Domain-domain Interactions Research Articles

KBDOCK 2013: a spatial classification of 3D protein domain family interactions

Comparing, classifying and modelling protein structural interactions can enrich our understanding of many biomolecular processes. This contribution describes Kbdock (http://kbdock.loria.fr/), a database system that combines the Pfam domain classification with coordinate data from the PDB to analyse and model 3D domain–domain interactions (DDIs). Kbdock can be queried using Pfam domain identifiers, protein sequences or 3D protein structures. For a given query domain or pair of domains, Kbdock retrieves and displays a non-redundant list of homologous DDIs or domain–peptide interactions in a common coordinate frame. Kbdock may also be used to search for and visualize interactions involving different, but structurally similar, Pfam families. Thus, structural DDI templates may be proposed even when there is little or no sequence similarity to the query domains.

Protein docking using case‐based reasoning

Protein docking algorithms aim to calculate the three-dimensional (3D) structure of a protein complex starting from its unbound components. Although ab initio docking algorithms are improving, there is a growing need to use homology modeling techniques to exploit the rapidly increasing volumes of structural information that now exist. However, most current homology modeling approaches involve finding a pair of complete single-chain structures in a homologous protein complex to use as a 3D template, despite the fact that protein complexes are often formed from one or more domain-domain interactions (DDIs). To model 3D protein complexes by domain-domain homology, we have developed a case-based reasoning approach called KBDOCK which systematically identifies and reuses domain family binding sites from our database of nonredundant DDIs. When tested on 54 protein complexes from the Protein Docking Benchmark, our approach provides a near-perfect way to model single-domain protein complexes when full-homology templates are available, and it extends our ability to model more difficult cases when only partial or incomplete templates exist. These promising early results highlight the need for a new and diverse docking benchmark set, specifically designed to assess homology docking approaches.

3did: a catalog of domain-based interactions of known three-dimensional structure

The database of 3D interacting domains (3did, available online for browsing and bulk download at http://3did.irbbarcelona.org) is a catalog of protein–protein interactions for which a high-resolution 3D structure is known. 3did collects and classifies all structural templates of domain–domain interactions in the Protein Data Bank, providing molecular details for such interactions. The current version also includes a pipeline for the discovery and annotation of novel domain–motif interactions. For every interaction, 3did identifies and groups different binding modes by clustering similar interfaces into ‘interaction topologies’. By maintaining a constantly updated collection of domain-based structural interaction templates, 3did is a reference source of information for the structural characterization of protein interaction networks. 3did is updated every 6 months.

Predicting Interacting Residues Using Long-Distance Information and Novel Decoding in Hidden Markov Models

Identification of interacting residues involved in protein-protein and protein-ligand interaction is critical for the prediction and understanding of the interaction and has practical impact on mutagenesis and drug design. In this work, we introduce a new decoding algorithm, ETB-Viterbi, with an early traceback mechanism, and apply it to interaction profile hidden Markov models (ipHMMs) to enable optimized incorporation of long-distance correlations between interacting residues, leading to improved prediction accuracy. The method was applied and tested to a set of domain-domain interaction families from the 3DID database, and showed statistically significant improvement in accuracy measured by F-score. To gauge and assess the method's effectiveness and robustness in capturing the correlation signals, sets of simulated data based on the 3DID dataset with controllable correlation between interacting residues were also used, as well as reversed sequence orientation. It was demonstrated that the prediction consistently improves as the correlations increase and is not significantly affected by sequence orientation.

