Catalysis of ADP-ATP exchange by nucleotide exchange factors (NEFs) is central to the activity of Hsp70 molecular chaperones. Yet, the mechanism of interaction of this family of chaperones with NEFs is not well understood in the context of the sequence evolution and structural dynamics of Hsp70 ATPase domains. We studied the interactions of Hsp70 ATPase domains with four different NEFs on the basis of the evolutionary trace and co-evolution of the ATPase domain sequence, combined with elastic network modeling of the collective dynamics of the complexes. Our study reveals a subtle balance between the intrinsic (to the ATPase domain) and specific (to interactions with NEFs) mechanisms shared by the four complexes. Two classes of key residues are distinguished in the Hsp70 ATPase domain: (i) highly conserved residues, involved in nucleotide binding, which mediate, via a global hinge-bending, the ATPase domain opening irrespective of NEF binding, and (ii) not-conserved but co-evolved and highly mobile residues, engaged in specific interactions with NEFs . The observed interplay between these respective intrinsic  and specific  interactions provides us with insights into the allosteric dynamics and functional evolution of the modular Hsp70 ATPase domain. The heat shock protein 70 (Hsp70) serves as a housekeeper in the cell, assisting in the correct folding, trafficking, and degradation of many proteins. The ATPase domain is the control unit of this molecular machine and its efficient functioning requires interactions with co-chaperones, including, in particular, the nucleotide exchange factors (NEFs). We examined the molecular motions of the ATPase domain in both NEF-bound and -unbound forms. We found that the NEF-binding surface enjoys large global movements prior to NEF binding, which presumably facilitates NEF recognition and binding. NEF binding stabilizes the ATPase domain in an open form and thereby facilitates the nucleotide exchange step of the chaperone cycle. A series of highly correlated amino acids were distinguished at the NEF-binding sites of the Hsp70 ATPase domain, which highlights the adaptability of the ATPase domain, both structurally and sequentially, to recognize NEFs. In contrast, the nucleotide-binding residues are tightly held near a global hinge center and are highly conserved. The contrasting properties of these two groups of residues point to an evolutionarily optimized balance between conserved/constrained and co-evolved/mobile amino acids, which enables the functional interactions of the modular Hps70 ATPase domains with NEFs. Many proteins are molecular machines. They function because their three-dimensional structure allows them to undergo cooperative changes in conformation that maintain the native fold while enabling their biological functions. The changes have been pointed out to be structure-encoded, intrinsically accessible to proteins, as can be deduced from simple physics-based approaches The interplay between these two effects becomes particularly important in the case of a number of proteins or domains that play a modular role in a variety of biomolecular interactions. The ATPase domain  of the Hsp70 family of proteins is a typical example. This domain plays a critical role in regulating the activities of these molecular chaperones, which, in turn, promote accurate folding, and prevent unwanted aggregation by either unfolding and refolding misfolded proteins or regulating their intracellular trafficking to the protein degradation machinery Chaperones of the Hsp70 family contain two domains: the N-terminal ATPase domain and the C-terminal substrate-binding domain (SBD), which regulate each other's activity via allosteric effects. ATP hydrolysis at the ATPase domain increases the substrate-binding affinity of the SBD, thus lowering the substrate exchange rate; on the other hand, the dissociation of the ADP produced upon ATP hydrolysis and its replacement by a new ATP trigger the release of substrate by the SBD, and therefore enhance the substrate exchange rate The precise functioning of the Hsp70 ATPase domain involves an interaction with two families of co-factors, also called co-chaperones: the J-domain proteins that catalyze ATP hydrolysis The Hsp70 ATPase domain is composed of four subdomains: IA and IB in lobe I, and, IIA and IIB in lobe II  Figure S1 and Text S1) to retrieve the closest orthologs to human (Hsc70) and bacterial (DnaK) chaperones; (ii) deletion of MSA columns that correspond to insertions with respect to the consensus sequence, and (iii) removal of the sequences containing more than 10 gaps. These three steps resulted in a MSA of 1627 sequences with N = 380 columns (corresponding to residues 6 to 385 in Hsc70 ATPase domain), which has been subjected to evolutionary trace (ET) and mutual information (MI) analyses for detecting residue conservation and co-evolution patterns, respectively.We began with 4839 sequences retrieved from the Pfam database 22.0 SMFigure S2 and Text S2.We retrieved from the Protein Data Bank (PDB) We performed Gaussian Network Model (GNM) global modes, as the major determinant of functional movements. In the GNM, each mode k is represented by an N-dimensional eigenvector, uk), respectively. The ith element, [uk)(]i2 as a function of residue index i defines the imobility profile Mk)( for the first (lowest frequency) mode accessible to Hsp70 ATPase domain in k. The reciprocal kλ−1 serves as the weight of mode k, such that the slower modes, also called softer modes, make larger contributions to observed dynamics. The mobility of residue i driven by a subset of m soft modes is found from the weighted average  using two metrics: (i) the correlation cosine (|vk)(·d|/|d|) between the kth ANM eigenvector vk) and d, and (ii) the cumulative overlap achieved by the m softest modes d, is obtained by superposing the known NEF-bound and -free structures of ATPase domain and evaluating the differences in the Cα-coordinates. Kabsch's algorithm The modes predicted by the ANM for the ATPase domain both in NEF-bound and –free forms