000737:chemistry
chemical_list: D029968:Escherichia coli Proteins; D008051:Lipid Bilayers; D008565:Membrane Proteins; D010743:Phospholipids; D014867:Water