Protein-protein interaction at crystal contacts

Time-series analysis of rhenium(I) organometallic covalent binding to a model protein for drug development

Metal-based complexes with their unique chemical properties, including multiple oxidation states, radio-nuclear capabilities and various coordination geometries yield value as potential pharmaceuticals. Understanding the interactions between metals and biological systems will prove key for site-specific coordination of new metal-based lead compounds. This study merges the concepts of target coordination with fragment-based drug methodologies, supported by varying the anomalous scattering of rhenium along with infrared spectroscopy, and has identified rhenium metal sites bound covalently with two amino acid types within the model protein. A time-based series of lysozyme-rhenium-imidazole (HEWL-Re-Imi) crystals was analysed systematically over a span of 38 weeks. The main rhenium covalent coordination is observed at His15, Asp101 and Asp119. Weak (i.e. noncovalent) interactions are observed at other aspartic, asparagine, proline, tyrosine and tryptophan side chains. Detailed bond distance comparisons, including precision estimates, are reported, utilizing the diffraction precision index supplemented with small-molecule data from the Cambridge Structural Database. Key findings include changes in the protein structure induced at the rhenium metal binding site, not observed in similar metal-free structures. The binding sites are typically found along the solvent-channel-accessible protein surface. The three primary covalent metal binding sites are consistent throughout the time series, whereas binding to neighbouring amino acid residues changes through the time series. Co-crystallization was used, consistently yielding crystals four days after setup. After crystal formation, soaking of the compound into the crystal over 38 weeks is continued and explains these structural adjustments. It is the covalent bond stability at the three sites, their proximity to the solvent channel and the movement of residues to accommodate the metal that are important, and may prove useful for future radiopharmaceutical development including target modification.

Computational Approaches to Predict Protein-Protein Interactions in Crowded Cellular Environments

Investigating protein-protein interactions is crucial for understanding cellular biological processes because proteins often function within molecular complexes rather than in isolation. While experimental and computational methods have provided valuable insights into these interactions, they often overlook a critical factor: the crowded cellular environment. This environment significantly impacts protein behavior, including structural stability, diffusion, and ultimately the nature of binding. In this review, we discuss theoretical and computational approaches that allow the modeling of biological systems to guide and complement experiments and can thus significantly advance the investigation, and possibly the predictions, of protein-protein interactions in the crowded environment of cell cytoplasm. We explore topics such as statistical mechanics for lattice simulations, hydrodynamic interactions, diffusion processes in high-viscosity environments, and several methods based on molecular dynamics simulations. By synergistically leveraging methods from biophysics and computational biology, we review the state of the art of computational methods to study the impact of molecular crowding on protein-protein interactions and discuss its potential revolutionizing effects on the characterization of the human interactome.

Structural and physical features that distinguish tumor-controlling from inactive cancer neoepitopes

Neoepitopes arising from amino acid substitutions due to single nucleotide polymorphisms are targets of T cell immune responses to cancer and are of significant interest in the development of cancer vaccines. However, understanding the characteristics of rare protective neoepitopes that truly control tumor growth has been a challenge, due to their scarcity as well as the challenge of verifying true, neoepitope-dependent tumor control in humans. Taking advantage of recent work in mouse models that circumvented these challenges, here, we compared the structural and physical properties of neoepitopes that range from fully protective to immunologically inactive. As neoepitopes are derived from self-peptides that can induce immune tolerance, we studied not only how the various neoepitopes differ from each other but also from their wild-type counterparts. We identified multiple features associated with protection, including features that describe how neoepitopes differ from self as well as features associated with recognition by diverse T cell receptor repertoires. We demonstrate both the promise and limitations of neoepitope structural analysis and predictive modeling and illustrate important aspects that can be incorporated into neoepitope prediction pipelines.

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.

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 and molecular dynamic studies of Pseudomonas aeruginosa OdaA reveal the regulation role of a C-terminal hinge element

BACKGROUND

Crotonase superfamily members exhibit great catalytic diversity towards various acyl-CoA substrates. A common CoA moiety binding pattern is usually observed in this family, understanding the substrate-binding mechanism would facilitate the rational engineering of crotonases for improved properties.

METHODS

We applied X-ray crystallography to investigate a putative enoyl-CoA hydratase/isomerase OdaA in Pseudomonas aeruginosa. Thermal shift assay (TSA) were performed to explore the binding of OdaA with CoA thioester substrates. Furthermore, we performed molecular dynamics (MD) simulations to elucidate the dynamics of its CoA-binding site.

RESULTS

We solved the crystal structures of the apo and CoA-bound OdaA. Thermal shift assay (TSA) showed that CoA thioester substrates bind to OdaA with a different degree. MD simulations demonstrated that the C-terminal alpha helix underwent a structural transition and a hinge region would associate with this conformational change.

CONCLUSIONS

TSA in combination with MD simulations elucidate that the dynamics of C-terminal alpha helix in CoA-binding, and a hinge region play an important role in conformational change.

GENERAL SIGNIFICANCE

Those results help to extend our knowledge about the nature of crotonases and would be informative for future mechanistic studies and industry applications.