were compared to the experimentally measured changes in structure (designated by a 3trace residues (conserved within classes but not across classes). The ET sequence for the particular level lists the fully conserved residues by their single-letter code, the trace residues by the symbol ‘X’, and the remaining residues as blank; and (iii) The ET sequences generated at each level are organized in rows (leftmost column) is assigned to each residue. A fully conserved residue is assigned the highest rank (rank of 1). In the present case, Gly201 is the only residue with ET rank 1, i.e., it is fully conserved among the set of 1627 sequences .The ET method The conservation of a given residue in all subfamilies is a very strict condition when large sets of aligned sequences are considered. This limitation restricted the previous applications of the ET method to MSA of 100 and 200 sequences N columns of the MSA is considered as a discrete random variable that takes on one of the 20 amino-acid types, or an insertion , with some probability. The MI associated with the ith and jth sequence positions is defined as an N×N matrix (for a MSA of N columns) of the formP is the joint probability of observing amino acid types x and y at the respective sequence positions i, and j; P(ix) is the marginal/singlet probability of amino acid of type x at the thi position. I varies in the range , where the lower and upper limits correspond to fully uncorrelated and most correlated pairs of residues.The ET method identifies conserved residues, but does not provide information on co-evolutionary relations between residues. Co-evolving residues are usually indicative of structural or functional constraints vice versa.Here is a brief summary of the approach and rationale. First, we examine the structural properties of known Hsp70 ATPase domain-NEF complexes from different organisms to identify the interfacial residues. Second, we analyze the intrinsic (structure-encoded) dynamics of the ATPase domain using the GNM, with an eye on the dynamic characteristics of the NEF-binding residues, on the one hand, and ATP/ADP-binding residues, on the other. A clear difference emerges between these two groups of functional residues: the former is distinguished by enhanced mobility in the softest modes while the latter is severely restricted. Third, calculations repeated with NEF-bound ATPase domains reveal how the open form of the ATPase domain is stabilized in order to facilitate ADP release, which is enabled by the intrinsic mobility of the NEF-binding regions. Nucleotide-binding sites, on the other hand, are shown to maintain their generic structure and dynamics irrespective of NEF binding, pointing to the robustness of the ATP-regulation by the ATPase domain. Fourth, detailed sequence analysis of Hsp70 family members reveals the distinctive sequence properties of the two regions: NEF-binding sites exhibit highly correlated mutations, consistent with the recognition of specific NEFs. Nucleotide-binding sites on the other hand, are almost fully conserved. In a sense, sequence variability is accompanied by conformation variability and Overall, Hsp70 ATPase domains appear to have been evolutionarily optimized to acquire a dual character: functional variability accompanied by structural variability at the co-chaperone binding sites and conservation/robustness both in terms of sequence and structural dynamics at the nucleotide-binding sites. This dual character is proposed to be essential for adapting to interactions with different co-factors while maintaining ATPase activity.SMTable S1 and Table S1 is based on atom-atom interactions closer than 4Å separation. Table S2 is based on the change in solvent-accessible surface areas, Δ(SASA), induced upon NEF binding. The entries in Table S2 form a subset of those in Table S1, thus helping consolidate the identity of the NEF-binding residues on the ATPase domain. We note in particular Asn57, Arg258, Arg261, Arg262 and Tyr134 shared by both mammalian and bacterial chaperones in their NEF binding activity. Table S2 also lists the interfacial NEF residues for each case, which draws attention to the abundance of salt bridges at the mammalian Hsc70/NEF interfaces. In contrast, DnaK-GrpE contacts are predominantly hydrophobic, consistent with previous observations We examined the interface between the Hsp70 ATPase domain and the corresponding NEF in four structurally resolved complexes: with GrpE, BAG-1, HspBP1 or Sse1. Despite their structural differences, all four NEFs make contacts with subdomain IIB (a displays the mobility profile iM(1) in the lowest frequency  mode of motion intrinsically favored by the overall ATPase domain architecture  is the one favored by the Hsp70 ATPase domain architecture to enjoy the largest mobility in the most cooperative mode of motion accessible to the ATPase domain. We also note, among NEF-contacting residues, a few exhibiting more restricted mobilities, located at the interface between subdomains IB and IIB, in particular. The tendency of biomolecules to involve their most mobile regions (peaks in the softest modes) in ligand recognition appears to be a design property noted in other applications; the ATPase domain subdomain IIB conforms to this rule. Its intrinsic mobility or conformational adaptability presumably allows for optimal interaction with the bound NEFs. On the other hand, final stabilization of a ‘bound’ conformer and communication of the conformational change locked upon substrate binding to other functional sites , may require the involvement of spatially constrained regions near the binding site The Hsp70 nucleotide-binding residues, on the other hand, represent a completely different type of behavior. These residues, indicated by the blue squares in We also note among nucleotide-binding residues three charged residues, K271, R272 and R342, which were distinguished by their ‘central’ role in mediating the communication between the nucleotide-binding site and the other parts of the Hsp70 ATPase domain The co-localization of chemically active sites