Interaction preferences across protein-protein interfaces of obligatory and non-obligatory components are different

Overcoming phenotypic switching: targeting protein-protein interactions in cancer

Alternative protein-protein interactions (PPIs) arising from mutations or post-translational modifications (PTMs), termed phenotypic switching (PS), are critical for the transmission of alternative pathogenic signals and are particularly significant in cancer. In recent years, PPIs have emerged as promising targets for rational drug design, primarily because their high specificity facilitates targeting of disease-related signaling pathways. However, obstacles exist at the molecular level that arise from the properties of the interaction interfaces and the propensity of small molecule drugs to interact with more than one cleft surface. The difficulty in identifying small molecules that act as activators or inhibitors to counteract the biological effects of mutations raises issues that have not been encountered before. For example, small molecules can bind tightly but may not act as drugs or bind to multiple sites (interaction promiscuity). Another reason is the absence of significant clefts on protein surfaces; if a pocket is present, it may be too small, or its geometry may prevent binding. PS, which arises from oncogenic (alternative) signaling, causes drug resistance and forms the basis for the systemic robustness of tumors. In this review, the properties of PPI interfaces relevant to the design and development of targeting drugs are examined. In addition, the interactions between three tyrosine kinase inhibitors (TKIs) employed as drugs are discussed. Finally, potential novel targets of one of these drugs were identified in silico.

Comparison of side-chain dispersion in protein structures determined by cryo-EM and X-ray crystallography

An evaluation of systematic differences in local structure and conformation in the interior of protein tertiary structures determined by crystallography and by cryo-electron microscopy (cryo-EM) is reported. The expectation is that any consistent differences between the derived atomic models could provide insights into variations in side-chain packing that result from differences in specimens prepared for analysis between these two methods. By computing an atomic packing score, which provides a quantitative measure of clustering of side-chain atoms in the core of the tertiary structures, it is found that, in general, for structures determined by cryo-EM, side chains are more dispersed than in structures determined by X-ray crystallography over a similar resolution range. This trend is also observed in the packing comparison at subunit interfaces. Similar trends were observed in the packing comparison at the core of tertiary structures of the same proteins determined by both X-ray and cryo-EM methods. It is proposed here that the reduced dispersion of side chains in protein crystals could be due to some level of dehydration in 3D crystals prepared for X-ray crystallography and also because the higher rate of freezing of protein samples for cryo-EM may enable preservation of a more native conformation.

Nonspecific Binding-Fundamental Concepts and Consequences for Biosensing Applications

Nature achieves differentiation of specific and nonspecific binding in molecular interactions through precise control of biomolecules in space and time. Artificial systems such as biosensors that rely on distinguishing specific molecular binding events in a sea of nonspecific interactions have struggled to overcome this issue. Despite the numerous technological advancements in biosensor technologies, nonspecific binding has remained a critical bottleneck due to the lack of a fundamental understanding of the phenomenon. To date, the identity, cause, and influence of nonspecific binding remain topics of debate within the scientific community. In this review, we discuss the evolution of the concept of nonspecific binding over the past five decades based upon the thermodynamic, intermolecular, and structural perspectives to provide classification frameworks for biomolecular interactions. Further, we introduce various theoretical models that predict the expected behavior of biosensors in physiologically relevant environments to calculate the theoretical detection limit and to optimize sensor performance. We conclude by discussing existing practical approaches to tackle the nonspecific binding challenge in vitro for biosensing platforms and how we can both address and harness nonspecific interactions for in vivo systems.

The activity and stability of a cold-active acylaminoacyl peptidase rely on its dimerization by domain swapping

The study of enzymes from extremophiles arouses interest in Protein Science because of the amazing solutions these proteins adopt to cope with extreme conditions. Recently solved, the structure of the psychrophilic acyl aminoacyl peptidase from Sporosarcina psychrophila (SpAAP) pinpoints a mechanism of dimerization unusual for this class of enzymes. The quaternary structure of SpAAP relies on a domain-swapping mechanism involving the N-terminal A1 helix. The A1 helix is conserved among homologous mesophilic and psychrophilic proteins and its deletion causes the formation of a monomeric enzyme, which is inactive and prone to aggregate. Here, we investigate the dimerization mechanism of SpAAP through the analysis of chimeric heterodimers where a protomer lacking the A1 helix combines with a protomer carrying the inactivated catalytic site. Our results indicate that the two active sites are independent, and that a single A1 helix is sufficient to partially recover the quaternary structure and the activity of chimeric heterodimers. Since catalytically competent protomers are unstable and inactive unless they dimerize, SpAAP reveals as an "obligomer" for both structural and functional reasons.

A survey of human histone H1 subtypes interaction networks: Implications for histones H1 functioning

To show a spectrum of histone H1 subtypes (H1.1-H1.5) activity realized through the protein-protein interactions, data selected from APID resources were processed with sequence-based bioinformatics approaches. Histone H1 subtypes participate in over half a thousand interactions with nuclear and cytosolic proteins (ComPPI database) engaged in the enzymatic activity and binding of nucleic acids and proteins (SIFTER tool). Small-scale networks of H1 subtypes (STRING network) have similar topological parameters (P > .05) which are, however, different for networks hubs between subtype H1.1 and H1.4 and subtype H1.3 and H1.5 (P < .05) (Cytoscape software). Based on enriched GO terms (g:Profiler toolset) of interacting proteins, molecular function and biological process of networks hubs is related to RNA binding and ribosome biogenesis (subtype H1.1 and H1.4), cell cycle and cell division (subtype H1.3 and H1.5) and protein ubiquitination and degradation (subtype H1.2). The residue propensity (BIPSPI predictor) and secondary structures of interacting surfaces (GOR algorithm) as well as a value of equilibrium dissociation constant (ISLAND predictor) indicate that a type of H1 subtypes interactions is transient in term of the stability and medium-strong in relation to the strength of binding. Histone H1 subtypes bind interacting partners in the intrinsic disorder-dependent mode (FoldIndex, PrDOS predictor), according to the coupled folding and binding and mutual synergistic folding mechanism. These results evidence that multifunctional H1 subtypes operate via protein interactions in the networks of crucial cellular processes and, therefore, confirm a new histone H1 paradigm relating to its functioning in the protein-protein interaction networks.