Thermodynamic assessment of domain‐domain interactions and in vitro activities of mesophilic and thermophilic ribosome recycling factors

Ribosome recycling factor (RRF) is essential for bacterial growth. Structural studies revealed that RRF consists of two domains connected by two short polypeptides at the hinge region. Here, we evaluated the intrinsic stabilities (ΔG*s) of the two domains and the free energy of the domain-domain interactions (ΔG(int)) for mesophilic RRF (RRF from Escherichia coli, EcRRF) and thermophilic RRF (RRF from Thermus thermophilus, TtRRF) by using differential scanning calorimetry and circular dichroic spectroscopy. Despite single endothermic peaks, a higher than unity value for the ratio of calorimetric enthalpy to van't Hoff enthalpy of the unfolding indicated the presence of unfolding intermediates for both RRFs. Deconvolution analysis based on statistical thermodynamics employing multiple states of the unfolding process with domain-domain interactions allowed us to determine ΔG*s of each domain and ΔG(int). The results indicated that domain I has a higher unfolding temperature than domain II and that it stabilizes domain II through ΔG(int), giving rise to an apparent single peak of unfolding. Interestingly, the estimated ΔG(int) values of 6.28 kJ/mol for EcRRF and 26.28 kJ/mol for TtRRF reflect the observation that only EcRRF has recycling activity at ambient temperature. Our present study suggests the importance of a moderate ΔG(int) for biological activity of multidomain proteins.

Reconstituting protein interaction networks using parameter-dependent domain-domain interactions

BackgroundWe can describe protein-protein interactions (PPIs) as sets of distinct domain-domain interactions (DDIs) that mediate the physical interactions between proteins. Experimental data confirm that DDIs are more consistent than their corresponding PPIs, lending support to the notion that analyses of DDIs may improve our understanding of PPIs and lead to further insights into cellular function, disease, and evolution. However, currently available experimental DDI data cover only a small fraction of all existing PPIs and, in the absence of structural data, determining which particular DDI mediates any given PPI is a challenge.ResultsWe present two contributions to the field of domain interaction analysis. First, we introduce a novel computational strategy to merge domain annotation data from multiple databases. We show that when we merged yeast domain annotations from six annotation databases we increased the average number of domains per protein from 1.05 to 2.44, bringing it closer to the estimated average value of 3. Second, we introduce a novel computational method, parameter-dependent DDI selection (PADDS), which, given a set of PPIs, extracts a small set of domain pairs that can reconstruct the original set of protein interactions, while attempting to minimize false positives. Based on a set of PPIs from multiple organisms, our method extracted 27% more experimentally detected DDIs than existing computational approaches.ConclusionsWe have provided a method to merge domain annotation data from multiple sources, ensuring large and consistent domain annotation for any given organism. Moreover, we provided a method to extract a small set of DDIs from the underlying set of PPIs and we showed that, in contrast to existing approaches, our method was not biased towards DDIs with low or high occurrence counts. Finally, we used these two methods to highlight the influence of the underlying annotation density on the characteristics of extracted DDIs. Although increased annotations greatly expanded the possible DDIs, the lack of knowledge of the true biological false positive interactions still prevents an unambiguous assignment of domain interactions responsible for all protein network interactions.Executable files and examples are given at: http://www.bhsai.org/downloads/padds/

Domain-Domain Interactions in Filamin A (16–23) Impose a Hierarchy of Unfolding Forces

The quaternary structure of Filamin A (FLNa) 16–23 was recently shown to exhibit multiple domain-domain interactions that lead to a propeller-like construction. Here we present single-molecule force spectroscopy experiments to show a wide variety of mechanical responses of this molecule and compare it with its linear counterpart FLNa 1–8. The compact structure of FLNa 16–23 leads to a broad distribution of rupture forces and end-to-end lengths in the force-extension mode and multiple unraveling timescales in the force-clamp mode. Moreover, a subset of force-extension trajectories reveals a mechanical hierarchy in which the rupture of domain-domain interactions at high forces (>200 pN) liberates the unfolding of individual domains at low forces (∼100 pN). This mechanism may also explain the order-of-magnitude difference in the rates of the biexponential fits to the distribution of unfolding dwell times under force-clamp. Overall, FLNa 16–23 under a force of 100 pN is more compliant than the linear FLNa 1–8. Because a physiological role of FLNa is to crosslink actin filaments, this range of responses allows it to accommodate a broad spectrum of forces exerted by the cell and its environment.