In-Cell Protein-Protein Contacts: Transient Interactions in the Crowd

Proteins in vivo are immersed in a crowded environment of water, ions, metabolites, and macromolecules. In-cell experiments highlight how transient weak protein-protein interactions promote (via functional "quinary structure") or hinder (via competitive binding or "sticking") complex formation. Computational models of the cytoplasm are expensive. We tackle this challenge with an all-atom model of a small volume of the E. coli cytoplasm to simulate protein-protein contacts up to the 5 μs time scale on the special-purpose supercomputer Anton 2. We use three CHARMM-derived force fields: C22*, C36m, and C36mCU (with CUFIX corrections). We find that both C36m and C36mCU form smaller contact surfaces than C22*. Although CUFIX was developed to reduce protein-protein sticking, larger contacts are observed with C36mCU than C36m. We show that the lifespan Δt of protein-protein contacts obeys a power law distribution between 0.03 and 3 μs, with ∼90% of all contacts lasting <1 μs (similar to the time scale for downhill folding).

Unit-cell response of tetragonal hen egg white lysozyme upon controlled relative humidity variation

Variation of relative humidity (rH) greatly affects the internal order of solvent-based protein crystals, and the rearrangement of molecules can be efficiently recorded in distinct diffraction patterns. This study focuses on this topic, reporting the effect of rH variation experiments on hen egg white lysozyme (HEWL) polycrystalline precipitates of tetragonal symmetry using X-ray powder diffraction (XRPD). In situ XRPD data were collected on HEWL specimens during dehydration and rehydration processes using laboratory instrumentation. A known polymorph [space group P43212, a = 79.07181 (1), c = 38.0776 (1) Å] was identified during gradual dehydration from 95 to 63% rH and vice versa. Pawley analysis of collected data sets and accurate extraction of unit-cell parameters indicated a characteristic evolution of the tetragonal axes with rH. In addition, there is a low humidity level below which samples do not retain their crystallinity. This work illustrates the accuracy of laboratory XRPD as a probe for time-resolved studies of proteins and in situ investigations of gradual structural modifications upon rH variation. These experiments provide essential information for improving production and post-production practices of microcrystalline protein-based pharmaceuticals.

Recombinant production, purification, crystallization, and structure analysis of human transforming growth factor β2 in a new conformation

Transforming growth factor β is a disulfide-linked dimeric cytokine that occurs in three highly related isoforms (TGFβ1–TGFβ3) engaged in signaling functions through binding of cognate TGFβ receptors. To regulate this pathway, the cytokines are biosynthesized as inactive pro-TGFβs with an N-terminal latency-associated protein preceding the mature moieties. Due to their pleiotropic implications in physiology and pathology, TGFβs are privileged objects of in vitro studies. However, such studies have long been limited by the lack of efficient human recombinant expression systems of native, glycosylated, and homogenous proteins. Here, we developed pro-TGFβ2 production systems based on human Expi293F cells, which yielded >2 mg of pure histidine- or Strep-tagged protein per liter of cell culture. We assayed this material biophysically and in crystallization assays and obtained a different crystal form of mature TGFβ2, which adopted a conformation deviating from previous structures, with a distinct dimeric conformation that would require significant rearrangement for binding of TGFβ receptors. This new conformation may be reversibly adopted by a certain fraction of the mature TGβ2 population and represent a hitherto undescribed additional level of activity regulation of the mature growth factor once the latency-associated protein has been separated.

High-symmetry protein assemblies: patterns and emerging applications

The accelerated elucidation of three-dimensional structures of protein complexes, both natural and designed, is providing new examples of large supramolecular assemblies with intriguing shapes. Those with high symmetry - based on the geometries of the Platonic solids - are particularly notable as their innately closed forms create interior spaces with varying degrees of enclosure. We survey known protein assemblies of this type and discuss their geometric features. The results bear on issues of protein function and evolution, while also guiding novel bioengineering applications. Recent successes using high-symmetry protein assemblies for applications in interior encapsulation and exterior display are highlighted.

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 .

Role of Computational Methods in Going beyond X-ray Crystallography to Explore Protein Structure and Dynamics

Protein structural biology came a long way since the determination of the first three-dimensional structure of myoglobin about six decades ago. Across this period, X-ray crystallography was the most important experimental method for gaining atomic-resolution insight into protein structures. However, as the role of dynamics gained importance in the function of proteins, the limitations of X-ray crystallography in not being able to capture dynamics came to the forefront. Computational methods proved to be immensely successful in understanding protein dynamics in solution, and they continue to improve in terms of both the scale and the types of systems that can be studied. In this review, we briefly discuss the limitations of X-ray crystallography in studying protein dynamics, and then provide an overview of different computational methods that are instrumental in understanding the dynamics of proteins and biomacromolecular complexes.

Integrating co-evolutionary signals and other properties of residue pairs to distinguish biological interfaces from crystal contacts

It remains challenging to accurately discriminate between biological and crystal interfaces. Most existing analyses and algorithms focused on the features derived from a single side of the interface. However, less attention has been paid to the properties of residue pairs across protein interfaces. To address this problem, we defined a novel co-evolutionary feature for homodimers through integrating direct coupling analysis and image processing techniques. The residue pairs across biological homodimeric interfaces were significantly enriched in co-evolving residues compared to those across crystal contacts, resulting in a promising classification accuracy with area under the curves (AUCs) of >0.85. Considering the availability of co-evolutionary feature, we also designed other residue pair based features that were useful for both homodimers and heterodimers. The most informative residue pairs were identified to reflect the interaction preferences across protein interfaces. Regarding the other extant properties, we designed the new descriptors at the interface residue level as well as at the pairwise contact level. Extensive validation showed that these single properties can be used to identify biological interfaces with AUCs ranging from 0.60 to 0.88. By integrating co-evolutionary feature with other residue pair based properties, our final prediction model output excellent performance with AUCs of >0.91 on different datasets. Compared to existing methods, our algorithm not only yielded better or comparable results but also provided complementary information. An easy-to-use web server is freely accessible at http://liulab.hzau.edu.cn/RPAIAnalyst.