with the global hinge region is another design feature consistent with previous observations reported for catalytic sites of enzymes vs highly constrained) dynamics in the global modes intrinsically accessible to the Hsp70 ATPase domain.It will be shown below that the NEF-contacting and nucleotide-binding residues form two groups fundamentally different in terms of their evolutionary properties, in addition to their contrasting (highly mobile Table S1). For clarity, the average over these three cases (red curve) is displayed in SMFigure S4. In each case, the ten top (lowest frequency) modes are used to display the weight-averaged square displacements, which provide an accurate representation of the overall collective dynamics. The results indicate that the NEF-bound form of the Hsp70 ATPase domain closely maintains the intrinsic dynamics accessible to its unbound form, i.e., the loci of peaks and minima remain practically unchanged; however, binding of a NEF alters the relative (quantitative) distribution of mobilities: in particular, a reduction is observed in the mobility of subdomain IIB. As can be seen in more details for each of the four complexes in the SMFigure S4, the peak around the β-hairpin loop (residues 285–292) in subdomain IIB is almost completely depressed in the case of Sse1 and BAG-1, while GrpE and HspBP1 binding suppresses the mobility of the C-terminal end of helix 8 in the same subdomain.SMFigure S5, as if their global fluctuations are conferred by the dissipation of those in the ATPase domain.By and large, all these observations support a common mechanism shared by all NEFs: they bind the most mobile subdomain of the Hsp70 ATPase domain - subdomain IIB, and in essence ‘lock’ the ATPase domain in a fixed conformation. This newly stabilized conformation is the ‘open’ form of the ATPase domain, as will be elaborated below.Comparison of the NEF-bound and –unbound structures of the Hsp70 ATPase domain shows that the distance between the subdomains IIB and IB is larger in the NEF-bound form. induced upon NEF binding, (ii) a pre-existing equilibrium/path where the open form is already sampled, or can be readily reached via a soft mode, by the NEF-free ATPase domain, (iii) pre-existing equilibrium/path followed by induced fit. The former would be NEF-specific; the latter, would be intrinsic to the ATPase domain; and the third is an intermediate behavior, i.e. the original recognition requires the pre-disposition of the suitable ‘binding’ conformation (pre-existing equilibrium); and binding of NEF induces further rearrangements to optimize the intermolecular interactions. For a more extensive discussion see for example our previous work The observed conformational change of the Hsp70 ATPase domain may be explained by three possible scenarios: (i) vk) predicted to be intrinsically accessible to the unbound ATPase domain and the experimentally observed deformation  = 1 is obtained by adding up all modes' contributions (see Equation 2). In the absence of correlations between the predicted modes and the experimentally observed changes, i.e., if the modes were completely random, their correlation cosine with d would therefore be (3N-6)−1/2 = 0.029, using N = 380. In contrast, a single mode alone (k = 3) is found here to exhibit a correlation cosine of 0.62 with the observed deformation, and the cumulative overlap reaches 86% by moving along 6 modes only (black curve with circles in In order to examine quantitatively to what extent the observed reconfiguration is an intrinsic property of the Hsp70 ATPase domain (as opposed to a property induced by NEF), we focused on the softest motions predicted by the ANM. The black curve in open form of the ATPase domain to restore its conformation back to the closed form in the absence of a NEF .We further explored the transition between the open and closed forms of the Hsp70 ATPase domain by examining the reverse process, i.e., we examined the ability of the Thermus thermophilus in the ADP-bound state. Interestingly, the conformational variabilities observed in this ensemble, as noted by the authors, were found to be consistent with the structural change crystallographically observed Notably, this type of intrinsic ability of the Hsp70 ATPase domain to undergo changes in its structure is consistent with the experimental observations made by Zuiderweg and co-workers sequences.The results presented above lend strong support to the evolutionary selection/stabilization of a fold (by the Hsp70 ATPase domain) that endows suitable mobility and flexibilities at particular sites so as to favor functional changes in conformation (between open and closed forms), and optimal recognition and binding of the co-chaperones (NEFs). Next, we take a closer look at the evolutionary properties of Hsp70 ATPase domain SMFigure S3 for an enlarged version of the same panel. The large majority, if not all, of the key residues reported in previous studies to be important to Hsp70 activity is captured by the ET peaks, including those participating in the hydrogen bond network proposed to form a proline switch  The results from ET analysis are presented in The color-coded ribbon diagram in M|1–10>ET for all residues that exhibit a given ET rank (abscissa), and the bars display the standard error in each case. The observed decrease in mobility with increased conservation suggests that constraints on the collective mechanics of the molecule may be as important as those associated with chemical activity, such that the residues at key mechanical sites also tend to be evolutionarily conserved.Towards a more detailed examination of this tendency, we have grouped residues based on their ET ranks, starting from the most conserved residues (ET rank = 1), and computed the average mobility profile of residues for each ET rank. SMFigure S7) shows that G34, D292 and L274 are outliers when comparing their ET rank with their global mobility (they are too mobile for their level of conservation). Their enhanced mobilities may however be explained by their functionalities: G34 is presumably