Elucidation of the molecular interactions that enable stable assembly and structural diversity in multicomponent immune receptors.

Multicomponent immune receptors are essential complexes in which distinct ligand-recognition and signaling subunits are held together by interactions between acidic and basic residues of their transmembrane helices. A 2:1 acidic-to-basic motif in the transmembrane domains of the subunits is necessary and sufficient to assemble these receptor complexes. Here, we study a prototype for these receptors, a DAP12-NKG2C 2:1 heterotrimeric complex, in which the two DAP12 subunits each contribute a single transmembrane Asp residue, and the NKG2C subunit contributes a Lys to form the complex. DAP12 can also associate with 20 other subunits using a similar motif. Here, we use molecular-dynamics simulations to understand the basis for the high affinity and diversity of interactions in this group of receptors. Simulations of the transmembrane helices with differing protonation states of the Asp-Asp-Lys triad identified a structurally stable interaction in which a singly-protonated Asp-Asp pair forms a hydrogen-bonded carboxyl-carboxylate clamp that clasps onto a charged Lys side chain. This polar motif was also supported by density functional theory and a Protein Data Bank-wide search. In contrast, the helices are dynamic at sites distal to the stable carboxyl-carboxylate clamp motif. Such a locally stable but globally dynamic structure is well suited to accommodate the sequence and structural variations in the transmembrane helices of multicomponent receptors, which mix and match subunits to create combinatorial functional diversity from a limited number of subunits. It also supports a signaling mechanism based on multisubunit clustering rather than propagation of rigid conformational changes through the membrane.

Elucidation of proteostasis defects caused by osteogenesis imperfecta mutations in the collagen-α2(I) C-propeptide domain.

Intracellular collagen assembly begins with the oxidative folding of ∼30-kDa C-terminal propeptide (C-Pro) domains. Folded C-Pro domains then template the formation of triple helices between appropriate partner strands. Numerous C-Pro missense variants that disrupt or delay triple-helix formation are known to cause disease, but our understanding of the specific proteostasis defects introduced by these variants remains immature. Moreover, it is unclear whether or not recognition and quality control of misfolded C-Pro domains is mediated by recognizing stalled assembly of triple-helical domains or by direct engagement of the C-Pro itself. Here, we integrate biochemical and cellular approaches to illuminate the proteostasis defects associated with osteogenesis imperfecta-causing mutations within the collagen-α2(I) C-Pro domain. We first show that "C-Pro-only" constructs recapitulate key aspects of the behavior of full-length Colα2(I) constructs. Of the variants studied, perhaps the most severe assembly defects are associated with C1163R C-Proα2(I), which is incapable of forming stable trimers and is retained within cells. We find that the presence or absence of an unassembled triple-helical domain is not the key feature driving cellular retention versus secretion. Rather, the proteostasis network directly engages the misfolded C-Pro domain itself to prevent secretion and initiate clearance. Using MS-based proteomics, we elucidate how the endoplasmic reticulum (ER) proteostasis network differentially engages misfolded C1163R C-Proα2(I) and targets it for ER-associated degradation. These results provide insights into collagen folding and quality control with the potential to inform the design of proteostasis network-targeted strategies for managing collagenopathies.

TMDIM: an improved algorithm for the structure prediction of transmembrane domains of bitopic dimers.

[Formula: see text]-Helical transmembrane proteins are the most important drug targets in rational drug development. However, solving the experimental structures of these proteins remains difficult, therefore computational methods to accurately and efficiently predict the structures are in great demand. We present an improved structure prediction method TMDIM based on Park et al. (Proteins 57:577-585, 2004) for predicting bitopic transmembrane protein dimers. Three major algorithmic improvements are introduction of the packing type classification, the multiple-condition decoy filtering, and the cluster-based candidate selection. In a test of predicting nine known bitopic dimers, approximately 78% of our predictions achieved a successful fit (RMSD <2.0 Å) and 78% of the cases are better predicted than the two other methods compared. Our method provides an alternative for modeling TM bitopic dimers of unknown structures for further computational studies. TMDIM is freely available on the web at https://cbbio.cis.umac.mo/TMDIM . Website is implemented in PHP, MySQL and Apache, with all major browsers supported.

Coarse Grained Molecular Dynamic Simulations for the Study of TNF Receptor Family Members' Transmembrane Organization.

The Tumor Necrosis Factor (TNF) and the TNF receptor (TNFR) superfamilies are composed of 19 ligands and 30 receptors, respectively. The oligomeric properties of ligands, both membrane bound and soluble, has been studied most. However, less is known about the oligomeric properties of TNFRs. Earlier reports identified the extracellular, membrane-distal, cysteine-rich domain as a pre-ligand assembly domain which stabilizes receptor dimers and/or trimers in the absence of ligand. Nevertheless, recent reports based on structural nuclear magnetic resonance (NMR) highlight the intrinsic role of the transmembrane domains to form dimers (p75NTR), trimers (Fas), or dimers of trimers (DR5). Thus, understanding the structural basis of transmembrane oligomerization may shed light on the mechanism for signal transduction and the impact of disease-associated mutations in this region. To this end, here we used an in silico coarse grained molecular dynamics approach with Martini force field to study TNFR transmembrane homotypic interactions. We have first validated this approach studying the three TNFR described by NMR (p75NTR, Fas, and DR5). We have simulated membrane patches composed of 36 helices of the same receptor equidistantly distributed in order to get unbiassed information on spontaneous proteins assemblies. Good agreement was found in the specific residues involved in homotypic interactions and we were able to observe dimers, trimers, and higher-order oligomers corresponding to those reported in NMR experiments. We have, applied this approach to study the assembly of disease-related mutations being able to assess their impact on oligomerization stability. In conclusion, our results showed the usefulness of coarse grained simulations with Martini force field to study in an unbiased manner higher order transmembrane oligomerization.

Transmembrane Helix Assembly by Max-Min Ant System Algorithm.

Because of the rapid progress in biochemical and structural studies of membrane proteins, considerable attention has been given on developing efficient computational methods for solving low-to-medium resolution structures using sparse structural data. In this study, we demonstrate a novel algorithm, max-min ant system (MMAS), designed to find an assembly of α-helical transmembrane proteins using a rigid helix arrangement guided by distance constraints. The new algorithm generates a large variety with finite number of orientations of transmembrane helix bundle and finds the solution that is matched with the provided distance constraints based on the behavior of ants to search for the shortest possible path between their nest and the food source. To demonstrate the efficiency of the novel search algorithm, MMAS is applied to determine the transmembrane packing of KcsA and MscL ion channels from a limited distance information extracted from the crystal structures, and the packing of KvAP voltage sensor domain using a set of 10 experimentally determined constraints, and the results are compared with those of two popular used stochastic methods, simulated annealing Monte Carlo method and genetic algorithm.