Galvanization of Protein-Protein Interactions in a Dynamic Zinc Interactome

The presence of Zn²⁺ at protein-protein interfaces modulates complex function, stability, and introduces structural flexibility/complexity, chemical selectivity, and reversibility driven in a Zn²⁺-dependent manner. Recent studies have demonstrated that dynamically changing Zn²⁺ affects numerous cellular processes, including protein-protein communication and protein complex assembly. How Zn²⁺-involved protein-protein interactions (ZPPIs) are formed and dissociate and how their stability and reactivity are driven in a zinc interactome remain poorly understood, mostly due to experimental obstacles. Here, we review recent research advances on the role of Zn²⁺ in the formation of interprotein sites, their architecture, function, and stability. Moreover, we underline the importance of zinc networks in intersystemic communication and highlight bioinformatic and experimental challenges required for the identification and investigation of ZPPIs.

Computational Analysis of Non-covalent Interactions in Phycocyanin Subunit Interfaces

Protein-protein interactions are an important phenomenon in biological processes and functions. We used the manually curated non-redundant dataset of 118 phycocyanin interfaces to gain additional insight into this phenomenon using a robust inter-atomic non-covalent interaction analyzing tool PPCheck. Our observations indicate that there is a relatively high composition of hydrophobic residues at the interfaces. Most of the interface residues are clustered at the middle of the range which we call "standard-size" interfaces. Furthermore, the multiple interaction patterns founded in the present study indicate that more than half of the residues involved in these interactions participate in multiple and water-bridged hydrogen bonds. Thus, hydrogen bonds contribute maximally towards the stability of protein-protein complexes. The analysis shows that hydrogen bond energies contribute to about 88 % to the total energy and it also increases with interface size. Van der Waals (vdW) energy contributes to 9.3 %±1.7 % on average in these complexes. Moreover, there is about 1.9 %±1.5 % contribution by electrostatic energy. Nevertheless, the role by vdW and electrostatic energy could not be ignored in interface binding. Results show that the total binding energy is more for large phycocyanin interfaces. The normalized energy per residue was less than -16 kJ mol^-1 , while most of them have energy in the range from -6 to -14 kJ mol^-1 . The non-covalent interacting residues in these proteins were found to be highly conserved. Obtained results might contribute to the understanding of structural stability of this class of evolutionary essential proteins with increased practical application and future designs of novel protein-bioactive compound interactions.

ProteinsPlus: a web portal for structure analysis of macromolecules

With currently more than 126 000 publicly available structures and an increasing growth rate, the Protein Data Bank constitutes a rich data source for structure-driven research in fields like drug discovery, crop science and biotechnology in general. Typical workflows in these areas involve manifold computational tools for the analysis and prediction of molecular functions. Here, we present the ProteinsPlus web server that offers a unified easy-to-use interface to a broad range of tools for the early phase of structure-based molecular modeling. This includes solutions for commonly required pre-processing tasks like structure quality assessment (EDIA), hydrogen placement (Protoss) and the search for alternative conformations (SIENA). Beyond that, it also addresses frequent problems as the generation of 2D-interaction diagrams (PoseView), protein-protein interface classification (HyPPI) as well as automatic pocket detection and druggablity assessment (DoGSiteScorer). The unified ProteinsPlus interface covering all featured approaches provides various facilities for intuitive input and result visualization, case-specific parameterization and download options for further processing. Moreover, its generalized workflow allows the user a quick familiarization with the different tools. ProteinsPlus also stores the calculated results temporarily for future request and thus facilitates convenient result communication and re-access. The server is freely available at http://proteins.plus.

Methods for Discovering and Targeting Druggable Protein-Protein Interfaces and Their Application to Repurposing

Drug repurposing is a creative and resourceful approach to increase the number of therapies by exploiting available and approved drugs. However, identifying new protein targets for previously approved drugs is challenging. Although new strategies have been developed for drug repurposing, there is broad agreement that there is room for further improvements. In this chapter, we review protein-protein interaction (PPI) interface-targeting strategies for drug repurposing applications. We discuss certain features, such as hot spot residue and hot region prediction and their importance in drug repurposing, and illustrate common methods used in PPI networks to identify drug off-targets. We also collect available online resources for hot spot prediction, binding pocket identification, and interface clustering which are effective resources in polypharmacology. Finally, we provide case studies showing the significance of protein interfaces and hot spots in drug repurposing.

Exploring the potential of 3D Zernike descriptors and SVM for protein-protein interface prediction

Background

The correct determination of protein–protein interaction interfaces is important for understanding disease mechanisms and for rational drug design. To date, several computational methods for the prediction of protein interfaces have been developed, but the interface prediction problem is still not fully understood. Experimental evidence suggests that the location of binding sites is imprinted in the protein structure, but there are major differences among the interfaces of the various protein types: the characterising properties can vary a lot depending on the interaction type and function. The selection of an optimal set of features characterising the protein interface and the development of an effective method to represent and capture the complex protein recognition patterns are of paramount importance for this task.

Results

In this work we investigate the potential of a novel local surface descriptor based on 3D Zernike moments for the interface prediction task. Descriptors invariant to roto-translations are extracted from circular patches of the protein surface enriched with physico-chemical properties from the HQI8 amino acid index set, and are used as samples for a binary classification problem. Support Vector Machines are used as a classifier to distinguish interface local surface patches from non-interface ones. The proposed method was validated on 16 classes of proteins extracted from the Protein–Protein Docking Benchmark 5.0 and compared to other state-of-the-art protein interface predictors (SPPIDER, PrISE and NPS-HomPPI).

