The many faces of protein-protein interactions: A compendium of interface geometry

Database-Driven Identification of Structurally Similar Protein-Protein Interfaces

Analyzing the similarity of protein interfaces in protein-protein interactions gives new insights into protein function and assists in discovering new drugs. Usually, tools that assess the similarity focus on the interactions between two protein interfaces, while sometimes we only have one predicted interface. Herein, we present PiMine, a database-driven protein interface similarity search. It compares interface residues of one or two interacting chains by calculating and searching tetrahedral geometric patterns of α-carbon atoms and calculating physicochemical and shape-based similarity. On a dedicated, tailor-made dataset, we show that PiMine outperforms commonly used comparison tools in terms of early enrichment when considering interfaces of sequentially and structurally unrelated proteins. In an application example, we demonstrate its usability for protein interaction partner prediction by comparing predicted interfaces to known protein-protein interfaces.

Importance of DJ-1 in autophagy regulation and disease

Autophagy is a highly conserved biological process that has evolved across evolution. It can be activated by various external stimuli including oxidative stress, amino acid starvation, infection, and hypoxia. Autophagy is the primary mechanism for preserving cellular homeostasis and is implicated in the regulation of metabolism, cell differentiation, tolerance to starvation conditions, and resistance to aging. As a multifunctional protein, DJ-1 is commonly expressed in vivo and is associated with a variety of biological processes. Its most widely studied role is its function as an oxidative stress sensor that inhibits the production of excessive reactive oxygen species (ROS) in the mitochondria and subsequently the cellular damage caused by oxidative stress. In recent years, many studies have identified DJ-1 as another important factor regulating autophagy; it regulates autophagy in various ways, most commonly by regulating the oxidative stress response. In particular, DJ-1-regulated autophagy is involved in cancer progression and plays a key role in alleviating neurodegenerative diseases(NDS) and defective reperfusion diseases. It could serve as a potential target for the regulation of autophagy and participate in disease treatment as a meaningful modality. Therefore, exploring DJ-1-regulated autophagy could provide new avenues for future disease treatment.

Large protein complex interfaces have evolved to promote cotranslational assembly

Assembly pathways of protein complexes should be precise and efficient to minimise misfolding and unwanted interactions with other proteins in the cell. One way to achieve this efficiency is by seeding assembly pathways during translation via the cotranslational assembly of subunits. While recent evidence suggests that such cotranslational assembly is widespread, little is known about the properties of protein complexes associated with the phenomenon. Here, using a combination of proteome-specific protein complex structures and publicly available ribosome profiling data, we show that cotranslational assembly is particularly common between subunits that form large intermolecular interfaces. To test whether large interfaces have evolved to promote cotranslational assembly, as opposed to cotranslational assembly being a non-adaptive consequence of large interfaces, we compared the sizes of first and last translated interfaces of heteromeric subunits in bacterial, yeast, and human complexes. When considering all together, we observe the N-terminal interface to be larger than the C-terminal interface 54% of the time, increasing to 64% when we exclude subunits with only small interfaces, which are unlikely to cotranslationally assemble. This strongly suggests that large interfaces have evolved as a means to maximise the chance of successful cotranslational subunit binding.

Next generation protein structure predictions and genetic variant interpretation

The need to make sense of the thousands of genetic variants uncovered every day in terms of pathology or biological mechanism is acute. Many insights into how genetic changes impact protein function can be gleaned if three-dimensional structures of the associated proteins are available. The availability of a highly accurate method of predicting structures from amino acid sequences is thus potentially a great boost to those wanting to understand genetic changes. In this paper we discuss the current state of protein structures known for the human and other proteomes and how better structure predictions might impact on variant interpretation efforts. For the human proteome in particular, the state of the available structural data suggests that the impact on variant interpretation might be less than anticipated. We also discuss additional efforts in structure prediction that could further aid the understanding of genetic variants.

Global dynamics in an age-structured HIV model with humoral immunity

In this paper, we formulate an age-structured HIV model, in which the influence of humoral immunity and the infection age of the infected cells are considered. The model is governed by three ordinary differential equations and two first-ordered partial differential equations and admits three equilibria: disease-free, immune-inactivated and immune-activated equilibria. We introduce two important thresholds: the basic reproduction number [Formula: see text] and immune-activated reproduction number [Formula: see text] and further show the global stability of above three equilibria in terms of [Formula: see text] and [Formula: see text], respectively. The numerical simulations are presented to illustrate our results.

