Structural adaptation of extreme halophilic proteins through decrease of conserved hydrophobic contact surface

Background

Disrupting protein-protein interactions by small organic molecules is nowadays a promising strategy employed to block protein targets involved in different pathologies. However, structural changes occurring at the binding interfaces make difficult drug discovery processes using structure-based drug design/virtual screening approaches. Here we focused on two homologous calcium binding proteins, calmodulin and human centrin 2, involved in different cellular functions via protein-protein interactions, and known to undergo important conformational changes upon ligand binding.

Results

In order to find suitable protein conformations of calmodulin and centrin for further structure-based drug design/virtual screening, we performed in silico structural/energetic analysis and molecular docking of terphenyl (a mimicking alpha-helical molecule known to inhibit protein-protein interactions of calmodulin) into X-ray and NMR ensembles of calmodulin and centrin. We employed several scoring methods in order to find the best protein conformations. Our results show that docking on NMR structures of calmodulin and centrin can be very helpful to take into account conformational changes occurring at protein-protein interfaces.

Conclusions

NMR structures of protein-protein complexes nowadays available could efficiently be exploited for further structure-based drug design/virtual screening processes employed to design small molecule inhibitors of protein-protein interactions.     

Xenorhodopsins, an enigmatic new class of microbial rhodopsins horizontally transferred between archaea and bacteria

Background

Currently a huge amount of protein-protein interaction data is available therefore extracting meaningful ones are a challenging task. In a protein-protein interaction network, hubs are considered as key proteins maintaining function and stability of the network. Therefore, studying protein-protein complexes from a structural perspective provides valuable information for predicted interactions.

Results

In this study, we have predicted by comparative modelling and docking methods protein-protein complexes of hubs of human NR-RTK network inferred from our earlier study. We found that some interactions are mutually excluded while others could occur simultaneously. This study revealed by structural analysis the key role played by Estrogen receptor (ESR1) in mediating the signal transduction between human Receptor Tyrosine kinases (RTKs) and nuclear receptors (NRs).

Conclusions

Although the methods require human intervention and judgment, they can identify the interactions that could occur together or ones that are mutually exclusive. This adds a fourth dimension to interaction network, that of time, and can assist in obtaining concrete predictions consistent with experiments.

Open peer review

This article was reviewed by Dr. Anthony Almudevar, Prof. James Faeder and Prof. Eugene Koonin. For the full reviews, please go to the Reviewers' comments.     

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.     

High throughput protein-protein interaction data: clues for the architecture of protein complexes

Background

High-throughput techniques are becoming widely used to study protein-protein interactions and protein complexes on a proteome-wide scale. Here we have explored the potential of these techniques to accurately determine the constituent proteins of complexes and their architecture within the complex.

Results

Two-dimensional representations of the 19S and 20S proteasome, mediator, and SAGA complexes were generated and overlaid with high quality pairwise interaction data, core-module-attachment classifications from affinity purifications of complexes and predicted domain-domain interactions. Pairwise interaction data could accurately determine the members of each complex, but was unexpectedly poor at deciphering the topology of proteins in complexes. Core and module data from affinity purification studies were less useful for accurately defining the member proteins of these complexes. However, these data gave strong information on the spatial proximity of many proteins. Predicted domain-domain interactions provided some insight into the topology of proteins within complexes, but was affected by a lack of available structural data for the co-activator complexes and the presence of shared domains in paralogous proteins.

Conclusion

The constituent proteins of complexes are likely to be determined with accuracy by combining data from high-throughput techniques. The topology of some proteins in the complexes will be able to be clearly inferred. We finally suggest strategies that can be employed to use high throughput interaction data to define the membership and understand the architecture of proteins in novel complexes.     