Conclusions

The 3D Zernike descriptors are able to capture the similarity among patterns of physico-chemical and biochemical properties mapped on the protein surface arising from the various spatial arrangements of the underlying residues, and their usage can be easily extended to other sets of amino acid properties. The results suggest that the choice of a proper set of features characterising the protein interface is crucial for the interface prediction task, and that optimality strongly depends on the class of proteins whose interface we want to characterise. We postulate that different protein classes should be treated separately and that it is necessary to identify an optimal set of features for each protein class.

Electronic supplementary material

The online version of this article (10.1186/s12859-018-2043-3) contains supplementary material, which is available to authorized users.

Computational Resources for Predicting Protein-Protein Interactions

Proteins are the essential building blocks and functional components of a cell. They account for the vital functions of an organism. Proteins interact with each other and form protein interaction networks. These protein interactions play a major role in all the biological processes and pathways. The previous methods of predicting protein interactions were experimental which focused on a small set of proteins or a particular protein. However, these experimental approaches are low-throughput as they are time-consuming and require a significant amount of human effort. This led to the development of computational techniques that uses high-throughput experimental data for analyzing protein-protein interactions. The main purpose of this review is to provide an overview on the computational advancements and tools for the prediction of protein interactions. The major databases for the deposition of these interactions are also described. The advantages, as well as the specific limitations of these tools, are highlighted which will shed light on the computational aspects that can help the biologist and researchers in their research.

Structural insights into the interaction of the conserved mammalian proteins GAPR-1 and Beclin 1, a key autophagy protein

Mammalian Golgi-associated plant pathogenesis-related protein 1 (GAPR-1) is a negative autophagy regulator that binds Beclin 1, a key component of the autophagosome nucleation complex. Beclin 1 residues 267-284 are required for binding GAPR-1. Here, sequence analyses, structural modeling, mutagenesis combined with pull-down assays, X-ray crystal structure determination and small-angle X-ray scattering were used to investigate the Beclin 1-GAPR-1 interaction. Five conserved residues line an equatorial GAPR-1 surface groove that is large enough to bind a peptide. A model of a peptide comprising Beclin 1 residues 267-284 docked onto GAPR-1, built using the CABS-dock server, indicates that this peptide binds to this GAPR-1 groove. Mutation of the five conserved residues lining this groove, H54A/E86A/G102K/H103A/N138G, abrogates Beclin 1 binding. The 1.27 Å resolution X-ray crystal structure of this pentad mutant GAPR-1 was determined. Comparison with the wild-type (WT) GAPR-1 structure shows that the equatorial groove of the pentad mutant is shallower and more positively charged, and therefore may not efficiently bind Beclin 1 residues 267-284, which include many hydrophobic residues. Both WT and pentad mutant GAPR-1 crystallize as dimers, and in each case the equatorial groove of one subunit is partially occluded by the other subunit, indicating that dimeric GAPR-1 is unlikely to bind Beclin 1. SAXS analysis of WT and pentad mutant GAPR-1 indicates that in solution the WT forms monomers, while the pentad mutant is primarily dimeric. Thus, changes in the structure of the equatorial groove combined with the improved dimerization of pentad mutant GAPR-1 are likely to abrogate binding to Beclin 1.

Non-interacting proteins may resemble interacting proteins: prevalence and implications

The vast majority of proteins do not form functional interactions in physiological conditions. We have considered several sets of protein pairs from S. cerevisiae with no functional interaction reported, denoted as non-interacting pairs, and compared their 3D structures to available experimental complexes. We identified some non-interacting pairs with significant structural similarity with experimental complexes, indicating that, even though they do not form functional interactions, they have compatible structures. We estimate that up to 8.7% of non-interacting protein pairs could have compatible structures. This number of interactions exceeds the number of functional interactions (around 0.2% of the total interactions) by a factor 40. Network analysis suggests that the interactions formed by non-interacting pairs with compatible structures could be particularly hazardous to the protein-protein interaction network. From a structural point of view, these interactions display no aberrant structural characteristics, and are even predicted as relatively stable and enriched in potential physical interactors, suggesting a major role of regulation to prevent them.

PIMADb: A Database of Protein-Protein Interactions in Huge Macromolecular Assemblies

Protein-protein interactions play a very important role in the process of cellular functionality. Intricate details about the interactions between the proteins in a macromolecular assembly are important to understand the function and significance of protein complexes. We are reporting about a database of protein-protein interactions in huge macromolecular assemblies (PIMADb) that records the intrinsic details of 189,532 interchain interactions in 40,049 complexes from the Protein Data Bank. These details include the results of the quantification and analysis of all the interactions in the complex. The availability of interprotomer interaction networks can enable the design of point mutation experiments. PIMADb can be accessed from the URL: http://caps.ncbs.res.in/pimadb.

Protein Ensembles: How Does Nature Harness Thermodynamic Fluctuations for Life? The Diverse Functional Roles of Conformational Ensembles in the Cell

All soluble proteins populate conformational ensembles that together constitute the native state. Their fluctuations in water are intrinsic thermodynamic phenomena, and the distributions of the states on the energy landscape are determined by statistical thermodynamics; however, they are optimized to perform their biological functions. In this review we briefly describe advances in free energy landscape studies of protein conformational ensembles. Experimental (nuclear magnetic resonance, small-angle X-ray scattering, single-molecule spectroscopy, and cryo-electron microscopy) and computational (replica-exchange molecular dynamics, metadynamics, and Markov state models) approaches have made great progress in recent years. These address the challenging characterization of the highly flexible and heterogeneous protein ensembles. We focus on structural aspects of protein conformational distributions, from collective motions of single- and multi-domain proteins, intrinsically disordered proteins, to multiprotein complexes. Importantly, we highlight recent studies that illustrate functional adjustment of protein conformational ensembles in the crowded cellular environment. We center on the role of the ensemble in recognition of small- and macro-molecules (protein and RNA/DNA) and emphasize emerging concepts of protein dynamics in enzyme catalysis. Overall, protein ensembles link fundamental physicochemical principles and protein behavior and the cellular network and its regulation.