Unraveling the structural landscape of intra-chain domain interfaces: Implication in the evolution of domain-domain interactions

Intra-chain domain interactions are known to play a significant role in the function and stability of multidomain proteins. These interactions are mediated through a physical interaction at domain-domain interfaces (DDIs). With a motivation to understand evolution of interfaces, we have investigated similarities among DDIs. Even though interfaces of protein-protein interactions (PPIs) have been previously studied by structurally aligning interfaces, similar analyses have not yet been performed on DDIs of either multidomain proteins or PPIs. For studying the structural landscape of DDIs, we have used iAlign to structurally align intra-chain domain interfaces of domains. The interface alignment of spatially constrained domains (due to inter-domain linkers) showed that ~88% of these could identify a structural matching interface having similar C-alpha geometry and contact pattern despite that aligned domain pairs are not structurally related. Moreover, the mean interface similarity score (IS-score) is 0.307, which is higher compared to the average random IS-score (0.207) suggesting domain interfaces are not random. The structural space of DDIs is highly connected as ~84% of all possible directed edges among interfaces are found to have at most path length of 8 when 0.26 is IS-score threshold. At this threshold, ~83% of interfaces form the largest strongly connected component. Thus, suggesting that structural space of intra-chain domain interfaces is degenerate and highly connected, as has been found in PPI interfaces. Interestingly, searching for structural neighbors of inter-chain interfaces among intra-chain interfaces showed that ~86% could find a statistically significant match to intra-chain interface with a mean IS-score of 0.311. This implies that domain interfaces are degenerate whether formed within a protein or between proteins. The interface degeneracy is most likely due to limited possible ways of packing secondary structures. In principle, interface similarities can be exploited to accurately model domain interfaces in structure prediction of multidomain proteins.

Allosteric Modulators of Protein-Protein Interactions (PPIs)

Protein-protein interactions (PPIs) represent promising drug targets of broad-spectrum therapeutic interests due to their critical implications in both health and disease circumstances. Hence, they are widely accepted as the Holy Grail of drug development. Historically, PPIs were rendered "undruggable" for their large, flat, and pocket-less structures. Current attempts to drug these "intractable" targets include orthosteric and allosteric methodologies. Previous efforts employing orthosteric approaches like protein therapeutics and orthosteric small molecules frequently suffered from poor performance caused by the difficulties in directly targeting PPI interfaces. As structural biology progresses rapidly, allosteric modulators, which direct to the allosteric regulatory sites remote to the PPI surfaces, have gradually established as a potential solution. Allosteric pockets are topologically distal from the PPI orthosteric sites, and their ligands do not need to compete with the PPI partners, which helps to improve the physiochemical and pharmacological properties of allosteric PPI modulators. Thus, exploiting allostery to tailor PPIs is regarded as a tempting strategy in future PPI drug discovery. Here, we provide a comprehensive review of our representative achievements along the way we utilize allosteric effects to tame the difficult PPI systems into druggable targets. Importantly, we provide an in-depth mechanistic analysis of this success, which will be instructive to future related lead optimizations and drug design. Finally, we discuss the current challenges in allosteric PPI drug discovery. Their solutions as well as future perspectives are also presented.

Catabolism of the Cholesterol Side Chain in Mycobacterium tuberculosis Is Controlled by a Redox-Sensitive Thiol Switch

Mycobacterium tuberculosis (Mtb), the causative agent of tuberculosis (TB), is a highly successful human pathogen and has infected approximately one-third of the world’s population. Multiple drug resistant (MDR) and extensively drug resistant (XDR) TB strains and coinfection with HIV have increased the challenges of successfully treating this disease pandemic. The metabolism of host cholesterol by Mtb is an important factor for both its virulence and pathogenesis. In Mtb, the cholesterol side chain is degraded through multiple cycles of β-oxidation and FadA5 (Rv3546) catalyzes side chain thiolysis in the first two cycles. Moreover, FadA5 is important during the chronic stage of infection in a mouse model of Mtb infection. Here, we report the redox control of FadA5 catalytic activity that results from reversible disulfide bond formation between Cys59-Cys91 and Cys93-Cys377. Cys93 is the thiolytic nucleophile, and Cys377 is the general acid catalyst for cleavage of the β-keto-acyl-CoA substrate. The disulfide bond formed between the two catalytic residues Cys93 and Cys377 blocks catalysis. The formation of the disulfide bonds is accompanied by a large domain swap at the FadA5 dimer interface that serves to bring Cys93 and Cys377 in close proximity for disulfide bond formation. The catalytic activity of FadA5 has a midpoint potential of −220 mV, which is close to the Mtb mycothiol potential in the activated macrophage. The redox profile of FadA5 suggests that FadA5 is fully active when Mtb resides in the unactivated macrophage to maximize flux into cholesterol catabolism. Upon activation of the macrophage, the oxidative shift in the mycothiol potential will decrease the thiolytic activity by 50%. Thus, the FadA5 midpoint potential is poised to rapidly restrict cholesterol side chain degradation in response to oxidative stress from the host macrophage environment.

