Dissecting subunit interfaces in homodimeric proteins

The subunit interfaces of 122 homodimers of known three-dimensional structure are analyzed and dissected into sets of surface patches by clustering atoms at the interface; 70 interfaces are single-patch, the others have up to six patches, often contributed by different structural domains. The average interface buries 1,940 A2 of the surface of each monomer, contains one or two patches burying 600-1,600 A2, is 65% nonpolar and includes 18 hydrogen bonds. However, the range of size and of hydrophobicity is wide among the 122 interfaces. Each interface has a core made of residues with atoms buried in the dimer, surrounded by a rim of residues with atoms that remain accessible to solvent. The core, which constitutes 77% of the interface on average, has an amino acid composition that resembles the protein interior except for the presence of arginine residues, whereas the rim is more like the protein surface. These properties of the interfaces in homodimers, which are permanent assemblies, are compared to those of protein-protein complexes where the components associate after they have independently folded. On average, subunit interfaces in homodimers are twice larger than in complexes, and much less polar due to the large fraction belonging to the core, although the amino acid compositions of the cores are similar in the two types of interfaces.     

Hydrophobic patches on protein subunit interfaces: Characteristics and prediction

Hydrophobic patches, defined as clusters of neighboring apolar atoms deemed accessible on a given protein surface, have been investigated on protein subunit interfaces. The data were taken from known tertiary structures of multimeric protein complexes. Amino acid composition and preference, patch size distribution, and patch contact complementarity across associating subunits were examined and compared with hydrophobic patches found on the solvent-accessible surface of the multimeric complexes. The largest or second largest patch on the accessible surface of the entire subunit was involved in multimeric interfaces in 90% of the cases. These results should prove useful for subunit design and engineering as well as for prediction of subunit interface regions. Proteins 28:333–343, 1997. © 1997 Wiley-Liss, Inc.     

Dissection, residue conservation, and structural classification of protein-DNA interfaces

The basic DNA-binding modules of 128 protein-DNA interfaces have been analyzed. Although these are less planar, like the protein-protein interfaces, the protein-DNA interfaces can also be dissected into core regions in which all the fully-buried atoms are located, and rim regions having atoms with residual accessibilities. The sequence entropy of the core residues is smaller than those in the rim, indicating that the former are better conserved and possibly contribute more towards the binding free energy, as has been implicated in protein-protein interactions. On the protein side, 1014 A(2) of the surface is buried of which 63% belong to the core. There are some differences in the propensities of residues to occur in the core and the rim. In the DNA strands, the nucleotide(s) containing fully-buried atoms in all three components usually occupy central positions of the binding region. A new classification scheme for the interfaces has been introduced based on the composition of secondary structural elements of residues and the results compared with the conventional classification of DNA-binding proteins, as well as the protein class of the molecule. It appears that a common framework may be developed to understand both protein-protein and protein-DNA interactions.     

A Simple Definition of Structural Regions in Proteins and Its Use in Analyzing Interface Evolution

Analysis of proteins commonly requires the partition of their structure into regions such as the surface, interior, or interface. Despite the frequent use of such categorization, no consensus definition seems to exist. This study thus aims at providing a definition that is general, is simple to implement, and yields new biological insights. This analysis relies on 397, 196, and 701 protein structures from Escherichia coli, Saccharomyces cerevisiae, and Homo sapiens, respectively, and the conclusions are consistent across all three species. A threshold of 25% relative accessible surface area best segregates amino acids at the interior and at the surface. This value is further used to extend the core-rim model of protein-protein interfaces and to introduce a third region called support. Interface core, rim, and support regions contain similar numbers of residues on average, but core residues contribute over two-thirds of the contact surface. The amino acid composition of each region remains similar across different organisms and interface types. The interface core composition is intermediate between the surface and the interior, but the compositions of the support and the rim are virtually identical with those of the interior and the surface, respectively. The support and rim could thus “preexist” in proteins, and evolving a new interaction could require mutations to form an interface core only. Using the interface regions defined, it is shown through simulations that only two substitutions are necessary to shift the average composition of a  1000-Ų surface patch involving ∼ 28 residues to that of an equivalent interface. This analysis and conclusions will help understand the notion of promiscuity in protein-protein interaction networks.     

