Large-scale determination of previously unsolved protein structures using evolutionary information

Combining Evolutionary Covariance and NMR Data for Protein Structure Determination

Accurate protein structure determination by solution-state NMR is challenging for proteins greater than about 20kDa, for which extensive perdeuteration is generally required, providing experimental data that are incomplete (sparse) and ambiguous. However, the massive increase in evolutionary sequence information coupled with advances in methods for sequence covariance analysis can provide reliable residue-residue contact information for a protein from sequence data alone. These "evolutionary couplings (ECs)" can be combined with sparse NMR data to determine accurate 3D protein structures. This hybrid "EC-NMR" method has been developed using NMR data for several soluble proteins and validated by comparison with corresponding reference structures determined by X-ray crystallography and/or conventional NMR methods. For small proteins, only backbone resonance assignments are utilized, while for larger proteins both backbone and some sidechain methyl resonance assignments are generally required. ECs can be combined with sparse NMR data obtained on deuterated, selectively protonated protein samples to provide structures that are more accurate and complete than those obtained using such sparse NMR data alone. EC-NMR also has significant potential for analysis of protein structures from solid-state NMR data and for studies of integral membrane proteins. The requirement that ECs are consistent with NMR data recorded on a specific member of a protein family, under specific conditions, also allows identification of ECs that reflect alternative allosteric or excited states of the protein structure.

Structure of the type VI secretion system TssK-TssF-TssG baseplate subcomplex revealed by cryo-electron microscopy

Type VI secretion systems (T6SSs) translocate effectors into target cells and are made of a contractile sheath and a tube docked onto a multi-protein transmembrane complex via a baseplate. Although some information is available about the mechanisms of tail contraction leading to effector delivery, the detailed architecture and function of the baseplate remain unknown. Here, we report the 3.7 Å resolution cryo-electron microscopy reconstruction of an enteroaggregative Escherichia coli baseplate subcomplex assembled from TssK, TssF and TssG. The structure reveals two TssK trimers interact with a locally pseudo-3-fold symmetrical complex comprising two copies of TssF and one copy of TssG. TssF and TssG are structurally related to each other and to components of the phage T4 baseplate and of the type IV secretion system, strengthening the evolutionary relationships among these macromolecular machines. These results, together with bacterial two-hybrid assays, provide a structural framework to understand the T6SS baseplate architecture.

Structure-Function Analysis of the Bifunctional CcsBA Heme Exporter and Cytochrome c Synthetase

The movement or trafficking of heme is critical for cellular functions (e.g., oxygen transport and energy production); however, intracellular heme is tightly regulated due to its inherent cytotoxicity. These factors, combined with the transient nature of transport, have resulted in a lack of direct knowledge on the mechanisms of heme binding and trafficking. Here, we used the cytochrome c biogenesis system II pathway as a model to study heme trafficking. System II is composed of two integral membrane proteins (CcsBA) which function to transport heme across the membrane and stereospecifically position it for covalent attachment to apocytochrome c. We mapped two heme binding domains in CcsBA and suggest a path for heme trafficking. These data, in combination with metagenomic coevolution data, are used to determine a structural model of CcsBA, leading to increased understanding of the mechanisms for heme transport and the cytochrome c synthetase function of CcsBA.

Although intracellular heme trafficking must occur for heme protein assembly, only a few heme transporters have been unequivocally discovered and nothing is known about their structure or mechanisms. Cytochrome c biogenesis in prokaryotes requires the transport of heme from inside to outside for stereospecific attachment to cytochrome c via two thioether bonds (at CXXCH). The CcsBA integral membrane protein was shown to transport and attach heme (and thus is a cytochrome c synthetase), but the structure and mechanisms underlying these two activities are poorly understood. We employed a new cysteine/heme crosslinking tool that traps endogenous heme in heme binding sites. We combined these data with a comprehensive imidazole correction approach (for heme ligand interrogation) to map heme binding sites. Results illuminate the process of heme transfer through the membrane to an external binding site (called the WWD domain). Using meta-genomic data (GREMLIN) and Rosetta modeling programs, a structural model of the transmembrane (TM) regions in CcsBA were determined. The heme mapping data were then incorporated to model the TM heme binding site (with TM-His1 and TM-His2 as ligands) and the external heme binding WWD domain (with P-His1 and P-His2 as ligands). Other periplasmic structure/function studies facilitated modeling of the full CcsBA protein as a framework for understanding the mechanisms. Mechanisms are proposed for heme transport from TM-His to WWD/P-His and subsequent stereospecific attachment of heme. A ligand exchange of the P-His1 for histidine of CXXCH at the synthetase active site is suggested.

Statistical investigations of protein residue direct couplings

Protein Direct Coupling Analysis (DCA), which predicts residue-residue contacts based on covarying positions within a multiple sequence alignment, has been remarkably effective. This suggests that there is more to learn from sequence correlations than is generally assumed, and calls for deeper investigations into DCA and perhaps into other types of correlations. Here we describe an approach that enables such investigations by measuring, as an estimated p-value, the statistical significance of the association between residue-residue covariance and structural interactions, either internal or homodimeric. Its application to thirty protein superfamilies confirms that direct coupling (DC) scores correlate with 3D pairwise contacts with very high significance. This method also permits quantitative assessment of the relative performance of alternative DCA methods, and of the degree to which they detect direct versus indirect couplings. We illustrate its use to assess, for a given protein, the biological relevance of alternative conformational states, to investigate the possible mechanistic implications of differences between these states, and to characterize subtle aspects of direct couplings. Our analysis indicates that direct pairwise correlations may be largely distinct from correlated patterns associated with functional specialization, and that the joint analysis of both types of correlations can yield greater power. Data, programs, and source code are freely available at http://evaldca.igs.umaryland.edu.

Synthetic protein alignments by CCMgen quantify noise in residue-residue contact prediction

Compensatory mutations between protein residues in physical contact can manifest themselves as statistical couplings between the corresponding columns in a multiple sequence alignment (MSA) of the protein family. Conversely, large coupling coefficients predict residue contacts. Methods for de-novo protein structure prediction based on this approach are becoming increasingly reliable. Their main limitation is the strong systematic and statistical noise in the estimation of coupling coefficients, which has so far limited their application to very large protein families. While most research has focused on improving predictions by adding external information, little progress has been made to improve the statistical procedure at the core, because our lack of understanding of the sources of noise poses a major obstacle. First, we show theoretically that the expectation value of the coupling score assuming no coupling is proportional to the product of the square roots of the column entropies, and we propose a simple entropy bias correction (EntC) that subtracts out this expectation value. Second, we show that the average product correction (APC) includes the correction of the entropy bias, partly explaining its success. Third, we have developed CCMgen, the first method for simulating protein evolution and generating realistic synthetic MSAs with pairwise statistical residue couplings. Fourth, to learn exact statistical models that reliably reproduce observed alignment statistics, we developed CCMpredPy, an implementation of the persistent contrastive divergence (PCD) method for exact inference. Fifth, we demonstrate how CCMgen and CCMpredPy can facilitate the development of contact prediction methods by analysing the systematic noise contributions from phylogeny and entropy. Using the entropy bias correction, we can disentangle both sources of noise and find that entropy contributes roughly twice as much noise as phylogeny.