Protein Assembly: Versatile Approaches to Construct Highly Ordered Nanostructures

Nature endows life with a wide variety of sophisticated, synergistic, and highly functional protein assemblies. Following Nature's inspiration to assemble protein building blocks into exquisite nanostructures is emerging as a fascinating research field. Dictating protein assembly to obtain highly ordered nanostructures and sophisticated functions not only provides a powerful tool to understand the natural protein assembly process but also offers access to advanced biomaterials. Over the past couple of decades, the field of protein assembly has undergone unexpected and rapid developments, and various innovative strategies have been proposed. This Review outlines recent advances in the field of protein assembly and summarizes several strategies, including biotechnological strategies, chemical strategies, and combinations of these approaches, for manipulating proteins to self-assemble into desired nanostructures. The emergent applications of protein assemblies as versatile platforms to design a wide variety of attractive functional materials with improved performances have also been discussed. The goal of this Review is to highlight the importance of this highly interdisciplinary field and to promote its growth in a diverse variety of research fields ranging from nanoscience and material science to synthetic biology.

Deciphering the cause of evolutionary variance within intrinsically disordered regions in human proteins

Why the intrinsically disordered regions evolve within human proteome has became an interesting question for a decade. Till date, it remains an unsolved yet an intriguing issue to investigate why some of the disordered regions evolve rapidly while the rest are highly conserved across mammalian species. Identifying the key biological factors, responsible for the variation in the conservation rate of different disordered regions within the human proteome, may revisit the above issue. We emphasized that among the other biological features (multifunctionality, gene essentiality, protein connectivity, number of unique domains, gene expression level and expression breadth) considered in our study, the number of unique protein domains acts as a strong determinant that negatively influences the conservation of disordered regions. In this context, we justified that proteins having a fewer types of domains preferably need to conserve their disordered regions to enhance their structural flexibility which in turn will facilitate their molecular interactions. In contrast, the selection pressure acting on the stretches of disordered regions is not so strong in the case of multi-domains proteins. Therefore, we reasoned that the presence of conserved disordered stretches may compensate the functions of multiple domains within a single domain protein. Interestingly, we noticed that the influence of the unique domain number and expression level acts differently on the evolution of disordered regions from that of well-structured ones.

3DIANA: 3D Domain Interaction Analysis: A Toolbox for Quaternary Structure Modeling

Electron microscopy (EM) is experiencing a revolution with the advent of a new generation of Direct Electron Detectors, enabling a broad range of large and flexible structures to be resolved well below 1 nm resolution. Although EM techniques are evolving to the point of directly obtaining structural data at near-atomic resolution, for many molecules the attainable resolution might not be enough to propose high-resolution structural models. However, accessing information on atomic coordinates is a necessary step toward a deeper understanding of the molecular mechanisms that allow proteins to perform specific tasks. For that reason, methods for the integration of EM three-dimensional maps with x-ray and NMR structural data are being developed, a modeling task that is normally referred to as fitting, resulting in the so called hybrid models. In this work, we present a novel application—3DIANA—specially targeted to those cases in which the EM map resolution is medium or low and additional experimental structural information is scarce or even lacking. In this way, 3DIANA statistically evaluates proposed/potential contacts between protein domains, presents a complete catalog of both structurally resolved and predicted interacting regions involving these domains and, finally, suggests structural templates to model the interaction between them. The evaluation of the proposed interactions is computed with DIMERO, a new method that scores physical binding sites based on the topology of protein interaction networks, which has recently shown the capability to increase by 200% the number of domain-domain interactions predicted in interactomes as compared to previous approaches. The new application displays the information at a sequence and structural level and is accessible through a web browser or as a Chimera plugin at http://3diana.cnb.csic.es.

Structural basis for collagen recognition by the immune receptor OSCAR

The osteoclast-associated receptor (OSCAR) is a collagen-binding immune receptor with important roles in dendritic cell maturation and activation of inflammatory monocytes as well as in osteoclastogenesis. The crystal structure of the OSCAR ectodomain is presented, both free and in complex with a consensus triple-helical peptide (THP). The structures revealed a collagen-binding site in each immunoglobulin-like domain (D1 and D2). The THP binds near a predicted collagen-binding groove in D1, but a more extensive interaction with D2 is facilitated by the unusually wide D1-D2 interdomain angle in OSCAR. Direct binding assays, combined with site-directed mutagenesis, confirm that the primary collagen-binding site in OSCAR resides in D2, in marked contrast to the related collagen receptors, glycoprotein VI (GPVI) and leukocyte-associated immunoglobulin-like receptor-1 (LAIR-1). Monomeric OSCAR D1D2 binds to the consensus THP with a KD of 28 µM measured in solution, but shows a higher affinity (KD 1.5 μM) when binding to a solid-phase THP, most likely due to an avidity effect. These data suggest a 2-stage model for the interaction of OSCAR with a collagen fibril, with transient, low-affinity interactions initiated by the membrane-distal D1, followed by firm adhesion to the primary binding site in D2.

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.