Protein-protein crystal-packing contacts.

Protein-protein contacts in monomeric protein crystal structures have been analyzed and compared to the physiological protein-protein contacts in oligomerization. A number of features differentiate the crystal-packing contacts from the natural contacts occurring in multimeric proteins. The area of the protein surface patches involved in packing contacts is generally smaller and its amino acid composition is indistinguishable from that of the protein surface accessible to the solvent. The fraction of protein surface in crystal contacts is very variable and independent of the number of packing contacts. The thermal motion at the crystal packing interface and that of the protein core, even for large packing interfaces, though the tendency is to be closer to that of the core. These results suggest that protein crystallization depends on random protein-protein interactions, which have little in common with physiological protein-protein recognition processes, and that the possibility of engineering macromolecular crystallization to improve crystal quality could be widened.     

Docking without docking: ISEARCH--prediction of interactions using known interfaces

The increasing number of solved protein structures provides a solid number of interfaces, if protein-protein interactions, domain-domain contacts, and contacts between biological units are taken into account. An interface library gives us the opportunity to identify surface regions on a target molecule that are similar by local structure and residue composition. If both unbound components of a possible protein complex exhibit structural similarities to a known interface, the unbound structures can be superposed onto the known interfaces. The approach is accompanied by two mathematical problems. Protein surfaces have to be quickly screened by thousands of patches, and similarity has to be evaluated by a suitable scoring scheme. The used algorithm (NeedleHaystack) identifies similar patches within minutes. Structurally related sites are recognized even if only parts of the template patches are structurally related to the interface region. A successful prediction of the protein complex depends on a suitable template of the library. However, the performed tests indicate that interaction sites are identified even if the similarity is very low. The approach complements existing ab initio methods and provides valuable results on standard benchmark sets.     

Protein-protein interaction and quaternary structure

Protein-protein recognition plays an essential role in structure and function. Specific non-covalent interactions stabilize the structure of macromolecular assemblies, exemplified in this review by oligomeric proteins and the capsids of icosahedral viruses. They also allow proteins to form complexes that have a very wide range of stability and lifetimes and are involved in all cellular processes. We present some of the structure-based computational methods that have been developed to characterize the quaternary structure of oligomeric proteins and other molecular assemblies and analyze the properties of the interfaces between the subunits. We compare the size, the chemical and amino acid compositions and the atomic packing of the subunit interfaces of protein-protein complexes, oligomeric proteins, viral capsids and protein-nucleic acid complexes. These biologically significant interfaces are generally close-packed, whereas the non-specific interfaces between molecules in protein crystals are loosely packed, an observation that gives a structural basis to specific recognition. A distinction is made within each interface between a core that contains buried atoms and a solvent accessible rim. The core and the rim differ in their amino acid composition and their conservation in evolution, and the distinction helps correlating the structural data with the results of site-directed mutagenesis and in vitro studies of self-assembly.     

Folding of beta/alpha-unit scrambled forms of S. cerevisiae triosephosphate isomerase: Evidence for autonomy of substructure formation and plasticity of hydrophobic and hydrogen bonding interactions in core of (beta/alpha)8-barrel