Insights into the role of the junctional region of Plasmodium falciparum dihydrofolate reductase-thymidylate synthase

BackgroundPlasmodium falciparum dihydrofolate reductase-thymidylate synthase (pfDHFR-TS) is a well-defined target of anti-malarial drug, such as pyrimethamine and cycloguanil. Emergence of malaria parasites resistant to these drugs has been shown to be associated with point mutations of the gene coding for the target enzymes. Although the 3D-structure of P. falciparum bifunctional pfDHFR-TS has been reported previously, relatively little is known about the interactions between the pfDHFR and pfTS domains and the roles of the junctional region that links the two domains together. Therefore, a thorough understanding of the interaction of the two domains and the role of the junctional region of this target is important as the knowledge could assist the development of new effective anti-malarial drugs aimed at overcoming drug-resistant malaria.MethodsA system was developed to investigate the interaction between pfDHFR and pfTS domains and the role of the junctional region on the activity of the recombinant pfTS. Based on the ability of co-transformed plasmids coding for pfDHFR and pfTS with truncated junctional region to complement the growth of TS-deficient Escherichia coli strain χ2913recA(DE3) on minimum media without thymidine supplementation, active pfTS mutants with minimal length of junctional region were identified. Interactions between active pfDHFR and the pfTS domains were demonstrated by using a bacterial two-hybrid system.ResultsUsing TS-deficient E. coli strain χ2913recA(DE3), the authors have shown for the first time that in P. falciparum a junctional region of at least 44 amino acids or longer was necessary for the pfTS domain to be active for the synthesis of thymidylate for the cells. Truncation of the junctional region of the bifunctional pfDHFR-TS further confirmed the above results, and suggested that a critical length of the junctional peptide of pfDHFR-TS would be essential for the activity of TS to catalyze the synthesis of thymidylate.ConclusionThe present study demonstrated the interactions between the pfDHFR and pfTS domains of the bifunctional pfDHFR-TS, and revealed that the junctional region linking the two protein domains is essential for the expression of catalytically active pfTS domain. The findings could be useful since inhibition of the pfDHFR-TS domain-domain interaction could form a basis for the development of new anti-malarial drugs based on targeting the non-active site region of this important enzyme.

Multidomain integration in the structure of the HNF-4α nuclear receptor complex

The hepatocyte nuclear factor 4 alpha (HNF4α, NR2A1) is a member of the nuclear receptor (NR) family of transcription factors that use conserved DNA binding domains (DBDs) and ligand binding domains (LBDs)1,2. HNF4α is the most abundant DNA-binding protein in the liver, where some 40% of the actively transcribed genes have a HNF4α response element 1,3,4. These regulated genes are largely involved in the hepatic gluconeogenic program and lipid metabolism3,5,6. In the pancreas too, HNF4α is a master regulator controlling an estimated 11% of islet genes7. HNF4α protein mutations are linked to Maturity Onset of Diabetes in Young 1 (MODY1) and hyperinsulinemic hypoglycemia (HH)8–11. Prior structural analyses of NRs, while productive with individual domains, have lagged in revealing the connectivity patterns of NR domains. Here, we describe the 2.9 Å crystal structure of the multi-domain HNF4α homodimer bound to its DNA response element and coactivator-derived peptides. A convergence zone connects multiple receptor domains in an asymmetric fashion joining distinct elements from each monomer. An arginine target of PRMT1 methylation protrudes directly into this convergence zone and sustains its integrity. A serine target of protein kinase C is also responsible for maintaining domain-domain interactions. These post-translational modifications manifest into changes in DNA binding by communicating through the tightly connected surfaces of the quaternary fold. We find that some MODY1 mutations, positioned on the LBD and hinge regions of the receptor, compromise DNA binding at a distance by communicating through the inter-junctional surfaces of the complex. The overall domain representation of the HNF4α homodimer is different from that of the PPARγ-RXRα heterodimer, even when both NR complexes are assembled on the same DNA element. Our findings suggest that unique quaternary folds and inter-domain connections in NRs could be exploited by small-molecule allosteric modulators that impact distal functions in these polypeptides.