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

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

Structure, dynamics, assembly, and evolution of protein complexes

The assembly of individual proteins into functional complexes is fundamental to nearly all biological processes. In recent decades, many thousands of homomeric and heteromeric protein complex structures have been determined, greatly improving our understanding of the fundamental principles that control symmetric and asymmetric quaternary structure organization. Furthermore, our conception of protein complexes has moved beyond static representations to include dynamic aspects of quaternary structure, including conformational changes upon binding, multistep ordered assembly pathways, and structural fluctuations occurring within fully assembled complexes. Finally, major advances have been made in our understanding of protein complex evolution, both in reconstructing evolutionary histories of specific complexes and in elucidating general mechanisms that explain how quaternary structure tends to evolve. The evolution of quaternary structure occurs via changes in self-assembly state or through the gain or loss of protein subunits, and these processes can be driven by both adaptive and nonadaptive influences.

Structural bioinformatics of the interactome

The past decade has seen a dramatic expansion in the number and range of techniques available to obtain genome-wide information and to analyze this information so as to infer both the functions of individual molecules and how they interact to modulate the behavior of biological systems. Here, we review these techniques, focusing on the construction of physical protein-protein interaction networks, and highlighting approaches that incorporate protein structure, which is becoming an increasingly important component of systems-level computational techniques. We also discuss how network analyses are being applied to enhance our basic understanding of biological systems and their disregulation, as well as how these networks are being used in drug development.

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.

Protein flexibility facilitates quaternary structure assembly and evolution

The flexibility of individual proteins aids their evolutionary recruitment into complexes with increasing numbers of distinct subunits.

The intrinsic flexibility of proteins allows them to undergo large conformational fluctuations in solution or upon interaction with other molecules. Proteins also commonly assemble into complexes with diverse quaternary structure arrangements. Here we investigate how the flexibility of individual protein chains influences the assembly and evolution of protein complexes. We find that flexibility appears to be particularly conducive to the formation of heterologous (i.e., asymmetric) intersubunit interfaces. This leads to a strong association between subunit flexibility and homomeric complexes with cyclic and asymmetric quaternary structure topologies. Similarly, we also observe that the more nonhomologous subunits that assemble together within a complex, the more flexible those subunits tend to be. Importantly, these findings suggest that subunit flexibility should be closely related to the evolutionary history of a complex. We confirm this by showing that evolutionarily more recent subunits are generally more flexible than evolutionarily older subunits. Finally, we investigate the very different explorations of quaternary structure space that have occurred in different evolutionary lineages. In particular, the increased flexibility of eukaryotic proteins appears to enable the assembly of heteromeric complexes with more unique components.

Proteins often interact with other proteins and assemble into complexes. Here we show that the flexibility of individual proteins is important for their recruitment to complexes, as it facilitates the formation of asymmetric interfaces between different subunits. The role of flexibility becomes increasingly important as a greater number of distinct proteins are packed together within a single complex: the more distinct subunits, the more flexible those subunits need to be. A consequence of this is that, when a protein complex gains a new subunit during evolution, the newer subunit will tend to be more flexible than the older subunits. This suggests that we may be able to partially reconstruct the evolutionary history of a protein complex by considering the flexibility of its subunits. We also find that the types of protein complexes an organism forms are closely related to the flexibility of its proteins, with eukaryotic species, and particularly animals, using their increased flexibility to assemble complexes involving more distinct components.

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.

Efficient computation of root mean square deviations under rigid transformations

The computation of root mean square deviations (RMSD) is an important step in many bioinformatics applications. If approached naively, each RMSD computation takes time linear in the number of atoms. In addition, a careful implementation is required to achieve numerical stability, which further increases runtimes. In practice, the structural variations under consideration are often induced by rigid transformations of the protein, or are at least dominated by a rigid component. In this work, we show how RMSD values resulting from rigid transformations can be computed in constant time from the protein's covariance matrix, which can be precomputed in linear time. As a typical application scenario is protein clustering, we will also show how the Ward-distance which is popular in this field can be reduced to RMSD evaluations, yielding a constant time approach for their computation.

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.

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.

Protein design by fusion: implications for protein structure prediction and evolution

Domain fusion is a useful tool in protein design. Here, the structure of a fusion of the heterodimeric flagella-assembly proteins FliS and FliC is reported. Although the ability of the fusion protein to maintain the structure of the heterodimer may be apparent, threading-based structural predictions do not properly fuse the heterodimer. Additional examples of naturally occurring heterodimers that are homologous to full-length proteins were identified. These examples highlight that the designed protein was engineered by the same tools as used in the natural evolution of proteins and that heterodimeric structures contain a wealth of information, currently unused, that can improve structural predictions.

SVM based model generation for binding site prediction on helix turn helix motif type of transcription factors in eukaryotes

Support vector machine is a class of machine learning algorithms which uses a set of related supervised learning methods for classification and regression. Nowadays this method is vividly applied to many detection problems related with secondary structure, tumor cell and binding residue prediction. In this work, support vector machines (SVMs) have been trained on 90 sequences of transcription factors with HTH motif. Four sequence features were used as attribute for the prediction of interaction site in HTH motif. A web page was also developed so that user can easily enter the protein sequence and receive the output as interaction site predicted or not predicted. The generated model shows a very high amount of accuracy, sensitivity and specificity which proves to be a good model for the selected case.