Knowledge about the three-dimensional structure of proteins is key to understanding their function and role in biological processes and diseases. The experimental structure determination techniques, such as X-ray crystallography or electron cryo-microscopy, are labour intensive, time-consuming and expensive. Therefore, complementary computational methods to predict a protein’s structure have become indispensable. Over the last years, immense progress has been made in predicting protein structures from their amino acid sequence by utilizing highly accurate predictions of spatial contacts between amino acid residues as constraints in folding simulations. However, contact prediction methods require large numbers of homologous protein sequences in order to discriminate between signal and noise. A major obstacle preventing progress on the statistical methodology is our limited understanding of the different components of noise that are known to affect the predictions. We provide two tools, CCMpredPy and CCMgen, that can be used to learn highly accurate statistical models for contact prediction and to simulate protein evolution according to the statistical constraints between positions of residues as specified by these models, respectively. We showcase their usefulness by quantifying the relative contribution of noise arising from entropy and phylogeny on the predicted contacts, which will facilitate the improvement of the statistical methodology.

Inferring interaction partners from protein sequences using mutual information

Functional protein-protein interactions are crucial in most cellular processes. They enable multi-protein complexes to assemble and to remain stable, and they allow signal transduction in various pathways. Functional interactions between proteins result in coevolution between the interacting partners, and thus in correlations between their sequences. Pairwise maximum-entropy based models have enabled successful inference of pairs of amino-acid residues that are in contact in the three-dimensional structure of multi-protein complexes, starting from the correlations in the sequence data of known interaction partners. Recently, algorithms inspired by these methods have been developed to identify which proteins are functional interaction partners among the paralogous proteins of two families, starting from sequence data alone. Here, we demonstrate that a slightly higher performance for partner identification can be reached by an approximate maximization of the mutual information between the sequence alignments of the two protein families. Our mutual information-based method also provides signatures of the existence of interactions between protein families. These results stand in contrast with structure prediction of proteins and of multi-protein complexes from sequence data, where pairwise maximum-entropy based global statistical models substantially improve performance compared to mutual information. Our findings entail that the statistical dependences allowing interaction partner prediction from sequence data are not restricted to the residue pairs that are in direct contact at the interface between the partner proteins.

Co-Evolution of Intrinsically Disordered Proteins with Folded Partners Witnessed by Evolutionary Couplings

Although improved strategies for the detection and analysis of evolutionary couplings (ECs) between protein residues already enable the prediction of protein structures and interactions, they are mostly restricted to conserved and well-folded proteins. Whereas intrinsically disordered proteins (IDPs) are central to cellular interaction networks, due to the lack of strict structural constraints, they undergo faster evolutionary changes than folded domains. This makes the reliable identification and alignment of IDP homologs difficult, which led to IDPs being omitted in most large-scale residue co-variation analyses. By preforming a dedicated analysis of phylogenetically widespread bacterial IDP–partner interactions, here we demonstrate that partner binding imposes constraints on IDP sequences that manifest in detectable interprotein ECs. These ECs were not detected for interactions mediated by short motifs, rather for those with larger IDP–partner interfaces. Most identified coupled residue pairs reside close (<10 Å) to each other on the interface, with a third of them forming multiple direct atomic contacts. EC-carrying interfaces of IDPs are enriched in negatively charged residues, and the EC residues of both IDPs and partners preferentially reside in helices. Our analysis brings hope that IDP–partner interactions difficult to study could soon be successfully dissected through residue co-variation analysis.

Crystal structure of the bacterial acetate transporter SatP reveals that it forms a hexameric channel

Acetate is found ubiquitously in the natural environment and can be used as an exogenous carbon source by bacteria, fungi, and mammalian cells. A representative member of the acetate uptake transporter (AceTr) family named SatP (also yaaH) has been preliminarily identified as a succinate-acetate/proton symporter in Escherichia coli However, the molecular mechanism of acetate uptake by SatP still remains elusive. Here, we report the crystal structure of SatP from E. coli at 2.8 Å resolution, determined with a molecular replacement approach using a previously developed predicted model algorithm, which revealed a hexameric UreI-like channel structure. Structural analysis identified six transmembrane (TM) helices surrounding the central channel pore in each protomer and three conserved hydrophobic residues, FLY, located in the middle of the TM region for pore constriction. According to single-channel conductance recordings, performed with purified SatP reconstituted into lipid bilayer, three conserved polar residues in the TM1 facing to the periplasmic side are closely associated with acetate translocation activity. These analyses provide critical insights into the mechanism of acetate translocation in bacteria and a first glimpse of a structure of an AceTr family transporter.

Deep generative models of genetic variation capture the effects of mutations

The functions of proteins and RNAs are defined by the collective interactions of many residues, and yet most statistical models of biological sequences consider sites nearly independently. Recent approaches have demonstrated benefits of including interactions to capture pairwise covariation, but leave higher-order dependencies out of reach. Here we show how it is possible to capture higher-order, context-dependent constraints in biological sequences via latent variable models with nonlinear dependencies. We found that DeepSequence ( https://github.com/debbiemarkslab/DeepSequence ), a probabilistic model for sequence families, predicted the effects of mutations across a variety of deep mutational scanning experiments substantially better than existing methods based on the same evolutionary data. The model, learned in an unsupervised manner solely on the basis of sequence information, is grounded with biologically motivated priors, reveals the latent organization of sequence families, and can be used to explore new parts of sequence space.

Widespread evolutionary crosstalk among protein domains in the context of multi-domain proteins

Domains are distinct units within proteins that typically can fold independently into recognizable three-dimensional structures to facilitate their functions. The structural and functional independence of protein domains is reflected by their apparent modularity in the context of multi-domain proteins. In this work, we examined the coupling of evolution of domain sequences co-occurring within multi-domain proteins to see if it proceeds independently, or in a coordinated manner. We used continuous information theory measures to assess the extent of correlated mutations among domains in multi-domain proteins from organisms across the tree of life. In all multi-domain architectures we examined, domains co-occurring within protein sequences had to some degree undergone concerted evolution. This finding challenges the notion of complete modularity and independence of protein domains, providing new perspective on the evolution of protein sequence and function.

