The Subunit Interfaces of Weakly Associated Homodimeric Proteins

We analyzed subunit interfaces in 315 homodimers with an X-ray structure in the Protein Data Bank, validated by checking the literature for data that indicate that the proteins are dimeric in solution and that, in the case of the “weak” dimers, the homodimer is in equilibrium with the monomer. The interfaces of the 42 weak dimers, which are smaller by a factor of 2.4 on average than in the remainder of the set, are comparable in size with antibody-antigen or protease-inhibitor interfaces. Nevertheless, they are more hydrophobic than in the average transient protein-protein complex and similar in amino acid composition to the other homodimer interfaces. The mean numbers of interface hydrogen bonds and hydration water molecules per unit area are also similar in homodimers and transient complexes. Parameters related to the atomic packing suggest that many of the weak dimer interfaces are loosely packed, and we suggest that this contributes to their low stability. To evaluate the evolutionary selection pressure on interface residues, we calculated the Shannon entropy of homologous amino acid sequences at 60% sequence identity. In 93% of the homodimers, the interface residues are better conserved than the residues on the protein surface. The weak dimers display the same high degree of interface conservation as other homodimers, but their homologs may be heterodimers as well as homodimers. Their interfaces may be good models in terms of their size, composition, and evolutionary conservation for the labile subunit contacts that allow protein assemblies to share and exchange components, allosteric proteins to undergo quaternary structure transitions, and molecular machines to operate in the cell.     

Protein-DNA interactions: structural, thermodynamic and clustering patterns of conserved residues in DNA-binding proteins

Amino acid residues, which play important roles in protein function, are often conserved. Here, we analyze thermodynamic and structural data of protein-DNA interactions to explore a relationship between free energy, sequence conservation and structural cooperativity. We observe that the most stabilizing residues or putative hotspots are those which occur as clusters of conserved residues. The higher packing density of the clusters and available experimental thermodynamic data of mutations suggest cooperativity between conserved residues in the clusters. Conserved singlets contribute to the stability of protein-DNA complexes to a lesser extent. We also analyze structural features of conserved residues and their clusters and examine their role in identifying DNA-binding sites. We show that about half of the observed conserved residue clusters are in the interface with the DNA, which could be identified from their amino acid composition; whereas the remaining clusters are at the protein-protein or protein-ligand interface, or embedded in the structural scaffolds. In protein-protein interfaces, conserved residues are highly correlated with experimental residue hotspots, contributing dominantly and often cooperatively to the stability of protein-protein complexes. Overall, the conservation patterns of the stabilizing residues in DNA-binding proteins also highlight the significance of clustering as compared to single residue conservation.     

ISIS: interaction sites identified from sequence

MOTIVATION: Large-scale experiments reveal pairs of interacting proteins but leave the residues involved in the interactions unknown. These interface residues are essential for understanding the mechanism of interaction and are often desired drug targets. Reliable identification of residues that reside in protein-protein interface typically requires analysis of protein structure. Therefore, for the vast majority of proteins, for which there is no high-resolution structure, there is no effective way of identifying interface residues. RESULTS: Here we present a machine learning-based method that identifies interacting residues from sequence alone. Although the method is developed using transient protein-protein interfaces from complexes of experimentally known 3D structures, it never explicitly uses 3D information. Instead, we combine predicted structural features with evolutionary information. The strongest predictions of the method reached over 90% accuracy in a cross-validation experiment. Our results suggest that despite the significant diversity in the nature of protein-protein interactions, they all share common basic principles and that these principles are identifiable from sequence alone.     

Structural similarity and classification of protein interaction interfaces

Interactions between proteins play a key role in many cellular processes. Studying protein-protein interactions that share similar interaction interfaces may shed light on their evolution and could be helpful in elucidating the mechanisms behind stability and dynamics of the protein complexes. When two complexes share structurally similar subunits, the similarity of the interaction interfaces can be found through a structural superposition of the subunits. However, an accurate detection of similarity between the protein complexes containing subunits of unrelated structure remains an open problem. Here, we present an alignment-free machine learning approach to measure interface similarity. The approach relies on the feature-based representation of protein interfaces and does not depend on the superposition of the interacting subunit pairs. Specifically, we develop an SVM classifier of similar and dissimilar interfaces and derive a feature-based interface similarity measure. Next, the similarity measure is applied to a set of 2,806×2,806 binary complex pairs to build a hierarchical classification of protein-protein interactions. Finally, we explore case studies of similar interfaces from each level of the hierarchy, considering cases when the subunits forming interactions are either homologous or structurally unrelated. The analysis has suggested that the positions of charged residues in the homologous interfaces are not necessarily conserved and may exhibit more complex conservation patterns.     