Ligand Access Channels in Cytochrome P450 Enzymes: A Review

Quantitative structure-activity relationships may bring invaluable information on structural elements of both enzymes and substrates that, together, govern substrate specificity. Buried active sites in cytochrome P450 enzymes are connected to the solvent by a network of channels exiting at the distal surface of the protein. This review presents different in silico tools that were developed to uncover such channels in P450 crystal structures. It also lists some of the experimental evidence that actually suggest that these predicted channels might indeed play a critical role in modulating P450 functions. Amino acid residues at the entrance of the channels may participate to a first global ligand recognition of ligands by P450 enzymes before they reach the buried active site. Moreover, different P450 enzymes show different networks of predicted channels. The plasticity of P450 structures is also important to take into account when looking at how channels might play their role.

Identifying and Overcoming Crystal Pathologies: Disorder and Twinning

Macromolecular crystals are prone to a number of pathologies that result from aberrant molecular packing. Two common pathologies encountered in macromolecular crystals are rigid-body disorder and twinning. When a crystal displays one of these pathologies, its diffraction pattern is altered in a way that generally complicates structure determination. The severity of the underlying abnormalities varies from case to case, and sometimes the resulting alterations to the diffraction pattern are immediately obvious, while at other times they may go entirely unnoticed. Structure determination from a crystal that suffers from disorder or twinning may or may not be possible, depending on the specific nature of the pathology, and on how the data are handled. This chapter provides an introduction to these pathologies, with an emphasis on providing guidelines for identifying and overcoming them when they pose a threat to successful structure determination.

Structural changes of homodimers in the PDB

Protein complexes are involved in various biological phenomena. These complexes are intrinsically flexible, and structural changes are essential to their functions. To perform a large-scale automated analysis of the structural changes of complexes, we combined two original methods. An application, SCPC, compares two structures of protein complexes and decides the match of binding mode. Another application, Motion Tree, identifies rigid-body motions in various sizes and magnitude from the two structural complexes with the same binding mode. This approach was applied to all available homodimers in the Protein Data Bank (PDB). We defined two complex-specific motions: interface motion and subunit-spanning motion. In the former, each subunit of a complex constitutes a rigid body, and the relative movement between subunits occurs at the interface. In the latter, structural parts from distinct subunits constitute a rigid body, providing the relative movement spanning subunits. All structural changes were classified and examined. It was revealed that the complex-specific motions were common in the homodimers, detected in around 40% of families. The dimeric interfaces were likely to be small and flat for interface motion, while large and rugged for subunit-spanning motion. Interface motion was accompanied by a drastic change in contacts at the interface, while the change in the subunit-spanning motion was moderate. These results indicate that the interface properties of homodimers correlated with the type of complex-specific motion. The study demonstrates that the pipeline of SCPC and Motion Tree is useful for the massive analysis of structural change of protein complexes.

Packing topology in crystals of proteins and small molecules: a comparison

We compared the topologies of protein and small molecule crystals, which have many common features - both are molecular crystals with intermolecular interactions much weaker than intramolecular interactions. They also have different features - a considerably large fraction of the volume of protein crystals is occupied by liquid water while no room is available to other molecules in small molecule crystals. We analyzed the overall and local topology and performed multilevel topological analyses (with the software package ToposPro) of carefully selected high quality sets of protein and small molecule crystal structures. Given the suboptimal packing of protein crystals, which is due the special shape and size of proteins, it would be reasonable to expect that the topology of protein crystals is different from the topology of small molecule crystals. Surprisingly, we discovered that these two types of crystalline compounds have strikingly similar topologies. This might suggest that molecular crystal formations share symmetry rules independent of molecular dimension.

What macromolecular crystallogenesis tells us - what is needed in the future

Crystallogenesis is a longstanding topic that has transformed into a discipline that is mainly focused on the preparation of crystals for practising crystallo-graphers. Although the idiosyncratic features of proteins have to be taken into account, the crystallization of proteins is governed by the same physics as the crystallization of inorganic materials. At present, a diversified panel of crystallization methods adapted to proteins has been validated, and although only a few methods are in current practice, the success rate of crystallization has increased constantly, leading to the determination of ∼105 X-ray structures. These structures reveal a huge repertoire of protein folds, but they only cover a restricted part of macromolecular diversity across the tree of life. In the future, crystals representative of missing structures or that will better document the structural dynamics and functional steps underlying biological processes need to be grown. For the pertinent choice of biologically relevant targets, computer-guided analysis of structural databases is needed. From another perspective, crystallization is a self-assembly process that can occur in the bulk of crowded fluids, with crystals being supramolecular assemblies. Life also uses self-assembly and supramolecular processes leading to transient, or less often stable, complexes. An integrated view of supramolecularity implies that proteins crystallizing either in vitro or in vivo or participating in cellular processes share common attributes, notably determinants and antideterminants that favour or disfavour their correct or incorrect associations. As a result, under in vivo conditions proteins show a balance between features that favour or disfavour association. If this balance is broken, disorders/diseases occur. Understanding crystallization under in vivo conditions is a challenge for the future. In this quest, the analysis of packing contacts and contacts within oligomers will be crucial in order to decipher the rules governing protein self-assembly and will guide the engineering of novel biomaterials. In a wider perspective, understanding such contacts will open the route towards supramolecular biology and generalized crystallogenesis.