ST proteins, a new family of plant tandem repeat proteins with a DUF2775 domain mainly found in Fabaceae and Asteraceae

Background

Many proteins with tandem repeats in their sequence have been described and classified according to the length of the repeats: I) Repeats of short oligopeptides (from 2 to 20 amino acids), including structural cell wall proteins and arabinogalactan proteins. II) Repeats that range in length from 20 to 40 residues, including proteins with a well-established three-dimensional structure often involved in mediating protein-protein interactions. (III) Longer repeats in the order of 100 amino acids that constitute structurally and functionally independent units. Here we analyse ShooT specific (ST) proteins, a family of proteins with tandem repeats of unknown function that were first found in Leguminosae, and their possible similarities to other proteins with tandem repeats.

Results

ST protein sequences were only found in dicotyledonous plants, limited to several plant families, mainly the Fabaceae and the Asteraceae. ST mRNAs accumulate mainly in the roots and under biotic interactions. Most ST proteins have one or several Domain(s) of Unknown Function 2775 (DUF2775). All deduced ST proteins have a signal peptide, indicating that these proteins enter the secretory pathway, and the mature proteins have tandem repeat oligopeptides that share a hexapeptide (E/D)FEPRP followed by 4 partially conserved amino acids, which could determine a putative N-glycosylation signal, and a fully conserved tyrosine. In a phylogenetic tree, the sequences clade according to taxonomic group. A possible involvement in symbiosis and abiotic stress as well as in plant cell elongation is suggested, although different STs could play different roles in plant development.

Conclusions

We describe a new family of proteins called ST whose presence is limited to the plant kingdom, specifically to a few families of dicotyledonous plants. They present 20 to 40 amino acid tandem repeat sequences with different characteristics (signal peptide, DUF2775 domain, conservative repeat regions) from the described group of 20 to 40 amino acid tandem repeat proteins and also from known cell wall proteins with repeat sequences. Several putative roles in plant physiology can be inferred from the characteristics found.     

On the perturbation nature of allostery: sites,mutations, and signal modulation

Highlights

• •Perturbation nature of signaling is a fundamental feature of allosteric regulation.
• •Experimental and theoretical studies of allostery using perturbation approach.
• •Structure-based statistical mechanical model of allostery.
• •Inducing and fine-tuning targeted allosteric response.
• •From current understanding of allosteric control to future tasks in its design.

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Regardless of the diversity of systems, allosteic signalling is found to be always caused by perturbations. This recurring trait of allostery serves as a foundation for developing different experimental efforts and theoretical models for the studies of allosteric mechanisms. Among computational approaches considered here particular emphasis is given to the structure-based statistical mechanical model of allostery (SBSMMA), which allows one to study the causality and energetics of allosteric communication. We argue that the reverse allosteric signaling on the basis of SBSMMA can be used for predicting latent allosteric sites and inducing a tunable allosteric response. Per-residue allosteric effects of mutations can also be explored and ‘latent drivers’ expanding the cancer mutational landscape can be predicted using SBSMMA. Most recent and important implementations of computational models in web-resources along with a brief outlook on future research directions are also discussed.     

HSPA1A conformational mutants reveal a conserved structural unit in Hsp70 proteins

BackgroundThe Hsp70 proteins maintain proteome integrity through the capacity of their nucleotide- and substrate-binding domains (NBD and SBD) to allosterically regulate substrate affinity in a nucleotide-dependent manner. Crystallographic studies showed that Hsp70 allostery relies on formation of contacts between ATP-bound NBD and an interdomain linker, accompanied by SBD subdomains docking onto distinct sites of the NBD leading to substrate release. However, the mechanics of ATP-induced SBD subdomains detachment is largely unknown.MethodsHere, we investigated the structural and allosteric properties of human HSPA1A using hydrogen/deuterium exchange mass spectrometry, ATPase assays, surface plasmon resonance and fluorescence polarization-based substrate binding assays.ResultsAnalysis of HSPA1A proteins bearing mutations at the interface of SBD subdomains close to the interdomain linker (amino acids L399, L510, I515, and D529) revealed that this region forms a folding unit stabilizing the structure of both SBD subdomains in the nucleotide-free state. The introduced mutations modulate HSPA1A allostery as they localize to the NBD-SBD interfaces in the ATP-bound protein.ConclusionsThese findings show that residues forming the hydrophobic structural unit stabilizing the SBD structure are relocated during ATP-activated detachment of the SBD subdomains to different NBD-SBD docking interfaces enabling HSPA1A allostery.General significanceMutation-induced perturbations tuned HSPA1A sensitivity to peptide/protein substrates and to Hsp40 in a way that is common for other Hsp70 proteins. Our results provide an insight into structural rearrangements in the SBD of Hsp70 proteins and highlight HSPA1A-specific allostery features, which is a prerequisite for selective targeting in Hsp-related pathologies.     