Molecular principles of the interactions of disordered proteins

Thorough knowledge of the molecular principles of protein-protein recognition is essential to our understanding of protein function at the cellular level. Whereas interactions of ordered proteins have been analyzed in great detail, complexes of intrinsically unstructured/disordered proteins (IUPs) have hardly been addressed so far. Here, we have collected a database of 39 complexes of experimentally verified IUPs, and compared their interfaces with those of 72 complexes of ordered, globular proteins. The characteristic differences found between the two types of complexes suggest that IUPs represent a distinct molecular implementation of the principles of protein-protein recognition. The interfaces do not differ in size, but those of IUPs cover a much larger part of the surface of the protein than for their ordered counterparts. Moreover, IUP interfaces are significantly more hydrophobic relative to their overall amino acid composition, but also in absolute terms. They rely more on hydrophobic-hydrophobic than on polar-polar interactions. Their amino acids in the interface realize more intermolecular contacts, which suggests a better fit with the partner due to induced folding upon binding that results in a better adaptation to the partner. The two modes of interaction also differ in that IUPs usually use only a single continuous segment for partner binding, whereas the binding sites of ordered proteins are more segmented. Probably, all these features contribute to the increased evolutionary conservation of IUP interface residues. These noted molecular differences are also manifested in the interaction energies of IUPs. Our approximation of these by low-resolution force-fields shows that IUPs gain much more stabilization energy from intermolecular contacts, than from folding, i.e. they use their binding energy for folding. Overall, our findings provide a structural rationale to the prior suggestions that many IUPs are specialized for functions realized by protein-protein interactions.     

Structural characterisation and functional significance of transient protein-protein interactions

Protein-protein complexes that dissociate and associate readily, often depending on the physiological condition or environment, play an important role in many biological processes. In order to characterise these "transient" protein-protein interactions, two sets of complexes were collected and analysed. The first set consists of 16 experimentally validated "weak" transient homodimers, which are known to exist as monomers and dimers at physiological concentration, with dissociation constants in the micromolar range. A set of 23 functionally validated transient (i.e. intracellular signalling) heterodimers comprise the second set. This set includes complexes that are more stable, with nanomolar binding affinities, and require a molecular trigger to form and break the interaction. In comparison to more stable homodimeric complexes, the weak homodimers demonstrate smaller contact areas between protomers and the interfaces are more planar and polar on average. The physicochemical and geometrical properties of these weak homodimers more closely resemble those of non-obligate hetero-oligomeric complexes, whose components can exist either as monomers or as complexes in vivo. In contrast to the weak transient dimers, "strong" transient dimers often undergo large conformational changes upon association/dissociation and are characterised with larger, less planar and sometimes more hydrophobic interfaces. From sequence alignments we find that the interface residues of the weak transient homodimers are generally more conserved than surface residues, consistent with being constrained to maintain the protein-protein interaction during evolution. Protein families that include members with different oligomeric states or structures are identified, and found to exhibit a lower sequence conservation at the interface. The results are discussed in terms of the physiological function and evolution of protein-protein interactions.     

Protein–protein interactions leave evolutionary footprints: High molecular coevolution at the core of interfaces

Protein–protein interactions are essential to all aspects of life. Specific interactions result from evolutionary pressure at the interacting interfaces of partner proteins. However, evolutionary pressure is not homogeneous within the interface: for instance, each residue does not contribute equally to the binding energy of the complex. To understand functional differences between residues within the interface, we analyzed their properties in the core and rim regions. Here, we characterized protein interfaces with two evolutionary measures, conservation and coevolution, using a comprehensive dataset of 896 protein complexes. These scores can detect different selection pressures at a given position in a multiple sequence alignment. We also analyzed how the number of interactions in which a residue is involved influences those evolutionary signals. We found that the coevolutionary signal is higher in the interface core than in the interface rim region. Additionally, the difference in coevolution between core and rim regions is comparable to the known difference in conservation between those regions. Considering proteins with multiple interactions, we found that conservation and coevolution increase with the number of different interfaces in which a residue is involved, suggesting that more constraints (i.e., a residue that must satisfy a greater number of interactions) allow fewer sequence changes at those positions, resulting in higher conservation and coevolution values. These findings shed light on the evolution of protein interfaces and provide information useful for identifying protein interfaces and predicting protein–protein interactions.     