We show that residues at the interfaces of protein-protein complexes have higher side-chain energy than other surface residues. Eight different sets of protein complexes were analyzed. For each protein pair, the complex structure was used to identify the interface residues in the unbound monomer structures. Side-chain energy was calculated for each surface residue in the unbound monomer using our previously developed scoring function.1 The mean energy was calculated for the interface residues and the other surface residues. In 15 of the 16 monomers, the mean energy of the interface residues was higher than that of other surface residues. By decomposing the scoring function, we found that the energy term of the buried surface area of non-hydrogen-bonded hydrophilic atoms is the most important factor contributing to the high energy of the interface regions. In spite of lacking hydrophilic residues, the interface regions were found to be rich in buried non-hydrogen-bonded hydrophilic atoms. Although the calculation results could be affected by the inaccuracy of the scoring function, patch analysis of side-chain energy on the surface of an isolated protein may be helpful in identifying the possible protein-protein interface. A patch was defined as 20 residues surrounding the central residue on the protein surface, and patch energy was calculated as the mean value of the side-chain energy of all residues in the patch. In 12 of the studied monomers, the patch with the highest energy overlaps with the observed interface. The results are more remarkable when only three residues with the highest energy in a patch are averaged to derive the patch energy. All three highest-energy residues of the top energy patch belong to interfacial residues in four of the eight small protomers. We also found that the residue with the highest energy score on the surface of a small protomer is very possibly the key interaction residue.     

Scoring optimisation of unbound protein-protein docking including protein binding site predictions

The prediction of the structure of the protein-protein complex is of great importance to better understand molecular recognition processes. During systematic protein-protein docking, the surface of a protein molecule is scanned for putative binding sites of a partner protein. The possibility to include external data based on either experiments or bioinformatic predictions on putative binding sites during docking has been systematically explored. The external data were included during docking with a coarse-grained protein model and on the basis of force field weights to bias the docking search towards a predicted or known binding region. The approach was tested on a large set of protein partners in unbound conformations. The significant improvement of the docking performance was found if reliable data on the native binding sites were available. This was possible even if data for single key amino acids at a binding interface are included. In case of binding site predictions with limited accuracy, only modest improvement compared with unbiased docking was found. The optimisation of the protocol to bias the search towards predicted binding sites was found to further improve the docking performance resulting in approximately 40% acceptable solutions within the top 10 docking predictions compared with 22% in case of unbiased docking of unbound protein structures.     

Mapping of protein surface cavities and prediction of enzyme class by a self-organizing neural network

An automated computer-based method for mapping of protein surface cavities was developed and applied to a set of 176 metalloproteinases containing zinc cations in their active sites. With very few exceptions, the cavity search routine detected the active site among the five largest cavities and produced reasonable active site surfaces. Cavities were described by means of solvent-accessible surface patches. For a given protein, these patches were calculated in three steps: (i) definition of cavity atoms forming surface cavities by a grid-based technique; (ii) generation of solvent accessible surfaces; (iii) assignment of an accessibility value and a generalized atom type to each surface point. Topological correlation vectors were generated from the set of surface points forming the cavities, and projected onto the plane by a self-organizing network. The resulting map of 865 enzyme cavities displays clusters of active sites that are clearly separated from the other cavities. It is demonstrated that both fully automated recognition of active sites, and prediction of enzyme class can be performed for novel protein structures at high accuracy.     

Are protein–protein interfaces more conserved in sequence than the rest of the protein surface?

Protein interfaces are thought to be distinguishable from the rest of the protein surface by their greater degree of residue conservation. We test the validity of this approach on an expanded set of 64 protein-protein interfaces using conservation scores derived from two multiple sequence alignment types, one of close homologs/orthologs and one of diverse homologs/paralogs. Overall, we find that the interface is slightly more conserved than the rest of the protein surface when using either alignment type, with alignments of diverse homologs showing marginally better discrimination. However, using a novel surface-patch definition, we find that the interface is rarely significantly more conserved than other surface patches when using either alignment type. When an interface is among the most conserved surface patches, it tends to be part of an enzyme active site. The most conserved surface patch overlaps with 39% (+/- 28%) and 36% (+/- 28%) of the actual interface for diverse and close homologs, respectively. Contrary to results obtained from smaller data sets, this work indicates that residue conservation is rarely sufficient for complete and accurate prediction of protein interfaces. Finally, we find that obligate interfaces differ from transient interfaces in that the former have significantly fewer alignment gaps at the interface than the rest of the protein surface, as well as having buried interface residues that are more conserved than partially buried interface residues.     