A coevolution-guided model for the rotor of the bacterial flagellar motor

The Salmonella typhimurium trans-membrane FliF MS ring templates assembly of the rotary bacterial flagellar motor, which also contains a cytoplasmic C-ring. A full-frame fusion of FliF with the rotor protein FliG assembles rings in non-motile expression hosts. 3D electron microscopy reconstructions of these FliFFliG rings show three high electron-density sub-volumes. 3D-classification revealed heterogeneity of the assigned cytoplasmic volume consistent with FliG lability. We used residue coevolution to construct homodimer building blocks for ring assembly, with X-ray crystal structures from other species and injectisome analogs. The coevolution signal validates folds and, importantly, indicates strong homodimer contacts for three ring building motifs (RBMs), initially identified in injectisome structures. It also indicates that the cofolded domains of the FliG N-terminal domain (FliG_N) with embedded α-helical FliF carboxy-terminal tail homo-oligomerize. The FliG middle and C-terminal domains (FliG_MC) have a weak signal for homo-dimerization but have coevolved to conserve their stacking contact. The homodimers and their ring models fit well into the 3D reconstruction. We hypothesize that a stable FliF periplasmic hub provides a platform for FliG ring self-assembly, but the FliG_MC ring has only limited stability without the C-ring. We also present a mechanical model for torque transmission in the FliFFliG ring.

Enhancing coevolution-based contact prediction by imposing structural self-consistency of the contacts

Based on the development of new algorithms and growth of sequence databases, it has recently become possible to build robust higher-order sequence models based on sets of aligned protein sequences. Such models have proven useful in de novo structure prediction, where the sequence models are used to find pairs of residues that co-vary during evolution, and hence are likely to be in spatial proximity in the native protein. The accuracy of these algorithms, however, drop dramatically when the number of sequences in the alignment is small. We have developed a method that we termed CE-YAPP (CoEvolution-YAPP), that is based on YAPP (Yet Another Peak Processor), which has been shown to solve a similar problem in NMR spectroscopy. By simultaneously performing structure prediction and contact assignment, CE-YAPP uses structural self-consistency as a filter to remove false positive contacts. Furthermore, CE-YAPP solves another problem, namely how many contacts to choose from the ordered list of covarying amino acid pairs. We show that CE-YAPP consistently improves contact prediction from multiple sequence alignments, in particular for proteins that are difficult targets. We further show that the structures determined from CE-YAPP are also in better agreement with those determined using traditional methods in structural biology.

Comparing co-evolution methods and their application to template-free protein structure prediction

Motivation

Co-evolution methods have been used as contact predictors to identify pairs of residues that share spatial proximity. Such contact predictors have been compared in terms of the precision of their predictions, but there is no study that compares their usefulness to model generation.

Results

We compared eight different co-evolution methods for a set of ∼3500 proteins and found that metaPSICOV stage 2 produces, on average, the most precise predictions. Precision of all the methods is dependent on SCOP class, with most methods predicting contacts in all α and membrane proteins poorly. The contact predictions were then used to assist in de novo model generation. We found that it was not the method with the highest average precision, but rather metaPSICOV stage 1 predictions that consistently led to the best models being produced. Our modelling results show a correlation between the proportion of predicted long range contacts that are satisfied on a model and its quality. We used this proportion to effectively classify models as correct/incorrect; discarding decoys classified as incorrect led to an enrichment in the proportion of good decoys in our final ensemble by a factor of seven. For 17 out of the 18 cases where correct answers were generated, the best models were not discarded by this approach. We were also able to identify eight cases where no correct decoy had been generated.

Availability and Implementation

Data is available for download from: http://opig.stats.ox.ac.uk/resources.

Contact

saulo.deoliveira@dtc.ox.ac.uk

Supplimentary Information

Supplementary data are available at Bioinformatics online.

Structure of the core of the type III secretion system export apparatus

Export of proteins through type III secretion systems is critical for motility and virulence of many major bacterial pathogens. Three putative integral membrane proteins (FliP, FliQ, FliR) are suggested to form the core of an export gate in the inner membrane, but their structure, assembly and location within the final nanomachine remain unclear. Here, we present the cryoelectron microscopy structure of the Salmonella Typhimurium FliP-FliQ-FliR complex at 4.2 Å. None of the subunits adopt canonical integral membrane protein topologies, and common helix-turn-helix structural elements allow them to form a helical assembly with 5:4:1 stoichiometry. Fitting of the structure into reconstructions of intact secretion systems, combined with cross-linking, localize the export gate as a core component of the periplasmic portion of the machinery. This study thereby identifies the export gate as a key element of the secretion channel and implies that it primes the helical architecture of the components assembling downstream.

Enzyme structures of the bacterial peptidoglycan and wall teichoic acid biogenesis pathways

The bacterial cell wall is a complex polymeric structure with essential roles in defence, survival and pathogenesis. Common to both Gram-positive and Gram-negative bacteria is the mesh-like peptidoglycan sacculus that surrounds the outer leaflet of the cytoplasmic membrane. Recent crystallographic studies of enzymes that comprise the peptidoglycan biosynthetic pathway have led to significant new understanding of all stages. These include initial multi-step cytosolic formation of sugar-pentapeptide precursors, transfer of the precursors to activated polyprenyl lipids at the membrane inner leaflet and flippase mediated relocalization of the resulting lipid II precursors to the outer leaflet where glycopolymerization and subsequent peptide crosslinking are finalized. Additional, species-specific enzymes allow customized peptidoglycan modifications and biosynthetic regulation that are important to bacterial virulence and survival. These studies have reinforced the unique and specific catalytic mechanisms at play in cell wall biogenesis and expanded the atomic foundation to develop novel, structure guided, antibacterial agents.

ConKit: a python interface to contact predictions

Summary

Recent advances in protein residue contact prediction algorithms have led to the emergence of many new methods and a variety of file formats. We present ConKit, an open source, modular and extensible Python interface which allows facile conversion between formats and provides an interface to analyses of sequence alignments and sets of contact predictions.

Availability and Implementation

ConKit is available via the Python Package Index. The documentation can be found at http://www.conkit.org. ConKit is licensed under the BSD 3-Clause.

Supplementary information

Supplementary data are available at Bioinformatics online.

Large-scale structure prediction by improved contact predictions and model quality assessment

Motivation

Accurate contact predictions can be used for predicting the structure of proteins. Until recently these methods were limited to very big protein families, decreasing their utility. However, recent progress by combining direct coupling analysis with machine learning methods has made it possible to predict accurate contact maps for smaller families. To what extent these predictions can be used to produce accurate models of the families is not known.

Results

We present the PconsFold2 pipeline that uses contact predictions from PconsC3, the CONFOLD folding algorithm and model quality estimations to predict the structure of a protein. We show that the model quality estimation significantly increases the number of models that reliably can be identified. Finally, we apply PconsFold2 to 6379 Pfam families of unknown structure and find that PconsFold2 can, with an estimated 90% specificity, predict the structure of up to 558 Pfam families of unknown structure. Out of these, 415 have not been reported before.