High pressure reveals that the stability of interdimeric contacts in the R- and T-state of HbA is influenced by allosteric effectors: Insights from computational simulations

The molecular details of the mechanism of action of allosteric effectors on hemoglobin oxygen affinity are not clearly understood. The global allostery model proposed by Yonetani et al. suggests that the binding of allosteric effectors can take place both in the R and T states and that they influence oxygen affinity through inducing global tertiary changes in the subunits. Recently published high pressure studies yielded dissociation constants at atmospheric pressure that showed a stabilizing effect of heterotropic allosteric effectors on the dimer interface in the R state, and a more pronounced destabilizing effect in a T state model. In the present work, we report on computational modeling used to interpret the high pressure experimental data. We show structural changes in the hemoglobin interdimeric interfaces, indicative of a global tertiary structural change induced by the binding of allosteric effectors. We also show that the number of water molecules bound at the interface is significantly influenced by binding effectors in the T state in accordance with the experimental data. Our results suggest that the binding of effectors at definite sites leads to tertiary changes that propagate to the interfaces and results in overall structural re-organizations.     

Structural interpretation of protein-protein interaction network

Background

Currently a huge amount of protein-protein interaction data is available from high throughput experimental methods. In a large network of protein-protein interactions, groups of proteins can be identified as functional clusters having related functions where a single protein can occur in multiple clusters. However experimental methods are error-prone and thus the interactions in a functional cluster may include false positives or there may be unreported interactions. Therefore correctly identifying a functional cluster of proteins requires the knowledge of whether any two proteins in a cluster interact, whether an interaction can exclude other interactions, or how strong the affinity between two interacting proteins is.

Methods

In the present work the yeast protein-protein interaction network is clustered using a spectral clustering method proposed by us in 2006 and the individual clusters are investigated for functional relationships among the member proteins. 3D structural models of the proteins in one cluster have been built – the protein structures are retrieved from the Protein Data Bank or predicted using a comparative modeling approach. A rigid body protein docking method (Cluspro) is used to predict the protein-protein interaction complexes. Binding sites of the docked complexes are characterized by their buried surface areas in the docked complexes, as a measure of the strength of an interaction.

Results

The clustering method yields functionally coherent clusters. Some of the interactions in a cluster exclude other interactions because of shared binding sites. New interactions among the interacting proteins are uncovered, and thus higher order protein complexes in the cluster are proposed. Also the relative stability of each of the protein complexes in the cluster is reported.

Conclusions

Although the methods used are computationally expensive and require human intervention and judgment, they can identify the interactions that could occur together or ones that are mutually exclusive. In addition indirect interactions through another intermediate protein can be identified. These theoretical predictions might be useful for crystallographers to select targets for the X-ray crystallographic determination of protein complexes.

     

Correlation between amino acid residues converted by RNA editing and functional residues in protein three-dimensional structures in plant organelles

Background

In plant organelles, specific messenger RNAs (mRNAs) are subjected to conversion editing, a process that often converts the first or second nucleotide of a codon and hence the encoded amino acid. No systematic patterns in converted sites were found on mRNAs, and the converted sites rarely encoded residues located at the active sites of proteins. The role and origin of RNA editing in plant organelles remain to be elucidated.

