Statistical analysis of interface similarity in crystals of homologous proteins

Establishing comprehensive quaternary structural proteomes from genome sequence

A critical body of knowledge has developed through advances in protein microscopy, protein-fold modeling, structural biology software, availability of sequenced bacterial genomes, large-scale mutation databases, and genome-scale models. Based on these recent advances, we develop a computational framework that; i) identifies the oligomeric structural proteome encoded by an organism's genome from available structural resources; ii) maps multi-strain alleleomic variation, resulting in the structural proteome for a species; and iii) calculates the 3D orientation of proteins across subcellular compartments with residue-level precision. Using the platform, we; iv) compute the quaternary E. coli K-12 MG1655 structural proteome; v) use a dataset of 12,000 mutations to build Random Forest classifiers that can predict the severity of mutations; and, in combination with a genome-scale model that computes proteome allocation, vi) obtain the spatial allocation of the E. coli proteome. Thus, in conjunction with relevant datasets and increasingly accurate computational models, we can now annotate quaternary structural proteomes, at genome-scale, to obtain a molecular-level understanding of whole-cell functions.

Significance

Advancements in experimental and computational methods have revealed the shapes of multi-subunit proteins. The absence of a unified platform that maps actionable datatypes onto these increasingly accurate structures creates a barrier to structural analyses, especially at the genome-scale. Here, we describe QSPACE, a computational annotation platform that evaluates existing resources to identify the best-available structure for each protein in a user's query, maps the 3D location of actionable datatypes ( e.g. , active sites, published mutations) onto the selected structures, and uses third-party APIs to determine the subcellular compartment of all amino acids of a protein. As proof-of-concept, we deployed QSPACE to generate the quaternary structural proteome of E. coli MG1655 and demonstrate two use-cases involving large-scale mutant analysis and genome-scale modelling.

The impact of AI-based modeling on the accuracy of protein assembly prediction: Insights from CASP15

In CASP15, 87 predictors submitted around 11 000 models on 41 assembly targets. The community demonstrated exceptional performance in overall fold and interface contact predictions, achieving an impressive success rate of 90% (compared to 31% in CASP14). This remarkable accomplishment is largely due to the incorporation of DeepMind's AF2-Multimer approach into custom-built prediction pipelines. To evaluate the added value of participating methods, we compared the community models to the baseline AF2-Multimer predictor. In over 1/3 of cases, the community models were superior to the baseline predictor. The main reasons for this improved performance were the use of custom-built multiple sequence alignments, optimized AF2-Multimer sampling, and the manual assembly of AF2-Multimer-built subcomplexes. The best three groups, in order, are Zheng, Venclovas, and Wallner. Zheng and Venclovas reached a 73.2% success rate over all (41) cases, while Wallner attained 69.4% success rate over 36 cases. Nonetheless, challenges remain in predicting structures with weak evolutionary signals, such as nanobody-antigen, antibody-antigen, and viral complexes. Expectedly, modeling large complexes also remains challenging due to their high memory compute demands. In addition to the assembly category, we assessed the accuracy of modeling interdomain interfaces in the tertiary structure prediction targets. Models on seven targets featuring 17 unique interfaces were analyzed. Best predictors achieved a 76.5% success rate, with the UM-TBM group being the leader. In the interdomain category, we observed that the predictors faced challenges, as in the case of the assembly category, when the evolutionary signal for a given domain pair was weak or the structure was large. Overall, CASP15 witnessed unprecedented improvement in interface modeling, reflecting the AI revolution seen in CASP14.

Discriminating physiological from non-physiological interfaces in structures of protein complexes: A community-wide study

Reliably scoring and ranking candidate models of protein complexes and assigning their oligomeric state from the structure of the crystal lattice represent outstanding challenges. A community-wide effort was launched to tackle these challenges. The latest resources on protein complexes and interfaces were exploited to derive a benchmark dataset consisting of 1677 homodimer protein crystal structures, including a balanced mix of physiological and non-physiological complexes. The non-physiological complexes in the benchmark were selected to bury a similar or larger interface area than their physiological counterparts, making it more difficult for scoring functions to differentiate between them. Next, 252 functions for scoring protein-protein interfaces previously developed by 13 groups were collected and evaluated for their ability to discriminate between physiological and non-physiological complexes. A simple consensus score generated using the best performing score of each of the 13 groups, and a cross-validated Random Forest (RF) classifier were created. Both approaches showed excellent performance, with an area under the Receiver Operating Characteristic (ROC) curve of 0.93 and 0.94, respectively, outperforming individual scores developed by different groups. Additionally, AlphaFold2 engines recalled the physiological dimers with significantly higher accuracy than the non-physiological set, lending support to the reliability of our benchmark dataset annotations. Optimizing the combined power of interface scoring functions and evaluating it on challenging benchmark datasets appears to be a promising strategy.

The protein common assembly database (ProtCAD)-a comprehensive structural resource of protein complexes

Proteins often act through oligomeric interactions with other proteins. X-ray crystallography and cryo-electron microscopy provide detailed information on the structures of biological assemblies, defined as the most likely biologically relevant structures derived from experimental data. In crystal structures, the most relevant assembly may be ambiguously determined, since multiple assemblies observed in the crystal lattice may be plausible. It is estimated that 10-15% of PDB entries may have incorrect or ambiguous assembly annotations. Accurate assemblies are required for understanding functional data and training of deep learning methods for predicting assembly structures. As with any other kind of biological data, replication via multiple independent experiments provides important validation for the determination of biological assembly structures. Here we present the Protein Common Assembly Database (ProtCAD), which presents clusters of protein assembly structures observed in independent structure determinations of homologous proteins in the Protein Data Bank (PDB). ProtCAD is searchable by PDB entry, UniProt identifiers, or Pfam domain designations and provides downloads of coordinate files, PyMol scripts, and publicly available assembly annotations for each cluster of assemblies. About 60% of PDB entries contain assemblies in clusters of at least 2 independent experiments. All clusters and coordinates are available on ProtCAD web site (http://dunbrack2.fccc.edu/protcad).

A recurring packing contact in crystals of InlB pinpoints functional binding sites in the internalin domain and the B repeat

InlB, a bacterial agonist of the human receptor tyrosine kinase MET, consists of an N-terminal internalin domain, a central B repeat and three C-terminal GW domains. In all previous structures of full-length InlB or an InlB construct lacking the GW domains (InlB392), there was no interpretable electron density for the B repeat. Here, three InlB392 crystal structures in which the B repeat is resolved are described. These are the first structures to reveal the relative orientation of the internalin domain and the B repeat. A wild-type structure and two structures of the T332E variant together contain five crystallographically independent molecules. Surprisingly, the threonine-to-glutamate substitution in the B repeat substantially improved the crystallization propensity and crystal quality of the T332E variant. The internalin domain and B repeat are quite rigid internally, but are flexibly linked to each other. The new structures show that inter-domain flexibility is the most likely cause of the missing electron density for the B repeat in previous InlB structures. A potential binding groove between B-repeat strand β2 and an adjacent loop forms an important crystal contact in all five crystallographically independent chains. This region may represent a hydrophobic `sticky patch' that supports protein–protein interactions. This assumption agrees with the previous finding that all known inactivating point mutations in the B repeat lie within strand β2. The groove formed by strand β2 and the adjacent loop may thus represent a functionally important protein–protein interaction site in the B repeat.