Predicting where Small Molecules Bind at Protein-Protein Interfaces

Small molecules that bind at protein-protein interfaces may either block or stabilize protein-protein interactions in cells. Thus, some of these binding interfaces may turn into prospective targets for drug design. Here, we collected 175 pairs of protein-protein (PP) complexes and protein-ligand (PL) complexes with known three-dimensional structures for which (1) one protein from the PP complex shares at least 40% sequence identity with the protein from the PL complex, and (2) the interface regions of these proteins overlap at least partially with each other. We found that those residues of the interfaces that may bind the other protein as well as the small molecule are evolutionary more conserved on average, have a higher tendency of being located in pockets and expose a smaller fraction of their surface area to the solvent than the remaining protein-protein interface region. Based on these findings we derived a statistical classifier that predicts patches at binding interfaces that have a higher tendency to bind small molecules. We applied this new prediction method to more than 10 000 interfaces from the protein data bank. For several complexes related to apoptosis the predicted binding patches were in direct contact to co-crystallized small molecules.     

Residue conservation in viral capsid assembly

To evaluate the evolutionary constraints placed on viral proteins by the structure and assembly of the capsid, we calculate Shannon entropies in the aligned sequences of 45 polypeptide chains in 32 icosahedral viruses, and relate these entropies to the residue location in the three-dimensional structure of the capsids. Three categories of residues have entropies lower than the chain average implying that they are better conserved than average: residues that are buried within a subunit (the protein core), residues that contain atoms buried at an interface between subunits (the interface core), and residues that contribute to several such interfaces. The interface core is also conserved in homomeric proteins and in transient protein-protein complexes, which have only one interface whereas capsids have many. In capsids, the subunit interfaces implicate most of the polypeptide chain: on average, 66% of the capsid residues are at an interface, 34% at more than one, and 47% at the interface core. Nevertheless, we observe that the degree of residue conservation can vary widely between interfaces within a capsid and between regions within an interface. The interfaces and regions of interfaces that show a low sequence variability are likely to play major roles in the self-assembly of the capsid, with implications on its mechanism that we discuss taking adeno-associated virus as an example.     

Expression cartography of human tissues using self organizing maps

Background

Although homology-based methods are among the most widely used methods for predicting the structure and function of proteins, the question as to whether interface sequence conservation can be effectively exploited in predicting protein-protein interfaces has been a subject of debate.

Results

We studied more than 300,000 pair-wise alignments of protein sequences from structurally characterized protein complexes, including both obligate and transient complexes. We identified sequence similarity criteria required for accurate homology-based inference of interface residues in a query protein sequence. Based on these analyses, we developed HomPPI, a class of sequence homology-based methods for predicting protein-protein interface residues. We present two variants of HomPPI: (i) NPS-HomPPI (Non partner-specific HomPPI), which can be used to predict interface residues of a query protein in the absence of knowledge of the interaction partner; and (ii) PS-HomPPI (Partner-specific HomPPI), which can be used to predict the interface residues of a query protein with a specific target protein. Our experiments on a benchmark dataset of obligate homodimeric complexes show that NPS-HomPPI can reliably predict protein-protein interface residues in a given protein, with an average correlation coefficient (CC) of 0.76, sensitivity of 0.83, and specificity of 0.78, when sequence homologs of the query protein can be reliably identified. NPS-HomPPI also reliably predicts the interface residues of intrinsically disordered proteins. Our experiments suggest that NPS-HomPPI is competitive with several state-of-the-art interface prediction servers including those that exploit the structure of the query proteins. The partner-specific classifier, PS-HomPPI can, on a large dataset of transient complexes, predict the interface residues of a query protein with a specific target, with a CC of 0.65, sensitivity of 0.69, and specificity of 0.70, when homologs of both the query and the target can be reliably identified. The HomPPI web server is available at http://homppi.cs.iastate.edu/.

Conclusions

Sequence homology-based methods offer a class of computationally efficient and reliable approaches for predicting the protein-protein interface residues that participate in either obligate or transient interactions. For query proteins involved in transient interactions, the reliability of interface residue prediction can be improved by exploiting knowledge of putative interaction partners.     

Insights from the structural analysis of protein heterodimer interfaces

Protein heterodimer complexes are often involved in catalysis, regulation, assembly, immunity and inhibition. This involves the formation of stable interfaces between the interacting partners. Hence, it is of interest to describe heterodimer interfaces using known structural complexes. We use a non-redundant dataset of 192 heterodimer complex structures from the protein databank (PDB) to identify interface residues and describe their interfaces using amino-acids residue property preference. Analysis of the dataset shows that the heterodimer interfaces are often abundant in polar residues. The analysis also shows the presence of two classes of interfaces in heterodimer complexes. The first class of interfaces (class A) with more polar residues than core but less than surface is known. These interfaces are more hydrophobic than surfaces, where protein-protein binding is largely hydrophobic. The second class of interfaces (class B) with more polar residues than core and surface is shown. These interfaces are more polar than surfaces, where binding is mainly polar. Thus, these findings provide insights to the understanding of protein-protein interactions.     