Discovering interacting domains and motifs in protein-protein interactions

Many important biological processes, such as the signaling pathways, require protein-protein interactions (PPIs) that are designed for fast response to stimuli. These interactions are usually transient, easily formed, and disrupted, yet specific. Many of these transient interactions involve the binding of a protein domain to a short stretch (3-10) of amino acid residues, which can be characterized by a sequence pattern, i.e., a short linear motif (SLiM). We call these interacting domains and motifs domain-SLiM interactions. Existing methods have focused on discovering SLiMs in the interacting proteins' sequence data. With the recent increase in protein structures, we have a new opportunity to detect SLiMs directly from the proteins' 3D structures instead of their linear sequences. In this chapter, we describe a computational method called SLiMDIet to directly detect SLiMs on domain interfaces extracted from 3D structures of PPIs. SLiMDIet comprises two steps: (1) interaction interfaces belonging to the same domain are extracted and grouped together using structural clustering and (2) the extracted interaction interfaces in each cluster are structurally aligned to extract the corresponding SLiM. Using SLiMDIet, de novo SLiMs interacting with protein domains can be computationally detected from structurally clustered domain-SLiM interactions for PFAM domains which have available 3D structures in the PDB database.

Protein complexes are under evolutionary selection to assemble via ordered pathways

Is the order in which proteins assemble into complexes important for biological function? Here, we seek to address this by searching for evidence of evolutionary selection for ordered protein complex assembly. First, we experimentally characterize the assembly pathways of several heteromeric complexes and show that they can be simply predicted from their three-dimensional structures. Then, by mapping gene fusion events identified from fully sequenced genomes onto protein complex assembly pathways, we demonstrate evolutionary selection for conservation of assembly order. Furthermore, using structural and high-throughput interaction data, we show that fusion tends to optimize assembly by simplifying protein complex topologies. Finally, we observe protein structural constraints on the gene order of fusion that impact the potential for fusion to affect assembly. Together, these results reveal the intimate relationships among protein assembly, quaternary structure, and evolution and demonstrate on a genome-wide scale the biological importance of ordered assembly pathways.

Proteome-wide prediction of self-interacting proteins based on multiple properties

Self-interacting proteins, whose two or more copies can interact with each other, play important roles in cellular functions and the evolution of protein interaction networks (PINs). Knowing whether a protein can self-interact can contribute to and sometimes is crucial for the elucidation of its functions. Previous related research has mainly focused on the structures and functions of specific self-interacting proteins, whereas knowledge on their overall properties is limited. Meanwhile, the two current most common high throughput protein interaction assays have limited ability to detect self-interactions because of biological artifacts and design limitations, whereas the bioinformatic prediction method of self-interacting proteins is lacking. This study aims to systematically study and predict self-interacting proteins from an overall perspective. We find that compared with other proteins the self-interacting proteins in the structural aspect contain more domains; in the evolutionary aspect they tend to be conserved and ancient; in the functional aspect they are significantly enriched with enzyme genes, housekeeping genes, and drug targets, and in the topological aspect tend to occupy important positions in PINs. Furthermore, based on these features, after feature selection, we use logistic regression to integrate six representative features, including Gene Ontology term, domain, paralogous interactor, enzyme, model organism self-interacting protein, and betweenness centrality in the PIN, to develop a proteome-wide prediction model of self-interacting proteins. Using 5-fold cross-validation and an independent test, this model shows good performance. Finally, the prediction model is developed into a user-friendly web service SLIPPER (SeLf-Interacting Protein PrEdictoR). Users may submit a list of proteins, and then SLIPPER will return the probability_scores measuring their possibility to be self-interacting proteins and various related annotation information. This work helps us understand the role self-interacting proteins play in cellular functions from an overall perspective, and the constructed prediction model may contribute to the high throughput finding of self-interacting proteins and provide clues for elucidating their functions.

Alternative protein-protein interfaces are frequent exceptions

The intricate molecular details of protein-protein interactions (PPIs) are crucial for function. Therefore, measuring the same interacting protein pair again, we expect the same result. This work measured the similarity in the molecular details of interaction for the same and for homologous protein pairs between different experiments. All scores analyzed suggested that different experiments often find exceptions in the interfaces of similar PPIs: up to 22% of all comparisons revealed some differences even for sequence-identical pairs of proteins. The corresponding number for pairs of close homologs reached 68%. Conversely, the interfaces differed entirely for 12-29% of all comparisons. All these estimates were calculated after redundancy reduction. The magnitude of interface differences ranged from subtle to the extreme, as illustrated by a few examples. An extreme case was a change of the interacting domains between two observations of the same biological interaction. One reason for different interfaces was the number of copies of an interaction in the same complex: the probability of observing alternative binding modes increases with the number of copies. Even after removing the special cases with alternative hetero-interfaces to the same homomer, a substantial variability remained. Our results strongly support the surprising notion that there are many alternative solutions to make the intricate molecular details of PPIs crucial for function.