Prediction of protein assemblies, the next frontier: The CASP14-CAPRI experiment

We present the results for CAPRI Round 50, the fourth joint CASP-CAPRI protein assembly prediction challenge. The Round comprised a total of twelve targets, including six dimers, three trimers, and three higher-order oligomers. Four of these were easy targets, for which good structural templates were available either for the full assembly, or for the main interfaces (of the higher-order oligomers). Eight were difficult targets for which only distantly related templates were found for the individual subunits. Twenty-five CAPRI groups including eight automatic servers submitted ~1250 models per target. Twenty groups including six servers participated in the CAPRI scoring challenge submitted ~190 models per target. The accuracy of the predicted models was evaluated using the classical CAPRI criteria. The prediction performance was measured by a weighted scoring scheme that takes into account the number of models of acceptable quality or higher submitted by each group as part of their five top-ranking models. Compared to the previous CASP-CAPRI challenge, top performing groups submitted such models for a larger fraction (70-75%) of the targets in this Round, but fewer of these models were of high accuracy. Scorer groups achieved stronger performance with more groups submitting correct models for 70-80% of the targets or achieving high accuracy predictions. Servers performed less well in general, except for the MDOCKPP and LZERD servers, who performed on par with human groups. In addition to these results, major advances in methodology are discussed, providing an informative overview of where the prediction of protein assemblies currently stands.

PDB-wide identification of physiological hetero-oligomeric assemblies based on conserved quaternary structure geometry

An accurate understanding of biomolecular mechanisms and diseases requires information on protein quaternary structure (QS). A critical challenge in inferring QS information from crystallography data is distinguishing biological interfaces from fortuitous crystal-packing contacts. Here, we employ QS conservation across homologs to infer the biological relevance of hetero-oligomers. We compare the structures and compositions of hetero-oligomers, which allow us to annotate 7,810 complexes as physiologically relevant, 1,060 as likely errors, and 1,432 with comparative information on subunit stoichiometry and composition. Excluding immunoglobulins, these annotations encompass over 51% of hetero-oligomers in the PDB. We curate a dataset of 577 hetero-oligomeric complexes to benchmark these annotations, which reveals an accuracy >94%. When homology information is not available, we compare QS across repositories (PDB, PISA, and EPPIC) to derive confidence estimates. This work provides high-quality annotations along with a large benchmark dataset of hetero-assemblies.

Structural Profiling of Bacterial Effectors Reveals Enrichment of Host-Interacting Domains and Motifs

Effector proteins are bacterial virulence factors secreted directly into host cells and, through extensive interactions with host proteins, rewire host signaling pathways to the advantage of the pathogen. Despite the crucial role of globular domains as mediators of protein-protein interactions (PPIs), previous structural studies of bacterial effectors are primarily focused on individual domains, rather than domain-mediated PPIs, which limits their ability to uncover systems-level molecular recognition principles governing host-bacteria interactions. Here, we took an interaction-centric approach and systematically examined the potential of structural components within bacterial proteins to engage in or target eukaryote-specific domain-domain interactions (DDIs). Our results indicate that: 1) effectors are about six times as likely as non-effectors to contain host-like domains that mediate DDIs exclusively in eukaryotes; 2) the average domain in effectors is about seven times as likely as that in non-effectors to co-occur with DDI partners in eukaryotes rather than in bacteria; and 3) effectors are about nine times as likely as non-effectors to contain bacteria-exclusive domains that target host domains mediating DDIs exclusively in eukaryotes. Moreover, in the absence of host-like domains or among pathogen proteins without domain assignment, effectors harbor a higher variety and density of short linear motifs targeting host domains that mediate DDIs exclusively in eukaryotes. Our study lends novel quantitative insight into the structural basis of effector-induced perturbation of host-endogenous PPIs and may aid in the design of selective inhibitors of host-pathogen interactions.

Structure, function, and regulation of thioesterases

Thioesterases are present in all living cells and perform a wide range of important biological functions by catalysing the cleavage of thioester bonds present in a diverse array of cellular substrates. Thioesterases are organised into 25 families based on their sequence conservation, tertiary and quaternary structure, active site configuration, and substrate specificity. Recent structural and functional characterisation of thioesterases has led to significant changes in our understanding of the regulatory mechanisms that govern enzyme activity and their respective cellular roles. The resulting dogma changes in thioesterase regulation include mechanistic insights into ATP and GDP-mediated regulation by oligomerisation, the role of new key regulatory regions, and new insights into a conserved quaternary structure within TE4 family members. Here we provide a current and comparative snapshot of our understanding of thioesterase structure, function, and regulation across the different thioesterase families.

ProtCID: a data resource for structural information on protein interactions

Structural information on the interactions of proteins with other molecules is plentiful, and for some proteins and protein families, there may be 100s of available structures. It can be very difficult for a scientist who is not trained in structural bioinformatics to access this information comprehensively. Previously, we developed the Protein Common Interface Database (ProtCID), which provided clusters of the interfaces of full-length protein chains as a means of identifying biological assemblies. Because proteins consist of domains that act as modular functional units, we have extended the analysis in ProtCID to the individual domain level. This has greatly increased the number of large protein-protein clusters in ProtCID, enabling the generation of hypotheses on the structures of biological assemblies of many systems. The analysis of domain families allows us to extend ProtCID to the interactions of domains with peptides, nucleic acids, and ligands. ProtCID provides complete annotations and coordinate sets for every cluster.

The authors previously developed the Protein Common Interface Database (ProtCID), which compares and clusters the interfaces of pairs of full-length protein chains with defined Pfam domain architectures in different PDB entries to identify biological assemblies. Here the authors extend ProtCID to the clustering of domain-domain interactions that also allows analyzing domain interactions with peptides, nucleic acids, and ligands.

Template-Based Modeling of Protein Complexes Using the PPI3D Web Server

There is a large gap between the numbers of known protein-protein interactions and the corresponding experimentally solved structures of protein complexes. Fortunately, this gap can be in part bridged by computational structure modeling methods. Currently, template-based modeling is the most accurate means to predict both individual protein structures and protein complexes. One of the major issues in template-based modeling is to identify homologous structures that could be utilized as templates. To simplify this task, we have developed the PPI3D web server. The server is not only able to search for homologous protein complexes, but also provides means to analyze identified interactions and to model protein complexes. In recent CASP and CAPRI experiments, PPI3D proved to be a useful tool for homology modeling of multimeric proteins. In this chapter, we provide a brief description of the PPI3D web server capabilities and how to use the server for modeling of protein complexes.

Blind prediction of homo- and hetero-protein complexes: The CASP13-CAPRI experiment

We present the results for CAPRI Round 46, the third joint CASP-CAPRI protein assembly prediction challenge. The Round comprised a total of 20 targets including 14 homo-oligomers and 6 heterocomplexes. Eight of the homo-oligomer targets and one heterodimer comprised proteins that could be readily modeled using templates from the Protein Data Bank, often available for the full assembly. The remaining 11 targets comprised 5 homodimers, 3 heterodimers, and two higher-order assemblies. These were more difficult to model, as their prediction mainly involved "ab-initio" docking of subunit models derived from distantly related templates. A total of ~30 CAPRI groups, including 9 automatic servers, submitted on average ~2000 models per target. About 17 groups participated in the CAPRI scoring rounds, offered for most targets, submitting ~170 models per target. The prediction performance, measured by the fraction of models of acceptable quality or higher submitted across all predictors groups, was very good to excellent for the nine easy targets. Poorer performance was achieved by predictors for the 11 difficult targets, with medium and high quality models submitted for only 3 of these targets. A similar performance "gap" was displayed by scorer groups, highlighting yet again the unmet challenge of modeling the conformational changes of the protein components that occur upon binding or that must be accounted for in template-based modeling. Our analysis also indicates that residues in binding interfaces were less well predicted in this set of targets than in previous Rounds, providing useful insights for directions of future improvements.