Peptide segments in protein-protein interfaces

An important component of functional genomics involves the understanding of protein association. The interfaces resulting from protein-protein interactions - (i) specific, as represented by the homodimeric quaternary structures and the complexes formed by two independently occurring protein components, and (ii) non-specific, as observed in the crystal lattice of monomeric proteins - have been analysed on the basis of the length and the number of peptide segments. In 1000 A2 of the interface area, contributed by a polypeptide chain, there would be 3.4 segments in homodimers, 5.6 in complexes and 6.3 in crystal contacts. Concomitantly, the segments are the longest (with 8.7 interface residues) in homodimers. Core segments (likely to contribute more towards binding) are more in number in homodimers (1.7) than in crystal contacts (0.5), and this number can be used as one of the parameters to distinguish between the two types of interfaces. Dominant segments involved in specific interactions, along with their secondary structural features, are enumerated.     

Predicted protein-protein interaction sites from local sequence information

Protein-protein interactions are facilitated by a myriad of residue-residue contacts on the interacting proteins. Identifying the site of interaction in the protein is a key for deciphering its functional mechanisms, and is crucial for drug development. Many studies indicate that the compositions of contacting residues are unique. Here, we describe a neural network that identifies protein-protein interfaces from sequence. For the most strongly predicted sites (in 34 of 333 proteins), 94% of the predictions were confirmed experimentally. When 70% of our predictions were right, we correctly predicted at least one interaction site in 20% of the complexes (66/333). These results indicate that the prediction of some interaction sites from sequence alone is possible. Incorporating evolutionary and predicted structural information may improve our method. However, even at this early stage, our tool might already assist wet-lab biology.     

Charged residues at protein interaction interfaces: unexpected conservation and orchestrated divergence

Protein-protein interactions play an essential role in the functioning of cell. The importance of charged residues and their diverse role in protein-protein interactions have been well studied using experimental and computational methods. Often, charged residues located in protein interaction interfaces are conserved across the families of homologous proteins and protein complexes. However, on a large scale, it has been recently shown that charged residues are significantly less conserved than other residue types in protein interaction interfaces. The goal of this work is to understand the role of charged residues in the protein interaction interfaces through their conservation patterns. Here, we propose a simple approach where the structural conservation of the charged residue pairs is analyzed among the pairs of homologous binary complexes. Specifically, we determine a large set of homologous interactions using an interaction interface similarity measure and catalog the basic types of conservation patterns among the charged residue pairs. We find an unexpected conservation pattern, which we call the correlated reappearance, occurring among the pairs of homologous interfaces more frequently than the fully conserved pairs of charged residues. Furthermore, the analysis of the conservation patterns across different superkingdoms as well as structural classes of proteins has revealed that the correlated reappearance of charged residues is by far the most prevalent conservation pattern, often occurring more frequently than the unconserved charged residues. We discuss a possible role that the new conservation pattern may play in the long-range electrostatic steering effect.     

A de novo protein binding pair by computational design and directed evolution

The de novo design of protein-protein interfaces is a stringent test of our understanding of the principles underlying protein-protein interactions and would enable unique approaches to biological and medical challenges. Here we describe a motif-based method to computationally design protein-protein complexes with native-like interface composition and interaction density. Using this method we designed a pair of proteins, Prb and Pdar, that heterodimerize with a Kd of 130 nM, 1000-fold tighter than any previously designed de novo protein-protein complex. Directed evolution identified two point mutations that improve affinity to 180 pM. Crystal structures of an affinity-matured complex reveal binding is entirely through the designed interface residues. Surprisingly, in the in vitro evolved complex one of the partners is rotated 180° relative to the original design model, yet still maintains the central computationally designed hotspot interaction and preserves the character of many peripheral interactions. This work demonstrates that high-affinity protein interfaces can be created by designing complementary interaction surfaces on two noninteracting partners and underscores remaining challenges.     

