Does structural and chemical divergence play a role in precluding undesirable protein interactions?

To understand the evolutionary forces establishing, maintaining, breaking, or precluding protein-protein interactions, a comprehensive data set of protein complexes has been analyzed to examine the overlap between protein interfaces and the most conserved or divergent protein surface areas. The most divergent areas tend to be found predominantly away from protein interfaces, although when found at interfaces, they are associated with specific lack of cross-reactivity between close homologues, like in antibody-antigen complexes. Moreover, the amino acid composition of highly variable regions is significantly different from any other protein surfaces. The variable regions present higher structural plasticity as a result of insertions and deletions, and favor charged over hydrophobic residues, a known strategy to minimize aggregation. This suggests that (1) a rapid rate of mutations at these regions might be continuously altering their properties, making difficult the coadaptation, in shape and chemical complementarity, to potential interacting partners; and (2) the existence of some form of selective pressure for variable areas away from interfaces to accumulate charged residues, perhaps as an evolutionary mechanism to increase solubility and minimize undesirable interactions within the crowded cellular environment. Finally, these results are placed into the context of the aberrant oligomerization of sickle-cell anemia hemoglobin and prion proteins.     

Evolvability of yeast protein-protein interaction interfaces

The functional importance of protein-protein interactions indicates that there should be strong evolutionary constraint on their interaction interfaces. However, binding interfaces are frequently affected by amino acid replacements. Change due to coevolution within interfaces can contribute to variability but is not ubiquitous. An alternative explanation for the ability of surfaces to accept replacements may be that many residues can be changed without affecting the interaction. Candidates for these types of residues are those that make interchain interaction only through the protein main chain, β-carbon, or associated hydrogen atoms. Since almost all residues have these atoms, we hypothesize that this subset of interface residues may be more easily substituted than those that make interactions through other atoms. We term such interactions "residue type independent." Investigating this hypothesis, we find that nearly a quarter of residues in protein interaction interfaces make exclusively interchain residue-type-independent contacts. These residues are less structurally constrained and less conserved than residues making residue-type-specific interactions. We propose that residue-type-independent interactions allow substitutions in binding interfaces while the specificity of binding is maintained.     

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.     

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.     

A "solvated rotamer" approach to modeling water-mediated hydrogen bonds at protein-protein interfaces

Water-mediated hydrogen bonds play critical roles at protein-protein and protein-nucleic acid interfaces, and the interactions formed by discrete water molecules cannot be captured using continuum solvent models. We describe a simple model for the energetics of water-mediated hydrogen bonds, and show that, together with knowledge of the positions of buried water molecules observed in X-ray crystal structures, the model improves the prediction of free-energy changes upon mutation at protein-protein interfaces, and the recovery of native amino acid sequences in protein interface design calculations. We then describe a "solvated rotamer" approach to efficiently predict the positions of water molecules, at protein-protein interfaces and in monomeric proteins, that is compatible with widely used rotamer-based side-chain packing and protein design algorithms. Finally, we examine the extent to which the predicted water molecules can be used to improve prediction of amino acid identities and protein-protein interface stability, and discuss avenues for overcoming current limitations of the approach.     

An evolution based classifier for prediction of protein interfaces without using protein structures

MOTIVATION: The number of available protein structures still lags far behind the number of known protein sequences. This makes it important to predict which residues participate in protein-protein interactions using only sequence information. Few studies have tackled this problem until now. RESULTS: We applied support vector machines to sequences in order to generate a classification of all protein residues into those that are part of a protein interface and those that are not. For the first time evolutionary information was used as one of the attributes and this inclusion of evolutionary importance rankings improves the classification. Leave-one-out cross-validation experiments show that prediction accuracy reaches 64%.     

Protein hot spots: the islands of stability

Understanding the structural basis of protein-protein interactions (PPIs) may shed light on the organization and functioning of signal transduction and metabolic networks and may assist in structure-based design of ligands (drugs) targeting protein-protein interfaces. The residues at the bimolecular interface, designated as the hot spots, contribute most of the free binding energy of PPI. To date, there is no conclusive atomistic explanation for the unique functional properties of the hot spots. We hypothesized that backbone compliance may play a role in protein-protein recognition and in the mechanism of binding of small-molecule compounds to protein surfaces. We used a steered molecular dynamics simulation to explore the compliance properties of the backbone of surface-exposed residues in several model proteins: interleukin-2, mouse double minute protein 2 and proliferating cell nuclear antigen. We demonstrated that protein surfaces exhibit distinct patterns in which highly immobile residues form defined clusters ("stability patches") alternating with areas of moderate to high mobility. These "stability patches" tend to localize in functionally important regions involved in protein-protein recognition. We propose a mechanism by which the distinct structural organization of the hot spots may contribute to their role in mediating PPI and facilitating binding of structurally diverse small-molecule compounds to protein surfaces.     

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.     

New measures for estimating surface complementarity and packing at protein-protein interfaces

A number of methods exist that use different approaches to assess geometric properties like the surface complementarity and atom packing at the protein-protein interface. We have developed two new and conceptually different measures using the Delaunay tessellation and interface slice selection to compute the surface complementarity and atom packing at the protein-protein interface in a straightforward manner. Our measures show a strong correlation among themselves and with other existing measures, and can be calculated in a highly time-efficient manner. The measures are discriminative for evaluating biological, as well as non-biological protein-protein contacts, especially from large protein complexes and large-scale structural studies (http://pallab.serc.iisc.ernet.in/nip_nsc).     