Availability and Implementation

Datasets as well as models of all the 558 Pfam families are available at http://c3.pcons.net/. All programs used here are freely available.

Integrated Structural Biology for α-Helical Membrane Protein Structure Determination

While great progress has been made, only 10% of the nearly 1,000 integral, α-helical, multi-span membrane protein families are represented by at least one experimentally determined structure in the PDB. Previously, we developed the algorithm BCL::MP-Fold, which samples the large conformational space of membrane proteins de novo by assembling predicted secondary structure elements guided by knowledge-based potentials. Here, we present a case study of rhodopsin fold determination by integrating sparse and/or low-resolution restraints from multiple experimental techniques including electron microscopy, electron paramagnetic resonance spectroscopy, and nuclear magnetic resonance spectroscopy. Simultaneous incorporation of orthogonal experimental restraints not only significantly improved the sampling accuracy but also allowed identification of the correct fold, which is demonstrated by a protein size-normalized transmembrane root-mean-square deviation as low as 1.2 Å. The protocol developed in this case study can be used for the determination of unknown membrane protein folds when limited experimental restraints are available.

Sequential search leads to faster, more efficient fragment-based de novo protein structure prediction

Motivation

Most current de novo structure prediction methods randomly sample protein conformations and thus require large amounts of computational resource. Here, we consider a sequential sampling strategy, building on ideas from recent experimental work which shows that many proteins fold cotranslationally.

Results

We have investigated whether a pseudo-greedy search approach, which begins sequentially from one of the termini, can improve the performance and accuracy of de novo protein structure prediction. We observed that our sequential approach converges when fewer than 20 000 decoys have been produced, fewer than commonly expected. Using our software, SAINT2, we also compared the run time and quality of models produced in a sequential fashion against a standard, non-sequential approach. Sequential prediction produces an individual decoy 1.5-2.5 times faster than non-sequential prediction. When considering the quality of the best model, sequential prediction led to a better model being produced for 31 out of 41 soluble protein validation cases and for 18 out of 24 transmembrane protein cases. Correct models (TM-Score > 0.5) were produced for 29 of these cases by the sequential mode and for only 22 by the non-sequential mode. Our comparison reveals that a sequential search strategy can be used to drastically reduce computational time of de novo protein structure prediction and improve accuracy.

Availability and implementation

Data are available for download from: http://opig.stats.ox.ac.uk/resources. SAINT2 is available for download from: https://github.com/sauloho/SAINT2.

Contact

saulo.deoliveira@dtc.ox.ac.uk.

Supplementary information

Supplementary data are available at Bioinformatics online.

Structure of the peptidoglycan polymerase RodA resolved by evolutionary coupling analysis

The Shape, Elongation, Division, and Sporulation (“SEDS”) proteins are a large family of ubiquitous and essential transmembrane enzymes with critical roles in bacterial cell wall biology. The exact function of SEDS proteins was long enigmatic, but recent work^1–3 has revealed that the prototypical SEDS family member RodA is a peptidoglycan polymerase – a role previously attributed exclusively to members of the penicillin binding protein family⁴. This discovery has made RodA and other SEDS proteins promising targets for the development of next-generation antibiotics. However, little is known regarding the molecular basis for SEDS activity, and no structural data are available for RodA or any homolog thereof. Here, we report the crystal structure of Thermus thermophilus RodA at a resolution of 2.9 Å, determined using evolutionary covariance-based fold prediction to enable molecular replacement. The structure reveals a novel ten-pass transmembrane fold with large extracellular loops, one of which is partially disordered. The protein contains a highly conserved cavity in the transmembrane domain, reminiscent of ligand binding sites in transmembrane receptors. Mutagenesis experiments in Bacillus subtilis and Escherichia coli show that perturbation of this cavity abolishes RodA function both in vitro and in vivo, indicating it is catalytically essential. These results provide a framework for understanding bacterial cell wall synthesis and SEDS protein function.

Succinate-acetate permease from Citrobacter koseri is an anion channel that unidirectionally translocates acetate

Acetate is an important metabolite in metabolism and cell signaling. Succinate-Acetate Permease (SatP) superfamily proteins are known to be responsible for acetate transport across membranes, but the nature of this transport remains unknown. Here, we show that the SatP homolog from Citrobacter koseri (SatP_Ck) is an anion channel that can unidirectionally translocate acetate at rates of the order of ~10⁷ ions/s. Crystal structures of SatP_Ck in complex with multiple acetates at 1.8 Å reveal that the acetate pathway consists of four acetate-binding sites aligned in a single file that are interrupted by three hydrophobic constrictions. The bound acetates at the four sites are each orientated differently. The acetate at the cytoplasmic vestibule is partially dehydrated, whereas those in the main pore body are fully dehydrated. Aromatic residues within the substrate pathway may coordinate translocation of acetates via anion-π interactions. SatP_Ck reveals a new type of selective anion channel and provides a structural and functional template for understanding organic anion transport.

Crystal structure of an intramembranal phosphatase central to bacterial cell-wall peptidoglycan biosynthesis and lipid recycling

Undecaprenyl pyrophosphate phosphatase (UppP) is an integral membrane protein that recycles the lipid carrier essential to the ongoing biosynthesis of the bacterial cell wall. Individual building blocks of peptidoglycan are assembled in the cytoplasm on undecaprenyl phosphate (C55-P) before being flipped to the periplasmic face, where they are polymerized and transferred to the existing cell wall sacculus, resulting in the side product undecaprenyl pyrophosphate (C55-PP). Interruption of UppP’s regeneration of C55-P from C55-PP leads to the buildup of cell wall intermediates and cell lysis. We present the crystal structure of UppP from Escherichia coli at 2.0 Å resolution, which reveals the mechanistic basis for intramembranal phosphatase action and substrate specificity using an inverted topology repeat. In addition, the observation of key structural motifs common to a variety of cross membrane transporters hints at a potential flippase function in the specific relocalization of the C55-P product back to the cytosolic space.

Undecaprenyl pyrophosphate phosphatase (UppP) recycles the lipid carrier essential for bacterial cell wall synthesis. Here authors present the crystal structure of UppP from E. coli at 2.0 Å resolution, which sheds light on its phosphatase mechanism and indicates a potential flippase role for UppP.

