SAbDab: the structural antibody database.

Structural antibody database (SAbDab; http://opig.stats.ox.ac.uk/webapps/sabdab) is an online resource containing all the publicly available antibody structures annotated and presented in a consistent fashion. The data are annotated with several properties including experimental information, gene details, correct heavy and light chain pairings, antigen details and, where available, antibody-antigen binding affinity. The user can select structures, according to these attributes as well as structural properties such as complementarity determining region loop conformation and variable domain orientation. Individual structures, datasets and the complete database can be downloaded.

Examining variable domain orientations in antigen receptors gives insight into TCR-like antibody design.

The variable domains of antibodies and T-Cell receptors (TCRs) share similar structures. Both molecules act as sensors for the immune system but recognise their respective antigens in different ways. Antibodies bind to a diverse set of antigenic shapes whilst TCRs only recognise linear peptides presented by a major histocompatibility complex (MHC). The antigen specificity and affinity of both receptors is determined primarily by the sequence and structure of their complementarity determining regions (CDRs). In antibodies the binding site is also known to be affected by the relative orientation of the variable domains, VH and VL. Here, the corresponding property for TCRs, the Vß-Vα orientation, is investigated and compared with that of antibodies. We find that TCR and antibody orientations are distinct. General antibody orientations are found to be incompatible with binding to the MHC in a canonical TCR-like mode. Finally, factors that cause the orientation of TCRs and antibodies to be different are investigated. Packing of the long Vα CDR3 in the domain-domain interface is found to be influential. In antibodies, a similar packing affect can be achieved using a bulky residue at IMGT position 50 on the VH domain. Along with IMGT VH 50, other positions are identified that may help to promote a TCR-like orientation in antibodies. These positions should provide useful considerations in the engineering of therapeutic TCR-like antibodies.

Camps 2.0: exploring the sequence and structure space of prokaryotic, eukaryotic, and viral membrane proteins.

Structural bioinformatics of membrane proteins is still in its infancy, and the picture of their fold space is only beginning to emerge. Because only a handful of three-dimensional structures are available, sequence comparison and structure prediction remain the main tools for investigating sequence-structure relationships in membrane protein families. Here we present a comprehensive analysis of the structural families corresponding to α-helical membrane proteins with at least three transmembrane helices. The new version of our CAMPS database (CAMPS 2.0) covers nearly 1300 eukaryotic, prokaryotic, and viral genomes. Using an advanced classification procedure, which is based on high-order hidden Markov models and considers both sequence similarity as well as the number of transmembrane helices and loop lengths, we identified 1353 structurally homogeneous clusters roughly corresponding to membrane protein folds. Only 53 clusters are associated with experimentally determined three-dimensional structures, and for these clusters CAMPS is in reasonable agreement with structure-based classification approaches such as SCOP and CATH. We therefore estimate that ∼1300 structures would need to be determined to provide a sufficient structural coverage of polytopic membrane proteins. CAMPS 2.0 is available at http://webclu.bio.wzw.tum.de/CAMPS2.0/.

Correlated mutations: a hallmark of phenotypic amino acid substitutions.

Point mutations resulting in the substitution of a single amino acid can cause severe functional consequences, but can also be completely harmless. Understanding what determines the phenotypical impact is important both for planning targeted mutation experiments in the laboratory and for analyzing naturally occurring mutations found in patients. Common wisdom suggests using the extent of evolutionary conservation of a residue or a sequence motif as an indicator of its functional importance and thus vulnerability in case of mutation. In this work, we put forward the hypothesis that in addition to conservation, co-evolution of residues in a protein influences the likelihood of a residue to be functionally important and thus associated with disease. While the basic idea of a relation between co-evolution and functional sites has been explored before, we have conducted the first systematic and comprehensive analysis of point mutations causing disease in humans with respect to correlated mutations. We included 14,211 distinct positions with known disease-causing point mutations in 1,153 human proteins in our analysis. Our data show that (1) correlated positions are significantly more likely to be disease-associated than expected by chance, and that (2) this signal cannot be explained by conservation patterns of individual sequence positions. Although correlated residues have primarily been used to predict contact sites, our data are in agreement with previous observations that (3) many such correlations do not relate to physical contacts between amino acid residues. Access to our analysis results are provided at http://webclu.bio.wzw.tum.de/~pagel/supplements/correlated-positions/.