Accurate Classification of Biological and non-Biological Interfaces in Protein Crystal Structures using Subtle Covariation Signals

Proteins often work as oligomers or multimers in vivo. Therefore, elucidating their oligomeric or multimeric form (quaternary structure) is crucially important to ascertain their function. X-ray crystal structures of numerous proteins have been accumulated, providing information related to their biological units. Extracting information of biological units from protein crystal structures represents a meaningful task for modern biology. Nevertheless, although many methods have been proposed for identifying biological units appearing in protein crystal structures, it is difficult to distinguish biological protein-protein interfaces from crystallographic ones. Therefore, our simple but highly accurate classifier was developed to infer biological units in protein crystal structures using large amounts of protein sequence information and a modern contact prediction method to exploit covariation signals (CSs) in proteins. We demonstrate that our proposed method is promising even for weak signals of biological interfaces. We also discuss the relation between classification accuracy and conservation of biological units, and illustrate how the selection of sequences included in multiple sequence alignments as sources for obtaining CSs affects the results. With increased amounts of sequence data, the proposed method is expected to become increasingly useful.

Structural modeling of protein complexes: Current capabilities and challenges

Proteins frequently interact with each other, and the knowledge of structures of the corresponding protein complexes is necessary to understand how they function. Computational methods are increasingly used to provide structural models of protein complexes. Not surprisingly, community-wide Critical Assessment of protein Structure Prediction (CASP) experiments have recently started monitoring the progress in this research area. We participated in CASP13 with the aim to evaluate our current capabilities in modeling of protein complexes and to gain a better understanding of factors that exert the largest impact on these capabilities. To model protein complexes in CASP13, we applied template-based modeling, free docking and hybrid techniques that enabled us to generate models of the topmost quality for 27 of 42 multimers. If templates for protein complexes could be identified, we modeled the structures with reasonable accuracy by straightforward homology modeling. If only partial templates were available, it was nevertheless possible to predict the interaction interfaces correctly or to generate acceptable models for protein complexes by combining template-based modeling with docking. If no templates were available, we used rigid-body docking with limited success. However, in some free docking models, despite the incorrect subunit orientation and missed interface contacts, the approximate location of protein binding sites was identified correctly. Apparently, our overall performance in docking was limited by the quality of monomer models and by the imperfection of scoring methods. The impact of human intervention on our results in modeling of protein complexes was significant indicating the need for improvements of automatic methods.

AraPPINet: An Updated Interactome for the Analysis of Hormone Signaling Crosstalk in Arabidopsis thaliana

Protein-protein interactions (PPIs) play fundamental roles in various cellular processes. Here, we present a new version of computational interactome that contains more than 345,000 predicted PPIs involving about 51.2% of the Arabidopsis proteins. Compared to the earlier version, the updated AraPPINet displays a higher accuracy in predicting protein interactions through performance evaluation with independent datasets. In addition to the experimental verifications of the previous version, the new version has been subjected to further validation test that demonstrates its ability to discover novel PPIs involved in hormone signaling pathways. Moreover, network analysis shows that many overlapping proteins are significantly involved in the interactions which mediated the crosstalk among plant hormones. The new version of AraPPINet provides a more reliable interactome which would facilitate the understanding of crosstalk among hormone signaling pathways in plants.

Principles and characteristics of biological assemblies in experimentally determined protein structures

More than half of all structures in the PDB are assemblies of two or more proteins, including both homooligomers and heterooligomers. Structural information on these assemblies comes from X-ray crystallography, NMR, and cryo-EM spectroscopy. The correct assembly in an X-ray structure is often ambiguous, and computational methods have been developed to identify the most likely biologically relevant assembly based on physical properties of assemblies and sequence conservation in interfaces. Taking advantage of the large number of structures now available, some of the most recent methods have relied on similarity of interfaces and assemblies across structures of homologous proteins.

Alignment-free similarity analysis for protein sequences based on fuzzy integral

Sequence comparison is an essential part of modern molecular biology research. In this study, we estimated the parameters of Markov chain by considering the frequencies of occurrence of the all possible amino acid pairs from each alignment-free protein sequence. These estimated Markov chain parameters were used to calculate similarity between two protein sequences based on a fuzzy integral algorithm. For validation, our result was compared with both alignment-based (ClustalW) and alignment-free methods on six benchmark datasets. The results indicate that our developed algorithm has a better clustering performance for protein sequence comparison.

Distinguishing crystallographic from biological interfaces in protein complexes: role of intermolecular contacts and energetics for classification

BACKGROUND

Study of macromolecular assemblies is fundamental to understand functions in cells. X-ray crystallography is the most common technique to solve their 3D structure at atomic resolution. In a crystal, however, both biologically-relevant interfaces and non-specific interfaces resulting from crystallographic packing are observed. Due to the complexity of the biological assemblies currently tackled, classifying those interfaces, i.e. distinguishing biological from crystal lattice interfaces, is not trivial and often prone to errors. In this context, analyzing the physico-chemical characteristics of biological/crystal interfaces can help researchers identify possible features that distinguish them and gain a better understanding of the systems.

RESULTS

In this work, we are providing new insights into the differences between biological and crystallographic complexes by focusing on "pair-properties" of interfaces that have not yet been fully investigated. We investigated properties such intermolecular residue-residue contacts (already successfully applied to the prediction of binding affinities) and interaction energies (electrostatic, Van der Waals and desolvation). By using the XtalMany and BioMany interface datasets, we show that interfacial residue contacts, classified as a function of their physico-chemical properties, can distinguish between biological and crystallographic interfaces. The energetic terms show, on average, higher values for crystal interfaces, reflecting a less stable interface due to crystal packing compared to biological interfaces. By using a variety of machine learning approaches, we trained a new interface classification predictor based on contacts and interaction energetic features. Our predictor reaches an accuracy in classifying biological vs crystal interfaces of 0.92, compared to 0.88 for EPPIC (one of the main state-of-the-art classifiers reporting same performance as PISA).

CONCLUSION

In this work we have gained insights into the nature of intermolecular contacts and energetics terms distinguishing biological from crystallographic interfaces. Our findings might have a broader applicability in structural biology, for example for the identification of near native poses in docking. We implemented our classification approach into an easy-to-use and fast software, freely available to the scientific community from http://github.com/haddocking/interface-classifier .