Crystal structure of undecaprenyl-pyrophosphate phosphatase and its role in peptidoglycan biosynthesis

As a protective envelope surrounding the bacterial cell, the peptidoglycan sacculus is a site of vulnerability and an antibiotic target. Peptidoglycan components, assembled in the cytoplasm, are shuttled across the membrane in a cycle that uses undecaprenyl-phosphate. A product of peptidoglycan synthesis, undecaprenyl-pyrophosphate, is converted to undecaprenyl-phosphate for reuse in the cycle by the membrane integral pyrophosphatase, BacA. To understand how BacA functions, we determine its crystal structure at 2.6 Å resolution. The enzyme is open to the periplasm and to the periplasmic leaflet via a pocket that extends into the membrane. Conserved residues map to the pocket where pyrophosphorolysis occurs. BacA incorporates an interdigitated inverted topology repeat, a topology type thus far only reported in transporters and channels. This unique topology raises issues regarding the ancestry of BacA, the possibility that BacA has alternate active sites on either side of the membrane and its possible function as a flippase.

Bacterial cell wall components are assembled in a transmembrane cycle that involves the membrane integral pyrophosphorylase, BacA. Here the authors solve the crystal structure of BacA which shows an interdigitated inverted topology repeat that hints towards a flippase function for BacA.

Using molecular simulation to explore the nanoscale dynamics of the plant kinome

Eukaryotic protein kinases (PKs) are a large family of proteins critical for cellular response to external signals, acting as molecular switches. PKs propagate biochemical signals by catalyzing phosphorylation of other proteins, including other PKs, which can undergo conformational changes upon phosphorylation and catalyze further phosphorylations. Although PKs have been studied thoroughly across the domains of life, the structures of these proteins are sparsely understood in numerous groups of organisms, including plants. In addition to efforts towards determining crystal structures of PKs, research on human PKs has incorporated molecular dynamics (MD) simulations to study the conformational dynamics underlying the switching of PK function. This approach of experimental structural biology coupled with computational biophysics has led to improved understanding of how PKs become catalytically active and why mutations cause pathological PK behavior, at spatial and temporal resolutions inaccessible to current experimental methods alone. In this review, we argue for the value of applying MD simulation to plant PKs. We review the basics of MD simulation methodology, the successes achieved through MD simulation in animal PKs, and current work on plant PKs using MD simulation. We conclude with a discussion of the future of MD simulations and plant PKs, arguing for the importance of molecular simulation in the future of plant PK research.

Structurally Mapping Endogenous Heme in the CcmCDE Membrane Complex for Cytochrome c Biogenesis

Although many putative heme transporters have been discovered, it has been challenging to prove that these proteins are directly involved with heme trafficking in vivo and to identify their heme binding domains. The prokaryotic pathways for cytochrome c biogenesis, Systems I and II, transport heme from inside the cell to outside for stereochemical attachment to cytochrome c, making them excellent models to study heme trafficking. System I is composed of eight integral membrane proteins (CcmA-H) and is proposed to transport heme via CcmC to an external "WWD" domain for presentation to the membrane-tethered heme chaperone, CcmE. Herein, we develop a new cysteine/heme crosslinking approach to trap and map endogenous heme in CcmC (WWD domain) and CcmE (defining "2-vinyl" and "4-vinyl" pockets for heme). Crosslinking occurs when either of the two vinyl groups of heme localize near a thiol of an engineered cysteine residue. Double crosslinking, whereby both vinyls crosslink to two engineered cysteines, facilitated a more detailed structural mapping of the heme binding sites, including stereospecificity. Using heme crosslinking results, heme ligand identification, and genomic coevolution data, we model the structure of the CcmCDE complex, including the WWD heme binding domain. We conclude that CcmC trafficks heme via its WWD domain and propose the structural basis for stereochemical attachment of heme.

HsTRPA of the Red Imported Fire Ant, Solenopsis invicta, Functions as a Nocisensor and Uncovers the Evolutionary Plasticity of HsTRPA Channels

Solenopsis invicta, the red imported fire ant, represents one of the most devastating invasive species. To understand their sensory physiology, we identified and characterized their Hymenoptera-specific (Hs) TRPA channel, SiHsTRPA. Consistent with the sensory functions of SiHsTRPA, it is activated by heat, an electrophile, and an insect repellent. Nevertheless, SiHsTRPA does not respond to most of the honey bee ortholog (AmHsTRPA)-activating compounds. The jewel wasp ortholog (NvHsTRPA) is activated by these compounds even though it outgroups both AmHsTRPA and SiHsTRPA. Characterization of AmHsTRPA/SiHsTRPA chimeric channels revealed that the amino acids in the N terminus, as well as ankyrin repeat 2 (AR2) of AmHsTRPA, are essential for the response to camphor. Furthermore, amino acids in ARs 3 and 5–7 were specifically required for the response to diallyl disulfide. Thus, amino acid substitutions in the corresponding domains of SiHsTRPA during evolution would be responsible for the loss of chemical sensitivity. SiHsTRPA-activating compounds repel red imported fire ants, suggesting that SiHsTRPA functions as a sensor for noxious compounds. SiHsTRPA represents an example of the species-specific modulation of orthologous TRPA channel properties by amino acid substitutions in multiple domains, and SiHsTRPA-activating compounds could be used to develop a method for controlling red imported fire ants.

The FtsLB subcomplex of the bacterial divisome is a tetramer with an uninterrupted FtsL helix linking the transmembrane and periplasmic regions

In Escherichia coli, FtsLB plays a central role in the initiation of cell division, possibly transducing a signal that will eventually lead to the activation of peptidoglycan remodeling at the forming septum. The molecular mechanisms by which FtsLB operates in the divisome, however, are not understood. Here, we present a structural analysis of the FtsLB complex, performed with biophysical, computational, and in vivo methods, that establishes the organization of the transmembrane region and proximal coiled coil of the complex. FRET analysis in vitro is consistent with formation of a tetramer composed of two FtsL and two FtsB subunits. We predicted subunit contacts through co-evolutionary analysis and used them to compute a structural model of the complex. The transmembrane region of FtsLB is stabilized by hydrophobic packing and by a complex network of hydrogen bonds. The coiled coil domain probably terminates near the critical constriction control domain, which might correspond to a structural transition. The presence of strongly polar amino acids within the core of the tetrameric coiled coil suggests that the coil may split into two independent FtsQ-binding domains. The helix of FtsB is interrupted between the transmembrane and coiled coil regions by a flexible Gly-rich linker. Conversely, the data suggest that FtsL forms an uninterrupted helix across the two regions and that the integrity of this helix is indispensable for the function of the complex. The FtsL helix is thus a candidate for acting as a potential mechanical connection to communicate conformational changes between periplasmic, membrane, and cytoplasmic regions.

The electron's spin and molecular chirality - how are they related and how do they affect life processes?

The recently discovered chiral induced spin selectivity (CISS) effect gives rise to a spin selective electron transmission through biomolecules. Here we review the mechanism behind the CISS effect and its implication for processes in Biology. Specifically, three processes are discussed: long-range electron transfer, spin effects on the oxidation of water, and enantioselectivity in bio-recognition events. These phenomena imply that chirality and spin may play several important roles in biology, which have not been considered so far.