Structural comparison and classification of alpha-helical transmembrane domains based on helix interaction patterns.

Structural classification of membrane proteins is still in its infancy due to the relative paucity of available three-dimensional structures compared with soluble proteins. However, recent technological advances in protein structure determination have led to a significant increase in experimentally known membrane protein folds, warranting exploration of the structural universe of membrane proteins. Here, a new and completely membrane protein specific structural classification system is introduced that classifies alpha-helical membrane proteins according to common helix architectures. Each membrane protein is represented by a helix interaction graph depicting transmembrane helices with their pairwise interactions resulting from individual residue contacts. Subsequently, proteins are clustered according to similarities among these helix interaction graphs using a newly developed structural similarity score called HISS. As HISS scores explicitly disregard structural properties of loop regions, they are more suitable to capture conserved transmembrane helix bundle architectures than other structural similarity scores. Importantly, we are able to show that a classification approach based on helix interaction similarity closely resembles conventional structural classification databases such as SCOP and CATH implying that helix interactions are one of the major determinants of alpha-helical membrane protein folds. Furthermore, the classification of all currently available membrane protein structures into 20 recurrent helix architectures and 15 singleton proteins demonstrates not only an impressive variability of membrane helix bundles but also the conservation of common helix interaction patterns among proteins with distinctly different sequences.

Current status of membrane protein structure classification.

For over 2 decades, continuous efforts to organize the jungle of available protein structures have been underway. Although a number of discrepancies between different classification approaches for soluble proteins have been reported, the classification of membrane proteins has so far not been comparatively studied because of the limited amount of available structural data. Here, we present an analysis of alpha-helical membrane protein classification in the SCOP and CATH databases. In the current set of 63 alpha-helical membrane protein chains having between 1 and 13 transmembrane helices, we observed a number of differently classified proteins both regarding their domain and fold assignment. The majority of all discrepancies affect single transmembrane helix, two helix hairpin, and four helix bundle domains, while domains with more than five helices are mostly classified consistently between SCOP and CATH. It thus appears that the structural constraints imposed by the lipid bilayer complicate the classification of membrane proteins with only few membrane-spanning regions. This problem seems to be specific for membrane proteins as soluble four helix bundles, not restrained by the membrane, are more consistently classified by SCOP and CATH. Our findings indicate that the structural space of small membrane helix bundles is highly continuous such that even minor differences in individual classification procedures may lead to a significantly different classification. Membrane proteins with few helices and limited structural diversity only seem to be reasonably classifiable if the definition of a fold is adapted to include more fine-grained structural features such as helix-helix interactions and reentrant regions.

Prediction of helix-helix contacts and interacting helices in polytopic membrane proteins using neural networks.

Despite rapidly increasing numbers of available 3D structures, membrane proteins still account for less than 1% of all structures in the Protein Data Bank. Recent high-resolution structures indicate a clearly broader structural diversity of membrane proteins than initially anticipated, motivating the development of reliable structure prediction methods specifically tailored for this class of molecules. One important prediction target capturing all major aspects of a protein's 3D structure is its contact map. Our analysis shows that computational methods trained to predict residue contacts in globular proteins perform poorly when applied to membrane proteins. We have recently published a method to identify interacting alpha-helices in membrane proteins based on the analysis of coevolving residues in predicted transmembrane regions. Here, we present a substantially improved algorithm for the same problem, which uses a newly developed neural network approach to predict helix-helix contacts. In addition to the input features commonly used for contact prediction of soluble proteins, such as windowed residue profiles and residue distance in the sequence, our network also incorporates features that apply to membrane proteins only, such as residue position within the transmembrane segment and its orientation toward the lipophilic environment. The obtained neural network can predict contacts between residues in transmembrane segments with nearly 26% accuracy. It is therefore the first published contact predictor developed specifically for membrane proteins performing with equal accuracy to state-of-the-art contact predictors available for soluble proteins. The predicted helix-helix contacts were employed in a second step to identify interacting helices. For our dataset consisting of 62 membrane proteins of solved structure, we gained an accuracy of 78.1%. Because the reliable prediction of helix interaction patterns is an important step in the classification and prediction of membrane protein folds, our method will be a helpful tool in compiling a structural census of membrane proteins.