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.     

SHARP2: protein-protein interaction predictions using patch analysis

SHARP2 is a flexible web-based bioinformatics tool for predicting potential protein-protein interaction sites on protein structures. It implements a predictive algorithm that calculates multiple parameters for overlapping patches of residues on the surface of a protein. Six parameters are calculated: solvation potential, hydrophobicity, accessible surface area, residue interface propensity, planarity and protrusion (SHARP2). Parameter scores for each patch are combined, and the patch with the highest combined score is predicted as a potential interaction site. SHARP2 enables users to upload 3D protein structure files in PDB format, to obtain information on potential interaction sites as downloadable HTML tables and to view the location of the sites on the 3D structure using Jmol. The server allows for the input of multiple structures and multiple combinations of parameters. Therefore predictions can be made for complete datasets, as well as individual structures. AVAILABILITY: http://www.bioinformatics.sussex.ac.uk/SHARP2.     

Association of putative concave protein-binding sites with the fluctuation behavior of residues

Here, we propose a binding site prediction method based on the high frequency end of the spectrum in the native state of the protein structural dynamics. The spectrum is obtained using an elastic network model (GNM). High frequency vibrating (HFV) residues are determined from the fastest modes dynamics. HFV residue clusters and the associated surface patch residues are tested for their likelihood to locate at the binding interfaces using two different data sets, the Benchmark Set of mainly enzymes and antigen/antibodies and the Cluster Set of more diverse structures. The binding interface is identified to be within 7.5 A of the HFV residue clusters in the Benchmark Set and Cluster Set, for 77% and 70% of the structures, respectively. The success rate increases to 88% and 84%, respectively, by using the surface patches. The results suggest that concave binding interfaces, typically those of enzyme-binding sites, are enriched by the HFV residues. Thus, we expect that the association of HFV residues with the interfaces is mostly for enzymes. If, however, a binding region has invaginations and cavities, as in some of the antigen/antibodies and in cases in the Cluster data set, we expect it would be detected there too. This implies that binding sites possess several (inter-related) properties such as cavities, high packing density, conservation, and disposition for hotspots at binding surfaces. It further suggests that the high frequency vibrating residue-based approach is a potential tool for identification of regions likely to serve as protein-binding sites. The software is available at http://www.prc.boun.edu.tr/PRC/software.html.     

Protein-protein interaction site predictions with three-dimensional probability distributions of interacting atoms on protein surfaces

Protein-protein interactions are key to many biological processes. Computational methodologies devised to predict protein-protein interaction (PPI) sites on protein surfaces are important tools in providing insights into the biological functions of proteins and in developing therapeutics targeting the protein-protein interaction sites. One of the general features of PPI sites is that the core regions from the two interacting protein surfaces are complementary to each other, similar to the interior of proteins in packing density and in the physicochemical nature of the amino acid composition. In this work, we simulated the physicochemical complementarities by constructing three-dimensional probability density maps of non-covalent interacting atoms on the protein surfaces. The interacting probabilities were derived from the interior of known structures. Machine learning algorithms were applied to learn the characteristic patterns of the probability density maps specific to the PPI sites. The trained predictors for PPI sites were cross-validated with the training cases (consisting of 432 proteins) and were tested on an independent dataset (consisting of 142 proteins). The residue-based Matthews correlation coefficient for the independent test set was 0.423; the accuracy, precision, sensitivity, specificity were 0.753, 0.519, 0.677, and 0.779 respectively. The benchmark results indicate that the optimized machine learning models are among the best predictors in identifying PPI sites on protein surfaces. In particular, the PPI site prediction accuracy increases with increasing size of the PPI site and with increasing hydrophobicity in amino acid composition of the PPI interface; the core interface regions are more likely to be recognized with high prediction confidence. The results indicate that the physicochemical complementarity patterns on protein surfaces are important determinants in PPIs, and a substantial portion of the PPI sites can be predicted correctly with the physicochemical complementarity features based on the non-covalent interaction data derived from protein interiors.     