critical to maintaining the loop structure near the nucleotide-binding site; D292 is a class-specific residue recognized to be a key element of the signature loop that differentiates subfamilies A closer examination . E268 and R272 are two other residues on helix 9 in contact with both NEF and nucleotide, and as such, they may be playing a role in initiating the allosteric communication between the bound NEF and the nucleotide-binding pocket. Like most of other nucleotide-binding residues, E268 is relatively conserved (ET rank = 11)  a highlights the residues distinguished by their co-evolutionary patterns. These are determined by analyzing the MI map for the complete sequence shown in c and d. These two regions (residues 246–305 and 16–75) include 90% of all NEF-contacting residues. The bar plots below the MI map in panel b indicate the contribution of individual residues to the most correlated pairs in the MI matrix (upper plot), and the frequency of NEF-ATPase domain contacts made by these residues in the examined three mammalian complexes (lower plot). The bar plots and enlarged panels c and d clearly show that NEF-contacting residues exhibit high sequence correlations. Residue pairs that exhibit the highest MI values are listed in SMTable S3.The results from the MI analysis of the 1627 Hsp70 ATPase domain sequences examined here are presented in a), and all pairs of subdomains (panel b). The strong co-evolutionary property of residues within the subdomain IIB is clearly seen from panel b, succeeded by that within subdomain IB. Among the inter-subdomain correlations, we distinguish the pair of subdomains IB and IIB. Consistent with these patterns, four groups of secondary structural elements are distinguished in panel a by their most correlated evolutions: sheet E and connecting loop (Q279-T298) in subdomain IIB, the β-strand R100-Y107, and loop (V59-N62) between helices 1 and 2 in subdomain IB, and a β-strand and preceding turn (His23-Ile28) in subdomain IA.th, 6th, and 27th respectively among all MI pairs, see Table S3). Notably, helix 9 also contains highly conserved residues  involved in nucleotide binding. This combination of co-evolving (NEF-recognition) and conserved (nucleotide-binding) residues endows helix 9 with a unique mediating role between the NEF-binding region and the nucleotide-binding pocket. Notably, the two β-strands and preceding α-helix on subdomain IIB emerge as a co-evolved structural entity, distinguished by its NEF-recognition and binding role, reminiscent of the functional ‘sectors’ pointed out by Ranganathan and coworkers Table S3), despite their long distance separation on the structure.SMFigure S8 and Text S3) which confirmed the results found from MI analysis, while the signals provided by MI are more pronounced due to the weighting strategy employed in SCA. Finally, we repeated the MI calculations (i) using an extended set of Hsp70 family sequences (retrieved from Pfam version 24), and (ii) allowing for larger fractions of gaps in the MSA. The results presented in the respective SMFigure S9 and Text S4).The observed sequence correlations may arise from several reasons SMFigure S3), and the corresponding region in the MI map (The ET diagram 7 providper se) vs. specific (to its interaction with different ligands/substrates such as NEFs). The structural regions where these two groups of residues are clustered and/or closely coupled  are suggested here to play a key role in reconciling the specific functions  of the Hsc70 ATPase domain with its intrinsic conserved properties (of nucleotide binding and ATP hydrolysis).Therefore, the sets of residues highlighted by these two analyses tend to be mutually exclusive, and involved in different roles, intrinsic  mutations indeed require compensating mutations that can be detected, even if such mutations are rare. NEF binding R262 and D292 (with respective ET rank of 12 and 9) belong to this group of residues, and can sustain mutations provided that these are accompanied by compensating substitutions. As mentioned above R292 is a class-specific residue involved in salt-bridge formation with NEF residues, and likewise, R262 takes part in conserved interactions with acidic residues on NEF in mammalian homologues . Note that its counterpart in DnaK (R261) makes a contact with M174. This can be explained by the fact that binding of GrpE to DnaK is based on hydrophobic interactions instead of salt bridges e.g., that stabilized upon NEF binding, allowing for ADP release; shown in Figure S11) to maintain this specificity.Organisms comply with the evolutionary pressure to maintain their phenotype by genotypic variations that are compensated or correlated as needed, conserving certain sequence fragments vital to preserving their functions An observation of interest is the similarity between the interactions of the Hsp70 ATPase domain with different NEFs, in terms of structural dynamics. While Hsp70 ATPase domains are highly conserved both sequentially and structurally, the four NEFs examined have distinct structures and consequently different dynamics. The key point is that their binding to the ATPase domain involves in all cases the subdomain IIB of the ATPase domain, although not in exactly the same arrangement. Their binding to a common interfacial region on the ATPase domain point to a shared mechanism of interaction: The ATPase subdomain IIB is originally distinguished by its high mobility in the slowest mode, especially at the β-sheet E and the exposed loop connecting the two strands of this sheet; and after NEF binding, there is a significant suppression in its mobility. The conserved dynamics of the complexes suggests a role of subdomain IIB as an “adjustable handle”, which regulates the Hsp70 chaperone machine, to facilitate other proteins making use of its SBD.Many applications using the ANM have shown that the substrate recognition involves a region distinguished by its enhanced mobility in the most cooperative (or softest) modes, which enables the molecule to optimize its interactions with the substrate. Here