Co-evolving residues in membrane proteins.

MOTIVATION: The analysis of co-evolving residues has been exhaustively evaluated for the prediction of intramolecular amino acid contacts in soluble proteins. Although a variety of different methods for the detection of these co-evolving residues have been developed, the fraction of correctly predicted contacts remained insufficient for their reliable application in the construction of structural models. Membrane proteins, which constitute between one-fourth and one-third of all proteins in an organism, were only considered in few individual case studies. RESULTS: We present the first general study of correlated mutations in alpha-helical membrane proteins. Using seven different prediction algorithms, we extracted co-evolving residues for 14 membrane proteins having a solved 3D structure. On average, distances between correlated pairs of residues lying on different transmembrane segments were found to be significantly smaller compared to a random prediction. Covariation of residues was frequently found in direct sequence neighborhood to helix-helix contacts. Based on the results obtained from individual prediction methods, we constructed a consensus prediction for every protein in the dataset that combines obtained correlations from different prediction algorithms and simultaneously removes likely false positives. Using this consensus prediction, 53% of all predicted residue pairs were found within one helix turn of an observed helix-helix contact. Based on the combination of co-evolving residues detected with the four best prediction algorithms, interacting helices could be predicted with a specificity of 83% and sensitivity of 42%. AVAILABILITY: http://webclu.bio.wzw.tum.de/helixcorr/

Molecular characterisation of the STRUBBELIG-RECEPTOR FAMILY of genes encoding putative leucine-rich repeat receptor-like kinases in Arabidopsis thaliana.

BACKGROUND: Receptor-like kinases are a prominent class of surface receptors that regulate many aspects of the plant life cycle. Despite recent advances the function of most receptor-like kinases remains elusive. Therefore, it is paramount to investigate these receptors. The task is complicated by the fact that receptor-like kinases belong to a large monophyletic family with many sub-clades. In general, functional analysis of gene family members by reverse genetics is often obscured by several issues, such as redundancy, subtle or difficult to detect phenotypes in mutants, or by decision problems regarding suitable biological and biochemical assays. Therefore, in many cases additional strategies have to be employed to allow inference of hypotheses regarding gene function. RESULTS: We approached the function of genes encoding the nine-member STRUBBELIG-RECEPTOR FAMILY (SRF) class of putative leucine-rich repeat receptor-like kinases. Sequence comparisons show overall conservation but also divergence in predicted functional domains among SRF proteins. Interestingly, SRF1 undergoes differential splicing. As a result, SRF1 is predicted to exist in a standard receptor configuration and in a membrane-anchored receptor-like version that lacks most of the intracellular domain. Furthermore, SRF1 is characterised by a high degree of polymorphism between the Ler and Col accessions. Two independent T-DNA-based srf4 mutants showed smaller leaves while 35S::SRF4 plants displayed enlarged leaves. This is in addition to the strubbelig phenotype which has been described before. Additional single and several key double mutant combinations did not reveal obvious mutant phenotypes. Ectopic expression of several SRF genes, using the 35S promoter, resulted in male sterility. To gain possible insights into SRF gene function we employed a computational analysis of publicly available microarray data. We performed global expression profiling, coexpression analysis, and an analysis of the enrichment of gene ontology terms among coexpressed genes. The bioinformatic analyses raise the possibility that some SRF genes affect different aspects of cell wall biology. The results also indicate that redundancy is a minor aspect of the SRF family. CONCLUSION: The results provide evidence that SRF4 is a positive regulator of leaf size. In addition, they suggest that the SRF family is characterised by functional diversity and that some SRF genes may function in cell wall biology. They also indicate that complementing reverse genetics with bioinformatical data mining of genome-wide expression data aids in inferring hypotheses on possible functions for members of a gene family.