Results

Here we study the relationship between amino acid residues encoded by edited codons and the structural characteristics of these residues within proteins, e.g., in protein-protein interfaces, elements of secondary structure, or protein structural cores. We find that the residues encoded by edited codons are significantly biased toward involvement in helices and protein structural cores. RNA editing can convert codons for hydrophilic to hydrophobic amino acids. Hence, only the edited form of an mRNA can be translated into a polypeptide with helix-preferring and core-forming residues at the appropriate positions, which is often required for a protein to form a functional three-dimensional (3D) structure.

Conclusion

We have performed a novel analysis of the location of residues affected by RNA editing in proteins in plant organelles. This study documents that RNA editing sites are often found in positions important for 3D structure formation. Without RNA editing, protein folding will not occur properly, thus affecting gene expression. We suggest that RNA editing may have conferring evolutionary advantage by acting as a mechanism to reduce susceptibility to DNA damage by allowing the increase in GC content in DNA while maintaining RNA codons essential to encode residues required for protein folding and activity.     

Predicting binding sites of hydrolase-inhibitor complexes by combining several methods

Background

Protein-protein interactions play a critical role in protein function. Completion of many genomes is being followed rapidly by major efforts to identify interacting protein pairs experimentally in order to decipher the networks of interacting, coordinated-in-action proteins. Identification of protein-protein interaction sites and detection of specific amino acids that contribute to the specificity and the strength of protein interactions is an important problem with broad applications ranging from rational drug design to the analysis of metabolic and signal transduction networks.

Results

In order to increase the power of predictive methods for protein-protein interaction sites, we have developed a consensus methodology for combining four different methods. These approaches include: data mining using Support Vector Machines, threading through protein structures, prediction of conserved residues on the protein surface by analysis of phylogenetic trees, and the Conservatism of Conservatism method of Mirny and Shakhnovich. Results obtained on a dataset of hydrolase-inhibitor complexes demonstrate that the combination of all four methods yield improved predictions over the individual methods.

Conclusions

We developed a consensus method for predicting protein-protein interface residues by combining sequence and structure-based methods. The success of our consensus approach suggests that similar methodologies can be developed to improve prediction accuracies for other bioinformatic problems.     

On the origin of distribution patterns of motifs in biological networks

Background

Data from high-throughput experiments of protein-protein interactions are commonly used to probe the nature of biological organization and extract functional relationships between sets of proteins. What has not been appreciated is that the underlying mechanisms involved in assembling these networks may exhibit considerable probabilistic behaviour.

Results

We find that the probability of an interaction between two proteins is generally proportional to the numerical product of their individual interacting partners, or degrees. The degree-weighted behaviour is manifested throughout the protein-protein interaction networks studied here, except for the high-degree, or hub, interaction areas. However, we find that the probabilities of interaction between the hubs are still high. Further evidence is provided by path length analyses, which show that these hubs are separated by very few links.

Conclusion

The results suggest that protein-protein interaction networks incorporate probabilistic elements that lead to scale-rich hierarchical architectures. These observations seem to be at odds with a biologically-guided organization. One interpretation of the findings is that we are witnessing the ability of proteins to indiscriminately bind rather than the protein-protein interactions that are actually utilized by the cell in biological processes. Therefore, the topological study of a degree-weighted network requires a more refined methodology to extract biological information about pathways, modules, or other inferred relationships among proteins.     

Protein mechanics: How force regulates molecular function

Background

Regulation of proteins is ubiquitous and vital for any organism. Protein activity can be altered chemically, by covalent modifications or non-covalent binding of co-factors. Mechanical forces are emerging as an additional way of regulating proteins, by inducing a conformational change or by partial unfolding.

Scope

We review some advances in experimental and theoretical techniques to study protein allostery driven by mechanical forces, as opposed to the more conventional ligand driven allostery. In this respect, we discuss recent single molecule pulling experiments as they have substantially augmented our view on the protein allostery by mechanical signals in recent years. Finally, we present a computational analysis technique, Force Distribution Analysis, that we developed to reveal allosteric pathways in proteins.

Major conclusions

Any kind of external perturbation, being it ligand binding or mechanical stretching, can be viewed as an external force acting on the macromolecule, rendering force-based experimental or computational techniques, a very general approach to the mechanics involved in protein allostery.