Structural basis of DNA target recognition by the B3 domain of Arabidopsis epigenome reader VAL1

Arabidopsis thaliana requires a prolonged period of cold exposure during winter to initiate flowering in a process termed vernalization. Exposure to cold induces epigenetic silencing of the FLOWERING LOCUS C (FLC) gene by Polycomb group (PcG) proteins. A key role in this epigenetic switch is played by transcriptional repressors VAL1 and VAL2, which specifically recognize Sph/RY DNA sequences within FLC via B3 DNA binding domains, and mediate recruitment of PcG silencing machinery. To understand the structural mechanism of site-specific DNA recognition by VAL1, we have solved the crystal structure of VAL1 B3 domain (VAL1-B3) bound to a 12 bp oligoduplex containing the canonical Sph/RY DNA sequence 5'-CATGCA-3'/5'-TGCATG-3'. We find that VAL1-B3 makes H-bonds and van der Waals contacts to DNA bases of all six positions of the canonical Sph/RY element. In agreement with the structure, in vitro DNA binding studies show that VAL1-B3 does not tolerate substitutions at any position of the 5'-TGCATG-3' sequence. The VAL1-B3-DNA structure presented here provides a structural model for understanding the specificity of plant B3 domains interacting with the Sph/RY and other DNA sequences.

Inferring and Using Protein Quaternary Structure Information from Crystallographic Data

A precise knowledge of the quaternary structure of proteins is essential to illuminate both their function and their evolution. The major part of our knowledge on quaternary structure is inferred from X-ray crystallography data, but this inference process is hard and error-prone. The difficulty lies in discriminating fortuitous protein contacts, which make up the lattice of protein crystals, from biological protein contacts that exist in the native cellular environment. Here, we review methods devised to discriminate between both types of contacts and describe resources for downloading protein quaternary structure information and identifying high-confidence quaternary structures. The use of high-confidence datasets of quaternary structures will be critical for the analysis of structural, functional, and evolutionary properties of proteins.

The challenge of modeling protein assemblies: the CASP12-CAPRI experiment

We present the quality assessment of 5613 models submitted by predictor groups from both CAPRI and CASP for the total of 15 most tractable targets from the second joint CASP-CAPRI protein assembly prediction experiment. These targets comprised 12 homo-oligomers and 3 hetero-complexes. The bulk of the analysis focuses on 10 targets (of CAPRI Round 37), which included all 3 hetero-complexes, and whose protein chains or the full assembly could be readily modeled from structural templates in the PDB. On average, 28 CAPRI groups and 10 CASP groups (including automatic servers), submitted models for each of these 10 targets. Additionally, about 16 groups participated in the CAPRI scoring experiments. A range of acceptable to high quality models were obtained for 6 of the 10 Round 37 targets, for which templates were available for the full assembly. Poorer results were achieved for the remaining targets due to the lower quality of the templates available for the full complex or the individual protein chains, highlighting the unmet challenge of modeling the structural adjustments of the protein components that occur upon binding or which must be accounted for in template-based modeling. On the other hand, our analysis indicated that residues in binding interfaces were correctly predicted in a sizable fraction of otherwise poorly modeled assemblies and this with higher accuracy than published methods that do not use information on the binding partner. Lastly, the strengths and weaknesses of the assessment methods are evaluated and improvements suggested.

Modeling protein quaternary structure of homo- and hetero-oligomers beyond binary interactions by homology

Cellular processes often depend on interactions between proteins and the formation of macromolecular complexes. The impairment of such interactions can lead to deregulation of pathways resulting in disease states, and it is hence crucial to gain insights into the nature of macromolecular assemblies. Detailed structural knowledge about complexes and protein-protein interactions is growing, but experimentally determined three-dimensional multimeric assemblies are outnumbered by complexes supported by non-structural experimental evidence. Here, we aim to fill this gap by modeling multimeric structures by homology, only using amino acid sequences to infer the stoichiometry and the overall structure of the assembly. We ask which properties of proteins within a family can assist in the prediction of correct quaternary structure. Specifically, we introduce a description of protein-protein interface conservation as a function of evolutionary distance to reduce the noise in deep multiple sequence alignments. We also define a distance measure to structurally compare homologous multimeric protein complexes. This allows us to hierarchically cluster protein structures and quantify the diversity of alternative biological assemblies known today. We find that a combination of conservation scores, structural clustering, and classical interface descriptors, can improve the selection of homologous protein templates leading to reliable models of protein complexes.

Structure of the human aminopeptidase XPNPEP3 and comparison of its in vitro activity with Icp55 orthologs: Insights into diverse cellular processes

The human aminopeptidase XPNPEP3 is associated with cystic kidney disease and TNF-TNFR2 cellular signaling. Its yeast and plant homolog Icp55 processes several imported mitochondrial matrix proteins leading to their stabilization. However, the molecular basis for the diverse roles of these enzymes in the cell is unknown. Here, we report the crystal structure of human XPNPEP3 with bound apstatin product at 1.65 Å resolution, and we compare its in vitro substrate specificity with those of fungal Icp55 enzymes. In contrast to the suggestions by earlier in vivo studies of mitochondrial processing, we found that these enzymes are genuine Xaa-Pro aminopeptidases, which hydrolyze peptides with proline at the second position (P1'). The mitochondrial processing activity involving cleavage of peptides lacking P1' proline was also detected in the purified enzymes. A wide proline pocket as well as molecular complementarity and capping at the S1 substrate site of XPNPEP3 provide the necessary structural features for processing the mitochondrial substrates. However, this activity was found to be significantly lower as compared with Xaa-Pro aminopeptidase activity. Because of similar activity profiles of Icp55 and XPNPEP3, we propose that XPNPEP3 plays the same mitochondrial role in humans as Icp55 does in yeast. Both Xaa-Pro aminopeptidase and mitochondrial processing activities of XPNPEP3 have implications toward mitochondrial fitness and cystic kidney disease. Furthermore, the presence of both these activities in Icp55 elucidates the unexplained processing of the mitochondrial cysteine desulfurase Nfs1 in yeast. The enzymatic and structural analyses reported here provide a valuable molecular framework for understanding the diverse cellular roles of XPNPEP3.

A computational interactome for prioritizing genes associated with complex agronomic traits in rice (Oryza sativa)

Rice (Oryza sativa) is one of the most important staple foods for more than half of the global population. Many rice traits are quantitative, complex and controlled by multiple interacting genes. Thus, a full understanding of genetic relationships will be critical to systematically identify genes controlling agronomic traits. We developed a genome-wide rice protein-protein interaction network (RicePPINet, http://netbio.sjtu.edu.cn/riceppinet) using machine learning with structural relationship and functional information. RicePPINet contained 708 819 predicted interactions for 16 895 non-transposable element related proteins. The power of the network for discovering novel protein interactions was demonstrated through comparison with other publicly available protein-protein interaction (PPI) prediction methods, and by experimentally determined PPI data sets. Furthermore, global analysis of domain-mediated interactions revealed RicePPINet accurately reflects PPIs at the domain level. Our studies showed the efficiency of the RicePPINet-based method in prioritizing candidate genes involved in complex agronomic traits, such as disease resistance and drought tolerance, was approximately 2-11 times better than random prediction. RicePPINet provides an expanded landscape of computational interactome for the genetic dissection of agronomically important traits in rice.