Protein-Protein Docking Using Evolutionary Information

The structural modeling of protein complexes by docking simulations has been attracting increasing interest with the rise of proteomics and of the number of experimentally identified binary interactions. Structures of unbound partners, either modeled or experimentally determined, can be used as input to sample as extensively as possible all putative binding modes and single out the most plausible ones. At the scoring step, evolutionary information contained in the joint multiple sequence alignments of both partners can provide key insights to recognize correct interfaces. Here, we describe a computational protocol based on the InterEvDock web server to exploit coevolution constraints in protein-protein docking methods. We provide methodology guidelines to prepare the input protein structures and generate improved alignments. We also explain how to extract and use the information returned by the server through the analysis of two representative examples.

A Hybrid Approach for Protein Structure Determination Combining Sparse NMR with Evolutionary Coupling Sequence Data

While 3D structure determination of small (<15 kDa) proteins by solution NMR is largely automated and routine, structural analysis of larger proteins is more challenging. An emerging hybrid strategy for modeling protein structures combines sparse NMR data that can be obtained for larger proteins with sequence co-variation data, called evolutionary couplings (ECs), obtained from multiple sequence alignments of protein families. This hybrid "EC-NMR" method can be used to accurately model larger (15-60 kDa) proteins, and more rapidly determine structures of smaller (5-15 kDa) proteins using only backbone NMR data. The resulting structures have accuracies relative to reference structures comparable to those obtained with full backbone and sidechain NMR resonance assignments. The requirement that evolutionary couplings (ECs) are consistent with NMR data recorded on a specific member of a protein family, under specific conditions, potentially also allows identification of ECs that reflect alternative allosteric or excited states of the protein structure.

Approaches to ab initio molecular replacement of α-helical transmembrane proteins

α-Helical transmembrane proteins are a ubiquitous and important class of proteins, but present difficulties for crystallographic structure solution. Here, the effectiveness of the AMPLE molecular replacement pipeline in solving α-helical transmembrane-protein structures is assessed using a small library of eight ideal helices, as well as search models derived from ab initio models generated both with and without evolutionary contact information. The ideal helices prove to be surprisingly effective at solving higher resolution structures, but ab initio-derived search models are able to solve structures that could not be solved with the ideal helices. The addition of evolutionary contact information results in a marked improvement in the modelling and makes additional solutions possible.

Protein structure prediction using Rosetta in CASP12

We describe several notable aspects of our structure predictions using Rosetta in CASP12 in the free modeling (FM) and refinement (TR) categories. First, we had previously generated (and published) models for most large protein families lacking experimentally determined structures using Rosetta guided by co-evolution based contact predictions, and for several targets these models proved better starting points for comparative modeling than any known crystal structure-our model database thus starts to fulfill one of the goals of the original protein structure initiative. Second, while our "human" group simply submitted ROBETTA models for most targets, for six targets expert intervention improved predictions considerably; the largest improvement was for T0886 where we correctly parsed two discontinuous domains guided by predicted contact maps to accurately identify a structural homolog of the same fold. Third, Rosetta all atom refinement followed by MD simulations led to consistent but small improvements when starting models were close to the native structure, and larger but less consistent improvements when starting models were further away.

Enhanced unbiased sampling of protein dynamics using evolutionary coupling information

One of the major challenges in atomistic simulations of proteins is efficient sampling of pathways associated with rare conformational transitions. Recent developments in statistical methods for computation of direct evolutionary couplings between amino acids within and across polypeptide chains have allowed for inference of native residue contacts, informing accurate prediction of protein folds and multimeric structures. In this study, we assess the use of distances between evolutionarily coupled residues as natural choices for reaction coordinates which can be incorporated into Markov state model-based adaptive sampling schemes and potentially used to predict not only functional conformations but also pathways of conformational change, protein folding, and protein-protein association. We demonstrate the utility of evolutionary couplings in sampling and predicting activation pathways of the β 2-adrenergic receptor (β 2-AR), folding of the FiP35 WW domain, and dimerization of the E. coli molybdopterin synthase subunits. We find that the time required for β 2-AR activation and folding of the WW domain are greatly diminished using evolutionary couplings-guided adaptive sampling. Additionally, we were able to identify putative molybdopterin synthase association pathways and near-crystal structure complexes from protein-protein association simulations.

An information theoretic framework reveals a tunable allosteric network in group II chaperonins

ATP-dependent allosteric regulation of the ring-shaped group II chaperonins remains ill defined, in part because their complex oligomeric topology has limited the success of structural techniques in suggesting allosteric determinants. Further, their high sequence conservation has hindered the prediction of allosteric networks using mathematical covariation approaches. Here, we develop an information theoretic strategy that is robust to residue conservation and apply it to group II chaperonins. We identify a contiguous network of covarying residues that connects all nucleotide-binding pockets within each chaperonin ring. An interfacial residue between the networks of neighboring subunits controls positive cooperativity by communicating nucleotide occupancy within each ring. Strikingly, chaperonin allostery is tunable through single mutations at this position. Naturally occurring variants at this position that double the extent of positive cooperativity are less prevalent in nature. We propose that being less cooperative than attainable allows chaperonins to support robust folding over a wider range of metabolic conditions.

Protein structure prediction: making AWSEM AWSEM-ER by adding evolutionary restraints

Protein sequences have evolved to fold into functional structures, resulting in families of diverse protein sequences that all share the same overall fold. One can harness protein family sequence data to infer likely contacts between pairs of residues. In the current study, we combine this kind of inference from coevolutionary information with a coarse-grained protein force field ordinarily used with single sequence input, the Associative memory, Water mediated, Structure and Energy Model (AWSEM), to achieve improved structure prediction. The resulting Associative memory, Water mediated, Structure and Energy Model with Evolutionary Restraints (AWSEM-ER) yields a significant improvement in the quality of protein structure prediction over the single sequence prediction from AWSEM when a sufficiently large number of homologous sequences are available. Free energy landscape analysis shows that the addition of the evolutionary term shifts the free energy minimum to more native-like structures, which explains the improvement in the quality of structures when performing predictions using simulated annealing. Simulations using AWSEM without coevolutionary information have proved useful in elucidating not only protein folding behavior, but also mechanisms of protein function. The success of AWSEM-ER in de novo structure prediction suggests that the enhanced model opens the door to functional studies of proteins even when no experimentally solved structures are available.