we can see that the C-terminal part of helix 8 and the loop of β-hairpin E enjoy this type of high mobility/adaptability. On the other hand, substrate ‘binding’ may also involve more constrained residues in the close neighborhood, which may play a role in transmitting allosteric effects. In the opposite case of a binding site composed exclusively of floppy residues, the structural changes induced upon substrate binding could dissipate locally and not efficiently transmitted. In this respect, we propose that the involvement of residues such as Arg258, Arg261 and Arg262 in subdomain IIB, or N57, A60 and M61 in subdomains IB is critically important in establishing the communication between subdomains and transmitting allosteric signals between NEF-binding and nucleotide binding sites.e.g. H23 in subdomain IA and N57, A60 and M61 in subdomain IB correlated with R258, R261, E283 and D292 in subdomain IIB). In a recent study, R272, R261, Y15 and Y41 have been identified to play a central role in establishing the allosteric communication in the unbound Hsp70 ATPase domain, along with highly conserved residues K71, R72, E175 and H227 A putative communication pathway that couples distant residues in different subdomains of the Hsp70 ATPase domain is suggested here by the structural mapping of correlated and conserved residues, which needs to be further established. Many recent studies have pointed out the validity of “pre-existing equilibrium” concept where a substrate or ligand simply selects from amongst an ensemble of conformations already accessible to the protein prior to binding Protein-ligand binding interfaces and protein-protein contact interfaces are characterized by different sequence variation patterns. The protein-protein contact interfaces usually expose larger contact areas The Hsp70 ATPase domain exhibits patterns in close agreement with these general features: Its ligand (nucleotide) binding site essentially consists of highly conserved residues, which not only precisely coordinate the ligand, but also take part in a global hinge-bending region so that they are both chemically and mechanically required to be highly conserved. NEF recognition sites, on the other hand, exhibit much lower conservation properties; and in addition to their sequence variability, the subdomain IIB, which is observed to be most often involved in NEF binding, enjoys enhanced mobility. Briefly, global dynamics requirements entail residue conservation, and specific recognition entails sequence variation along with enhanced mobility. However, neither the sequence variability, nor the conformational mobility at NEF recognition sites, is random. The sequence variability takes place under unique restrictions, compensating mutations, as unraveled by the MI map. Conformational variability, on the other hand, is uniquely defined by the ATPase architecture, and precisely adept to accommodate the passage to the functional open state that is stabilized upon NEF binding. The ATPase domain uniquely juxtaposes such structure-encoded dynamics and sequence-specific interactions, which underlie its ubiquitous activities.E. coli Hsp70, DnaK, to understand the molecular mechanism of activity of the molecular chaperones in the Hsp70 family. The analysis in the present paper will guide our interpretation of the NMR, FRET, and EPR data on different states accessible to DnaK. Each of these methods gives us a different window into the ensemble of conformational states populated in response to ATP, ADP and NEFs. Excitingly, a detailed chemical shift analysis of six different ligand bound states for the nucleotide-binding domain of DnaK, with and without the linker that connects it to the substrate-binding domain  has pointed to the same subdomain interface rearrangements indicated in the present study . Moreover, the NMR results point to the fundamental feature that subdomain IIB can undergo a hinge-like movement to enable nucleotide entry and release. It is this fundamental movement, intrinsic to Hsp70 ATPase domains, that different NEFs have exploited. They bind in different, sequence-specific ways, but modulate the same fundamental movement. Further detailed analysis of the ensemble distributions and rates of interconversion between states can be achieved using a synergistic battery of computational and experimental tools.In general, subdomains IA and IIA are more conserved and more rigid than subdomains IB and IIB Table S1ATPase domain residues making close atom-atom contacts with different NEFs. (a) Close atom-atom contacts are defined as those having interatomic distance less than 4 Å. (b)Amino acids are grouped according to their subdomain locations; those written in boldface are also detected by SASA calculations  to exhib(0.06 MB DOC)Click here for additional data file.Table S2bound−SASAapo. Calculations were performed using PyMol get_area function. All residues with ΔSASA<0 are listed below. The unit for the surface area is Å2. We also listed in the 5th column the identity of NEF residues that make contacts (interatomic distance of <4 Å) with the ATPase domain residue listed in the 1st column. Note that all these residues are a subset of the residues listed in Hsp70 ATPase domain residues exhibiting change in the solvent-accessible surface area (SASA) upon binding NEF. We calculated the solvent accessibility surface area (SASA) of each residue of the Hsp70 NBD for both the NEF-bound and -unbound forms, denoted by SASAapo and SASAbound, respectively. The change induced upon NEF-binding is designated as ΔSASA = SASA(0.09 MB DOC)Click here for additional data file.Table S3Residue pairs distinguished by their sequence correlation . Residue pairs are separated by at least two amino acids along the sequence.(0.06 MB DOC)Click here for additional data file.Text S1Smith-Waterman threshold score and percent identity of aligned sequences.(0.05 MB DOC)Click here for additional data file.Text S2Reconstructing the missing lobe I in the ATPase+NEF structure.(0.05 MB DOC)Click here for additional data file.Text S3Comparison of MI values with results from statistical coupling analysis.