Resurrecting the ancestral enzymatic role of a modulatory subunit

In the post-genomic era, functional prediction of genes is largely based on sequence similarity searches, but sometimes the homologues bear different roles because of evolutionary adaptations. For instance, the existence of enzyme and non-enzyme homologues poses a difficult case for function prediction and the extent of this phenomenon is just starting to be surveyed. Different evolutionary paths are theoretically possible for the loss or acquisition of enzyme function. Here we studied the ancestral role of a model non-catalytic modulatory subunit. With a rational approach, we "resurrected" enzymatic activity from that subunit to experimentally prove that it derived from a catalytic ancestor. We show that this protein (L subunit ADP-glucose pyrophosphorylase) evolved to have a regulatory role, losing catalytic residues more than 130 million years ago, but preserving, possibly as a by-product, the substrate site architecture. Inactivation of catalytic subunits could be the consequence of a general evolutionary strategy to explore new regulatory roles in hetero-oligomers.     

Engineered oligomerization state of OmpF protein through computational design decouples oligomer dissociation from unfolding

Biogenesis of β-barrel membrane proteins is a complex, multistep, and as yet incompletely characterized process. The bacterial porin family is perhaps the best-studied protein family among β-barrel membrane proteins that allows diffusion of small solutes across the bacterial outer membrane. In this study, we have identified residues that contribute significantly to the protein-protein interaction (PPI) interface between the chains of outer membrane protein F (OmpF), a trimeric porin, using an empirical energy function in conjunction with an evolutionary analysis. By replacing these residues through site-directed mutagenesis either with energetically favorable residues or substitutions that do not occur in natural bacterial outer membrane proteins, we succeeded in engineering OmpF mutants with dimeric and monomeric oligomerization states instead of a trimeric oligomerization state. Moreover, our results suggest that the oligomerization of OmpF proceeds through a series of interactions involving two distinct regions of the extensive PPI interface: two monomers interact to form a dimer through the PPI interface near G19. This dimer then interacts with another monomer through the PPI interface near G135 to form a trimer. We have found that perturbing the PPI interface near G19 results in the formation of the monomeric OmpF only. Thermal denaturation of the designed dimeric OmpF mutant suggests that oligomer dissociation can be separated from the process of protein unfolding. Furthermore, the conserved site near G57 and G59 is important for the PPI interface and might provide the essential scaffold for PPIs.     

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.     

Parameterization and classification of the protein universe via geometric techniques

We present a scheme for the classification of 3487 non-redundant protein structures into 1207 non-hierarchical clusters by using recurring structural patterns of three to six amino acids as keys of classification. This results in several signature patterns, which seem to decide membership of a protein in a functional category. The patterns provide clues to the key residues involved in functional sites as well as in protein-protein interaction. The discovered patterns include a "glutamate double bridge" of superoxide dismutase, the functional interface of the serine protease and inhibitor, interface of homo/hetero dimers, and functional sites of several enzyme families. We use geometric invariants to decide superimposability of structural patterns. This allows the parameterization of patterns and discovery of recurring patterns via clustering. The geometric invariant-based approach eliminates the computationally explosive step of pair-wise comparison of structures. The results provide a vast resource for the biologists for experimental validation of the proposed functional sites, and for the design of synthetic enzymes, inhibitors and drugs.     

Interactome-Wide Prediction of Protein-Protein Binding Sites Reveals Effects of Protein Sequence Variation in Arabidopsis thaliana

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

Automated structure-based prediction of functional sites in proteins: applications to assessing the validity of inheriting protein function from homology in genome annotation and to protein docking

A major problem in genome annotation is whether it is valid to transfer the function from a characterised protein to a homologue of unknown activity. Here, we show that one can employ a strategy that uses a structure-based prediction of protein functional sites to assess the reliability of functional inheritance. We have automated and benchmarked a method based on the evolutionary trace approach. Using a multiple sequence alignment, we identified invariant polar residues, which were then mapped onto the protein structure. Spatial clusters of these invariant residues formed the predicted functional site. For 68 of 86 proteins examined, the method yielded information about the observed functional site. This algorithm for functional site prediction was then used to assess the validity of transferring the function between homologues. This procedure was tested on 18 pairs of homologous proteins with unrelated function and 70 pairs of proteins with related function, and was shown to be 94 % accurate. This automated method could be linked to schemes for genome annotation. Finally, we examined the use of functional site prediction in protein-protein and protein-DNA docking. The use of predicted functional sites was shown to filter putative docked complexes with a discrimination similar to that obtained by manually including biological information about active sites or DNA-binding residues.     

Protein-protein interaction hotspots carved into sequences

Protein-protein interactions, a key to almost any biological process, are mediated by molecular mechanisms that are not entirely clear. The study of these mechanisms often focuses on all residues at protein-protein interfaces. However, only a small subset of all interface residues is actually essential for recognition or binding. Commonly referred to as "hotspots," these essential residues are defined as residues that impede protein-protein interactions if mutated. While no in silico tool identifies hotspots in unbound chains, numerous prediction methods were designed to identify all the residues in a protein that are likely to be a part of protein-protein interfaces. These methods typically identify successfully only a small fraction of all interface residues. Here, we analyzed the hypothesis that the two subsets correspond (i.e., that in silico methods may predict few residues because they preferentially predict hotspots). We demonstrate that this is indeed the case and that we can therefore predict directly from the sequence of a single protein which residues are interaction hotspots (without knowledge of the interaction partner). Our results suggested that most protein complexes are stabilized by similar basic principles. The ability to accurately and efficiently identify hotspots from sequence enables the annotation and analysis of protein-protein interaction hotspots in entire organisms and thus may benefit function prediction and drug development. The server for prediction is available at http://www.rostlab.org/services/isis.