MET-activating Residues in the B-repeat of the Listeria monocytogenes Invasion Protein InlB

The facultative intracellular pathogen Listeria monocytogenes causes listeriosis, a rare but life-threatening disease. Host cell entry begins with activation of the human receptor tyrosine kinase MET through the bacterial invasion protein InlB, which contains an internalin domain, a B-repeat, and three GW domains. The internalin domain is known to bind MET, but no interaction partner is known for the B-repeat. Adding the B-repeat to the internalin domain potentiates MET activation and is required to stimulate Madin-Darby canine kidney (MDCK) cell scatter. Therefore, it has been hypothesized that the B-repeat may bind a co-receptor on host cells. To test this hypothesis, we mutated residues that might be important for binding an interaction partner. We identified two adjacent residues in strand β2 of the β-grasp fold whose mutation abrogated induction of MDCK cell scatter. Biophysical analysis indicated that these mutations do not alter protein structure. We then tested these mutants in human HT-29 cells that, in contrast to the MDCK cells, were responsive to the internalin domain alone. These assays revealed a dominant negative effect, reducing the activity of a construct of the internalin domain and mutated B-repeat below that of the individual internalin domain. Phosphorylation assays of MET and its downstream targets AKT and ERK confirmed the dominant negative effect. Attempts to identify a host cell receptor for the B-repeat were not successful. We conclude that there is limited support for a co-receptor hypothesis and instead suggest that the B-repeat contributes to MET activation through low affinity homodimerization.

The V-motifs facilitate the substrate capturing step of the PTS elevator mechanism

We propose that the alternative crystal forms of outward open UlaA (which are experimental, not simulated, and contain the substrate in the cavity) can be used to interpret/validate the MD results from MalT (the substrate capture step, which involves the mobile second TMSs of the V-motifs, TMSs 2 and 7). Since the crystal contacts are the same between the two alternative crystal forms of outward open UlaA, the striking biological differences noted, including rearranged hydrogen bonds and salt bridge coordination, are not attributable to crystal packing differences. Using transport assays, we identified G58 and G286 as essential for normal vitamin C transport, but the comparison of alternative crystal forms revealed that these residues to unhinge TMS movements from substrate-binding side chains, rendering the mid-TMS regions of homologous TMSs 2 and 7 relatively immobile. While the TMS that is involved in substrate binding in MalT is part of the homologous bundle that holds the two separate halves of the transport assembly (two proteins) together, an unequal effect of the two knockouts was observed for UlaA where both V-motifs are free from such dimer interface interactions.

Biological function derived from predicted structures in CASP11

In CASP11, the organizers sought to bring the biological inferences from predicted structures to the fore. To accomplish this, we assessed the models for their ability to perform quantifiable tasks related to biological function. First, for 10 targets that were probable homodimers, we measured the accuracy of docking the models into homodimers as a function of GDT-TS of the monomers, which produced characteristic L-shaped plots. At low GDT-TS, none of the models could be docked correctly as homodimers. Above GDT-TS of ∼60%, some models formed correct homodimers in one of the largest docked clusters, while many other models at the same values of GDT-TS did not. Docking was more successful when many of the templates shared the same homodimer. Second, we docked a ligand from an experimental structure into each of the models of one of the targets. Docking to the models with two different programs produced poor ligand RMSDs with the experimental structure. Measures that evaluated similarity of contacts were reasonable for some of the models, although there was not a significant correlation with model accuracy. Finally, we assessed whether models would be useful in predicting the phenotypes of missense mutations in three human targets by comparing features calculated from the models with those calculated from the experimental structures. The models were successful in reproducing accessible surface areas but there was little correlation of model accuracy with calculation of FoldX evaluation of the change in free energy between the wild-type and the mutant. Proteins 2016; 84(Suppl 1):370-391. © 2016 Wiley Periodicals, Inc.

Genome-Wide Inference of Protein-Protein Interaction Networks Identifies Crosstalk in Abscisic Acid Signaling

Protein-protein interactions (PPIs) are essential to almost all cellular processes. To better understand the relationships of proteins in Arabidopsis (Arabidopsis thaliana), we have developed a genome-wide protein interaction network (AraPPINet) that is inferred from both three-dimensional structures and functional evidence and that encompasses 316,747 high-confidence interactions among 12,574 proteins. AraPPINet exhibited high predictive power for discovering protein interactions at a 50% true positive rate and for discriminating positive interactions from similar protein pairs at a 70% true positive rate. Experimental evaluation of a set of predicted PPIs demonstrated the ability of AraPPINet to identify novel protein interactions involved in a specific process at an approximately 100-fold greater accuracy than random protein-protein pairs in a test case of abscisic acid (ABA) signaling. Genetic analysis of an experimentally validated, predicted interaction between ARR1 and PYL1 uncovered cross talk between ABA and cytokinin signaling in the control of root growth. Therefore, we demonstrate the power of AraPPINet (http://netbio.sjtu.edu.cn/arappinet/) as a resource for discovering gene function in converging signaling pathways and complex traits in plants.

Biochemical properties and crystal structure of the flavin reductase FerA from Paracoccus denitrificans

The Pden_2689 gene encoding FerA, an NADH:flavin oxidoreductase required for growth of Paracoccus denitrificans under iron limitation, was cloned and overexpressed as a C-terminally His6-tagged derivative. The binding of substrates and products was detected and quantified by isothermal titration calorimetry and fluorometric titration. FerA binds FMN and FAD with comparable affinity in an enthalpically driven, entropically opposed process. The reduced flavin is bound more loosely than the oxidized one, which was confirmed by a negative shift in the redox potential of FMN after addition of FerA. Initial velocity and substrate analogs inhibition studies showed that FerA follows a random-ordered sequence of substrate (NADH and FMN) binding. The primary kinetic isotope effects from stereospecifically deuterated nicotinamide nucleotides demonstrated that hydride transfer occurs from the pro-S position and contributes to rate limitation for the overall reaction. The crystal structure of FerA revealed a twisted seven-stranded antiparallel β-barrel similar to that of other short chain flavin reductases. Only minor structural changes around Arg106 took place upon FMN binding. The solution structure FerA derived from small angle X-ray scattering (SAXS) matched the dimer assembly predicted from the crystal structure. Site-directed mutagenesis pinpointed a role of Arg106 and His146 in binding of flavin and NADH, respectively. Pull down experiments performed with cytoplasmic extracts resulted in a negative outcome indicating that FerA might physiologically act without association with other proteins. Rapid kinetics experiments provided evidence for a stabilizing effect of another P. denitrificans protein, the

NAD(P)H

acceptor oxidoreducase FerB, against spontaneous oxidation of the FerA-produced dihydroflavin.

Mapping of Fab-1:VEGF Interface Using Carboxyl Group Footprinting Mass Spectrometry

A proof-of-concept study was performed to demonstrate that carboxyl group footprinting, a relatively simple, bench-top method, has utility for first-pass analysis to determine epitope regions of therapeutic mAb:antigen complexes. The binding interface of vascular endothelial growth factor (VEGF) and the Fab portion of a neutralizing antibody (Fab-1) was analyzed using carboxyl group footprinting with glycine ethyl ester (GEE) labeling. Tryptic peptides involved in the binding interface between VEGF and Fab-1 were identified by determining the specific GEE-labeled residues that exhibited a reduction in the rate of labeling after complex formation. A significant reduction in the rate of GEE labeling was observed for E93 in the VEGF tryptic peptide V5, and D28 and E57 in the Fab-1 tryptic peptides HC2 and HC4, respectively. Results from the carboxyl group footprinting were compared with the binding interface identified from a previously characterized crystal structure (PDB: 1BJ1). All of these residues are located at the Fab-1:VEGF interface according to the crystal structure, demonstrating the potential utility of carboxyl group footprinting with GEE labeling for mapping epitopes. Graphical Abstract ᅟ.