(0.05 MB DOC)Click here for additional data file.Text S4Evaluation of MSA quality.(0.04 MB DOC)Click here for additional data file.Figure S1Left: Correlation between the SW score and the percent sequence identity of aligned sequences based on four different definitions, shown in panels (a)–(d); (e): distribution of sequence identity with respect to the reference sequence Hsc70, evaluated for the 4839 sequences retrieved from the Pfam v22 database for the Hsp70 family. The subset of sequences included in our MSA is shown by the upper bracket.Smith-Waterman threshold score and percent identity of aligned sequences. (0.07 MB PNG)Click here for additional data file.Figure S2Reconstruction of the ATPase domain complexed with HspBP1. 1XQS is colored cyan, and 1S3X is colored orange.(0.55 MB PNG)Click here for additional data file.Figure S3High resolution version of panel (b) in the main text (0.35 MB PNG)Click here for additional data file.Figure S4Intrinsic mobilities of residues in the ATPase domain. (a) The profiles represent the GNM-predicted weighted average mobilities (squared) of all residues, as driven by the first ten slowest modes, calculated for the three structures of mammalian homologs of Hsp70 listed in the inset Click here for additional data file.Figure S5Global dynamics of the ATPase domain. (a) Ribbon diagram of the ATPase domain in the unbound state color-coded by the mobilities in the first  GNM mode. Figure generated with PDB entry 1HPM. The slowest mode of the ATPase domain complexed with NEF is displayed for four different cases: (b) DnaK in contact with GrpE. (c) Hsc70 in contact with BAG-1. (d) Hsc70 in contact with Sse1. (e) Hsc70 in contact with HspBP1. Structural diagrams are generated with PDB entries (b) 1DKG (c) 1HX1 (d) 3D2E (e) 1XQS. The ATPase domain backbones are shown in stick representation, all in the same orientation, and the NEFs, as ribbon diagrams. In each case the complex is color-coded according to mobility .(0.82 MB PNG)Click here for additional data file.Figure S6d.Comparison of experimentally observed and computationally predicted structural changes in ATPase domain. The experimentally observed changes refer to the structural difference between the sse1-bound and -free forms of the NBD (respective PDB files 3D2E and 1HPM). The computational results are obtained by the ANM applied to the respective two structures. (a) Structural alignment of NEF-bound and unbound ATPase fragments. The unbound ATPase fragment (1HPM) is colored gray. The NEF-bound ATPase fragment  s displays the results for the unbound (black curves) and Sse1-bound (red curves) ATPase domain. The solid thick curves with the squares represent the correlation cosine between the deformation vector d and the ANM modes (eigenvectors). The thin curves with the circles describe the cumulative overlap (Eq (2)). The results show that a subset of 9 slow modes accessible to the unbound form (panel b) ensures the passage to the NEF-bound conformer with an overlap of 0.82. The NEF-bound form exhibits an even stronger potential to be reconfigured back into its closed form, consistent with the preferred conformation of the NBD in the unbound form: top ranking mode yield an overlap of 0.92 with the experimental deformation vector (0.17 MB PNG)Click here for additional data file.Figure S7M>|1–10) for residues with the same discrete ConSurf score. The discretization is performed by sorting all residues according to the ConSurf score, grouping every 20 consecutive residues and evaluating the mean mobility for each group. The correlation coefficient between average mobility and discrete ConSurf score is 0.88.Comparison of residue mobilities with their evolutionary conservation properties. (a) The mobility for each residue averaged ver the first 10 GNM modes is plotted against its ET rank. Three outliers for ET rank 6, 8 and 9 are labeled, two of which (Gly34 and Asp292) are NEF-contacting residues. (b) Proportionality between the discretized ConSurf score and the average mobility (average <(0.14 MB PNG)Click here for additional data file.Figure S8Comparison of correlated mutations map obtained by MI analysis and by the SCA. The first two panels are the correlation maps calculated using (a) SCA and (b) MI. (c) The SCA correlation map after hierarchical clustering  The MI correlation map with residues re-ordered according to the same permutation in panel (c).(2.18 MB PNG)Click here for additional data file.Figure S9Comparison of MI matrices. Comparison of MI matrices obtained with (a) the original dataset of Hsp70 family sequences (4839 of them), retrieved from Pfam release 22, and (b) the larger dataset retrieved from the Pfam release 24.(1.08 MB PNG)Click here for additional data file.Figure S10MI Results from MSA with different gap tolerances. The upper panels, (a) (b) and (c), display the MI matrices calculated by allowing sequences with different coverages of the reference sequence, i.e. 2%, 25% and 50% gaps. Panels (d) (e) and (f), show the number of gaps observed in each column of the three respective MSAs.(1.69 MB PNG)Click here for additional data file.Figure S11ConSurf score for residues in each NEF. Lower score corresponds to higher conservation. Residues that are in contact with the Hsp70 ATPase domain are colored green.(0.14 MB PNG)Click here for additional data file.