Prediction of contact matrix for protein–protein interaction

Prediction of protein-protein interaction has become an important part of systems biology in reverse engineering the biological networks for better understanding the molecular biology of the cell. Although significant progress has been made in terms of prediction accuracy, most computational methods only predict whether two proteins interact but not their interacting residues-the information that can be very valuable for understanding the interaction mechanisms and designing modulation of the interaction. In this work, we developed a computational method to predict the interacting residue pairs-contact matrix for interacting protein domains, whose rows and columns correspond to the residues in the two interacting domains respectively and whose values (1 or 0) indicate whether the corresponding residues (do or do not) interact. Our method is based on supervised learning using support vector machines. For each domain involved in a given domain-domain interaction (DDI), an interaction profile hidden Markov model (ipHMM) is first built for the domain family, and then each residue position for a member domain sequence is represented as a 20-dimension vector of Fisher scores, characterizing how similar it is as compared with the family profile at that position. Each element of the contact matrix for a sequence pair is now represented by a feature vector from concatenating the vectors of the two corresponding residues, and the task is to predict the element value (1 or 0) from the feature vector. A support vector machine is trained for a given DDI, using either a consensus contact matrix or contact matrices for individual sequence pairs, and is tested by leave-one-out cross validation. The performance averaged over a set of 115 DDIs collected from the 3 DID database shows significant improvement (sensitivity up to 85%, and specificity up to 85%), as compared with a multiple sequence alignment-based method (sensitivity 57%, and specificity 78%) previously reported in the literature. Supplementary data are available at Bioinformatics online.

Inferring homologous protein-protein interactions through pair position specific scoring matrix

BackgroundThe protein-protein interaction (PPI) is one of the most important features to understand biological processes. For a PPI, the physical domain-domain interaction (DDI) plays the key role for biology functions. In the post-genomic era, to rapidly identify homologous PPIs for analyzing the contact residue pairs of their interfaces within DDIs on a genomic scale is essential to determine PPI networks and the PPI interface evolution across multiple species.ResultsIn this study, we proposed "pair Position Specific Scoring Matrix (pairPSSM)" to identify homologous PPIs. The pairPSSM can successfully distinguish the true protein complexes from unreasonable protein pairs with about 90% accuracy. For the test set including 1,122 representative heterodimers and 2,708,746 non-interacting protein pairs, the mean average precision and mean false positive rate of pairPSSM were 0.42 and 0.31, respectively. Moreover, we applied pairPSSM to identify ~450,000 homologous PPIs with their interacting domains and residues in seven common organisms (e.g. Homo sapiens, Mus musculus, Saccharomyces cerevisiae and Escherichia coli).ConclusionsOur pairPSSM is able to provide statistical significance of residue pairs using evolutionary profiles and a scoring system for inferring homologous PPIs. According to our best knowledge, the pairPSSM is the first method for searching homologous PPIs across multiple species using pair position specific scoring matrix and a 3D dimer as the template to map interacting domain pairs of these PPIs. We believe that pairPSSM is able to provide valuable insights for the PPI evolution and networks across multiple species.

Protein-Protein Interactions Inferred from Domain-Domain Interactions in Genogroup II Genotype 4 Norovirus Sequences

Severe gastroenteritis and foodborne illness caused by Noroviruses (NoVs) during the winter are a worldwide phenomenon. Vulnerable populations including young children and elderly and immunocompromised people often require hospitalization and may die. However, no efficient vaccine for NoVs exists because of their variable genome sequences. This study investigates the infection processes in protein-protein interactions between hosts and NoVs. Protein-protein interactions were collected from related Pfam NoV domains. The related Pfam domains were accumulated incrementally from the protein domain interaction database. To examine the influence of domain intimacy, the 7 NoV domains were grouped by depth. The number of domain-domain interactions increased exponentially as the depth increased. Many protein-protein interactions were relevant; therefore, cloud techniques were used to analyze data because of their computational capacity. The infection relationship between hosts and NoVs should be used in clinical applications and drug design.