Identifying three-dimensional structures of autophosphorylation complexes in crystals of protein kinases

Protein kinase autophosphorylation is a common regulatory mechanism in cell signaling pathways. Crystal structures of several homomeric protein kinase complexes have a serine, threonine, or tyrosine autophosphorylation site of one kinase monomer located in the active site of another monomer, a structural complex that we call an "autophosphorylation complex." We developed and applied a structural bioinformatics method to identify all such autophosphorylation complexes in x-ray crystallographic structures in the Protein Data Bank (PDB). We identified 15 autophosphorylation complexes in the PDB, of which five complexes had not previously been described in the publications describing the crystal structures. These five complexes consist of tyrosine residues in the N-terminal juxtamembrane regions of colony-stimulating factor 1 receptor (CSF1R, Tyr(561)) and ephrin receptor A2 (EPHA2, Tyr(594)), tyrosine residues in the activation loops of the SRC kinase family member LCK (Tyr(394)) and insulin-like growth factor 1 receptor (IGF1R, Tyr(1166)), and a serine in a nuclear localization signal region of CDC-like kinase 2 (CLK2, Ser(142)). Mutations in the complex interface may alter autophosphorylation activity and contribute to disease; therefore, we mutated residues in the autophosphorylation complex interface of LCK and found that two mutations impaired autophosphorylation (T445V and N446A) and mutation of Pro(447) to Ala, Gly, or Leu increased autophosphorylation. The identified autophosphorylation sites are conserved in many kinases, suggesting that, by homology, these complexes may provide insight into autophosphorylation complex interfaces of kinases that are relevant drug targets.

Understanding the fabric of protein crystals: computational classification of biological interfaces and crystal contacts

Modern structural biology still draws the vast majority of information from crystallography, a technique where the objects being investigated are embedded in a crystal lattice. Given the complexity and variety of those objects, it becomes fundamental to computationally assess which of the interfaces in the lattice are biologically relevant and which are simply crystal contacts. Since the mid-1990s, several approaches have been applied to obtain high-accuracy classification of crystal contacts and biological protein–protein interfaces. This review provides an overview of the concepts and main approaches to protein interface classification: thermodynamic estimation of interface stability, evolutionary approaches based on conservation of interface residues, and co-occurrence of the interface across different crystal forms. Among the three categories, evolutionary approaches offer the strongest promise for improvement, thanks to the incessant growth in sequence knowledge. Importantly, protein interface classification algorithms can also be used on multimeric structures obtained using other high-resolution techniques or for protein assembly design or validation purposes. A key issue linked to protein interface classification is the identification of the biological assembly of a crystal structure and the analysis of its symmetry. Here, we highlight the most important concepts and problems to be overcome in assembly prediction. Over the next few years, tools and concepts of interface classification will probably become more frequently used and integrated in several areas of structural biology and structural bioinformatics. Among the main challenges for the future are better addressing of weak interfaces and the application of interface classification concepts to prediction problems like protein–protein docking.

Supplementary information: Supplementary data are available at Bioinformatics online.

Contact:guido.capitani@psi.ch

Structural characterization and modeling of the Borrelia burgdorferi hybrid histidine kinase Hk1 periplasmic sensor: A system for sensing small molecules associated with tick feeding

Two-component signal transduction systems are the primary mechanisms by which bacteria perceive and respond to changes in their environment. The Hk1/Rrp1 two-component system (TCS) in Borrelia burgdorferi consists of a hybrid histidine kinase and a response regulator with diguanylate cyclase activity, respectively. Phosphorylated Rrp1 catalyzes the synthesis of c-di-GMP, a second messenger associated with bacterial life-style control networks. Spirochetes lacking either Hk1 or Rrp1 are virulent in mice but destroyed within feeding ticks. Activation of Hk1 by exogenous stimuli represents the seminal event for c-di-GMP signaling. We reasoned that structural characterization of Hk1's sensor would provide insights into the mechanism underlying signal transduction and aid in the identification of activating ligands. The Hk1 sensor is composed of three ligand-binding domains (D1-3), each with homology to periplasmic solute-binding proteins (PBPs) typically associated with ABC transporters. Herein, we determined the structure for D1, the most N-terminal PBP domain. As expected, D1 displays a bilobed Venus Fly Trap-fold. Similar to the prototypical sensor PBPs HK29S from Geobacter sulfurreducens and VFT2 from Bordetella pertussis, apo-D1 adopts a closed conformation. Using complementary approaches, including SAXS, we established that D1 forms a dimer in solution. The D1 structure enabled us to model the D2 and D3 domains. Differences in the ligand-binding pockets suggest that each PBP recognizes a different ligand. The ability of Hk1 to recognize multiple stimuli provides spirochetes with a means of distinguishing between the acquisition and transmission blood meals and generate a graded output response that is reflective of the perceived environmental threats.

The Landscape of Intertwined Associations in Homooligomeric Proteins

We present an overview of the full repertoire of intertwined associations in homooligomeric proteins. This overview summarizes recent findings on the different categories of intertwined associations in known protein structures, their assembly modes, the properties of their interfaces, and their structural plasticity. Furthermore, the current body of knowledge on the so-called three-dimensional domain-swapped systems is reexamined in the context of the wider landscape of intertwined homooligomers, with a particular focus on the mechanistic aspects that underpin intertwined self-association processes in proteins. Insights gained from this integrated overview into the physical and biological roles of intertwining are highlighted.

Conservation of inner nuclear membrane targeting sequences in mammalian Pom121 and yeast Heh2 membrane proteins

This study examines whether active transport to the inner nuclear membrane, as shown for yeast membrane proteins Heh1 and Heh2, is conserved in metazoans. In support of this, the nuclear localization signal of metazoan Pom121 shares biochemical, structural, and functional properties with those of Heh1 and Heh2, and a Heh2-derived reporter protein targets to the inner membrane in Hek293T cells.

Endoplasmic reticulum–synthesized membrane proteins traffic through the nuclear pore complex (NPC) en route to the inner nuclear membrane (INM). Although many membrane proteins pass the NPC by simple diffusion, two yeast proteins, ScSrc1/ScHeh1 and ScHeh2, are actively imported. In these proteins, a nuclear localization signal (NLS) and an intrinsically disordered linker encode the sorting signal for recruiting the transport factors for FG-Nup and RanGTP-dependent transport through the NPC. Here we address whether a similar import mechanism applies in metazoans. We show that the (putative) NLSs of metazoan HsSun2, MmLem2, HsLBR, and HsLap2β are not sufficient to drive nuclear accumulation of a membrane protein in yeast, but the NLS from RnPom121 is. This NLS of Pom121 adapts a similar fold as the NLS of Heh2 when transport factor bound and rescues the subcellular localization and synthetic sickness of Heh2ΔNLS mutants. Consistent with the conservation of these NLSs, the NLS and linker of Heh2 support INM localization in HEK293T cells. The conserved features of the NLSs of ScHeh1, ScHeh2, and RnPom121 and the effective sorting of Heh2-derived reporters in human cells suggest that active import is conserved but confined to a small subset of INM proteins.