MoFvAb: Modeling the Fv region of antibodies.

Knowledge of the 3-dimensional structure of the antigen-binding region of antibodies enables numerous useful applications regarding the design and development of antibody-based drugs. We present a knowledge-based antibody structure prediction methodology that incorporates concepts that have arisen from an applied antibody engineering environment. The protocol exploits the rich and continuously growing supply of experimentally derived antibody structures available to predict CDR loop conformations and the packing of heavy and light chain quickly and without user intervention. The homology models are refined by a novel antibody-specific approach to adapt and rearrange sidechains based on their chemical environment. The method achieves very competitive all-atom root mean square deviation values in the order of 1.5 Å on different evaluation datasets consisting of both known and previously unpublished antibody crystal structures.

Classification of α-helical membrane proteins using predicted helix architectures.

Despite significant methodological advances in protein structure determination high-resolution structures of membrane proteins are still rare, leaving sequence-based predictions as the only option for exploring the structural variability of membrane proteins at large scale. Here, a new structural classification approach for α-helical membrane proteins is introduced based on the similarity of predicted helix interaction patterns. Its application to proteins with known 3D structure showed that it is able to reliably detect structurally similar proteins even in the absence of any sequence similarity, reproducing the SCOP and CATH classifications with a sensitivity of 65% at a specificity of 90%. We applied the new approach to enhance our comprehensive structural classification of α-helical membrane proteins (CAMPS), which is primarily based on sequence and topology similarity, in order to find protein clusters that describe the same fold in the absence of sequence similarity. The total of 151 helix architectures were delineated for proteins with more than four transmembrane segments. Interestingly, we observed that proteins with 8 and more transmembrane helices correspond to fewer different architectures than proteins with up to 7 helices, suggesting that in large membrane proteins the evolutionary tendency to re-use already available folds is more pronounced.

Coevolution predicts direct interactions between mtDNA-encoded and nDNA-encoded subunits of oxidative phosphorylation complex i.

Despite years of research, the structure of the largest mammalian oxidative phosphorylation (OXPHOS) complex, NADH-ubiquinone oxidoreductase (complex I), and the interactions among its 45 subunits are not fully understood. Since complex I harbors subunits encoded by mitochondrial DNA (mtDNA) and nuclear DNA (nDNA) genomes, with the former evolving ∼10 times faster than the latter, tight cytonuclear coevolution is expected and observed. Recently, we identified three nDNA-encoded complex I subunits that underwent accelerated amino acid replacement, suggesting their adjustment to the elevated mtDNA rate of change. Hence, they constitute excellent candidates for binding mtDNA-encoded subunits. Here, we further disentangle the network of physical cytonuclear interactions within complex I by analyzing subunits coevolution. Firstly, relying on the bioinformatic analysis of 10 protein complexes possessing solved structures, we show that signals of coevolution identified physically interacting subunits with nearly 90% accuracy, thus lending support to our approach. When applying this approach to cytonuclear interaction within complex I, we predict that the 'rate-accelerated' nDNA-encoded subunits of complex I, NDUFC2 and NDUFA1, likely interact with the mtDNA-encoded subunits ND5/ND4 and ND5/ND4/ND1, respectively. Furthermore, we predicted interactions among mtDNA-encoded complex I subunits. Using the yeast two-hybrid system, we experimentally confirmed the predicted interactions of human NDUFC2 with ND4, the interactions of human NDUFA1 with ND1 and ND4, and the lack of interaction of NDUFC2 with ND3 and NDUFA1, thus providing a proof of concept for our approach. Our study shows, for the first time, evidence for direct interactions between nDNA-encoded and mtDNA-encoded subunits of human OXPHOS complex I and paves the path towards deciphering subunit interactions within complexes lacking three-dimensional structures. Our subunit-interactions-predicting method, ComplexCorr, is available at http://webclu.bio.wzw.tum.de/complexcorr.