Prediction of interface residues in protein-protein complexes by a consensus neural network method: test against NMR data

The number of structures of protein-protein complexes deposited to the Protein Data Bank is growing rapidly. These structures embed important information for predicting structures of new protein complexes. This motivated us to develop the PPISP method for predicting interface residues in protein-protein complexes. In PPISP, sequence profiles and solvent accessibility of spatially neighboring surface residues were used as input to a neural network. The network was trained on native interface residues collected from the Protein Data Bank. The prediction accuracy at the time was 70% with 47% coverage of native interface residues. Now we have extensively improved PPISP. The training set now consisted of 1156 nonhomologous protein chains. Test on a set of 100 nonhomologous protein chains showed that the prediction accuracy is now increased to 80% with 51% coverage. To solve the problem of over-prediction and under-prediction associated with individual neural network models, we developed a consensus method that combines predictions from multiple models with different levels of accuracy and coverage. Applied on a benchmark set of 68 proteins for protein-protein docking, the consensus approach outperformed the best individual models by 3-8 percentage points in accuracy. To demonstrate the predictive power of cons-PPISP, eight complex-forming proteins with interfaces characterized by NMR were tested. These proteins are nonhomologous to the training set and have a total of 144 interface residues identified by chemical shift perturbation. cons-PPISP predicted 174 interface residues with 69% accuracy and 47% coverage and promises to complement experimental techniques in characterizing protein-protein interfaces. .     

Cluster conservation as a novel tool for studying protein-protein interactions evolution

Protein-protein interactions networks has come to be a buzzword associated with nets containing edges that represent a pair of interacting proteins (e.g. hormone-receptor, enzyme-inhibitor, antigen-antibody, and a subset of multichain biological machines). Yet, each such interaction composes its own unique network, in which vertices represent amino acid residues, and edges represent atomic contacts. Recent studies have shown that analyses of the data encapsulated in these detailed networks may impact predictions of structure-function correlation. Here, we study homologous families of protein-protein interfaces, which share the same fold but vary in sequence. In this context, we address what properties of the network are shared among relatives with different sequences (and hence different atomic interactions) and which are not. Herein, we develop the general mathematical framework needed to compare the modularity of homologous networks. We then apply this analysis to the structural data of a few interface families, including hemoglobin alpha-beta, growth hormone-receptor, and Serine protease-inhibitor. Our results suggest that interface modularity is an evolutionarily conserved property. Hence, protein-protein interfaces can be clustered down to a few modules, with the boundaries being evolutionarily conserved along homologous complexes. This suggests that protein engineering of protein-protein binding sites may be simplified by varying each module, but retaining the overall modularity of the interface.     

Statistical analysis of predominantly transient protein-protein interfaces

A non-redundant set of 170 protein-protein interfaces of known structure was statistically analyzed for residue and secondary-structure compositions, pairing preferences and side-chain-backbone interaction frequencies. By focussing mainly on transient protein-protein interfaces, the results underline previous findings for protein-protein interfaces but also show some new interesting aspects of transient interfaces. The residue compositions at interfaces found in this study correlate well with the results of other studies. On average, contacts between pairs of hydrophobic and polar residues were unfavorable, and the charged residues tended to pair subject to charge complementarity. Secondary structure composition analysis shows that neither helices nor beta-sheets are dominantly populated at interfaces. Analyzing the pairing preferences of the secondary structure elements revealed a higher affinity within the same elements and alludes to tight packings. In addition, the results for the side-chain and backbone interaction frequencies, which were measured under more stringent conditions, showed a high occurrence of side-chain-backbone interactions. Taking a closer look at the helix and beta-sheet binding frequencies for a given side-chain and backbone interaction underlined the relevance of tight packings. The polarity of interfaces increased with decreasing interface size. These types of information may be useful for scoring complexes in protein-protein docking studies or for prediction of protein-protein interfaces from the sequences alone.     

Predicting protein-protein interactions on a proteome scale by matching evolutionary and structural similarities at interfaces using PRISM

Prediction of protein-protein interactions at the structural level on the proteome scale is important because it allows prediction of protein function, helps drug discovery and takes steps toward genome-wide structural systems biology. We provide a protocol (termed PRISM, protein interactions by structural matching) for large-scale prediction of protein-protein interactions and assembly of protein complex structures. The method consists of two components: rigid-body structural comparisons of target proteins to known template protein-protein interfaces and flexible refinement using a docking energy function. The PRISM rationale follows our observation that globally different protein structures can interact via similar architectural motifs. PRISM predicts binding residues by using structural similarity and evolutionary conservation of putative binding residue 'hot spots'. Ultimately, PRISM could help to construct cellular pathways and functional, proteome-scale annotation. PRISM is implemented in Python and runs in a UNIX environment. The program accepts Protein Data Bank-formatted protein structures and is available at http://prism.ccbb.ku.edu.tr/prism_protocol/.