A three-dimensional model of the Cdc2 protein kinase: localization of cyclin- and Suc1-binding regions and phosphorylation sites.

The Cdc2 protein kinase requires cyclin binding for activity and also binds to a small protein, Suc1. Charged-to-alanine scanning mutagenesis of Cdc2 was used previously to localize cyclin A- and B- and Suc1-binding sites (B. Ducommun, P. Brambilla, and G. Draetta, Mol. Cell. Biol. 11:6177-6184, 1991). Those sites were mapped by building a Cdc2 model based on the crystallographic coordinates of the catalytic subunit of cyclic AMP-dependent protein kinase (cAPK) (D. R. Knighton, J. Zheng, L. F. Ten Eyck, V. A. Ashford, N.-H. Xuong, S. S. Taylor, and J. M. Sowadski, Science 253:407-414, 1991). On the basis of this model, additional mutations were made and tested for cyclin A and Suc1 binding and for kinase activity. Mutations that interfere with cyclin A binding are localized primarily on the small lobe near its interface with the cleft and include an acidic patch on the B helix and R-50 in the highly conserved PSTAIRE sequence. Two residues in the large lobe, R-151 and T-161, influence cyclin binding, and both are at the surface of the cleft near its interface with the PSTAIRE motif. Cyclin-dependent phosphorylation of T-161 in Cdc2 is essential for activation, and the model provides insights into the importance of this site. T-161 is equivalent to T-197, a stable phosphorylation site in cAPK. On the basis of the model, cyclin binding very likely alters the surface surrounding T-161 to allow for T-161 phosphorylation. The two major ligands to T-197 in cAPK are conserved as R-127 and R-151 in Cdc2. The equivalent of the third ligand, H-87, is T-47 in the PSTAIRE sequence motif. Once phosphorylated, T-161 is predicted to play a major structural role in Cdc2, comparable to that of T-197 in cAPK, by assembling the active conformation required for peptide recognition. The inhibitory phosphorylation at Y-15 also comes close to the cleft interface and on the basis of this model would disrupt the cleft interface and the adjacent peptide recognition site rather than prevent ATP binding. In contrast to cyclin A, both lobes influence Suc1 binding; however, the Suc1-binding sites are far from the active site. Several mutants map to the surface in cAPK, which is masked in part by the N-terminal 40 residues that lie outside the conserved catalytic core. The other Suc1-binding site maps to the large lobe near a 25-residue insert and includes R-215.     

Variation in larval properties of the Atlantic brooding coral <Emphasis Type="Italic">Porites astreoides</Emphasis> between different reef sites in Bermuda

Recent research has documented phenotypic differences among larvae released from corals with a brooding reproductive mode, both among species and within broods from a single species. We studied larvae released from the common Atlantic coral Porites astreoides in Bermuda to further evaluate phenotypic variability. Inter-site differences were investigated in larvae from conspecifics at a rim and patch reef site. Larvae were collected daily for one lunar cycle from several colonies per site each year over 5 yr. Larval volume varied with reef site of origin, with colonies from the rim reef site producing larger larvae than colonies from the patch reef site. This inter-site variation in larval size could not be explained by corallite size and may be a response to different environmental conditions at the sites. Larvae from both reef sites also varied in size depending on lunar day of release over 4 yr of study. Regardless of site of origin, smaller larvae were released earlier in the lunar cycle. Over 1 yr of study, lipid and zooxanthellae content and settlement success after 48 h covaried with larval size. However, there may be a trade-off between larger larvae and reduced fecundity. Overall, larvae released from colonies from the rim reef site were larger and had greater settlement success than those from colonies from the patch reef site. This study documents larval phenotypic variability and a distinct inter-site difference in larval ecology among conspecifics within the same geographic area, which may have implications for recruitment success, population dynamics, and resilience.     

