Comparison of the average relative accessible surface areas.

<p>The average relative accessible surface area of the representative interfaces and their nearby residues are showed. We suggested using 40% RASA value which corresponded to 99% of the average interface RASA values in order to extract interface residues using RASA values of the residues.</p

Comparison of the silhouette values.

<p>The silhouette value distribution over the protein interfaces generated by hierarchical clustering and community finding algorithm is presented. The new clusters are better clustered according to the silhouette values.</p

Protein interfaces and interface clusters based on years.

<p>(a) Protein interface and interface clusters evaluation during years. (b) Distribution of protein interface cluster sizes throughout years. While the number of protein interface clusters is increasing, the cluster sizes are getting denser. The largest cluster in 1999 had 238 members that increased to 1361 in 2011. The minimum cluster size criterion is used to stop the algorithm in order to prevent the network nodes as network size of 1. During separation of the networks, for example, if one of the networks divided into two networks which have 4 and 6 nodes respectively, the algorithm only tries to divide the network which is above 5 (if it is possible) because the other network reached its final state according to our stopping criterion.</p

Similar interfaces with similar and dissimilar global protein folds.

<p>Complexes are shown in cartoon representation and interface residues are shown in ball representation. (a) Bence-Jones Kappa I Protein Bre complex (1BRECF) and Immunoglobulin Light and Heavy chain complex (43C9AC) have 79% interface similarity. (b) Asportakinase complex (3AB2CG) and Thioredoxin complex (3O6TCD) which have different global fold have 77% interface similarity.</p

Human Proteome-scale Structural Modeling of E2–E3 Interactions Exploiting Interface Motifs

Ubiquitination is crucial for many cellular processes such as protein degradation, DNA repair, transcription regulation, and cell signaling. Ubiquitin attachment takes place via a sequential enzymatic cascade involving ubiquitin activation (by E1 enzymes), ubiquitin conjugation (by E2 enzymes), and ubiquitin substrate tagging (by E3 enzymes). E3 ligases mediate ubiquitin transfer from E2s to substrates and as such confer substrate specificity. Although E3s can interact and function with numerous E2s, it is still unclear how they choose which E2 to use. Identifying all E2 partners of an E3 is essential for inferring the principles guiding E2 selection by an E3. Here we model the interactions of E3 and E2 proteins in a large, proteome-scale strategy based on <i>interface structural motifs</i>, which allows elucidation of (1) which E3s interact with which E2s in the human ubiquitination pathway and (2) how they interact with each other. Interface analysis of E2–E3 complexes reveals that loop L1 of E2s is critical for binding; the residue in the sixth position in loop L1 is widely utilized as an interface hot spot and appears indispensible for E2 interactions. Other loop L1 residues also confer specificity on the E2–E3 interactions: HECT E3s are in contact with the residue in the second position in loop L1 of E2s, but this is not the case for the RING finger type E3s. Our modeled E2–E3 complexes illuminate how slight sequence variations in E2 residues may contribute to specificity in E3 binding. These findings may be important for discovering drug candidates targeting E3s, which have been implicated in many diseases

Multi-interface binding strategy for the same protein pairs.

