Computational Characterization of Structural & Thermodynamic Properties of Beta-barrel Membrane Proteins

Beta-barrel membrane proteins are found in the outer membrane of gram-negative bacteria, mitochondria, and chloroplasts. They carry out diverse biological functions, including pore formation, membrane anchoring, enzyme activity, and are often responsible for bacterial virulence. By using a novel statistical mechanical approach that allows the computation of full partition function and thermodynamic properties such as melting temperature, I have developed a method to identify weakly stable regions in the transmembrane (TM) domain and discovered that out-clamps, in-plugs, protein-lipid interactions and oligomerization are four general mechanisms stabilizing the beta-barrel membrane proteins. This method can predict the oligomerization state and can identify the interfaces of protein-protein interaction in the TM region. It is based on fundamental physical principles and only sequence information alone is required. In a blind test, oligomerization state of beta-barrel membrane proteins can be predicted with 100% accuracy, and the protein-protein interfaces can be identified with 86% accuracy. Experiments involving site-directed mutagenesis, SDS-PAGE, CD spectroscopy, tryptophan flourescence, thermal and chemical denaturation have confirmed predictions of important residues in the protein-protein interaction interface of OmpF, VDAC, and Tom40 proteins. Difficulties in experimental determination of structures of beta-barrel membrane proteins have increased the importance of computational modeling for these proteins. I have further developed a computational method called 3D-SPoT for predicting three dimensional structures of the TM domains of beta-barrel membrane proteins. Using a combinatorial model, this method can construct 3D structures of the TM region of beta-barrel membrane proteins using sequence information only. The average RMSD between predicted and true TM region is about 4 Angstrom. This method successfully predicted the structure of the TM domain of VDAC, a mitochondrial membrane protein with no known homologous structure. I also describe a framework to study the assembly of multi-chain proteins into a single beta-barrel structure

Lipid-Binding Surfaces of Membrane Proteins: Evidence From Evolutionary and Structural Analysis

Membrane proteins function in the diverse environment of the lipid bilayer. Experimental evidence suggests that some lipid molecules bind tightly to specific sites on the membrane protein surface. These lipid molecules often act as co-factors and play important functional roles. In this study, we have assessed the evolutionary selection pressure experienced at lipid-binding sites in a set of alpha-helical and beta-barrel membrane proteins using posterior probability analysis of the ratio of synonymous vs. nonsynonymous substitutions (omega-ratio). We have also carried out a geometric analysis of the membrane protein structures to identify residues in close contact with co-crystallized lipids. We found that residues forming cholesterol-binding sites in both beta(2)-adrenergic receptor and Na+-K+-ATPase exhibit strong conservation, which can be characterized by an expanded cholesterol consensus motif for GPCRs. Our results suggest the functional importance of aromatic stacking interactions and interhelical hydrogen bonds in facilitating protein-cholesterol interactions, which is now reflected in the expanded motif. We also find that residues forming the cardiolipin-binding site in formate dehydrogenase-N gamma-subunit and the phosphatidylglycerol binding site in KcsA are under strong purifying selection pressure. Although the lipopolysaccharide (LPS)-binding site in ferric hydroxamate uptake receptor (FhuA) is only weakly conserved, we show using a statistical mechanical model that LPS binds to the least stable FhuA beta-strand and protects it from the bulk lipid. Our results suggest that specific lipid binding may be a general mechanism employed by beta-barrel membrane proteins to stabilize weakly stable regions. Overall, we find that the residues forming specific lipid binding sites on the surfaces of membrane proteins often experience strong purifying selection pressure

Predicting Three-Dimensional Structures of Transmembrane Domains of β-Barrel Membrane Proteins

β-Barrel membrane proteins are found in the outer membrane of gram-negative bacteria, mitochondria, and chloroplasts. They are important for pore formation, membrane anchoring, and enzyme activity. These proteins are also often responsible for bacterial virulence. Due to difficulties in experimental structure determination, they are sparsely represented in the protein structure databank. We have developed a computational method for predicting structures of the transmembrane (TM) domains of β-barrel membrane proteins. Based on physical principles, our method can predict structures of the TM domain of β-barrel membrane proteins of novel topology, including those from eukaryotic mitochondria. Our method is based on a model of physical interactions, a discrete conformational state space, an empirical potential function, as well as a model to account for interstrand loop entropy. We are able to construct three-dimensional atomic structure of the TM domains from sequences for a set of 23 nonhomologous proteins (resolution 1.8–3.0 Å). The median rmsd of TM domains containing 75–222 residues between predicted and measured structures is 3.9 Å for main chain atoms. In addition, stability determinants and protein–protein interaction sites can be predicted. Such predictions on eukaryotic mitochondria outer membrane protein Tom40 and VDAC are confirmed by independent mutagenesis and chemical cross-linking studies. These results suggest that our model captures key components of the organization principles of β-barrel membrane protein assembly