Shape complementarity and hydrogen bond preferences in protein-protein interfaces: implications for antibody modeling and protein-protein docking

MOTIVATIONS

Characterizing protein-protein interfaces and the hydrogen bonds is a first step to better understand proteins' structures and functions toward high-resolution protein design. However, there are few large-scale surveys of hydrogen bonds of interfaces. In addition, previous work of shape complementarity of protein complexes suggested that lower shape complementarity in antibody-antigen interfaces is related to their evolutionary origin.

RESULTS

Using 6637 non-redundant protein-protein interfaces, we revealed peculiar features of various protein complex types. In contrast to previous findings, the shape complementarity of antibody-antigen interfaces resembles that of the other interface types. These results highlight the importance of hydrogen bonds during evolution of protein interfaces and rectify the prevailing belief that antibodies have lower shape complementarity.

CONTACT

jgray@jhu.edu

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Antibody Binding Selectivity: Alternative Sets of Antigen Residues Entail High-Affinity Recognition

Understanding the relationship between protein sequence and molecular recognition selectivity remains a major challenge. The antibody fragment scFv1F4 recognizes with sub nM affinity a decapeptide (sequence 6TAMFQDPQER15) derived from the N-terminal end of human papilloma virus E6 oncoprotein. Using this decapeptide as antigen, we had previously shown that only the wild type amino-acid or conservative replacements were allowed at positions 9 to 12 and 15 of the peptide, indicating a strong binding selectivity. Nevertheless phenylalanine (F) was equally well tolerated as the wild type glutamine (Q) at position 13, while all other amino acids led to weaker scFv binding. The interfaces of complexes involving either Q or F are expected to diverge, due to the different physico-chemistry of these residues. This would imply that high-affinity binding can be achieved through distinct interfacial geometries. In order to investigate this point, we disrupted the scFv-peptide interface by modifying one or several peptide positions. We then analyzed the effect on binding of amino acid changes at the remaining positions, an altered susceptibility being indicative of an altered role in complex formation. The 23 starting variants analyzed contained replacements whose effects on scFv1F4 binding ranged from minor to drastic. A permutation analysis (effect of replacing each peptide position by all other amino acids except cysteine) was carried out on the 23 variants using the PEPperCHIP® Platform technology. A comparison of their permutation patterns with that of the wild type peptide indicated that starting replacements at position 11, 12 or 13 modified the tolerance to amino-acid changes at the other two positions. The interdependence between the three positions was confirmed by SPR (Biacore® technology). Our data demonstrate that binding selectivity does not preclude the existence of alternative high-affinity recognition modes.

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.

Weak conservation of structural features in the interfaces of homologous transient protein-protein complexes

Residue types at the interface of protein-protein complexes (PPCs) are known to be reasonably well conserved. However, we show, using a dataset of known 3-D structures of homologous transient PPCs, that the 3-D location of interfacial residues and their interaction patterns are only moderately and poorly conserved, respectively. Another surprising observation is that a residue at the interface that is conserved is not necessarily in the interface in the homolog. Such differences in homologous complexes are manifested by substitution of the residues that are spatially proximal to the conserved residue and structural differences at the interfaces as well as differences in spatial orientations of the interacting proteins. Conservation of interface location and the interaction pattern at the core of the interfaces is higher than at the periphery of the interface patch. Extents of variability of various structural features reported here for homologous transient PPCs are higher than the variation in homologous permanent homomers. Our findings suggest that straightforward extrapolation of interfacial nature and inter-residue interaction patterns from template to target could lead to serious errors in the modeled complex structure. Understanding the evolution of interfaces provides insights to improve comparative modeling of PPC structures.

The Solution Structure and Dynamics of Full-length Human Cerebral Dopamine Neurotrophic Factor and Its Neuroprotective Role against α-Synuclein Oligomers

Cerebral dopamine neurotrophic factor (CDNF) is a promising therapeutic agent for Parkinson disease. As such, there has been great interest in studying its mode of action, which remains unknown. The three-dimensional crystal structure of the N terminus (residues 9-107) of CDNF has been determined, but there have been no published structural studies on the full-length protein due to proteolysis of its C-terminal domain, which is considered intrinsically disordered. An improved purification protocol enabled us to obtain active full-length CDNF and to determine its three-dimensional structure in solution. CDNF contains two well folded domains (residues 10-100 and 111-157) that are linked by a loop of intermediate flexibility. We identified two surface patches on the N-terminal domain that were characterized by increased conformational dynamics that should allow them to embrace active sites. One of these patches is formed by residues Ser-33, Leu-34, Ala-66, Lys-68, Ile-69, Leu-70, Ser-71, and Glu-72. The other includes a flexibly disordered N-terminal tail (residues 1-9), followed by the N-terminal portion of α-helix 1 (residues Cys-11, Glu-12, Val-13, Lys-15, and Glu-16) and residue Glu-88. The surface of the C-terminal domain contains two conserved active sites, which have previously been identified in mesencephalic astrocyte-derived neurotrophic factor, a CDNF paralog, which corresponds to its intracellular mode of action. We also showed that CDNF was able to protect dopaminergic neurons against injury caused by α-synuclein oligomers. This advises its use against physiological damages caused by α-synuclein oligomers, as observed in Parkinson disease and several other neurodegenerative diseases.