Origins of coevolution between residues distant in protein 3D structures

Residue pairs that directly coevolve in protein families are generally close in protein 3D structures. Here we study the exceptions to this general trend-directly coevolving residue pairs that are distant in protein structures-to determine the origins of evolutionary pressure on spatially distant residues and to understand the sources of error in contact-based structure prediction. Over a set of 4,000 protein families, we find that 25% of directly coevolving residue pairs are separated by more than 5 Å in protein structures and 3% by more than 15 Å. The majority (91%) of directly coevolving residue pairs in the 5-15 Å range are found to be in contact in at least one homologous structure-these exceptions arise from structural variation in the family in the region containing the residues. Thirty-five percent of the exceptions greater than 15 Å are at homo-oligomeric interfaces, 19% arise from family structural variation, and 27% are in repeat proteins likely reflecting alignment errors. Of the remaining long-range exceptions (<1% of the total number of coupled pairs), many can be attributed to close interactions in an oligomeric state. Overall, the results suggest that directly coevolving residue pairs not in repeat proteins are spatially proximal in at least one biologically relevant protein conformation within the family; we find little evidence for direct coupling between residues at spatially separated allosteric and functional sites or for increased direct coupling between residue pairs on putative allosteric pathways connecting them.

Co-evolution techniques are reshaping the way we do structural bioinformatics

Co-evolution techniques were originally conceived to assist in protein structure prediction by inferring pairs of residues that share spatial proximity. However, the functional relationships that can be extrapolated from co-evolution have also proven to be useful in a wide array of structural bioinformatics applications. These techniques are a powerful way to extract structural and functional information in a sequence-rich world.

Cryo-EM structure of the protein-conducting ERAD channel Hrd1 in complex with Hrd3

Misfolded endoplasmic reticulum (ER) proteins are retro-translocated through the membrane into the cytosol, where they are poly-ubiquitinated, extracted from the ER membrane, and degraded by the proteasome 1–4, a pathway termed ER-associated protein degradation (ERAD). Proteins with misfolded domains in the ER lumen or membrane are discarded through the ERAD-L and –M pathways, respectively. In S. cerevisiae, both pathways require the ubiquitin ligase Hrd1, a multi-spanning membrane protein with a cytosolic RING finger domain 5,6. Hrd1 is the crucial membrane component for retro-translocation 7,8, but whether it forms a protein-conducting channel is unclear. Here, we report a cryo-electron microscopy (cryo-EM) structure of S. cerevisiae Hrd1 in complex with its ER luminal binding partner Hrd3. Hrd1 forms a dimer within the membrane with one or two Hrd3 molecules associated at its luminal side. Each Hrd1 molecule has eight trans-membrane segments, five of which form an aqueous cavity extending from the cytosol almost to the ER lumen, while a segment of the neighboring Hrd1 molecule forms a lateral seal. The aqueous cavity and lateral gate are reminiscent of features in protein-conducting conduits that facilitate polypeptide movement in the opposite direction, i.e. from the cytosol into or across membranes 9–11. Our results suggest that Hrd1 forms a retro-translocation channel for the movement of misfolded polypeptides through the ER membrane.

Toward high-resolution computational design of the structure and function of helical membrane proteins

The computational design of α-helical membrane proteins is still in its infancy but has already made great progress. De novo design allows stable, specific and active minimal oligomeric systems to be obtained. Computational reengineering can improve the stability and function of naturally occurring membrane proteins. Currently, the major hurdle for the field is the experimental characterization of the designs. The emergence of new structural methods for membrane proteins will accelerate progress.

Protein structural motifs in prediction and design

The Protein Data Bank (PDB) has been an integral resource for shaping our fundamental understanding of protein structure and for the advancement of such applications as protein design and structure prediction. Over the years, information from the PDB has been used to generate models ranging from specific structural mechanisms to general statistical potentials. With accumulating structural data, it has become possible to mine for more complete and complex structural observations, deducing more accurate generalizations. Motif libraries, which capture recurring structural features along with their sequence preferences, have exposed modularity in the structural universe and found successful application in various problems of structural biology. Here we summarize recent achievements in this arena, focusing on subdomain level structural patterns and their applications to protein design and structure prediction, and suggest promising future directions as the structural database continues to grow.

Coevolution of Domain Interactions in the Chloroplast TGD1, 2, 3 Lipid Transfer Complex Specific to Brassicaceae and Poaceae Plants

The import of lipids into the chloroplast is essential for photosynthetic membrane biogenesis. This process requires an ABC transporter in the inner envelope membrane with three subunits, TRIGALACTOSYLDIACYLGLYCEROL (TGD) 1, 2, and 3, named after the oligogalactolipids that accumulate in the respective Arabidopsis thaliana mutants. Unlike Arabidopsis, in the model grass Brachypodium distachyon, chloroplast lipid biosynthesis is largely dependent on imported precursors, resulting in a characteristic difference in chloroplast lipid acyl composition between the two plants. Accordingly, Arabidopsis is designated as a 16:3 (acyl carbons:double bounds) plant and Brachypodium as an 18:3 plant. Repression of TGD1 (BdTGD1) in Brachypodium affected growth without triggering oligogalactolipid biosynthesis. Moreover, expressing BdTGD1 in the Arabidopsis tgd1-1 mutant restored some phenotypes but did not reverse oligogalactolipid biosynthesis. A 27-amino acid loop (L45) is solely responsible for the incomplete functioning of BdTGD1 in Arabidopsis tgd1-1 Coevolutionary analysis and coimmunoprecipitation assays showed that the TGD1 L45 loop interacts with the mycobacterial cell entry domain of TGD2. To explain the observed differences in oligogalactolipid biosynthesis between the two species, we suggest that excess monogalactosyldiacylglycerol derived from chloroplast-derived precursors in Arabidopsis tgd1-1 is converted into oligogalactolipids, a process absent from Brachypodium with reduced TGD1 levels, which assembles monogalactosyldiacylglycerol exclusively from imported precursors.

Applications of contact predictions to structural biology

Evolutionary pressure on residue interactions, intramolecular or intermolecular, that are important for protein structure or function can lead to covariance between the two positions. Recent methodological advances allow much more accurate contact predictions to be derived from this evolutionary covariance signal. The practical application of contact predictions has largely been confined to structural bioinformatics, yet, as this work seeks to demonstrate, the data can be of enormous value to the structural biologist working in X-ray crystallo-graphy, cryo-EM or NMR. Integrative structural bioinformatics packages such as Rosetta can already exploit contact predictions in a variety of ways. The contribution of contact predictions begins at construct design, where structural domains may need to be expressed separately and contact predictions can help to predict domain limits. Structure solution by molecular replacement (MR) benefits from contact predictions in diverse ways: in difficult cases, more accurate search models can be constructed using ab initio modelling when predictions are available, while intermolecular contact predictions can allow the construction of larger, oligomeric search models. Furthermore, MR using supersecondary motifs or large-scale screens against the PDB can exploit information, such as the parallel or antiparallel nature of any β-strand pairing in the target, that can be inferred from contact predictions. Contact information will be particularly valuable in the determination of lower resolution structures by helping to assign sequence register. In large complexes, contact information may allow the identity of a protein responsible for a certain region of density to be determined and then assist in the orientation of an available model within that density. In NMR, predicted contacts can provide long-range information to extend the upper size limit of the technique in a manner analogous but complementary to experimental methods. Finally, predicted contacts can distinguish between biologically relevant interfaces and mere lattice contacts in a final crystal structure, and have potential in the identification of functionally important regions and in foreseeing the consequences of mutations.