General significance

This unifying view might aid to decipher how complex allosteric protein machineries are regulated on the single molecular level.     

Computational structural analysis: multiple proteins bound to DNA

Background

With increasing numbers of crystal structures of protein∶DNA and protein∶protein∶DNA complexes publically available, it is now possible to extract sufficient structural, physical-chemical and thermodynamic parameters to make general observations and predictions about their interactions. In particular, the properties of macromolecular assemblies of multiple proteins bound to DNA have not previously been investigated in detail.

Methodology/Principal Findings

We have performed computational structural analyses on macromolecular assemblies of multiple proteins bound to DNA using a variety of different computational tools: PISA; PROMOTIF; X3DNA; ReadOut; DDNA and DCOMPLEX. Additionally, we have developed and employed an algorithm for approximate collision detection and overlapping volume estimation of two macromolecules. An implementation of this algorithm is available at http://promoterplot.fmi.ch/Collision1/. The results obtained are compared with structural, physical-chemical and thermodynamic parameters from protein∶protein and single protein∶DNA complexes. Many of interface properties of multiple protein∶DNA complexes were found to be very similar to those observed in binary protein∶DNA and protein∶protein complexes. However, the conformational change of the DNA upon protein binding is significantly higher when multiple proteins bind to it than is observed when single proteins bind. The water mediated contacts are less important (found in less quantity) between the interfaces of components in ternary (protein∶protein∶DNA) complexes than in those of binary complexes (protein∶protein and protein∶DNA).The thermodynamic stability of ternary complexes is also higher than in the binary interactions. Greater specificity and affinity of multiple proteins binding to DNA in comparison with binary protein-DNA interactions were observed. However, protein-protein binding affinities are stronger in complexes without the presence of DNA.

Conclusions/Significance

Our results indicate that the interface properties: interface area; number of interface residues/atoms and hydrogen bonds; and the distribution of interface residues, hydrogen bonds, van der Walls contacts and secondary structure motifs are independent of whether or not a protein is in a binary or ternary complex with DNA. However, changes in the shape of the DNA reduce the off-rate of the proteins which greatly enhances the stability and specificity of ternary complexes compared to binary ones.     

Protein-protein interactions of the hyperthermophilic archaeon Pyrococcus horikoshii OT3

Background

Although 2,061 proteins of Pyrococcus horikoshii OT3, a hyperthermophilic archaeon, have been predicted from the recently completed genome sequence, the majority of proteins show no similarity to those from other organisms and are thus hypothetical proteins of unknown function. Because most proteins operate as parts of complexes to regulate biological processes, we systematically analyzed protein-protein interactions in Pyrococcus using the mammalian two-hybrid system to determine the function of the hypothetical proteins.

Results

We examined 960 soluble proteins from Pyrococcus and selected 107 interactions based on luciferase reporter activity, which was then evaluated using a computational approach to assess the reliability of the interactions. We also analyzed the expression of the assay samples by western blot, and a few interactions by in vitro pull-down assays. We identified 11 hetero-interactions that we considered to be located at the same operon, as observed in Helicobacter pylori. We annotated and classified proteins in the selected interactions according to their orthologous proteins. Many enzyme proteins showed self-interactions, similar to those seen in other organisms.

Conclusion

We found 13 unannotated proteins that interacted with annotated proteins; this information is useful for predicting the functions of the hypothetical Pyrococcus proteins from the annotations of their interacting partners. Among the heterogeneous interactions, proteins were more likely to interact with proteins within the same ortholog class than with proteins of different classes. The analysis described here can provide global insights into the biological features of the protein-protein interactions in P. horikoshii.     

The Proteomic Code: a molecular recognition code for proteins

Background

The Proteomic Code is a set of rules by which information in genetic material is transferred into the physico-chemical properties of amino acids. It determines how individual amino acids interact with each other during folding and in specific protein-protein interactions. The Proteomic Code is part of the redundant Genetic Code.