Computational protein structure refinement: Almost there, yet still so far to go

Protein structures are essential in modern biology yet experimental methods are far from being able to catch up with the rapid increase in available genomic data. Computational protein structure prediction methods aim to fill the gap while the role of protein structure refinement is to take approximate initial template-based models and bring them closer to the true native structure. Current methods for computational structure refinement rely on molecular dynamics simulations, related sampling methods, or iterative structure optimization protocols. The best methods are able to achieve moderate degrees of refinement but consistent refinement that can reach near-experimental accuracy remains elusive. Key issues revolve around the accuracy of the energy function, the inability to reliably rank multiple models, and the use of restraints that keep sampling close to the native state but also limit the degree of possible refinement. A different aspect is the question of what exactly the target of high-resolution refinement should be as experimental structures are affected by experimental conditions and different biological questions require varying levels of accuracy. While improvement of the global protein structure is a difficult problem, high-resolution refinement methods that improves local structural quality such as favorable stereochemistry and the avoidance of atomic clashes are much more successful.

The "Sticky Patch" Model of Crystallization and Modification of Proteins for Enhanced Crystallizability

Crystallization of macromolecules has long been perceived as a stochastic process, which cannot be predicted or controlled. This is consistent with another popular notion that the interactions of molecules within the crystal, i.e., crystal contacts, are essentially random and devoid of specific physicochemical features. In contrast, functionally relevant surfaces, such as oligomerization interfaces and specific protein-protein interaction sites, are under evolutionary pressures so their amino acid composition, structure, and topology are distinct. However, current theoretical and experimental studies are significantly changing our understanding of the nature of crystallization. The increasingly popular "sticky patch" model, derived from soft matter physics, describes crystallization as a process driven by interactions between select, specific surface patches, with properties thermodynamically favorable for cohesive interactions. Independent support for this model comes from various sources including structural studies and bioinformatics. Proteins that are recalcitrant to crystallization can be modified for enhanced crystallizability through chemical or mutational modification of their surface to effectively engineer "sticky patches" which would drive crystallization. Here, we discuss the current state of knowledge of the relationship between the microscopic properties of the target macromolecule and its crystallizability, focusing on the "sticky patch" model. We discuss state-of-the-art in silico methods that evaluate the propensity of a given target protein to form crystals based on these relationships, with the objective to design variants with modified molecular surface properties and enhanced crystallization propensity. We illustrate this discussion with specific cases where these approaches allowed to generate crystals suitable for structural analysis.

Common Evolutionary Origin of Procapsid Proteases, Phage Tail Tubes, and Tubes of Bacterial Type VI Secretion Systems

Many large viruses, including tailed dsDNA bacteriophages and herpesviruses, assemble their capsids via formation of precursors, called procapsids or proheads. The prohead has an internal core, made of scaffolding proteins, and an outer shell, formed by the major capsid protein. The prohead usually contains a protease, which is activated during capsid maturation to destroy the inner core and liberate space for the genome. Here, we report a 2.0 Å resolution structure of the pentameric procapsid protease of bacteriophage T4, gene product (gp)21. The structure corresponds to the enzyme's pre-active state in which its N-terminal region blocks the catalytic center, demonstrating that the activation mechanism involves self-cleavage of nine N-terminal residues. We describe similarities and differences between T4 gp21 and related herpesvirus proteases. We found that gp21 and the herpesvirus proteases have similarity with proteins forming the tubes of phage tails and bacterial type VI secretion systems, suggesting their common evolutionary origin.

Computational approaches to detect allosteric pathways in transmembrane molecular machines

Many of the functions of transmembrane proteins involved in signal processing and transduction across the cell membrane are determined by allosteric couplings that propagate the functional effects well beyond the original site of activation. Data gathered from breakthroughs in biochemistry, crystallography, and single molecule fluorescence have established a rich basis of information for the study of molecular mechanisms in the allosteric couplings of such transmembrane proteins. The mechanistic details of these couplings, many of which have therapeutic implications, however, have only become accessible in synergy with molecular modeling and simulations. Here, we review some recent computational approaches that analyze allosteric coupling networks (ACNs) in transmembrane proteins, and in particular the recently developed Protein Interaction Analyzer (PIA) designed to study ACNs in the structural ensembles sampled by molecular dynamics simulations. The power of these computational approaches in interrogating the functional mechanisms of transmembrane proteins is illustrated with selected examples of recent experimental and computational studies pursued synergistically in the investigation of secondary active transporters and GPCRs. This article is part of a Special Issue entitled: Membrane Proteins edited by J.C. Gumbart and Sergei Noskov.