Hydrational Control of ETS-Family Transcription Factors: A Possible Resolution of the “Specificity Conundrum”

The ETS family of transcription factors is widely distributed among the metazoan phyla and regulates the expression of a wide range of genes. Despite their functional diversity, all ETS proteins share a structurally conserved DNA-binding (or ETS) domain. Given the highly overlapping sequence preferences among ETS members, it is as yet unclear how ETS proteins achieve functional specificity, a problem known as the “specificity conundrum.” Compounding this problem is a current lack of understanding of the biophysical mechanism of sequence selectivity among ETS binding sites. We hypothesize that the structural conservation among ETS domains disguises physicochemical heterogeneity in their mechanisms of sequence recognition. We have previously demonstrated that the ETS-family member PU.1 (Spi-1) recruits a cooperative network of water-mediated contacts along the protein-DNA interface for high-affinity binding. We have now compared the thermodynamics and kinetics of sequence recognition between the ETS domains of PU.1 and ETS1 which represent extremes of sequence divergence (∼30% homology) in the ETS family. We found that the thermodynamics and kinetics between the two structurally conserved ETS domains are highly differentiated under physiological conditions. More precisely, whereas high-affinity PU.1 ETS-DNA binding is enthalpically driven against an entropic penalty, ETS1 ETS-DNA binding is entropically driven. Kinetically, whereas ETS1 ETS associates rapidly with a high-affinity cognate site (ka > 107 M−1 s−1), PU.1 ETS is strikingly slow (ka ∼104 M−1 s−1). This profound difference in association rate constants means that the high-affinity PU.1 ETS-DNA complex, despite being somewhat thermodynamically less stable than the corresponding complex with ETS1, is significantly longer-lived. If these differences that underlie the intrinsic heterogeneity in site recognition by ETS proteins extend to protein-protein and domain-domain interactions, they offer one potential biophysical resolution to the specificity conundrum.

Domain information based prediction of protein-protein interactions of glucosinolate biosynthesis

Protein-protein interactions PPIs are of biological interest because they orchestrate a number of cellular processes such as metabolic pathways and immunological recognition. This paper aims at exploring more and removing PPIs falsely predicted PPIs involved in glucosinolate biosynthesis in Arabidopsis. A symmetric kernel function is proposed according to the approach of feature representation which combines the domain and domain-domain interaction DDI information in this paper. The performance of this kernel indicates SVM based PPIs predictor trained with this kernel is highly effective. According to the prediction result, proteins with Arabidopsis Genome Initiative AGI numbers AT4G14800 and AT5G54810, AT5G05730 and AT4G18040, AT1G04510 and AT5G05260 are affirmed as interactive among the 237 low level of confidence PPIs pairs. Furthermore, the SVM-based PPIs predictor is used to explore PPIs of AT1G74090 and AT5G07690 both of which are members of the four glucosinolate biosynthesis pathway proteins absent from AtPIN.

Inferring the Brassica rapa Interactome Using Protein–Protein Interaction Data from Arabidopsis thaliana

Following successful completion of the Brassica rapa sequencing project, the next step is to investigate functions of individual genes/proteins. For Arabidopsis thaliana, large amounts of protein–protein interaction (PPI) data are available from the major PPI databases (DBs). It is known that Brassica crop species are closely related to A. thaliana. This provides an opportunity to infer the B. rapa interactome using PPI data available from A. thaliana. In this paper, we present an inferred B. rapa interactome that is based on the A. thaliana PPI data from two resources: (i) A. thaliana PPI data from three major DBs, BioGRID, IntAct, and TAIR. (ii) ortholog-based A. thaliana PPI predictions. Linking between B. rapa and A. thaliana was accomplished in three complementary ways: (i) ortholog predictions, (ii) identification of gene duplication based on synteny and collinearity, and (iii) BLAST sequence similarity search. A complementary approach was also applied, which used known/predicted domain–domain interaction data. Specifically, since the two species are closely related, we used PPI data from A. thaliana to predict interacting domains that might be conserved between the two species. The predicted interactome was investigated for the component that contains known A. thaliana meiotic proteins to demonstrate its usability.