Accelerating physical simulations of proteins by leveraging external knowledge

It is challenging to compute structure-function relationships of proteins using molecular physics. The problem arises from the exponential scaling of the computational searching and sampling of large conformational spaces. This scaling challenge is not met by today's methods, such as Monte Carlo, simulated annealing, genetic algorithms, or molecular dynamics (MD) or its variants such as replica exchange. Such methods of searching for optimal states on complex probabalistic landscapes are referred to more broadly as Explore-and-Exploit (EE), including in contexts such as computational learning, games, industrial planning and modeling military strategies. Here we describe a Bayesian method, called MELD, that 'melds' together explore-and-exploit approaches with externally added information that can be vague, combinatoric, noisy, intuitive, heuristic, or from experimental data. MELD is shown to accelerate physical MD simulations when using experimental data to determine protein structures; for predicting protein structures by using heuristic directives; and when predicting binding affinities of proteins from limited information about the binding site. Such Guided Explore-and-Exploit approaches might also be useful beyond proteins and beyond molecular science.

Architectures of Lipid Transport Systems for the Bacterial Outer Membrane

How phospholipids are trafficked between the bacterial inner and outer membranes through the hydrophilic space of the periplasm is not known. We report that members of the mammalian cell entry (MCE) protein family form hexameric assemblies with a central channel capable of mediating lipid transport. The E. coli MCE protein, MlaD, forms a ring associated with an ABC transporter complex in the inner membrane. A soluble lipid-binding protein, MlaC, ferries lipids between MlaD and an outer membrane protein complex. In contrast, EM structures of two other E. coli MCE proteins show that YebT forms an elongated tube consisting of seven stacked MCE rings, and PqiB adopts a syringe-like architecture. Both YebT and PqiB create channels of sufficient length to span the periplasmic space. This work reveals diverse architectures of highly conserved protein-based channels implicated in the transport of lipids between the membranes of bacteria and some eukaryotic organelles.

Large-scale identification of coevolution signals across homo-oligomeric protein interfaces by direct coupling analysis

Proteins have evolved to perform diverse cellular functions, from serving as reaction catalysts to coordinating cellular propagation and development. Frequently, proteins do not exert their full potential as monomers but rather undergo concerted interactions as either homo-oligomers or with other proteins as hetero-oligomers. The experimental study of such protein complexes and interactions has been arduous. Theoretical structure prediction methods are an attractive alternative. Here, we investigate homo-oligomeric interfaces by tracing residue coevolution via the global statistical direct coupling analysis (DCA). DCA can accurately infer spatial adjacencies between residues. These adjacencies can be included as constraints in structure prediction techniques to predict high-resolution models. By taking advantage of the ongoing exponential growth of sequence databases, we go significantly beyond anecdotal cases of a few protein families and apply DCA to a systematic large-scale study of nearly 2,000 Pfam protein families with sufficient sequence information and structurally resolved homo-oligomeric interfaces. We find that large interfaces are commonly identified by DCA. We further demonstrate that DCA can differentiate between subfamilies with different binding modes within one large Pfam family. Sequence-derived contact information for the subfamilies proves sufficient to assemble accurate structural models of the diverse protein-oligomers. Thus, we provide an approach to investigate oligomerization for arbitrary protein families leading to structural models complementary to often-difficult experimental methods. Combined with ever more abundant sequential data, we anticipate that this study will be instrumental to allow the structural description of many heteroprotein complexes in the future.

Correlation of fitness landscapes from three orthologous TIM barrels originates from sequence and structure constraints

Sequence divergence of orthologous proteins enables adaptation to environmental stresses and promotes evolution of novel functions. Limits on evolution imposed by constraints on sequence and structure were explored using a model TIM barrel protein, indole-3-glycerol phosphate synthase (IGPS). Fitness effects of point mutations in three phylogenetically divergent IGPS proteins during adaptation to temperature stress were probed by auxotrophic complementation of yeast with prokaryotic, thermophilic IGPS. Analysis of beneficial mutations pointed to an unexpected, long-range allosteric pathway towards the active site of the protein. Significant correlations between the fitness landscapes of distant orthologues implicate both sequence and structure as primary forces in defining the TIM barrel fitness landscape and suggest that fitness landscapes can be translocated in sequence space. Exploration of fitness landscapes in the context of a protein fold provides a strategy for elucidating the sequence-structure-fitness relationships in other common motifs.

The TIM barrel fold is an evolutionarily conserved motif found in proteins with a variety of enzymatic functions. Here the authors explore the fitness landscape of the TIM barrel protein IGPS and uncover evolutionary constraints on both sequence and structure, accompanied by long range allosteric interactions.

Systematic evaluation of CS-Rosetta for membrane protein structure prediction with sparse NOE restraints

We critically test and validate the CS-Rosetta methodology for de novo structure prediction of α-helical membrane proteins (MPs) from NMR data, such as chemical shifts and NOE distance restraints. By systematically reducing the number and types of NOE restraints, we focus on determining the regime in which MP structures can be reliably predicted and pinpoint the boundaries of the approach. Five MPs of known structure were used as test systems, phototaxis sensory rhodopsin II (pSRII), a subdomain of pSRII, disulfide binding protein B (DsbB), microsomal prostaglandin E2 synthase-1 (mPGES-1), and translocator protein (TSPO). For pSRII and DsbB, where NMR and X-ray structures are available, resolution-adapted structural recombination (RASREC) CS-Rosetta yields structures that are as close to the X-ray structure as the published NMR structures if all available NMR data are used to guide structure prediction. For mPGES-1 and Bacillus cereus TSPO, where only X-ray crystal structures are available, highly accurate structures are obtained using simulated NMR data. One main advantage of RASREC CS-Rosetta is its robustness with respect to even a drastic reduction of the number of NOEs. Close-to-native structures were obtained with one randomly picked long-range NOEs for every 14, 31, 38, and 8 residues for full-length pSRII, the pSRII subdomain, TSPO, and DsbB, respectively, in addition to using chemical shifts. For mPGES-1, atomically accurate structures could be predicted even from chemical shifts alone. Our results show that atomic level accuracy for helical membrane proteins is achievable with CS-Rosetta using very sparse NOE restraint sets to guide structure prediction. Proteins 2017; 85:812-826. © 2016 Wiley Periodicals, Inc.