Structure-guided sequence specificity engineering of the modification-dependent restriction endonuclease LpnPI

The eukaryotic Set and Ring Associated (SRA) domains and structurally similar DNA recognition domains of prokaryotic cytosine modification-dependent restriction endonucleases recognize methylated, hydroxymethylated or glucosylated cytosine in various sequence contexts. Here, we report the apo-structure of the N-terminal SRA-like domain of the cytosine modification-dependent restriction enzyme LpnPI that recognizes modified cytosine in the 5'-C(mC)DG-3' target sequence (where mC is 5-methylcytosine or 5-hydroxymethylcytosine and D = A/T/G). Structure-guided mutational analysis revealed LpnPI residues involved in base-specific interactions and demonstrated binding site plasticity that allowed limited target sequence degeneracy. Furthermore, modular exchange of the LpnPI specificity loops by structural equivalents of related enzymes AspBHI and SgrTI altered sequence specificity of LpnPI. Taken together, our results pave the way for specificity engineering of the cytosine modification-dependent restriction enzymes.

Structure of a single-chain Fv bound to the 17 N-terminal residues of huntingtin provides insights into pathogenic amyloid formation and suppression

Huntington's disease is triggered by misfolding of fragments of mutant forms of the huntingtin protein (mHTT) with aberrant polyglutamine expansions. The C4 single-chain Fv antibody (scFv) binds to the first 17 residues of huntingtin [HTT(1-17)] and generates substantial protection against multiple phenotypic pathologies in situ and in vivo. We show in this paper that C4 scFv inhibits amyloid formation by exon1 fragments of huntingtin in vitro and elucidate the structural basis for this inhibition and protection by determining the crystal structure of the complex of C4 scFv and HTT(1-17). The peptide binds with residues 3-11 forming an amphipathic helix that makes contact with the antibody fragment in such a way that the hydrophobic face of this helix is shielded from the solvent. Residues 12-17 of the peptide are in an extended conformation and interact with the same region of another C4 scFv:HTT(1-17) complex in the asymmetric unit, resulting in a β-sheet interface within a dimeric C4 scFv:HTT(1-17) complex. The nature of this scFv-peptide complex was further explored in solution by high-resolution NMR and physicochemical analysis of species in solution. The results provide insights into the manner in which C4 scFv inhibits the aggregation of HTT, and hence into its therapeutic potential, and suggests a structural basis for the initial interactions that underlie the formation of disease-associated amyloid fibrils by HTT.

Burial Level Change Defines a High Energetic Relevance for Protein Binding Interfaces

Protein-protein interfaces defined through atomic contact or solvent accessibility change are widely adopted in structural biology studies. But, these definitions cannot precisely capture energetically important regions at protein interfaces. The burial depth of an atom in a protein is related to the atom's energy. This work investigates how closely the change in burial level of an atom/residue upon complexation is related to the binding. Burial level change is different from burial level itself. An atom deeply buried in a monomer with a high burial level may not change its burial level after an interaction and it may have little burial level change. We hypothesize that an interface is a region of residues all undergoing burial level changes after interaction. By this definition, an interface can be decomposed into an onion-like structure according to the burial level change extent. We found that our defined interfaces cover energetically important residues more precisely, and that the binding free energy of an interface is distributed progressively from the outermost layer to the core. These observations are used to predict binding hot spots. Our approach's F-measure performance on a benchmark dataset of alanine mutagenesis residues is much superior or similar to those by complicated energy modeling or machine learning approaches.

The structure of bradyzoite-specific enolase from Toxoplasma gondii reveals insights into its dual cytoplasmic and nuclear functions

In addition to catalyzing a central step in glycolysis, enolase assumes a remarkably diverse set of secondary functions in different organisms, including transcription regulation as documented for the oncogene c-Myc promoter-binding protein 1. The apicomplexan parasite Toxoplasma gondii differentially expresses two nuclear-localized, plant-like enolases: enolase 1 (TgENO1) in the latent bradyzoite cyst stage and enolase 2 (TgENO2) in the rapidly replicative tachyzoite stage. A 2.75 Å resolution crystal structure of bradyzoite enolase 1, the second structure to be reported of a bradyzoite-specific protein in Toxoplasma, captures an open conformational state and reveals that distinctive plant-like insertions are located on surface loops. The enolase 1 structure reveals that a unique residue, Glu164, in catalytic loop 2 may account for the lower activity of this cyst-stage isozyme. Recombinant TgENO1 specifically binds to a TTTTCT DNA motif present in the cyst matrix antigen 1 (TgMAG1) gene promoter as demonstrated by gel retardation. Furthermore, direct physical interactions of both nuclear TgENO1 and TgENO2 with the TgMAG1 gene promoter are demonstrated in vivo using chromatin immunoprecipitation (ChIP) assays. Structural and biochemical studies reveal that T. gondii enolase functions are multifaceted, including the coordination of gene regulation in parasitic stage development. Enolase 1 provides a potential lead in the design of drugs against Toxoplasma brain cysts.

Fast photochemical oxidation of proteins (FPOP) maps the epitope of EGFR binding to adnectin

Epitope mapping is an important tool for the development of monoclonal antibodies, mAbs, as therapeutic drugs. Recently, a class of therapeutic mAb alternatives, adnectins, has been developed as targeted biologics. They are derived from the 10th type III domain of human fibronectin ((10)Fn3). A common approach to map the epitope binding of these therapeutic proteins to their binding partners is X-ray crystallography. Although the crystal structure is known for Adnectin 1 binding to human epidermal growth factor receptor (EGFR), we seek to determine complementary binding in solution and to test the efficacy of footprinting for this purpose. As a relatively new tool in structural biology and complementary to X-ray crystallography, protein footprinting coupled with mass spectrometry is promising for protein-protein interaction studies. We report here the use of fast photochemical oxidation of proteins (FPOP) coupled with MS to map the epitope of EGFR-Adnectin 1 at both the peptide and amino-acid residue levels. The data correlate well with the previously determined epitopes from the crystal structure and are consistent with HDX MS data, which are presented in an accompanying paper. The FPOP-determined binding interface involves various amino-acid and peptide regions near the N terminus of EGFR. The outcome adds credibility to oxidative labeling by FPOP for epitope mapping and motivates more applications in the therapeutic protein area as a stand-alone method or in conjunction with X-ray crystallography, NMR, site-directed mutagenesis, and other orthogonal methods.

Landscape of intertwined associations in multi-domain homo-oligomeric proteins

This study charts the landscape of multi-domain protein structures that form intertwined homodimers by exchanging structural domains between subunits. A representative dataset of such homodimers was derived from the Protein Data Bank, and their structural and topological properties were compared to those of a representative set of non-intertwined homodimers. Most of the intertwined dimers form closed assemblies with head-to-tail arrangements, where the subunit interface involves contacts between dissimilar domains. In contrast, the non-intertwined dimers form preferentially head-to-head arrangements, where the subunit interface involves contacts between identical domains. Most of these contacts engage only one structural domain from each subunit, leaving the remaining domains free to form other associations. Remarkably, we find that multi-domain proteins closely related to the intertwined homodimers are significantly more likely than relatives of the non-intertwined versions to adopt alternative intramolecular domain arrangements. In ~40% of the intertwined dimers, the plasticity in domain arrangements among relatives affords maintenance of the head-to-head or head-to-tail topology and conservation of the corresponding subunit interface. This property seems to be exploited in several systems to regulate DNA binding. In ~58%, however, intramolecular domain re-arrangements are associated with changes in oligomeric states and poorly conserved interfaces among relatives. This time, the corresponding structural plasticity appears to be exploited by evolution to modulate function by switching between active and inactive states of the protein. Surprisingly, in total, only three systems were found to undergo the classical monomer to intertwined dimer conversion associated with three-dimensional domain swapping.