An overview of recent advances in structural bioinformatics of protein-protein interactions and a guide to their principles

Rich data bearing on the structural and evolutionary principles of protein-protein interactions are paving the way to a better understanding of the regulation of function in the cell. This is particularly the case when these interactions are considered in the framework of key pathways. Knowledge of the interactions may provide insights into the mechanisms of crucial 'driver' mutations in oncogenesis. They also provide the foundation toward the design of protein-protein interfaces and inhibitors that can abrogate their formation or enhance them. The main features to learn from known 3-D structures of protein-protein complexes and the extensive literature which analyzes them computationally and experimentally include the interaction details which permit undertaking structure-based drug discovery, the evolution of complexes and their interactions, the consequences of alterations such as post-translational modifications, ligand binding, disease causing mutations, host pathogen interactions, oligomerization, aggregation and the roles of disorder, dynamics, allostery and more to the protein and the cell. This review highlights some of the recent advances in these areas, including design, inhibition and prediction of protein-protein complexes. The field is broad, and much work has been carried out in these areas, making it challenging to cover it in its entirety. Much of this is due to the fast increase in the number of molecules whose structures have been determined experimentally and the vast increase in computational power. Here we provide a concise overview.

Non-redundant unique interface structures as templates for modeling protein interactions

Improvements in experimental techniques increasingly provide structural data relating to protein-protein interactions. Classification of structural details of protein-protein interactions can provide valuable insights for modeling and abstracting design principles. Here, we aim to cluster protein-protein interactions by their interface structures, and to exploit these clusters to obtain and study shared and distinct protein binding sites. We find that there are 22604 unique interface structures in the PDB. These unique interfaces, which provide a rich resource of structural data of protein-protein interactions, can be used for template-based docking. We test the specificity of these non-redundant unique interface structures by finding protein pairs which have multiple binding sites. We suggest that residues with more than 40% relative accessible surface area should be considered as surface residues in template-based docking studies. This comprehensive study of protein interface structures can serve as a resource for the community. The dataset can be accessed at http://prism.ccbb.ku.edu.tr/piface.

Accurate prediction of interfacial residues in two-domain proteins using evolutionary information: implications for three-dimensional modeling

With the preponderance of multidomain proteins in eukaryotic genomes, it is essential to recognize the constituent domains and their functions. Often function involves communications across the domain interfaces, and the knowledge of the interacting sites is essential to our understanding of the structure-function relationship. Using evolutionary information extracted from homologous domains in at least two diverse domain architectures (single and multidomain), we predict the interface residues corresponding to domains from the two-domain proteins. We also use information from the three-dimensional structures of individual domains of two-domain proteins to train naïve Bayes classifier model to predict the interfacial residues. Our predictions are highly accurate (∼85%) and specific (∼95%) to the domain-domain interfaces. This method is specific to multidomain proteins which contain domains in at least more than one protein architectural context. Using predicted residues to constrain domain-domain interaction, rigid-body docking was able to provide us with accurate full-length protein structures with correct orientation of domains. We believe that these results can be of considerable interest toward rational protein and interaction design, apart from providing us with valuable information on the nature of interactions.

The role of electrostatic energy in prediction of obligate protein-protein interactions

BACKGROUND

Prediction and analysis of protein-protein interactions (PPI) and specifically types of PPIs is an important problem in life science research because of the fundamental roles of PPIs in many biological processes in living cells. In addition, electrostatic interactions are important in understanding inter-molecular interactions, since they are long-range, and because of their influence in charged molecules. This is the main motivation for using electrostatic energy for prediction of PPI types.

RESULTS

We propose a prediction model to analyze protein interaction types, namely obligate and non-obligate, using electrostatic energy values as properties. The prediction approach uses electrostatic energy values for pairs of atoms and amino acids present in interfaces where the interaction occurs. The main features of the complexes are found and then the prediction is performed via several state-of-the-art classification techniques, including linear dimensionality reduction (LDR), support vector machine (SVM), naive Bayes (NB) and k-nearest neighbor (k-NN). For an in-depth analysis of classification results, some other experiments were performed by varying the distance cutoffs between atom pairs of interacting chains, ranging from 5Å to 13Å. Moreover, several feature selection algorithms including gain ratio (GR), information gain (IG), chi-square (Chi2) and minimum redundancy maximum relevance (mRMR) are applied on the available datasets to obtain more discriminative pairs of atom types and amino acid types as features for prediction.

CONCLUSIONS

Our results on two well-known datasets of obligate and non-obligate complexes confirm that electrostatic energy is an important property to predict obligate and non-obligate protein interaction types on the basis of all the experimental results, achieving accuracies of over 98%. Furthermore, a comparison performed by changing the distance cutoff demonstrates that the best values for prediction of PPI types using electrostatic energy range from 9Å to 12Å, which show that electrostatic interactions are long-range and cover a broader area in the interface. In addition, the results on using feature selection before prediction confirm that (a) a few pairs of atoms and amino acids are appropriate for prediction, and (b) prediction performance can be improved by eliminating irrelevant and noisy features and selecting the most discriminative ones.

Bioengineering of AAV2 capsid at specific serine, threonine, or lysine residues improves its transduction efficiency in vitro and in vivo

We hypothesized that the AAV2 vector is targeted for destruction in the cytoplasm by the host cellular kinase/ubiquitination/proteasomal machinery and that modification of their targets on AAV2 capsid may improve its transduction efficiency. In vitro analysis with pharmacological inhibitors of cellular serine/threonine kinases (protein kinase A, protein kinase C, casein kinase II) showed an increase (20-90%) on AAV2-mediated gene expression. The three-dimensional structure of AAV2 capsid was then analyzed to predict the sites of ubiquitination and phosphorylation. Three phosphodegrons, which are the phosphorylation sites recognized as degradation signals by ubiquitin ligases, were identified. Mutation targets comprising eight serine (S) or seven threonine (T) or nine lysine (K) residues were selected in and around phosphodegrons on the basis of their solvent accessibility, overlap with the receptor binding regions, overlap with interaction interfaces of capsid proteins, and their evolutionary conservation across AAV serotypes. AAV2-EGFP vectors with the wild-type (WT) capsid or mutant capsids (15 S/T→alanine [A] or 9 K→arginine [R] single mutant or 2 double K→R mutants) were then evaluated in vitro. The transduction efficiencies of 11 S/T→A and 7 K→R vectors were significantly higher (~63-90%) than the AAV2-WT vectors (~30-40%). Further, hepatic gene transfer of these mutant vectors in vivo resulted in higher vector copy numbers (up to 4.9-fold) and transgene expression (up to 14-fold) than observed from the AAV2-WT vector. One of the mutant vectors, S489A, generated ~8-fold fewer antibodies that could be cross-neutralized by AAV2-WT. This study thus demonstrates the feasibility of the use of these novel AAV2 capsid mutant vectors in hepatic gene therapy.

