Computational and Experimental Investigations of Folding Principles and Design of Outer Membrane Proteins

Outer membrane proteins are found in the outer membrane of Gram-negative bacteria, mitochondria and chloroplasts, and serve essential functions for cell viability. They are also important therapeutic targets of developing drugs, and are the focuses of development of single-molecule sensors. Despite their importance, our understanding of outer membrane protein folding as well as outer membrane protein design have lagged far behind compared to soluble proteins due to the difficulties in studying them in their natural form. Computational methods can compliment experimental methods to expand our knowledge in outer membrane proteins. The main goal of this study is developing computational methods to gain a fundamental understanding of protein structure and function, and in turn using this knowledge to design outer membrane proteins. We first developed a computational approach to assess the folding free energy of outer membrane proteins by combining an empirical energy function with a reduced discrete state space. With this approach, we calculated depth-dependent transfer free energy scales of 20 amino acids, determined the native topology of proteins in the outer membrane, and assessed the effect of asymmetric bacterial outer membranes on the thermodynamic stability of proteins. We then developed a computation-guided strategy to design a novel outer membrane protein. A set of sequence motifs that may be important for the outer membrane protein organization was identified from combinatorial analysis. Strands that are involved in oligomerization have few such motifs, and were replaced with those that are enriched with motifs. Experimental validation proved the success of our design as the resulting protein OmpGF folded into predominant β-sheet structures, and formed a monomeric ion-conducting pore. We also discovered a general mechanism that drives oligomerization of outer membrane proteins. A sequence motif that is specific to trimeric porin family was identified, and was found to form inter-molecular interactions that are conserved in this family. Experimental examination of OmpF and PorB proteins proved that these inter-molecular interactions are essential for trimer formation. Overall, findings from this study provided additional understanding of folding principles of outer membrane proteins, and paved the road for designing novel outer membrane proteins

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

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

IL18R1-Related Molecules as Biomarkers for Asthma Severity and Prognostic Markers for Idiopathic Pulmonary Fibrosis

To determine the role of inflammation-related proteins in predicting asthma severity and outcome, 92 inflammation-related proteins were measured in the asthmatic serum using Olink analysis. Different bioinformatics algorithms were developed to cross analyze with the single-cell or transcriptome data sets from the Gene Expression Omnibus database to explore the role of IL18R1 and related genes in asthma and idiopathic pulmonary fibrosis (IPF). Olink identified 52 differentially expressed proteins in asthma. They were strongly linked to the cytokine–cytokine receptor interaction, TNF, and NF-κB signaling pathway. Seven proteins were found in both single-cell RNA and Olink analyses. Among them, IL18R1 was predominantly expressed in mast cells, and the results suggested enhanced communication between mast cells and CD 8+ T cells. IL18R1 was upregulated in serum and induced sputum and bronchoalveolar lavage fluid of patients with uncontrolled or severe asthma. IL18R1 was positively correlated with TNFSF1 and OSM and S100A12. The diagnostic efficacy of these serum IL18R1-related molecules for asthma ranged from 0.839 to 0.921. Moreover, high levels of IL18R1, TNFSF1, OSM, and S100A12 were significantly associated with shorter survival times and worse lung function. IL18R1-related molecules may serve as biomarkers for monitoring uncontrolled or severe asthma and as prognostic markers for IPF