Review

The 25-year-old history of this concept is reviewed from the first independent suggestions by Biro and Mekler, through the works of Blalock, Root-Bernstein, Siemion, Miller and others, followed by the discovery of a Common Periodic Table of Codons and Nucleic Acids in 2003 and culminating in the recent conceptualization of partial complementary coding of interacting amino acids as well as the theory of the nucleic acid-assisted protein folding.

Methods and conclusions

A novel cloning method for the design and production of specific, high-affinity-reacting proteins (SHARP) is presented. This method is based on the concept of proteomic codes and is suitable for large-scale, industrial production of specifically interacting peptides.     

Allosteric effectors influence the tetramer stability of both R- and T-states of hemoglobin A

The contribution of heterotropic effectors to hemoglobin allostery is still not completely understood. With the recently proposed global allostery model, this question acquires crucial significance, because it relates tertiary conformational changes to effector binding in both the R- and T-states. In this context, an important question is how far the induced conformational changes propagate from the binding site(s) of the allosteric effectors. We present a study in which we monitored the interdimeric interface when the effectors such as Cl-, 2,3-diphosphoglycerate, inositol hexaphosphate, and bezafibrate were bound. We studied oxy-Hb and a hybrid form (alphaFeO2)2-(betaZn)2 as the T-state analogue by monitoring heme absorption and Trp intrinsic fluorescence under hydrostatic pressure. We observed a pressure-dependent change in the intrinsic fluorescence, which we attribute to a pressure-induced tetramer to dimer transition with characteristic pressures in the 70-200-megapascal range. The transition is sensitive to the binding of allosteric effectors. We fitted the data with a simple model for the tetramer-dimer transition and determined the dissociation constants at atmospheric pressure. In the R-state, we observed a stabilizing effect by the allosteric effectors, although in the T-analogue a stronger destabilizing effect was seen. The order of efficiency was the same in both states, but with the opposite trend as inositol hexaphosphate > 2,3-diphosphoglycerate > Cl-. We detected intrinsic fluorescence from bound bezafibrate that introduced uncertainty in the comparison with other effectors. The results support the global allostery model by showing that conformational changes propagate from the effector binding site to the interdimeric interfaces in both quaternary states.     

Interaction profile-based protein classification of death domain

Background

The increasing number of protein sequences and 3D structure obtained from genomic initiatives is leading many of us to focus on proteomics, and to dedicate our experimental and computational efforts on the creation and analysis of information derived from 3D structure. In particular, the high-throughput generation of protein-protein interaction data from a few organisms makes such an approach very important towards understanding the molecular recognition that make-up the entire protein-protein interaction network. Since the generation of sequences, and experimental protein-protein interactions increases faster than the 3D structure determination of protein complexes, there is tremendous interest in developing in silico methods that generate such structure for prediction and classification purposes. In this study we focused on classifying protein family members based on their protein-protein interaction distinctiveness. Structure-based classification of protein-protein interfaces has been described initially by Ponstingl et al. [1] and more recently by Valdar et al. [2] and Mintseris et al. [3], from complex structures that have been solved experimentally. However, little has been done on protein classification based on the prediction of protein-protein complexes obtained from homology modeling and docking simulation.

Results

We have developed an in silico classification system entitled HODOCO (Homology modeling, Docking and Classification Oracle), in which protein Residue Potential Interaction Profiles (RPIPS) are used to summarize protein-protein interaction characteristics. This system applied to a dataset of 64 proteins of the death domain superfamily was used to classify each member into its proper subfamily. Two classification methods were attempted, heuristic and support vector machine learning. Both methods were tested with a 5-fold cross-validation. The heuristic approach yielded a 61% average accuracy, while the machine learning approach yielded an 89% average accuracy.

Conclusion

We have confirmed the reliability and potential value of classifying proteins via their predicted interactions. Our results are in the same range of accuracy as other studies that classify protein-protein interactions from 3D complex structure obtained experimentally. While our classification scheme does not take directly into account sequence information our results are in agreement with functional and sequence based classification of death domain family members.