Ionic interactions promote transmembrane helix-helix association depending on sequence context.

Folding and oligomerization of integral membrane proteins frequently depend on specific interactions of transmembrane helices. Interacting amino acids of helix-helix interfaces may form complex motifs and exert different types of molecular forces. Here, a set of strongly self-interacting transmembrane domains (TMDs), as isolated from a combinatorial library, was found to contain basic and acidic residues, in combination with polar nonionizable amino acids and C-terminal GxxxG motifs. Mutational analyses of selected sequences and reconstruction of high-affinity interfaces confirmed the cooperation of these residues in homotypic interactions. Probing heterotypic interaction indicated the presence of interhelical charge-charge interactions. Furthermore, simple motifs of an ionizable residue and GxxxG are significantly overrepresented in natural TMDs, and a specific combination of these motifs exhibits high-affinity heterotypic interaction. We conclude that intramembrane charge-charge interactions depend on sequence context. Moreover, they appear important for homotypic and heterotypic interactions of numerous natural TMDs.

Complex patterns of histidine, hydroxylated amino acids and the GxxxG motif mediate high-affinity transmembrane domain interactions.

Specific interactions of transmembrane helices play a pivotal role in the folding and oligomerization of integral membrane proteins. The helix-helix interfaces frequently depend on specific amino acid patterns. In this study, a heptad repeat pattern was randomized with all naturally occurring amino acids to uncover novel sequence motifs promoting transmembrane domain interactions. Self-interacting transmembrane domains were selected from the resulting combinatorial library by means of the ToxR/POSSYCCAT system. A comparison of the amino acid composition of high-and low-affinity sequences revealed that high-affinity transmembrane domains exhibit position-specific enrichment of histidine. Further, sequences containing His preferentially display Gly, Ser, and/or Thr residues at flanking positions and frequently contain a C-terminal GxxxG motif. Mutational analysis of selected sequences confirmed the importance of these residues in homotypic interaction. Probing heterotypic interaction indicated that His interacts in trans with hydroxylated residues. Reconstruction of minimal interaction motifs within the context of an oligo-Leu sequence confirmed that His is part of a hydrogen bonded cluster that is brought into register by the GxxxG motif. Notably, a similar motif contributes to self-interaction of the BNIP3 transmembrane domain.

Phenylalanine promotes interaction of transmembrane domains via GxxxG motifs.

Interactions of transmembrane helices play a crucial role in the folding and oligomerisation of integral membrane proteins. In order to uncover novel sequence motifs mediating these interactions, we randomised one face of a transmembrane helix with a set of non-polar or moderately polar amino acids. Those sequences capable of self-interaction upon integration into bacterial inner membranes were selected by means of the ToxR/POSSYCCAT system. A comparison between low/medium-affinity and high-affinity sequences reveals that high-affinity sequences are strongly enriched in phenylalanine residues that are frequently observed at the -3 position of GxxxG motifs, thus yielding FxxGxxxG motifs. Mutation of Phe or GxxxG in selected sequences significantly reduces self-interaction of the transmembrane domains without affecting their efficiency of membrane integration. Conversely, grafting FxxGxxxG onto unrelated transmembrane domains strongly enhances their interaction. Further, we find that FxxGxxxG is significantly over-represented in transmembrane domains of bitopic membrane proteins. The same motif contributes to self-interaction of the vesicular stomatitis virus G protein transmembrane domain. We conclude that Phe stabilises membrane-spanning GxxxG motifs. This is one example of how the role of certain side-chains in helix-helix interfaces is modulated by sequence context.