Structure and Sequence Analyses of Clustered Protocadherins Reveal Antiparallel Interactions that Mediate Homophilic Specificity

Clustered protocadherin (Pcdh) proteins mediate dendritic self-avoidance in neurons via specific homophilic interactions in their extracellular cadherin (EC) domains. We determined crystal structures of EC1-EC3, containing the homophilic specificity-determining region, of two mouse clustered Pcdh isoforms (PcdhγA1 and PcdhγC3) to investigate the nature of the homophilic interaction. Within the crystal lattices, we observe antiparallel interfaces consistent with a role in trans cell-cell contact. Antiparallel dimerization is supported by evolutionary correlations. Two interfaces, located primarily on EC2-EC3, involve distinctive clustered Pcdh structure and sequence motifs, lack predicted glycosylation sites, and contain residues highly conserved in orthologs but not paralogs, pointing toward their biological significance as homophilic interaction interfaces. These two interfaces are similar yet distinct, reflecting a possible difference in interaction architecture between clustered Pcdh subfamilies. These structures initiate a molecular understanding of clustered Pcdh assemblies that are required to produce functional neuronal networks.

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

DynaFace: Discrimination between Obligatory and Non-obligatory Protein-Protein Interactions Based on the Complex's Dynamics

Protein-protein interfaces have been evolutionarily-designed to enable transduction between the interacting proteins. Thus, we hypothesize that analysis of the dynamics of the complex can reveal details about the nature of the interaction, and in particular whether it is obligatory, i.e., persists throughout the entire lifetime of the proteins, or not. Indeed, normal mode analysis, using the Gaussian network model, shows that for the most part obligatory and non-obligatory complexes differ in their decomposition into dynamic domains, i.e., the mobile elements of the protein complex. The dynamic domains of obligatory complexes often mix segments from the interacting chains, and the hinges between them do not overlap with the interface between the chains. In contrast, in non-obligatory complexes the interface often hinges between dynamic domains, held together through few anchor residues on one side of the interface that interact with their counterpart grooves in the other end. In automatic analysis, 117 of 139 obligatory (84.2%) and 203 of 246 non-obligatory (82.5%) complexes are correctly classified by our method: DynaFace. We further use DynaFace to predict obligatory and non-obligatory interactions among a set of 300 putative protein complexes. DynaFace is available at: http://safir.prc.boun.edu.tr/dynaface.

SdsA polymorph isolation and improvement of their crystal quality using nonconventional crystallization techniques

SdsA, a sodium dodecyl sulfate hydrolase, fromPseudomonas aeruginosawas crystallized in three different crystal polymorphs and their three-dimensional structure was determined. The different polymorphs present different crystal packing habits. One of the polymorphs suggests the existence of a tetramer, an oligomeric state not observed previously, while the crystal packing of the remaining two polymorphs obstructs the active site entrance but stabilizes flexible regions of the protein. Nonconventional crystallization methods that minimize convection, such as counterdiffusion in polyvinyl alcohol gel coupled with the influence of a 500 MHz (10.2 T) magnetic field, were necessary to isolate the poorest diffracting polymorph and increase its internal order to determine its structure by X-ray diffraction. The results obtained show the effectiveness of nonconventional crystallographic methods to isolate different crystal polymorphs.

A structural dissection of large protein-protein crystal packing contacts

With the rapid increase in crystal structures of protein-protein complexes deposited in the Protein Data Bank (PDB), more and more crystal contacts have been shown to have similar or even larger interface areas than biological interfaces. However, little attention has been paid to these large crystal packing contacts and their structural principles remain unknown. To address this issue, we used a comparative feature analysis to analyze the geometric and physicochemical properties of large crystal packing contacts by comparing two types of specific protein-protein interactions (PPIs), weak transient complexes and permanent homodimers. Our results show that although large crystal packing contacts have a similar interface area and contact size as permanent homodimers, they tend to be more planar, loosely packed and less hydrophobic than permanent homodimers and cannot form a central core region that is fully buried during interaction. However, the properties of large crystal packing contacts, except for the interface area and contact size, more closely resemble those of weak transient complexes. The large overlap between biological and large crystal packing contacts indicates that interface properties are not efficient indicators for classification of biological interfaces from large crystal packing contacts and finding other specific features urgently needed.

Structural Perspectives on the Evolutionary Expansion of Unique Protein-Protein Binding Sites

Structures of protein complexes provide atomistic insights into protein interactions. Human proteins represent a quarter of all structures in the Protein Data Bank; however, available protein complexes cover less than 10% of the human proteome. Although it is theoretically possible to infer interactions in human proteins based on structures of homologous protein complexes, it is still unclear to what extent protein interactions and binding sites are conserved, and whether protein complexes from remotely related species can be used to infer interactions and binding sites. We considered biological units of protein complexes and clustered protein-protein binding sites into similarity groups based on their structure and sequence, which allowed us to identify unique binding sites. We showed that the growth rate of the number of unique binding sites in the Protein Data Bank was much slower than the growth rate of the number of structural complexes. Next, we investigated the evolutionary roots of unique binding sites and identified the major phyletic branches with the largest expansion in the number of novel binding sites. We found that many binding sites could be traced to the universal common ancestor of all cellular organisms, whereas relatively few binding sites emerged at the major evolutionary branching points. We analyzed the physicochemical properties of unique binding sites and found that the most ancient sites were the largest in size, involved many salt bridges, and were the most compact and least planar. In contrast, binding sites that appeared more recently in the evolution of eukaryotes were characterized by a larger fraction of polar and aromatic residues, and were less compact and more planar, possibly due to their more transient nature and roles in signaling processes.