GUIDING PROTEIN DOCKING WITH GEOMETRIC AND EVOLUTIONARY INFORMATION

Structural modeling of molecular assemblies promises to improve our understanding of molecular interactions and biological function. Even when focusing on modeling structures of protein dimers from knowledge of monomeric native structure, docking two rigid structures onto one another entails exploring a large configurational space. This paper presents a novel approach for docking protein molecules and elucidating native-like configurations of protein dimers. The approach makes use of geometric hashing to focus the docking of monomeric units on geometrically complementary regions through rigid-body transformations. This geometry-based approach improves the feasibility of searching the combined configurational space. The search space is narrowed even further by focusing the sought rigid-body transformations around molecular surface regions composed of amino acids with high evolutionary conservation. This condition is based on recent findings, where analysis of protein assemblies reveals that many functional interfaces are significantly conserved throughout evolution. Different search procedures are employed in this work to search the resulting narrowed configurational space. A proof-of-concept energy-guided probabilistic search procedure is also presented. Results are shown on a broad list of 18 protein dimers and additionally compared with data reported by other labs. Our analysis shows that focusing the search around evolutionary-conserved interfaces results in lower lRMSDs.

GIRAF: a method for fast search and flexible alignment of ligand binding interfaces in proteins at atomic resolution

Comparison and classification of protein structures are fundamental means to understand protein functions. Due to the computational difficulty and the ever-increasing amount of structural data, however, it is in general not feasible to perform exhaustive all-against-all structure comparisons necessary for comprehensive classifications. To efficiently handle such situations, we have previously proposed a method, now called GIRAF. We herein describe further improvements in the GIRAF protein structure search and alignment method. The GIRAF method achieves extremely efficient search of similar structures of ligand binding sites of proteins by exploiting database indexing of structural features of local coordinate frames. In addition, it produces refined atom-wise alignments by iterative applications of the Hungarian method to the bipartite graph defined for a pair of superimposed structures. By combining the refined alignments based on different local coordinate frames, it is made possible to align structures involving domain movements. We provide detailed accounts for the database design, the search and alignment algorithms as well as some benchmark results.

Evolution of oligomeric state through geometric coupling of protein interfaces

Oligomerization plays an important role in the function of many proteins. Thus, understanding, predicting, and, ultimately, engineering oligomerization presents a long-standing interest. From the perspective of structural biology, protein-protein interactions have mainly been analyzed in terms of the biophysical nature and evolution of protein interfaces. Here, our aim is to quantify the importance of the larger structural context of protein interfaces in protein interaction evolution. Specifically, we ask to what extent intersubunit geometry affects oligomerization state. We define a set of structural parameters describing the overall geometry and relative positions of interfaces of homomeric complexes with different oligomeric states. This allows us to quantify the contribution of direct sequence changes in interfaces versus indirect changes outside the interface that affect intersubunit geometry. We find that such indirect, or allosteric mutations affecting intersubunit geometry via indirect mechanisms are as important as interface sequence changes for evolution of oligomeric states.

Comparison of tertiary structures of proteins in protein-protein complexes with unbound forms suggests prevalence of allostery in signalling proteins

BACKGROUND

Most signalling and regulatory proteins participate in transient protein-protein interactions during biological processes. They usually serve as key regulators of various cellular processes and are often stable in both protein-bound and unbound forms. Availability of high-resolution structures of their unbound and bound forms provides an opportunity to understand the molecular mechanisms involved. In this work, we have addressed the question "What is the nature, extent, location and functional significance of structural changes which are associated with formation of protein-protein complexes?"

RESULTS

A database of 76 non-redundant sets of high resolution 3-D structures of protein-protein complexes, representing diverse functions, and corresponding unbound forms, has been used in this analysis. Structural changes associated with protein-protein complexation have been investigated using structural measures and Protein Blocks description. Our study highlights that significant structural rearrangement occurs on binding at the interface as well as at regions away from the interface to form a highly specific, stable and functional complex. Notably, predominantly unaltered interfaces interact mainly with interfaces undergoing substantial structural alterations, revealing the presence of at least one structural regulatory component in every complex.Interestingly, about one-half of the number of complexes, comprising largely of signalling proteins, show substantial localized structural change at surfaces away from the interface. Normal mode analysis and available information on functions on some of these complexes suggests that many of these changes are allosteric. This change is largely manifest in the proteins whose interfaces are altered upon binding, implicating structural change as the possible trigger of allosteric effect. Although large-scale studies of allostery induced by small-molecule effectors are available in literature, this is, to our knowledge, the first study indicating the prevalence of allostery induced by protein effectors.

CONCLUSIONS

The enrichment of allosteric sites in signalling proteins, whose mutations commonly lead to diseases such as cancer, provides support for the usage of allosteric modulators in combating these diseases.