Composition and diversity of herbaceous patches in woody vegetation: The effects of grazing,soil seed bank,patch spatial properties and scale

Herbaceous plants contribute much to plant diversity in Mediterranean-type ecosystems though mostly occupying relatively small patches within the dense woody vegetation. While studying species diversity in the herbaceous patches, we hypothesized that grazing, soil seed bank, and spatial properties of the patch affect plant diversity and composition at different spatial scales. The study site was in an LTER site located in the Mediterranean region in north Israel. We determined herbaceous species composition in: (1) randomly sampled quadrats in herbaceous patches in grazed and un-grazed plots; (2) soil seed bank samples taken from the same patches and germinated under optimal greenhouse conditions; (3) quadrats in the same patches sown with a homogenous mixture of local soil samples. Using GIS methods, we determined small-scale spatial characteristics of the herbaceous patches. Alpha and beta diversities were calculated at the patch and plot scales using Shannon's entropy H. Grazing increased alpha diversity of local untreated seed bank samples but decreased alpha diversity of the artificial homogenous soil seed bank mixture at both patch and plot scales. Positive relation between alpha diversity and patch area was detected only under grazing. Grazing increased beta diversity in all three treatments at the patch scale. Grazing decreased the similarity in species composition between above-ground vegetation and soil seed bank. The results indicate that moderate cattle-grazing affects species diversity in the herbaceous patches within the dense maquis. This effect is scale-dependent, and interacts with the effects of soil seed bank and patch spatial-properties: without grazing soil seed bank plays a more important role than patch spatial properties, but under grazing the size and the accessibility of the patch are more important in the determination of herbaceous species composition.     

Ice-binding surface of fish type III antifreeze.

We employed computational techniques, including molecular docking, energy minimization, and molecular dynamics simulation, to investigate the ice-binding surface of fish type III antifreeze protein (AFP). The putative ice-binding site was previously identified by mutagenesis, structural analysis, and flatness evaluation. Using a high-resolution x-ray structure of fish type III AFP as a model, we calculated the ice-binding interaction energy of 11 surface patches chosen to cover the entire surface of the protein. These various surface patches exhibit small but significantly different ice-binding interaction energies. For both the prism ice plane and an "ice" plane in which water O atoms are randomly positioned, our calculations show that a surface patch containing 14 residues (L19, V20, T18, S42, V41, Q9, P12, A16, M21, T15, Q44, I13, N14, K61) has the most favorable interaction energy and corresponds to the previously identified ice-binding site of type III AFP. Although in general agreement with the earlier studies, our results also suggest that the ice-binding site may be larger than the previously identified "core" cluster that includes mostly hydrophilic residues. The enlargement mainly results from the inclusion of peripheral hydrophobic residues and K61.     

Statistical analysis and prediction of protein-protein interfaces

Predicting protein-protein interfaces from a three-dimensional structure is a key task of computational structural proteomics. In contrast to geometrically distinct small molecule binding sites, protein-protein interface are notoriously difficult to predict. We generated a large nonredundant data set of 1494 true protein-protein interfaces using biological symmetry annotation where necessary. The data set was carefully analyzed and a Support Vector Machine was trained on a combination of a new robust evolutionary conservation signal with the local surface properties to predict protein-protein interfaces. Fivefold cross validation verifies the high sensitivity and selectivity of the model. As much as 97% of the predicted patches had an overlap with the true interface patch while only 22% of the surface residues were included in an average predicted patch. The model allowed the identification of potential new interfaces and the correction of mislabeled oligomeric states.