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Title: High-Resolution Modeling of Transmembrane Helical Protein Structures from Distant Homologues}
Article:
Abstract:
Eukaryotic transmembrane helical( TMH) proteins perform a wide diversity of critical cellular functions, but remain structurally largely uncharacterized and their high-resolution structure prediction is currently hindered by the lack of close structural homologues. To address this problem, we present a novel and generic method for accurately modeling large TMH protein structures from distant homologues exhibiting distinct loop and TMH conformations. Models of the adenosine A2AR and chemokine CXCR4 receptors were first ranked in GPCR-DOCK blind prediction contests in the receptor structure accuracy category. In a benchmark of 50 TMH protein homolog pairs of diverse topology( from 5 to 12 TMHs), size( from 183 to 420 residues) and sequence identity( from 15% to 70%), the method improves most starting templates, and achieves near-atomic accuracy prediction of membrane-embedded regions. Unlike starting templates, the models are of suitable quality for computer-based protein engineering: redesigned models and redesigned X-ray structures exhibit very similar native interactions. The method should prove useful for the atom-level modeling and design of a large fraction of structurally uncharacterized TMH proteins from a wide range of structural homologues.
Introduction:
Membrane proteins perform a wide diversity of critical functions in living cells but are also involved in serious diseases and represent more than 60% of current drug targets[1],[2]. Despite recent tremendous progress in membrane protein expression, biochemistry and X-ray crystallography, eukaryotic membrane protein structures remain difficult to characterize experimentally[3]. The lack of high-resolution structures hinders the design of more effective therapeutics and of receptors with novel function for systems/synthetic biology applications which rely on atomic-resolution information[4]. The high-resolution prediction of membrane protein structures is therefore an important alternative approach but remains a major challenge in absence of close structural homologues[5]. Although numerous methods have been developed to model G protein-coupled receptor( GPCR) structures[6]–[9], much fewer techniques have been developed and applied to the entire class of alpha helical membrane proteins. Current state-of-the-art de novo structure prediction techniques of alpha helical membrane proteins can generate low-resolution models with native-like topologies[10]–[12] and, despite some insightful applications[13], most current comparative modeling methods do not significantly improve starting templates[14],[15]. The main structural differences between distant homolog transmembrane alpha-helical( TMH) proteins are found in loop regions and in helical conformations shaping TMH core structures and ligand/effector binding sites. While the problem of rebuilding protein loops has been extensively studied[16],[17], the accurate modeling of membrane protein structures from distant homologues diverging in both loop and TMH core regions is a remaining unsolved challenge[5]. The origins of TMH conformational diversity are multiple and range from the presence of localized sequence-specific distortions( e. g. Proline-induced kinks) to local bends and global tilts stabilized by specific tertiary contacts[18]–[23]. Many of these features cannot be accurately predicted from sequence information alone and requires the explicit modeling of atom-level physical interactions stabilizing these structures[18]–[20],[24]. The large size of TM proteins and associated number of degrees of freedom combined with the ruggedness of the all-atom energy landscape make their prediction at atomic resolution computationally intractable using an exhaustive conformational search in torsional angle space. To address this problem, we have developed a general modeling strategy based on efficient sampling techniques of alternative TMH structures to reconstruct both TMH core and loop regions from distant structural homologues. The method was stringently validated in two blind predictions where the generated models were top-ranked[14],[15] and in a large benchmark dominated by pairs of membrane protein distant homologues where starting templates were almost all significantly improved. Computational design calculations suggest that the models should be of suitable accuracy for rational protein engineering applications.
Discussion:
The prediction of membrane protein structures represents an important approach in light of their difficult experimental determination but remains a challenging problem. Current prediction techniques are limited to the generation of low-resolution models from sequence information alone[10]–[12] or of near-atomic resolution models from close structural homologues[5]. However, close structural homologs are currently not available for a large fraction of membrane proteins and often only one distant structural homolog hit can be found for these proteins( Fig. S1), making their structure prediction at high-resolution a real challenge. To address this problem, we developed a generic method that can efficiently reconstruct TMH and loop regions from single distant or closer homologues. The method was stringently validated in two blind predictions and in a large benchmark consisting of pairs of membrane protein homologues with wide diversity in length, topology and sequence identity. Submitted models were first-ranked in the blind predictions[14],[15] for the accuracy of the full-length receptor structure and the method was able to improve most starting templates in the benchmark to reach near atomic accuracy prediction in the TMH regions( Fig. 2, Fig. 5, Table S1). In local regions of the TMH structures where distortions differed between template and target, the method was able to significantly improve the starting template and to predict distorted helical structures with an average Cα RMSD of only 0. 8 Å to the native structures( Fig. 3, Table S2). As a stringent proof of the model's accuracy, complete redesign of their TMH regions recapitulated similar native interactions than the redesign of the same regions in the X-ray structures. In contrast, the methods MEDELLER[30], a comparative modeling technique developed for membrane proteins, the widely used homology modeling software MODELLER[31], and I-TASSER, a web-server for protein structure prediction[32],[33], did not significantly improve homologous templates( Fig. 5). The improvements observed for most distant or closer homologues with diverse length and topology indicate that the method provides a general and efficient approach for reconstructing the structure of a large diversity of transmembrane helical folds. Starting templates with sequence identity to the target as low as 15% were significantly improved, suggesting that the technique should be effective at generating atomic-level models more accurate than available templates for many structurally uncharacterized TMH proteins( Fig. S1). Because the conformational heterogeneity and poor stability of eukaryotic membrane proteins in detergents is a major bottleneck to their crystallization, their stabilization has been a very intensive area of research but has only been achieved with limited success using labor-intensive cycles of random or scanning mutagenesis[36]–[38]. According to our design calculations, our technique can predict stabilizing physical interactions in structurally uncharacterized receptors and should therefore be particularly useful for predicting mutational effects on receptor's conformational stability, for engineering receptors with altered conformational energy landscape and for precisely guiding structure/function studies. Future developments will involve 1) the explicit modeling of water molecules to improve the prediction of TMH core regions, and 2) the simultaneous modeling of loop and bound ligand conformations to improve the prediction of loop structures and allow accurate prediction of receptor-ligand bound conformations and interactions for ligand docking and virtual screening applications. In conclusion, the method may prove useful for the atom-level modeling and design of structurally uncharacterized classes of alpha-helical membrane receptors which are particularly challenging to study experimentally and for which close homologues are currently often not available.
Membrane proteins perform crucial cellular functions and can be involved in serious diseases but remain difficult to study experimentally . Hence , high-resolution membrane protein structures are scarce which hinders the design of selective therapeutics and of receptors with novel function for systems/synthetic biology applications . The computational modeling of membrane protein structures represents an important alternative approach but , to achieve high accuracy , usually requires structural information from closely related proteins currently unavailable for most membrane proteins . To address this limitation , we have developed a novel method to predict membrane protein structures from the structures of non-closely related proteins that differ both in loop and transmembrane regions . Using this approach , we show that a large diversity of membrane proteins can be reconstructed at a level of accuracy suitable for computer-based protein engineering applications . Because requiring information from a single distant homolog only , we expect that around 60% of human membrane proteins can reliably be modeled using our approach , thereby allowing precise structure/function studies on a large fraction of structurally uncharacterized membrane proteins .