Protein-Protein Interactions (PPIs) play important roles in many biological functions. Protein domains, which are defined as independently folding structural blocks of proteins, physically interact with each other to perform these biological functions. Therefore, the identification of Domain-Domain Interactions (DDIs) is of great biological interests because it is generally accepted that PPIs are mediated by DDIs. As a result, much effort has been put on the prediction of domain pair interactions based on computational methods. Many DDI prediction tools using PPIs network and domain evolution information have been reported. However, tools that combine the primary sequences, domain annotations, and structural annotations of proteins have not been evaluated before.Saccharomyces cerevisiae (budding yeast) and identifies a list of n-grams that may contribute to DDIs and PPIs based on the frequencies of their appearance. GAIA also reports the coordinate position of gram pairs on each interacting domain pair. We demonstrate that our approach improves on other DDI prediction approaches when tested against a gold-standard data set and achieves a true positive rate of 82% and a false positive rate of 21%. We also identify a list of 4-gram pairs that are significantly over-represented in the DDI data set and may mediate PPIs.In this study, we report a novel approach called Gram-bAsed Interaction Analysis (GAIA). GAIA extracts peptide segments that are composed of fixed length of continuous amino acids, called n-grams (where n is the number of amino acids), from the annotated domain and DDI data set in GAIA represents a novel and reliable way to predict DDIs that mediate PPIs. Our results, which show the localizations of interacting grams/hotspots, provide testable hypotheses for experimental validation. Complemented with other prediction methods, this study will allow us to elucidate the interactome of cells. Saccharomyces cerevisiae  that contain this gram and the number of hits of this gram in each domain;Step A. For each 4-gram Gj appearing in query protein B, we also generated a list of Pfam annotated domains dlistG[j] that contain this gram and the number of hits of this gram in each domain;Step B. For each 4-gram Gi, Gj) between the query proteins A and B, we calculated the frequency of hits freq[i][j] for this gram-pair represented in interacting domain-domain pairs previously established in Pfam [score[i][j] for this gram-pair was weighted by weightScore[i][j] to determine if the number of its occurrences in the interacting domain pairs is statistically significant. The hit scores and weight scores are calculated by the following formulas:Step C. For each gram-pair  [i] [j] is the probability of the number of occurrences of Gram[i][j] in the interacting domain pairs is expected at random. Comparable control domain pairs were randomly generated by pairing domains from the DDI data set.Here, Pc and this gram-pair was located in a domain region, then this gram-pair and their corresponding domain pair was predicted to interact with each other. A profile containing the number of hits and the positions of the gram-pairs in the input query protein pair was simultaneously generated. This profile is important because it provides information on the amino acid hotspots that are potentially contributing to the physical interaction between the pair of query proteins  curve and the Area Under the Curve (AUC). The area under the curve was calculated by the Wilcoxon rank sum test. ROC curve provides us an indicator of the sensitivity and how it is affected by the specificity. The area under the curve highlights discrimination . The ROC curve was generated by calculating the true positive rate (sensitivity) and the false positive rate (1-specificity) at the different thresholds on scores derived from PPIs and DDIs in the network, and combined scores from both kinds of interactions against the "gold-standard" data set. If the number of hits of any domain pair in a protein pair was above the threshold and it was in the DDIs of positive portion of the "gold-standard" data set, then it was regarded as a true positive. Alternatively, if it was not in the positive portion of the "gold-standard" dataset, then it was a false positive. If the number of hits of a domain pair in a protein pair was below the threshold and it was in the negative portion of the "gold-standard" data set, then it was regarded as a true negative. Alternatively, if it was not in the negative portion of the "gold-standard" data set, then it was a false negative.The sensitivity, specificity and positive prediction value (PPV) were calculated as follows:The related data sets and scripts, source code, and binaries are available for download from . All scrThe authors declare that they have no competing interests.KXZ and BFFO conceived and designed the experiments. KXZ performed the experiments. KXZ analyzed the data. KXZ performed analysis and wrote the tools. KXZ and BFFO wrote the paper. All authors read and approved the final manuscript.


In addition to structural domains, most eukaryotic proteins possess intrinsically disordered (ID) regions. Although ID regions often play important functional roles, their accurate identification is difficult. As human transcription factors (TFs) constitute a typical group of proteins with long ID regions, we regarded them as a model of all proteins and attempted to accurately classify TFs into structural domains and ID regions. Although an extremely high fraction of ID regions besides DNA binding and/or other domains was detected in human TFs in our previous investigation, 20% of the residues were left unassigned. In this report, we exploit the generally higher sequence divergence in ID regions than in structural regions to completely divide proteins into structural domains and ID regions.Escherichia coli (229 proteins), in which only 5% fell in ID regions. The method also revealed that 4.0% and 11.8% of the total length in human and E. coli TFs, respectively, are comprised of structural domains whose structures have not been determined.The new dichotomic system first identifies domains of known structures, followed by assignment of structural domains and ID regions with a combination of pre-existing tools and a newly developed program based on sequence divergence, taking un-aligned regions into consideration. The system was found to be highly accurate: its application to a set of proteins with experimentally verified ID regions had an error rate as low as 2%. Application of this system to human TFs (401 proteins) showed that 38% of the residues were in structural domains, while 62% were in ID regions. The preponderance of ID regions makes a sharp contrast to TFs of The present system