Versatility and invariance in the evolution of homologous heteromeric interfaces

Evolutionary pressures act on protein complex interfaces so that they preserve their complementarity. Nonetheless, the elementary interactions which compose the interface are highly versatile throughout evolution. Understanding and characterizing interface plasticity across evolution is a fundamental issue which could provide new insights into protein-protein interaction prediction. Using a database of 1,024 couples of close and remote heteromeric structural interologs, we studied protein-protein interactions from a structural and evolutionary point of view. We systematically and quantitatively analyzed the conservation of different types of interface contacts. Our study highlights astonishing plasticity regarding polar contacts at complex interfaces. It also reveals that up to a quarter of the residues switch out of the interface when comparing two homologous complexes. Despite such versatility, we identify two important interface descriptors which correlate with an increased conservation in the evolution of interfaces: apolar patches and contacts surrounding anchor residues. These observations hold true even when restricting the dataset to transiently formed complexes. We show that a combination of six features related either to sequence or to geometric properties of interfaces can be used to rank positions likely to share similar contacts between two interologs. Altogether, our analysis provides important tracks for extracting meaningful information from multiple sequence alignments of conserved binding partners and for discriminating near-native interfaces using evolutionary information.

Unraveling how interfaces of protein complexes coevolved is of major importance to improve our ability to predict their structures and design novel binders. Proteins whose interaction was maintained throughout evolution generally have their homologs binding in a similar manner while their sequences can have significantly diverged. Constraints holding proteins together should be captured from the growing body of available multiple sequence alignments. However, it remains unclear which features of the interfaces provide most tolerance to mutations and it is unknown whether any invariant properties may help to extract meaningful signals from sequence alignments. To solve this issue, we tackled an unprecedented large scale analysis of more than 1000 non-redundant couples of structural interologs. Structural interologs are pairs of complexes of known structure whose chains are homologs. We quantitatively measured how the networks of contacts varied between two interfaces. Although highly versatile, we found that contact networks were more conserved for residues acting as anchors and for apolar contacts when they are clustered into surface patches. Altogether, our results provide major guidelines for exploiting the wealth of evolutionary information contained in the sequences of binding partners. On those bases we developed a method to predict which residues most likely conserve their contacts.

A cell free protein fragment complementation assay for monitoring the core interaction of the human cytomegalovirus nuclear egress complex

Certain viral protein-protein interactions provide attractive targets for antiviral drug development. Recently, we described a β-lactamase based protein fragment complementation assay (PCA) to study the core interaction of the nuclear egress complex (NEC) of different herpesviruses in cells. Now, to have a cell free assay for inhibitor screens, we expressed split β-lactamase tagged interaction domains of the viral pUL50 and pUL53 proteins representing the NEC of human cytomegalovirus (HCMV) in bacteria. After validation and basic characterization of this NEC-PCA, we tested peptide inhibitors of the pUL50-pUL53 complex. We show that peptides resembling sequences of the first conserved region of pUL53 can inhibit the NEC-PCA. This, on one hand, indicated that the core interaction in the HCMV NEC is mediated by a linear motif. On the other hand it proved that this new pUL50-pUL53 interaction assay allows a simple cell free test for small molecular inhibitors.

Roles of residues in the interface of transient protein-protein complexes before complexation

Transient protein-protein interactions play crucial roles in all facets of cellular physiology. Here, using an analysis on known 3-D structures of transient protein-protein complexes, their corresponding uncomplexed forms and energy calculations we seek to understand the roles of protein-protein interfacial residues in the unbound forms. We show that there are conformationally near invariant and evolutionarily conserved interfacial residues which are rigid and they account for ∼65% of the core interface. Interestingly, some of these residues contribute significantly to the stabilization of the interface structure in the uncomplexed form. Such residues have strong energetic basis to perform dual roles of stabilizing the structure of the uncomplexed form as well as the complex once formed while they maintain their rigid nature throughout. This feature is evolutionarily well conserved at both the structural and sequence levels. We believe this analysis has general bearing in the prediction of interfaces and understanding molecular recognition.

Deciphering the shape and deformation of secondary structures through local conformation analysis

Background

Protein deformation has been extensively analysed through global methods based on RMSD, torsion angles and Principal Components Analysis calculations. Here we use a local approach, able to distinguish among the different backbone conformations within loops, α-helices and β-strands, to address the question of secondary structures' shape variation within proteins and deformation at interface upon complexation.

Results

Using a structural alphabet, we translated the 3 D structures of large sets of protein-protein complexes into sequences of structural letters. The shape of the secondary structures can be assessed by the structural letters that modeled them in the structural sequences. The distribution analysis of the structural letters in the three protein compartments (surface, core and interface) reveals that secondary structures tend to adopt preferential conformations that differ among the compartments. The local description of secondary structures highlights that curved conformations are preferred on the surface while straight ones are preferred in the core. Interfaces display a mixture of local conformations either preferred in core or surface. The analysis of the structural letters transition occurring between protein-bound and unbound conformations shows that the deformation of secondary structure is tightly linked to the compartment preference of the local conformations.