Use B-factor related features for accurate classification between protein binding interfaces and crystal packing contacts

Background

Distinction between true protein interactions and crystal packing contacts is important for structural bioinformatics studies to respond to the need of accurate classification of the rapidly increasing protein structures. There are many unannotated crystal contacts and there also exist false annotations in this rapidly expanding volume of data. Previous tools have been proposed to address this problem. However, challenging issues still remain, such as low performance when the training and test data contain mixed interfaces having diverse sizes of contact areas.

Methods and results

B factor is a measure to quantify the vibrational motion of an atom, a more relevant feature than interface size to characterize protein binding. We propose to use three features related to B factor for the classification between biological interfaces and crystal packing contacts. The first feature is the sum of the normalized B factors of the interfacial atoms in the contact area, the second is the average of the interfacial B factor per residue in the chain, and the third is the average number of interfacial atoms with a negative normalized B factor per residue in the chain. We investigate the distribution properties of these basic features and a compound feature on four datasets of biological binding and crystal packing, and on a protein binding-only dataset with known binding affinity. We also compare the cross-dataset classification performance of these features with existing methods and with a widely-used and the most effective feature interface area. The results demonstrate that our features outperform the interface area approach and the existing prediction methods remarkably for many tests on all of these datasets.

Conclusions

The proposed B factor related features are more effective than interface area to distinguish crystal packing from biological binding interfaces. Our computational methods have a potential for large-scale and accurate identification of biological interactions from the experimentally determined structural data stored at PDB which may have diverse interface sizes.

Effects of protein engineering and rational mutagenesis on crystal lattice of single chain antibody fragments

Protein crystallization is dependent upon, and sensitive to, the intermolecular contacts that assist in ordering proteins into a three-dimensional lattice. Here we used protein engineering and mutagenesis to affect the crystallization of single chain antibody fragments (scFvs) that recognize the EE epitope (EYMPME) with high affinity. These hypercrystallizable scFvs are under development to assist difficult proteins, such as membrane proteins, in forming crystals, by acting as crystallization chaperones. Guided by analyses of intermolecular crystal lattice contacts, two second-generation anti-EE scFvs were produced, which bind to proteins with installed EE tags. Surprisingly, although noncomplementarity determining region (CDR) lattice residues from the parent scFv framework remained unchanged through the processes of protein engineering and rational design, crystal lattices of the derivative scFvs differ. Comparison of energy calculations and the experimentally-determined lattice interactions for this basis set provides insight into the complexity of the forces driving crystal lattice choice and demonstrates the availability of multiple well-ordered surface features in our scFvs capable of forming versatile crystal contacts.

Statistical analysis of crystallization database links protein physico-chemical features with crystallization mechanisms

X-ray crystallography is the predominant method for obtaining atomic-scale information about biological macromolecules. Despite the success of the technique, obtaining well diffracting crystals still critically limits going from protein to structure. In practice, the crystallization process proceeds through knowledge-informed empiricism. Better physico-chemical understanding remains elusive because of the large number of variables involved, hence little guidance is available to systematically identify solution conditions that promote crystallization. To help determine relationships between macromolecular properties and their crystallization propensity, we have trained statistical models on samples for 182 proteins supplied by the Northeast Structural Genomics consortium. Gaussian processes, which capture trends beyond the reach of linear statistical models, distinguish between two main physico-chemical mechanisms driving crystallization. One is characterized by low levels of side chain entropy and has been extensively reported in the literature. The other identifies specific electrostatic interactions not previously described in the crystallization context. Because evidence for two distinct mechanisms can be gleaned both from crystal contacts and from solution conditions leading to successful crystallization, the model offers future avenues for optimizing crystallization screens based on partial structural information. The availability of crystallization data coupled with structural outcomes analyzed through state-of-the-art statistical models may thus guide macromolecular crystallization toward a more rational basis.

Coverage of protein domain families with structural protein-protein interactions: current progress and future trends

Protein interactions have evolved into highly precise and regulated networks adding an immense layer of complexity to cellular systems. The most accurate atomistic description of protein binding sites can be obtained directly from structures of protein complexes. The availability of structurally characterized protein interfaces significantly improves our understanding of interactomes, and the progress in structural characterization of protein-protein interactions (PPIs) can be measured by calculating the structural coverage of protein domain families. We analyze the coverage of protein domain families (defined according to CDD and Pfam databases) by structures, structural protein-protein complexes and unique protein binding sites. Structural PPI coverage of currently available protein families is about 30% without any signs of saturation in coverage growth dynamics. Given the current growth rates of domain databases and structural PPI deposition, complete domain coverage with PPIs is not expected in the near future. As a result of this study we identify families without any protein-protein interaction evidence (listed on a supporting website http://www.ncbi.nlm.nih.gov/Structure/ibis/coverage/) and propose them as potential targets for structural studies with a focus on protein interactions.