Structural and Functional Analysis of Multi-Interface Domains

A multi-interface domain is a domain that can shape multiple and distinctive binding sites to contact with many other domains, forming a hub in domain-domain interaction networks. The functions played by the multiple interfaces are usually different, but there is no strict bijection between the functions and interfaces as some subsets of the interfaces play the same function. This work applies graph theory and algorithms to discover fingerprints for the multiple interfaces of a domain and to establish associations between the interfaces and functions, based on a huge set of multi-interface proteins from PDB. We found that about 40% of proteins have the multi-interface property, however the involved multi-interface domains account for only a tiny fraction (1.8%) of the total number of domains. The interfaces of these domains are distinguishable in terms of their fingerprints, indicating the functional specificity of the multiple interfaces in a domain. Furthermore, we observed that both cooperative and distinctive structural patterns, which will be useful for protein engineering, exist in the multiple interfaces of a domain.

Revisiting the Nucleotide and Aminoglycoside Substrate Specificity of the Bifunctional Aminoglycoside Acetyltransferase(6′)-Ie/Aminoglycoside Phosphotransferase(2″)-Ia Enzyme

The bifunctional aminoglycoside-modifying enzyme aminoglycoside acetyltransferase(6')-Ie/aminoglycoside phosphotransferase(2″)-Ia, or AAC(6')-Ie/APH(2″)-Ia, is the major source of aminoglycoside resistance in gram-positive bacterial pathogens. In previous studies, using ATP as the cosubstrate, it was reported that the APH(2″)-Ia domain of this enzyme is unique among aminoglycoside phosphotransferases, having the ability to inactivate an unusually broad spectrum of aminoglycosides, including 4,6- and 4,5-disubstituted and atypical. We recently demonstrated that GTP, and not ATP, is the preferred cosubstrate of this enzyme. We now show, using competition assays between ATP and GTP, that GTP is the exclusive phosphate donor at intracellular nucleotide levels. In light of these findings, we reevaluated the substrate profile of the phosphotransferase domain of this clinically important enzyme. Steady-state kinetic characterization using the phosphate donor GTP demonstrates that AAC(6')-Ie/APH(2″)-Ia phosphorylates 4,6-disubstituted aminoglycosides with high efficiency (k(cat)/K(m) = 10(5)-10(7) M(-1) s(-1)). Despite this proficiency, no resistance is conferred to some of these antibiotics by the enzyme in vivo. We now show that phosphorylation of 4,5-disubstituted and atypical aminoglycosides are negligible and thus these antibiotics are not substrates. Instead, these aminoglycosides tend to stimulate an intrinsic GTPase activity of the enzyme. Taken together, our data show that the bifunctional enzyme efficiently phosphorylates only 4,6-disubstituted antibiotics; however, phosphorylation does not necessarily result in bacterial resistance. Hence, the APH(2″)-Ia domain of the bifunctional AAC(6')-Ie/APH(2″)-Ia enzyme is a bona fide GTP-dependent kinase with a narrow substrate profile, including only 4,6-disubstituted aminoglycosides.

DbPTM 3.0: an informative resource for investigating substrate site specificity and functional association of protein post-translational modifications

Protein modification is an extremely important post-translational regulation that adjusts the physical and chemical properties, conformation, stability and activity of a protein; thus altering protein function. Due to the high throughput of mass spectrometry (MS)-based methods in identifying site-specific post-translational modifications (PTMs), dbPTM (http://dbPTM.mbc.nctu.edu.tw/) is updated to integrate experimental PTMs obtained from public resources as well as manually curated MS/MS peptides associated with PTMs from research articles. Version 3.0 of dbPTM aims to be an informative resource for investigating the substrate specificity of PTM sites and functional association of PTMs between substrates and their interacting proteins. In order to investigate the substrate specificity for modification sites, a newly developed statistical method has been applied to identify the significant substrate motifs for each type of PTMs containing sufficient experimental data. According to the data statistics in dbPTM, >60% of PTM sites are located in the functional domains of proteins. It is known that most PTMs can create binding sites for specific protein-interaction domains that work together for cellular function. Thus, this update integrates protein–protein interaction and domain–domain interaction to determine the functional association of PTM sites located in protein-interacting domains. Additionally, the information of structural topologies on transmembrane (TM) proteins is integrated in dbPTM in order to delineate the structural correlation between the reported PTM sites and TM topologies. To facilitate the investigation of PTMs on TM proteins, the PTM substrate sites and the structural topology are graphically represented. Also, literature information related to PTMs, orthologous conservations and substrate motifs of PTMs are also provided in the resource. Finally, this version features an improved web interface to facilitate convenient access to the resource.