Outer Membrane Protein Folding and Topology from a Computational Transfer Free Energy Scale

Knowledge of the transfer free energy of amino acids from aqueous solution to a lipid bilayer is essential for understanding membrane protein folding and for predicting membrane protein structure. Here we report a computational approach that can calculate the folding free energy of the transmembrane region of outer membrane β-barrel proteins (OMPs) by combining an empirical energy function with a reduced discrete state space model. We quantitatively analyzed the transfer free energies of 20 amino acid residues at the center of the lipid bilayer of OmpLA. Our results are in excellent agreement with the experimentally derived hydrophobicity scales. We further exhaustively calculated the transfer free energies of 20 amino acids at all positions in the TM region of OmpLA. We found that the asymmetry of the Gram-negative bacterial outer membrane as well as the TM residues of an OMP determine its functional fold in vivo. Our results suggest that the folding process of an OMP is driven by the lipid-facing residues in its hydrophobic core, and its NC-IN topology is determined by the differential stabilities of OMPs in the asymmetrical outer membrane. The folding free energy is further reduced by lipid A and assisted by general depth-dependent cooperativities that exist between polar and ionizable residues. Moreover, context-dependency of transfer free energies at specific positions in OmpLA predict regions important for protein function as well as structural anomalies. Our computational approach is fast, efficient and applicable to any OMP

The degree of alignment by an index ϱ.

<p>It is calculated by drawing a vertical line in the middle of each stripe and summing the number of cells that lie between the bristles and the line. This is then normalized with the total number of bristles in the stripe. Here, a) is an example of good alignment with ϱ = 0.5 and b) is an example of bad alignment with ϱ = 2.0</p

Algorithm 1. UpdateCellPattern (<b><i>V</i></b>(<i>t</i>), Δ (<i>t</i>), Δ<i>η</i>(<i>t</i>), <i>σ</i>, <i>k</i>).

<p>Algorithm 1. UpdateCellPattern (<b><i>V</i></b>(<i>t</i>), Δ (<i>t</i>), Δ<i>η</i>(<i>t</i>), <i>σ</i>, <i>k</i>).</p

High-resolution structure prediction of β-barrel membrane proteins

β-Barrel membrane proteins (βMPs) play important roles, but knowledge of their structures is limited. We have developed a method to predict their 3D structures. We predict strand registers and construct transmembrane (TM) domains of βMPs accurately, including proteins for which no prediction has been attempted before. Our method also accurately predicts structures from protein families with a limited number of sequences and proteins with novel folds. An average main-chain rmsd of 3.48 Å is achieved between predicted and experimentally resolved structures of TM domains, which is a significant improvement (>3 Å) over a recent study. For βMPs with NMR structures, the deviation between predictions and experimentally solved structures is similar to the difference among the NMR structures, indicating excellent prediction accuracy. Moreover, we can now accurately model the extended β-barrels and loops in non-TM domains, increasing the overall coverage of structure prediction by >30%. Our method is general and can be applied to genome-wide structural prediction of βMPs

Bristle Plotting Puzzle.

<p>Simulation results of bristle pattern formation using different models. a) The pattern of gene expression used for the stripe models. Green stripes have almost equal expression of <i>Dl</i> and <i>N</i> genes but <i>ac</i> is not expressed. Blue stripes have high expression of <i>ac</i> and <i>Dl</i> genes but low expression of <i>N</i> gene. Red stripes have high expression of <i>ac</i> and <i>N</i> genes but low expression of <i>Dl</i> gene. Bristles only form in the blue stripes. b) Lateral inhibition with stripes does not ensure equal spacing or good alignment. c) Inhibition field with out stripes ensures proper spacing but does not produce a good alignment. d) Inhibition field with stripes produces equal spacing as well as good alignment.</p

Model of tissue development starting from a single cell.

<p>(a) A single cell and its plane of first division; (b) Two daughter cells after the first division, each is slight deformed from its shape in (a); (c) The formation of four cells after two cell divisions.</p

Model of feedback circuits for tissue size control.

<p>a) Division types of stem cells and progenitor cells. Red sphere labeled with (S) indicates stem cells, blue hexagon (P) indicates progenitor cells, and white diamond (D) indicates differentiated cell. The same color code is used for illustration of resulting tissues. b) Feedback controls of stem cell model. Blue arrows indicate self-renewal or proliferation divisions. Black arrows indicate symmetric differentiation divisions. Red arrows indicate asymmetric divisions. Flat-head arrows extending from differentiated cell with corresponding colors indicate inhibitions to respective type of divisions.</p