Conclusion

The conformation of secondary structures can be further analysed and detailed thanks to a structural alphabet which allows a better description of protein surface, core and interface in terms of secondary structures' shape and deformation. Induced-fit modification tendencies described here should be valuable information to identify and characterize regions under strong structural constraints for functional reasons.

Designing coarse grained-and atom based-potentials for protein-protein docking

BACKGROUND

Protein-protein docking is a challenging computational problem in functional genomics, particularly when one or both proteins undergo conformational change(s) upon binding. The major challenge is to define a scoring function soft enough to tolerate these changes and specific enough to distinguish between near-native and "misdocked" conformations.

RESULTS

Using a linear programming (LP) technique, we developed two types of potentials: (i) Side chain-based and (ii) Heavy atom-based. To achieve this we considered a set of 161 transient complexes and generated a large set of putative docked structures (decoys), based on a shape complementarity criterion, for each complex. The demand on the potentials was to yield, for the native (correctly docked) structure, a potential energy lower than those of any of the non-native (misdocked) structures. We show that the heavy atom-based potentials were able to comply with this requirement but not the side chain-based one. Thus, despite the smaller number of parameters, the capability of heavy atom-based potentials to discriminate between native and "misdocked" conformations is improved relative to those of the side chain-based potentials. The performance of the atom-based potentials was evaluated by a jackknife test on a set of 50 complexes taken from the Zdock2.3 decoys set.

CONCLUSIONS

Our results show that, using the LP approach, we were able to train our potentials using a dataset of transient complexes only the newly developed potentials outperform three other known potentials in this test.

Beauty is in the eye of the beholder: proteins can recognize binding sites of homologous proteins in more than one way

Understanding the mechanisms of protein-protein interaction is a fundamental problem with many practical applications. The fact that different proteins can bind similar partners suggests that convergently evolved binding interfaces are reused in different complexes. A set of protein complexes composed of non-homologous domains interacting with homologous partners at equivalent binding sites was collected in 2006, offering an opportunity to investigate this point. We considered 433 pairs of protein-protein complexes from the ABAC database (AB and AC binary protein complexes sharing a homologous partner A) and analyzed the extent of physico-chemical similarity at the atomic and residue level at the protein-protein interface. Homologous partners of the complexes were superimposed using Multiprot, and similar atoms at the interface were quantified using a five class grouping scheme and a distance cut-off. We found that the number of interfacial atoms with similar properties is systematically lower in the non-homologous proteins than in the homologous ones. We assessed the significance of the similarity by bootstrapping the atomic properties at the interfaces. We found that the similarity of binding sites is very significant between homologous proteins, as expected, but generally insignificant between the non-homologous proteins that bind to homologous partners. Furthermore, evolutionarily conserved residues are not colocalized within the binding sites of non-homologous proteins. We could only identify a limited number of cases of structural mimicry at the interface, suggesting that this property is less generic than previously thought. Our results support the hypothesis that different proteins can interact with similar partners using alternate strategies, but do not support convergent evolution.

Protein binding hot spots and the residue-residue pairing preference: a water exclusion perspective

Background

A protein binding hot spot is a small cluster of residues tightly packed at the center of the interface between two interacting proteins. Though a hot spot constitutes a small fraction of the interface, it is vital to the stability of protein complexes. Recently, there are a series of hypotheses proposed to characterize binding hot spots, including the pioneering O-ring theory, the insightful 'coupling' and 'hot region' principle, and our 'double water exclusion' (DWE) hypothesis. As the perspective changes from the O-ring theory to the DWE hypothesis, we examine the physicochemical properties of the binding hot spots under the new hypothesis and compare with those under the O-ring theory.

Results

The requirements for a cluster of residues to form a hot spot under the DWE hypothesis can be mathematically satisfied by a biclique subgraph if a vertex is used to represent a residue, an edge to indicate a close distance between two residues, and a bipartite graph to represent a pair of interacting proteins. We term these hot spots as DWE bicliques. We identified DWE bicliques from crystal packing contacts, obligate and non-obligate interactions. Our comparative study revealed that there are abundant unique bicliques to the biological interactions, indicating specific biological binding behaviors in contrast to crystal packing. The two sub-types of biological interactions also have their own signature bicliques. In our analysis on residue compositions and residue pairing preferences in DWE bicliques, the focus was on interaction-preferred residues (ipRs) and interaction-preferred residue pairs (ipRPs). It is observed that hydrophobic residues are heavily involved in the ipRs and ipRPs of the obligate interactions; and that aromatic residues are in favor in the ipRs and ipRPs of the biological interactions, especially in those of the non-obligate interactions. In contrast, the ipRs and ipRPs in crystal packing are dominated by hydrophilic residues, and most of the anti-ipRs of crystal packing are the ipRs of the obligate or non-obligate interactions.

Conclusions

These ipRs and ipRPs in our DWE bicliques describe a diverse binding features among the three types of interactions. They also highlight the specific binding behaviors of the biological interactions, sharply differing from the artifact interfaces in the crystal packing. It can be noted that DWE bicliques, especially the unique bicliques, can capture deep insights into the binding characteristics of protein interfaces.

A study of interface roughness of heteromeric obligate and non-obligate protein-protein complexes

A number of studies aimed to distinguish the structural patterns at the interfaces of obligate and non-obligate protein-protein complexes. These studies revealed better geometric complementarity of protomers in obligate complexes over non-obligates. We showed that protein surface roughness can be used to explain this observation. Using smoothened atomic fractal dimension (SAFD) as a descriptor, this work investigates the role of interface roughness in the molecular recognition of these two types of protein-protein complexes. We studied 52 obligate and 62 nonobligate heteromeric high quality crystal structures from benchmark data sets. We found that distribution of interface roughness values obligate and non-obligates are quite similar. However, we observed a distinct preference for obligate protomers to complex with chains having similar roughness. The roughness pairing is correlated in obligates only. The later indicates, an increase/decrease of roughness in one chain causes a proportional change in roughness in its binding partner. Based on these observations we proposed that similar and correlated roughness pairing leads to more interdigitation and contacts at the interface leading to better geometric fit in obligates. We propose that roughness information can find useful application in improving machine learning based complex type classifiers and filtering protein-protein docking solutions.