The molecular mechanism for human metallothionein-3 to protect against the neuronal cytotoxicity of Aβ1–42 with Cu ions

Aggregation and cytotoxicity of Aβ with redox-active metals in neuronal cells have been implicated in the progression of Alzheimer disease. Human metallothionein (MT) 3 is highly expressed in the normal human brain and is downregulated in Alzheimer disease. Zn(7)MT3 can protect against the neuronal toxicity of Aβ by preventing copper-mediated Aβ aggregation, abolishing the production of reactive oxygen species (ROS) and the related cellular toxicity. In this study, we intended to decipher the roles of single-domain proteins (α/β) and the α-β domain-domain interaction of Zn(7)MT3 to determine the molecular mechanism for protection against the neuronal cytotoxicity of Aβ(1-42) with copper ions. With this in mind, the α and β single-domain proteins, heterozygous β(MT3)-α(MT1), and a linker-truncated mutant ∆31-34 were prepared and characterized. In the presence/absence of various Zn(7)MT3 proteins, the Aβ(1-42)-Cu(2+)-mediated aggregation, the production of ROS, and the cellular toxicity were investigated by transmission electron microscopy, ROS assay by means of a fluorescent probe, and SH-SY5Y cell viability, respectively. The β domain cannot abolish Aβ(1-42)-Cu(2+)-induced aggregation, and neither the β domain nor the α domain can quench the production of ROS because of the redox cycling of Aβ-Cu(2+). Similarly to wild-type Zn(7)MT3, the heterozygous β(MT3)-α(MT1) possesses the characteristic of alleviating Aβ(1-42) aggregation and oxidative stress to neuronal cells. Therefore, the two domains through the linker Lys-Lys-Ser form a cooperative unit, and each of them is indispensable in conducting its bioactivity. The α domain plays an important role in modulating the stability of the metal-thiolate cluster, and the α-β domain-domain interaction through the linker is critical for its protective role in the brain.

Structural and Dynamic Properties of Incomplete Immunoglobulin-like Fold Domains

The immunoglobulin fold (Ig-fold) is a widespread structural motif that is detected in a variety of proteins involved in diversified biological processes. The Ig-fold contains 70-110 residues that are assembled in a characteristic sandwich-like structure formed by two facing β-sheets each made of antiparallel β-strands. A number of variations on this common theme have been detected and described (Ig-like fold). One of the most intriguing variants is characterized by the lack of a strand compared to the canonical motif (incomplete Ig-like fold). Interestingly, proteins exhibiting incomplete Ig-like fold have been shown to play an important role in mediating either protein-protein or domain-domain interactions. Protein-protein interactions mediated by incomplete Ig-like folds play a key structural role in the chaperone-usher pathway, a process that generates multi-protein assemblies essential for the adhesion of gram negative bacteria. Domains with incomplete Ig-like fold have also been discovered in the mechanism of action of adhesins belonging to the family of MSCRAMMs (microbial surface components recognizing adhesive matrix molecules). Recently, a stable incomplete Iglike fold has been detected in the peptidoglycan-binding extra-cellular portion of Staphylococcus aureus PrkC, an important Ser/Thr membrane kinase involved in bacterial growth and revival from latency. It is important to note that the occurrence of proteins with incomplete Ig-like fold is often related to cell adhesion and infectivity of bacterial pathological agents. We here report a survey of the structural data available on this peculiar structural motif highlighting analogies and differences of incomplete Ig-like fold involved in different processes. The dynamical behavior of these domains, investigated by molecular dynamics techniques, will be also commented.