DrugScorePPI knowledge-based potentials used as scoring and objective function in protein-protein docking

The distance-dependent knowledge-based DrugScore^PPI potentials, previously developed for in silico alanine scanning and hot spot prediction on given structures of protein-protein complexes, are evaluated as a scoring and objective function for the structure prediction of protein-protein complexes. When applied for ranking “unbound perturbation” (“unbound docking”) decoys generated by Baker and coworkers a 4-fold (1.5-fold) enrichment of acceptable docking solutions in the top ranks compared to a random selection is found. When applied as an objective function in FRODOCK for bound protein-protein docking on 97 complexes of the ZDOCK benchmark 3.0, DrugScore^PPI/FRODOCK finds up to 10% (15%) more high accuracy solutions in the top 1 (top 10) predictions than the original FRODOCK implementation. When used as an objective function for global unbound protein-protein docking, fair docking success rates are obtained, which improve by ∼2-fold to 18% (58%) for an at least acceptable solution in the top 10 (top 100) predictions when performing knowledge-driven unbound docking. This suggests that DrugScore^PPI balances well several different types of interactions important for protein-protein recognition. The results are discussed in view of the influence of crystal packing and the type of protein-protein complex docked. Finally, a simple criterion is provided with which to estimate a priori if unbound docking with DrugScore^PPI/FRODOCK will be successful.

Factors correlating with significant differences between X-ray structures of myoglobin

Validation of general ideas about the origins of conformational differences in proteins is critical in order to arrive at meaningful functional insights. Here, principal component analysis (PCA) and distance difference matrices are used to validate some such ideas about the conformational differences between 291 myoglobin structures from sperm whale, horse and pig. Almost all of the horse and pig structures form compact PCA clusters with only minor coordinate differences and outliers that are easily explained. The 222 whale structures form a few dense clusters with multiple outliers. A few whale outliers with a prominent distortion of the GH loop are very similar to the cluster of horse structures, which all have a similar GH-loop distortion apparently owing to intermolecular crystal lattice hydrogen bonds to the GH loop from residues near the distal histidine His64. The variations of the GH-loop coordinates in the whale structures are likely to be owing to the observed alternative intermolecular crystal lattice bond, with the change to the GH loop distorting bonds correlated with the binding of specific `unusual' ligands. Such an alternative intermolecular bond is not observed in horse myoglobins, obliterating any correlation with the ligands. Intermolecular bonds do not usually cause significant coordinate differences and cannot be validated as their universal cause. Most of the native-like whale myoglobin structure outliers can be correlated with a few specific factors. However, these factors do not always lead to coordinate differences beyond the previously determined uncertainty thresholds. The binding of unusual ligands by myoglobin, leading to crystal-induced distortions, suggests that some of the conformational differences between the apo and holo structures might not be `functionally important' but rather artifacts caused by the binding of `unusual' substrate analogs. The causes of P6 symmetry in myoglobin crystals and the relationship between crystal and solution structures are also discussed.

Characterizing protein crystal contacts and their role in crystallization: rubredoxin as a case study

The fields of structural biology and soft matter have independently sought out fundamental principles to rationalize protein crystallization. Yet the conceptual differences and the limited overlap between the two disciplines have thus far prevented a comprehensive understanding of the phenomenon to emerge. We conduct a computational study of proteins from the rubredoxin family that bridges the two fields. Using atomistic simulations, we characterize the protein crystal contacts, and accordingly parameterize patchy particle models. Comparing the phase diagrams of these schematic models with experimental results enables us to critically examine the assumptions behind the two approaches. The study also reveals features of protein–protein interactions that can be leveraged to crystallize proteins more generally.

The rotational order-disorder structure of the reversibly photoswitchable red fluorescent protein rsTagRFP

The rotational order-disorder (OD) structure of the reversibly photoswitchable fluorescent protein rsTagRFP is discussed in detail. The structure is composed of tetramers of 222 symmetry incorporated into the lattice in two different orientations rotated 90° with respect to each other around the crystal c axis and with tetramer axes coinciding with the crystallographic twofold axes. The random distribution of alternatively oriented tetramers in the crystal creates the rotational OD structure with statistically averaged I422 symmetry. Despite order-disorder pathology, the structure of rsTagRFP has electron-density maps of good quality for both non-overlapping and overlapping parts of the model. The crystal contacts, crystal internal architecture and a possible mechanism of rotational OD crystal formation are discussed.

KBDOCK 2013: a spatial classification of 3D protein domain family interactions

Comparing, classifying and modelling protein structural interactions can enrich our understanding of many biomolecular processes. This contribution describes Kbdock (http://kbdock.loria.fr/), a database system that combines the Pfam domain classification with coordinate data from the PDB to analyse and model 3D domain–domain interactions (DDIs). Kbdock can be queried using Pfam domain identifiers, protein sequences or 3D protein structures. For a given query domain or pair of domains, Kbdock retrieves and displays a non-redundant list of homologous DDIs or domain–peptide interactions in a common coordinate frame. Kbdock may also be used to search for and visualize interactions involving different, but structurally similar, Pfam families. Thus, structural DDI templates may be proposed even when there is little or no sequence similarity to the query domains.

An analysis of oligomerization interfaces in transmembrane proteins

BACKGROUND

The amount of transmembrane protein (TM) structures solved to date is now large enough to attempt large scale analyses. In particular, extensive studies of oligomeric interfaces in the transmembrane region are now possible.

RESULTS

We have compiled the first fully comprehensive set of validated transmembrane protein interfaces in order to study their features and assess what differentiates them from their soluble counterparts.