<p>(<b>a</b>) Histogram of protein-protein interactions which have different binding structures at the same shared site or different binding sites. 7962 protein-protein pairwise interactions use more than one interface conformation in order to interact with the same partner. Complex with one interface structure is pair of Phenylethanolamine N-methyltransferase with Phenylethanolamine N-methyltransferase, with two interface structures are pairs of probable two-component response regulator with probable two-component response regulator, with three interface structures are pairs of Hypothetical oxidoreductase yiaK with Hypothetical oxidoreductase yiaK, with four interface structures are pairs of Neurotoxin BoNT/A with Neurotoxin BoNT/A, with five different interface structures are pairs of Cytochrome P450 3A4 with Cytochrome P450 3A4 and eighteen interface structures are pairs of Calmodulin 2 with Calmodulin 2. (<b>b</b>) Multiple interaction sites of Histone deacetylase 8 (red-1VKGA). Six different interface architectures of interaction between Histone deacetylase 8 and Histone deacetylase 8 are shown. One of the binding sites is shared by three partners. The others are different. Gray-1VKGB, orange-3RQDB, green-1W22B are at binding site A, yellow-3F0RC is at binding site B, blue-3F07B is at binding site C, purple-1T64B is at binding site D. The balls represent the carbon alpha atoms of the interface residues of the complexes. Carbon alpha atoms are labeled according to interaction partners of the 1VKGA. (<b>c</b>) Histone deacetylase 8 (red monomer) uses the same binding site to bind different partners. Small conformational changes in the interface residues assist binding the partners. On the left hand-side, protein complexes are shown and in the center, interface structures are shown in ball and stick. Blue and yellow balls are the interface residues of histone deacetylase 8 (red), and yellow balls also showed the hotspot residues of the interface. Pink and purple balls are the interface residues of the partner monomer shown in gray, orange and green, and pink balls also show the hotspot residues of the interface. In the right side, hotspot residues of the interfaces are given.</p

Network of Interfaces.

<p>(a) Community structure. (b) Node and edge representation in protein interface network. (c) The nodes in the left network which have similarity values higher than 0.80 are grouped as a single node in the right network. The new node and the neighbors’ similarity values are chosen as the maximum similarity value of the edges between the two nodes which were grouped and the neighbor nodes (e.g. Node G and node H).</p

Exploiting Conformational Ensembles in Modeling Protein–Protein Interactions on the Proteome Scale

Cellular functions are performed through protein–protein interactions; therefore, identification of these interactions is crucial for understanding biological processes. Recent studies suggest that knowledge-based approaches are more useful than “blind” docking for modeling at large scales. However, a caveat of knowledge-based approaches is that they treat molecules as rigid structures. The Protein Data Bank (PDB) offers a wealth of conformations. Here, we exploited an ensemble of the conformations in predictions by a knowledge-based method, PRISM. We tested “difficult” cases in a docking-benchmark data set, where the unbound and bound protein forms are structurally different. Considering alternative conformations for each protein, the percentage of successfully predicted interactions increased from ∼26 to 66%, and 57% of the interactions were successfully predicted in an “unbiased” scenario, in which data related to the bound forms were not utilized. If the appropriate conformation, or relevant template interface, is unavailable in the PDB, PRISM could not predict the interaction successfully. The pace of the growth of the PDB promises a rapid increase of ensemble conformations emphasizing the merit of such knowledge-based ensemble strategies for higher success rates in protein–protein interaction predictions on an interactome scale. We constructed the structural network of ERK interacting proteins as a case study

A Strategy Based on Protein–Protein Interface Motifs May Help in Identifying Drug Off-Targets

Networks are increasingly used to study the impact of drugs at the systems level. From the algorithmic standpoint, a drug can “attack” nodes or edges of a protein–protein interaction network. In this work, we propose a new network strategy, “The Interface Attack”, based on protein–protein interfaces. Similar interface architectures can occur between unrelated proteins. Consequently, in principle, a drug that binds to one has a certain probability of binding to others. The interface attack strategy simultaneously removes from the network all interactions that consist of similar interface motifs. This strategy is inspired by network pharmacology and allows inferring potential off-targets. We introduce a network model that we call “Protein Interface and Interaction Network (P2IN)”, which is the integration of protein–protein interface structures and protein interaction networks. This interface-based network organization clarifies which protein pairs have structurally similar interfaces and which proteins may compete to bind the same surface region. We built the P2IN with the p53 signaling network and performed network robustness analysis. We show that (1) “hitting” frequent interfaces (a set of edges distributed around the network) might be as destructive as eleminating high degree proteins (hub nodes), (2) frequent interfaces are not always topologically critical elements in the network, and (3) interface attack may reveal functional changes in the system better than the attack of single proteins. In the off-target detection case study, we found that drugs blocking the interface between CDK6 and CDKN2D may also affect the interaction between CDK4 and CDKN2D