Knitting and untying the protein network: modulation of protein ensembles as a therapeutic strategy

Proteins constitute the working machinery and structural support of all organisms. In performing a given function, they must adopt highly specific structures that can change with their level of activity, often through the direct or indirect action of other proteins. Indeed, proteins typically function within an ensemble, rather than individually. Hence, they must be sufficiently flexible to interact with each other and execute diverse tasks. The discovery that errors within these groups can ultimately cause disease has led to a paradigm shift in drug discovery, from an emphasis on single protein targets to a holistic approach whereby entire ensembles are targeted.

A survey of available tools and web servers for analysis of protein-protein interactions and interfaces

The unanimous agreement that cellular processes are (largely) governed by interactions between proteins has led to enormous community efforts culminating in overwhelming information relating to these proteins; to the regulation of their interactions, to the way in which they interact and to the function which is determined by these interactions. These data have been organized in databases and servers. However, to make these really useful, it is essential not only to be aware of these, but in particular to have a working knowledge of which tools to use for a given problem; what are the tool advantages and drawbacks; and no less important how to combine these for a particular goal since usually it is not one tool, but some combination of tool-modules that is needed. This is the goal of this review.

Prediction of protein-protein interaction types using association rule based classification

Background

Protein-protein interactions (PPI) can be classified according to their characteristics into, for example obligate or transient interactions. The identification and characterization of these PPI types may help in the functional annotation of new protein complexes and in the prediction of protein interaction partners by knowledge driven approaches.

Results

This work addresses pattern discovery of the interaction sites for four different interaction types to characterize and uses them for the prediction of PPI types employing Association Rule Based Classification (ARBC) which includes association rule generation and posterior classification. We incorporated domain information from protein complexes in SCOP proteins and identified 354 domain-interaction sites. 14 interface properties were calculated from amino acid and secondary structure composition and then used to generate a set of association rules characterizing these domain-interaction sites employing the APRIORI algorithm. Our results regarding the classification of PPI types based on a set of discovered association rules shows that the discriminative ability of association rules can significantly impact on the prediction power of classification models. We also showed that the accuracy of the classification can be improved through the use of structural domain information and also the use of secondary structure content.

Conclusion

The advantage of our approach is that we can extract biologically significant information from the interpretation of the discovered association rules in terms of understandability and interpretability of rules. A web application based on our method can be found at

VASCo: computation and visualization of annotated protein surface contacts

BACKGROUND

Structural data from crystallographic analyses contain a vast amount of information on protein-protein contacts. Knowledge on protein-protein interactions is essential for understanding many processes in living cells. The methods to investigate these interactions range from genetics to biophysics, crystallography, bioinformatics and computer modeling. Also crystal contact information can be useful to understand biologically relevant protein oligomerisation as they rely in principle on the same physico-chemical interaction forces. Visualization of crystal and biological contact data including different surface properties can help to analyse protein-protein interactions.

RESULTS

VASCo is a program package for the calculation of protein surface properties and the visualization of annotated surfaces. Special emphasis is laid on protein-protein interactions, which are calculated based on surface point distances. The same approach is used to compare surfaces of two aligned molecules. Molecular properties such as electrostatic potential or hydrophobicity are mapped onto these surface points. Molecular surfaces and the corresponding properties are calculated using well established programs integrated into the package, as well as using custom developed programs. The modular package can easily be extended to include new properties for annotation. The output of the program is most conveniently displayed in PyMOL using a custom-made plug-in.

CONCLUSION

VASCo supplements other available protein contact visualisation tools and provides additional information on biological interactions as well as on crystal contacts. The tool provides a unique feature to compare surfaces of two aligned molecules based on point distances and thereby facilitates the visualization and analysis of surface differences.

Common physical basis of macromolecule-binding sites in proteins

Protein–DNA/RNA/protein interactions play critical roles in many biological functions. Previous studies have focused on the different features characterizing the different macromolecule-binding sites and approaches to detect these sites. However, no common unique signature of these sites had been reported. Thus, this work aims to provide a ‘common’ principle dictating the location of the different macromolecule-binding sites founded upon fundamental principles of binding thermodynamics. To achieve this aim, a comprehensive set of structurally nonhomologous DNA-, RNA-, obligate protein- and nonobligate protein-binding proteins, both free and bound to their respective macromolecules, was created and a novel strategy for detecting clusters of residues with electrostatic or steric strain given the protein structure was developed. The results show that regardless of the macromolecule type, the binding strength and conformational changes upon binding, macromolecule-binding sites are energetically less stable than nonmacromolecule-binding sites. They also reveal new energetic features distinguishing DNA- from RNA-binding sites and obligate protein- from nonobligate protein-binding sites in both free/bound protein structures.

Nanoscale organization of hedgehog is essential for long-range signaling

Hedgehog (Hh) plays crucial roles in tissue-patterning and activates signaling in Patched (Ptc)-expressing cells. Paracrine signaling requires release and transport over many cell diameters away by a process that requires interaction with heparan sulfate proteoglycans (HSPGs). Here, we examine the organization of functional, fluorescently tagged variants in living cells by using optical imaging, FRET microscopy, and mutational studies guided by bioinformatics prediction. We find that cell-surface Hh forms suboptical oligomers, further concentrated in visible clusters colocalized with HSPGs. Mutation of a conserved Lys in a predicted Hh-protomer interaction interface results in an autocrine signaling-competent Hh isoform--incapable of forming dense nanoscale oligomers, interacting with HSPGs, or paracrine signaling. Thus, Hh exhibits a hierarchical organization from the nanoscale to visible clusters with distinct functions.