CONCLUSIONS

The general features of TM interfaces do not differ much from those of soluble proteins: they are large, tightly packed and possess many interface core residues. In our set, membrane lipids were not found to significantly mediate protein-protein interfaces. Although no G protein-coupled receptor (GPCR) was included in the validated set, we analyzed the crystallographic dimerization interfaces proposed in the literature. We found that the putative dimer interfaces proposed for class A GPCRs do not show the usual patterns of stable biological interfaces, neither in terms of evolution nor of packing, thus they likely correspond to crystal interfaces. We cannot however rule out the possibility that they constitute transient or weak interfaces. In contrast we do observe a clear signature of biological interface for the proposed dimer of the class F human Smoothened receptor.

Expanding the druggable space of the LSD1/CoREST epigenetic target: new potential binding regions for drug-like molecules, peptides, protein partners, and chromatin

Lysine specific demethylase-1 (LSD1/KDM1A) in complex with its corepressor protein CoREST is a promising target for epigenetic drugs. No therapeutic that targets LSD1/CoREST, however, has been reported to date. Recently, extended molecular dynamics (MD) simulations indicated that LSD1/CoREST nanoscale clamp dynamics is regulated by substrate binding and highlighted key hinge points of this large-scale motion as well as the relevance of local residue dynamics. Prompted by the urgent need for new molecular probes and inhibitors to understand LSD1/CoREST interactions with small-molecules, peptides, protein partners, and chromatin, we undertake here a configurational ensemble approach to expand LSD1/CoREST druggability. The independent algorithms FTMap and SiteMap and our newly developed Druggable Site Visualizer (DSV) software tool were used to predict and inspect favorable binding sites. We find that the hinge points revealed by MD simulations at the SANT2/Tower interface, at the SWIRM/AOD interface, and at the AOD/Tower interface are new targets for the discovery of molecular probes to block association of LSD1/CoREST with chromatin or protein partners. A fourth region was also predicted from simulated configurational ensembles and was experimentally validated to have strong binding propensity. The observation that this prediction would be prevented when using only the X-ray structures available (including the X-ray structure bound to the same peptide) underscores the relevance of protein dynamics in protein interactions. A fifth region was highlighted corresponding to a small pocket on the AOD domain. This study sets the basis for future virtual screening campaigns targeting the five novel regions reported herein and for the design of LSD1/CoREST mutants to probe LSD1/CoREST binding with chromatin and various protein partners.

Evolutionary, physicochemical, and functional mechanisms of protein homooligomerization

Protein homooligomers afford several important benefits for the cell; they mediate and regulate gene expression, activity of many enzymes, ion channels, receptors, and cell-cell adhesion processes. The evolutionary and physical mechanisms of oligomer formation are very diverse and are not well understood. Certain homooligomeric states may be conserved within protein subfamilies and between different subfamilies, therefore providing the specificity to particular substrates while minimizing interactions with unwanted partners. In addition, transitions between different oligomeric states may regulate protein activity and support the switch between different pathways. In this chapter, we summarize the biological importance of homooligomeric assemblies, physicochemical properties of their interfaces, experimental methods for their identification, their evolution, and role in human diseases.

Interactome-wide prediction of protein-protein binding sites reveals effects of protein sequence variation in Arabidopsis thaliana

The specificity of protein-protein interactions is encoded in those parts of the sequence that compose the binding interface. Therefore, understanding how changes in protein sequence influence interaction specificity, and possibly the phenotype, requires knowing the location of binding sites in those sequences. However, large-scale detection of protein interfaces remains a challenge. Here, we present a sequence- and interactome-based approach to mine interaction motifs from the recently published Arabidopsis thaliana interactome. The resultant proteome-wide predictions are available via www.ab.wur.nl/sliderbio and set the stage for further investigations of protein-protein binding sites. To assess our method, we first show that, by using a priori information calculated from protein sequences, such as evolutionary conservation and residue surface accessibility, we improve the performance of interface prediction compared to using only interactome data. Next, we present evidence for the functional importance of the predicted sites, which are under stronger selective pressure than the rest of protein sequence. We also observe a tendency for compensatory mutations in the binding sites of interacting proteins. Subsequently, we interrogated the interactome data to formulate testable hypotheses for the molecular mechanisms underlying effects of protein sequence mutations. Examples include proteins relevant for various developmental processes. Finally, we observed, by analysing pairs of paralogs, a correlation between functional divergence and sequence divergence in interaction sites. This analysis suggests that large-scale prediction of binding sites can cast light on evolutionary processes that shape protein-protein interaction networks.

Structural determinants of protein kinase CK2 regulation by autoinhibitory polymerization

CK2 is a Ser/Thr protein kinase essential for cell viability whose activity is anomalously high in several cancers. CK2 is a validated target for cancer therapy with one small molecule inhibitor in phase I clinical trials. This enzyme is not regulated by mechanisms common to other protein kinases, and how its activity is controlled is still unclear. We present a new crystal structure of the CK2 holoenzyme that supports an autoinhibitory mechanism of regulation whereby the β-subunit plays an essential role in the formation of inactive polymeric assemblies. The derived structural model of (down)regulation by aggregation contributes to the interpretation of biochemical and functional data and paves the way for new strategies in the modulation of CK2 activity and for the design of non-ATP-competitive inhibitors targeting the interaction between the α catalytic and the β regulatory subunits.