A PDB-wide, evolution-based assessment of protein-protein interfaces

Background

Thanks to the growth in sequence and structure databases, more than 50 million sequences are now available in UniProt and 100,000 structures in the PDB. Rich information about protein–protein interfaces can be obtained by a comprehensive study of protein contacts in the PDB, their sequence conservation and geometric features.

Results

An automated computational pipeline was developed to run our Evolutionary Protein–Protein Interface Classifier (EPPIC) software on the entire PDB and store the results in a relational database, currently containing > 800,000 interfaces. This allows the analysis of interface data on a PDB-wide scale. Two large benchmark datasets of biological interfaces and crystal contacts, each containing about 3000 entries, were automatically generated based on criteria thought to be strong indicators of interface type. The BioMany set of biological interfaces includes NMR dimers solved as crystal structures and interfaces that are preserved across diverse crystal forms, as catalogued by the Protein Common Interface Database (ProtCID) from Xu and Dunbrack. The second dataset, XtalMany, is derived from interfaces that would lead to infinite assemblies and are therefore crystal contacts. BioMany and XtalMany were used to benchmark the EPPIC approach. The performance of EPPIC was also compared to classifications from the Protein Interfaces, Surfaces, and Assemblies (PISA) program on a PDB-wide scale, finding that the two approaches give the same call in about 88% of PDB interfaces. By comparing our safest predictions to the PDB author annotations, we provide a lower-bound estimate of the error rate of biological unit annotations in the PDB. Additionally, we developed a PyMOL plugin for direct download and easy visualization of EPPIC interfaces for any PDB entry. Both the datasets and the PyMOL plugin are available at http://www.eppic-web.org/ewui/#downloads.

Conclusions

Our computational pipeline allows us to analyze protein–protein contacts and their sequence conservation across the entire PDB. Two new benchmark datasets are provided, which are over an order of magnitude larger than existing manually curated ones. These tools enable the comprehensive study of several aspects of protein–protein contacts in the PDB and represent a basis for future, even larger scale studies of protein–protein interactions.

ATP-induced dimerization of the F0F1 ε subunit from Bacillus PS3: a hydrogen exchange-mass spectrometry study

F0F1 ATP synthase harnesses a transmembrane electrochemical gradient for the production of ATP. When operated in reverse, this multiprotein complex catalyzes ATP hydrolysis. In bacteria, the ε subunit is involved in regulating this ATPase activity. Also, ε is essential for coupling ATP hydrolysis (or synthesis) to proton translocation. The ε subunit consists of a β sandwich and two C-terminal helices, α1 and α2. The protein can switch from a compact fold to an alternate conformation where α1 and α2 are separated, resulting in an extended structure. ε from the thermophile Bacillus PS3 (Tε) binds ATP with high affinity such that this protein may function as an intracellular ATP level sensor. ATP binding to isolated Tε triggers a major conformational transition. Earlier data were interpreted in terms of an ATP + Tεextended → ATP·Tεcompact transition that may mimic aspects of the regulatory switching within F0F1 (Yagi et al. (2007) Proc. Natl. Acad. Sci. U.S.A., 104, 11233–11238). In this work, we employ complementary biophysical techniques for examining the ATP-induced conformational switching of isolated Tε. CD spectroscopy confirmed the occurrence of a large-scale conformational transition upon ATP binding, consistent with the formation of stable helical structure. Hydrogen/deuterium exchange (HDX) mass spectrometry revealed that this transition is accompanied by a pronounced stabilization in the vicinity of the ATP-binding pocket. Surprisingly, dramatic stabilization is also seen in the β8−β9 region, which is remote from the site of ATP interaction. Analytical ultracentrifugation uncovered a previously unrecognized feature of Tε: a high propensity to undergo dimerization in the presence of ATP. Comparison with existing crystallography data strongly suggests that the unexpected β8−β9 HDX protection is due to newly formed protein–protein contacts. Hence, ATP binding to isolated Tε proceeds according to 2ATP + 2Tεextended → (ATP·Tεcompact)2. Implications of this dimerization propensity for the possible role of Tε as an antibiotic target are discussed.

Assessment of protein side-chain conformation prediction methods in different residue environments

Computational prediction of side-chain conformation is an important component of protein structure prediction. Accurate side-chain prediction is crucial for practical applications of protein structure models that need atomic-detailed resolution such as protein and ligand design. We evaluated the accuracy of eight side-chain prediction methods in reproducing the side-chain conformations of experimentally solved structures deposited to the Protein Data Bank. Prediction accuracy was evaluated for a total of four different structural environments (buried, surface, interface, and membrane-spanning) in three different protein types (monomeric, multimeric, and membrane). Overall, the highest accuracy was observed for buried residues in monomeric and multimeric proteins. Notably, side-chains at protein interfaces and membrane-spanning regions were better predicted than surface residues even though the methods did not all use multimeric and membrane proteins for training. Thus, we conclude that the current methods are as practically useful for modeling protein docking interfaces and membrane-spanning regions as for modeling monomers.

BioAssemblyModeler (BAM): user-friendly homology modeling of protein homo- and heterooligomers

Many if not most proteins function in oligomeric assemblies of one or more protein sequences. The Protein Data Bank provides coordinates for biological assemblies for each entry, at least 60% of which are dimers or larger assemblies. BioAssemblyModeler (BAM) is a graphical user interface to the basic steps in homology modeling of protein homooligomers and heterooligomers from the biological assemblies provided in the PDB. BAM takes as input up to six different protein sequences and begins by assigning Pfam domains to the target sequences. The program utilizes a complete assignment of Pfam domains to sequences in the PDB, PDBfam (http://dunbrack2.fccc.edu/protcid/pdbfam), to obtain templates that contain any or all of the domains assigned to the target sequence(s). The contents of the biological assemblies of potential templates are provided, and alignments of the target sequences to the templates are produced with a profile-profile alignment algorithm. BAM provides for visual examination and mouse-editing of the alignments supported by target and template secondary structure information and a 3D viewer of the template biological assembly. Side-chain coordinates for a model of the biological assembly are built with the program SCWRL4. A built-in protocol navigation system guides the user through all stages of homology modeling from input sequences to a three-dimensional model of the target complex. Availability: http://dunbrack.fccc.edu/BAM.

Analysis of SecA dimerization in solution

The Sec pathway mediates translocation of protein across the inner membrane of bacteria. SecA is a motor protein that drives translocation of preprotein through the SecYEG channel. SecA reversibly dimerizes under physiological conditions, but different dimer interfaces have been observed in SecA crystal structures. Here, we have used biophysical approaches to address the nature of the SecA dimer that exists in solution. We have taken advantage of the extreme salt sensitivity of SecA dimerization to compare the rates of hydrogen–deuterium exchange of the monomer and dimer and have analyzed the effects of single-alanine substitutions on dimerization affinity. Our results support the antiparallel dimer arrangement observed in one of the crystal structures of Bacillus subtilis SecA. Additional residues lying within the preprotein binding domain and the C-terminus are also protected from exchange upon dimerization, indicating linkage to a conformational transition of the preprotein binding domain from an open to a closed state. In agreement with this interpretation, normal mode analysis demonstrates that the SecA dimer interface influences the global dynamics of SecA such that dimerization stabilizes the closed conformation.