Fast protein binding site comparisons using visual words representation

MOTIVATION

Finding geometrically similar protein binding sites is crucial for understanding protein functions and can provide valuable information for protein-protein docking and drug discovery. As the number of known protein-protein interaction structures has dramatically increased, a high-throughput and accurate protein binding site comparison method is essential. Traditional alignment-based methods can provide accurate correspondence between the binding sites but are computationally expensive.

RESULTS

In this article, we present a novel method for the comparisons of protein binding sites using a 'visual words' representation (PBSword). We first extract geometric features of binding site surfaces and build a vocabulary of visual words by clustering a large set of feature descriptors. We then describe a binding site surface with a high-dimensional vector that encodes the frequency of visual words, enhanced by the spatial relationships among them. Finally, we measure the similarity of binding sites by utilizing metric space operations, which provide speedy comparisons between protein binding sites. Our experimental results show that PBSword achieves a comparable classification accuracy to an alignment-based method and improves accuracy of a feature-based method by 36% on a non-redundant dataset. PBSword also exhibits a significant efficiency improvement over an alignment-based method.

Novel insights through the integration of structural and functional genomics data with protein networks

In recent years, major advances in genomics, proteomics, macromolecular structure determination, and the computational resources capable of processing and disseminating the large volumes of data generated by each have played major roles in advancing a more systems-oriented appreciation of biological organization. One product of systems biology has been the delineation of graph models for describing genome-wide protein-protein interaction networks. The network organization and topology which emerges in such models may be used to address fundamental questions in an array of cellular processes, as well as biological features intrinsic to the constituent proteins (or "nodes") themselves. However, graph models alone constitute an abstraction which neglects the underlying biological and physical reality that the network's nodes and edges are highly heterogeneous entities. Here, we explore some of the advantages of introducing a protein structural dimension to such models, as the marriage of conventional network representations with macromolecular structural data helps to place static node and edge constructs in a biologically more meaningful context. We emphasize that 3D protein structures constitute a valuable conceptual and predictive framework by discussing examples of the insights provided, such as enabling in silico predictions of protein-protein interactions, providing rational and compelling classification schemes for network elements, as well as revealing interesting intrinsic differences between distinct node types, such as disorder and evolutionary features, which may then be rationalized in light of their respective functions within networks.

Dissection of the dimerization modes in the DJ-1 superfamily

The DJ-1 superfamily (DJ-1/ThiJ/PfpI superfamily) is distributed across all three kingdoms of life. These proteins are involved in a highly diverse range of cellular functions, including chaperone and protease activity. DJ-1 proteins usually form dimers or hexamers in vivo and show at least four different binding orientations via distinct interface patches. Abnormal oligomerization of human DJ-1 is related to neurodegenerative disorders including Parkinson's disease, suggesting important functional roles of quaternary structures. However, the quaternary structures of the DJ-1 superfamily have not been extensively studied. Here, we focus on the diverse oligomerization modes among the DJ-1 superfamily proteins and investigate the functional roles of quaternary structures both computationally and experimentally. The oligomerization modes are classified into 4 types (DJ-1, YhbO, Hsp, and YDR types) depending on the distinct interface patches (I-IV) upon dimerization. A unique, rotated interface via patch I is reported, which may potentially be related to higher order oligomerization. In general, the groups based on sequence similarity are consistent with the quaternary structural classes, but their biochemical functions cannot be directly inferred using sequence information alone. The observed phyletic pattern suggests the dynamic nature of quaternary structures in the course of evolution. The amino acid residues at the interfaces tend to show lower mutation rates than those of non-interfacial surfaces.

Computational design of a symmetric homodimer using β-strand assembly

Computational design of novel protein-protein interfaces is a test of our understanding of protein interactions and has the potential to allow modification of cellular physiology. Methods for designing high-affinity interactions that adopt a predetermined binding mode have proved elusive, suggesting the need for new strategies that simplify the design process. A solvent-exposed backbone on a β-strand is thought of as "sticky" and β-strand pairing stabilizes many naturally occurring protein complexes. Here, we computationally redesign a monomeric protein to form a symmetric homodimer by pairing exposed β-strands to form an intermolecular β-sheet. A crystal structure of the designed complex closely matches the computational model (rmsd = 1.0 Å). This work demonstrates that β-strand pairing can be used to computationally design new interactions with high accuracy.

Simplified method to predict mutual interactions of human transcription factors based on their primary structure

Background

Physical interactions between transcription factors (TFs) are necessary for forming regulatory protein complexes and thus play a crucial role in gene regulation. Currently, knowledge about the mechanisms of these TF interactions is incomplete and the number of known TF interactions is limited. Computational prediction of such interactions can help identify potential new TF interactions as well as contribute to better understanding the complex machinery involved in gene regulation.

Methodology

We propose here such a method for the prediction of TF interactions. The method uses only the primary sequence information of the interacting TFs, resulting in a much greater simplicity of the prediction algorithm. Through an advanced feature selection process, we determined a subset of 97 model features that constitute the optimized model in the subset we considered. The model, based on quadratic discriminant analysis, achieves a prediction accuracy of 85.39% on a blind set of interactions. This result is achieved despite the selection for the negative data set of only those TF from the same type of proteins, i.e. TFs that function in the same cellular compartment (nucleus) and in the same type of molecular process (transcription initiation). Such selection poses significant challenges for developing models with high specificity, but at the same time better reflects real-world problems.

Conclusions

The performance of our predictor compares well to those of much more complex approaches for predicting TF and general protein-protein interactions, particularly when taking the reduced complexity of model utilisation into account.

Studies on the inference of protein binding regions across fold space based on structural similarities

The emerging picture of a continuous protein fold space highlights the existence of non obvious structural similarities between proteins with apparent different topologies. The identification of structure resemblances across fold space and the analysis of similar recognition regions may be a valuable source of information towards protein structure-based functional characterization. In this work, we use non-sequential structural alignment methods (ns-SAs) to identify structural similarities between protein pairs independently of their SCOP hierarchy, and we calculate the significance of binding region conservation using the interacting residues overlap in the ns-SA. We cluster the binding inferences for each family to distinguish already known family binding regions from putative new ones. Our methodology exploits the enormous amount of data available in the PDB to identify binding region similarities within protein families and to propose putative binding regions. Our results indicate that there is a plethora of structurally common binding regions among proteins, independently of current fold classifications. We obtain a 6- to 8-fold enrichment of novel binding regions, and identify binding inferences for 728 protein families that so far lack binding information in the PDB. We explore binding mode analogies between ligands from commonly clustered binding regions to investigate the utility of our methodology. A comprehensive analysis of the obtained binding inferences may help in the functional characterization of protein recognition and assist rational engineering. The data obtained in this work is available in the download link at www.scowlp.org.

Three-dimensional modeling of protein interactions and complexes is going 'omics

High-throughput interaction discovery initiatives have revealed the existence of hundreds of multiprotein complexes whose functions are regulated through thousands of protein-protein interactions (PPIs). However, the structural details of these interactions, often necessary to understand their function, are only available for a tiny fraction, and the experimental difficulties surrounding complex structure determination make computational modeling techniques paramount. In this manuscript, we critically review some of the most recent developments in the field of structural bioinformatics applied to the modeling of protein interactions and complexes, from large macromolecular machines to domain-domain and peptide-mediated interactions. In particular, we place a special emphasis on those methods that can be applied in a proteome-wide manner, and discuss how they will help in the ultimate objective of building 3D interactome networks.

Systematic classification and analysis of themes in protein-DNA recognition

Protein-DNA recognition plays a central role in the regulation of gene expression. With the rapidly increasing number of protein-DNA complex structures available at atomic resolution in recent years, a systematic, complete, and intuitive framework to clarify the intrinsic relationship between the global binding modes of these complexes is needed. In this work, we modified, extended, and applied previously defined RNA-recognition themes to describe protein-DNA recognition and used a protocol that incorporates automatic methods into manual inspection to plant a comprehensive classification tree for currently available high-quality protein-DNA structures. Further, a nonredundant (representative) data set consisting of 200 thematically diverse complexes was extracted from the leaves of the classification tree by using a locally sensitive interface comparison algorithm. On the basis of the representative data set, various physical and chemical properties associated with protein-DNA interactions were analyzed using empirical or semiempirical methods. We also examined the individual energetic components involved in protein-DNA interactions and highlighted the importance of conformational entropy, which has been almost completely ignored in previous studies of protein-DNA binding energy.

Structural space of protein-protein interfaces is degenerate, close to complete, and highly connected

At the heart of protein-protein interactions are protein-protein interfaces where the direct physical interactions occur. By developing and applying an efficient structural alignment method, we study the structural similarity of representative protein-protein interfaces involving interactions between dimers. Even without structural similarity between individual monomers that form dimeric complexes, ∼90% of native interfaces have a close structural neighbor with similar backbone C(α) geometry and interfacial contact pattern. About 80% of the interfaces form a dense network, where any two interfaces are structurally related using a transitive set of at most seven intermediate interfaces. The degeneracy of interface space is largely due to the packing of compact, hydrogen-bonded secondary structure elements. This packing generates relatively flat interacting surfaces whose geometries are highly degenerate. Comparative study of artificial and native interfaces argues that the library of protein interfaces is close to complete and comprised of roughly 1,000 distinct interface types. In contrast, the number of possible quaternary structures of dimers is estimated to be about 10(4) times larger; thus, an experimentally determined database of all representative quaternary structures is not likely in the near future. Nevertheless, one could in principle exploit the completeness of protein interfaces to predict most dimeric quaternary structures. Finally, our results provide a structural explanation for the prevalence of promiscuous protein interactions. By side-chain packing adjustments, we illustrate how multiprotein specificity can be attained at a promiscuous interface.