verifies that sequence divergence including information of unaligned regions is a good indicator of ID regions. The system for the first time estimates the complete fractioning of structured/un-structured regions in human TFs, also revealing structural domains without homology to known structures. These predicted novel structural domains are good targets of structural genomics. When applied to other proteins, the system is expected to uncover more novel structural domains. In vitro experimental studies, particularly those with NMR spectroscopy, revealed that TADs of various TFs are unstructured in isolation, but become structured upon binding to their interaction partners [Recent studies revealed that a high fraction of proteins in eukaryotes have long stretches of intrinsically disordered (ID) regions ,2. Protepartners ,11-17. Ppartners ,19. The partners . Highly partners  and HMMEpartners , made itpartners , if at lEfficient computer programs have been developed for prediction of ID regions from protein sequences -27 and uBesides the skewed amino acid composition, ID regions are characterized by higher sequence divergence as compared to structural domains. It is known that structural domains are well conserved through evolution and can be detected by homology search methods even across different kingdoms  ,33. SequIn the present study, we developed a program to CLAssify DIsorder regions and STructural domains, CLADIST, incorporating information of un-aligned sites into amino acid composition. We developed a combined system that uses structural domain identification, disorder prediction, and the CLADIST program to make order/disorder assignments to the entire length of proteins. We found that our system, DICHOT, divides proteins into structural domains and ID regions highly accurately. Application of the system to human TFs identifies ID regions and structural domains, including a number of cryptic structural domains.Figure We thus developed the CLADIST program that uses the amino acid composition considering information of highly divergent sequences in ID regions to divide the entire proteins into structural domains and ID regions. The program treats alignment gaps and un-aligned regions as the 21st element of amino acid composition and uses support vector machine for classification .-3 were used for estimation of the local amino acid composition. 92.4% of the residues were correctly assigned to either structural domains or ID regions. In order to examine the cases in which only close homologues of a query are available, we only took BLAST homologues with e-value less than 10-100 to perform test 2. In this test, the percentage of correctly assigned residues dropped to 86.1% . We built the DICHOT system by giving the first priority to trans-membrane (TM) domains and structural domains assigned by alignments to PDB sequences, then to ID regions predicted by DISOPRED2, and finally to both structural domains and ID regions assigned by CLADIST. In four steps, DICHOT classifies the entire sequence of a query into structural domains and ID regions. Structural domains consist of "known domains", i.e., structural domains with similarity to known 3D structures, and "cryptic domains" signifying structural domains without similarity to known 3D structures.A flow chart of the DICHOT system is presented in Figure Application of the DICHOT system to the above-mentioned test data showed that the accuracy increases to 97.7%  in a different format. For example, the tallest bar in the upper panel indicates that between 10 and 20% of the residues in 91 human TFs were in structural domains, or, equivalently between 80 and 90% were in ID regions. The cumulative graph shows that 60% of the human TFs have less than 40% of the residues in structural domains, in other words, have more than 60% in ID regions. In contrast, in most E. coli TFs structural domains account for more than 90% of their lengths and long ID regions are absent.Figure Cryptic structural domains were found in 53 human TFs  is composed of DBD (HLH), two PAS domains, an oxygen-dependent degradation domain (ODD), and two TADs termed N-TAD and C-TAD Fig. . AlthougA natural first step in analyzing the molecular architecture of proteins with ID regions is an accurate classification into structural domains and ID regions. For the dichotomic purpose, we have developed the DICHOT system, which combines existing methods of domain identification and disorder prediction with CLADIST, a newly developed classification program. The most reliable among the components is the identification methods that, along the sequence of a query protein, locate structural domains homologous to data compiled in SCOP and/or PDB. DICHOT identifies known domains by this method first and divides the remainder into structural domains and ID regions by a combination of the DISOPRED2 and CLADIST programs. The resolving power largely depends on the number of homologues available. The CLADIST program, in which the effect of sequence conservation is incorporated through local amino acid composition, maintains high accuracy even in cases in which only a limited number of homologues are available Table .E. coli TFs is as high as 95%  searches, i.e. BLAST and reverse PSI-BLAST searches against the PDB  and SCOP  . This length criterion was adopted by assessing the lengths of known TAD fragments. For the shorter regions, the SCOP classification of aligned domains was examined and those aligned to the SCOP structural domains of classes from 'a' , 'b' , 'c' (α/β), 'd' (α+β), 'e' (multidomain), 'f' (membrane), and 'g' , but not 'h' (coiled coil), 'i' (low resolution protein structure), and 'j' (peptide) were classified as known domains. As SCOP domains of classes h and j, as found in synuclein  and a p5A structural domain predicted by CLADIST is not accepted if more than half of the region overlaps with structural domains assigned by SD searches. Out of the two structural domains predicted by CLADIST (yellowish green bars), the right one was neglected because a majority of the region overlaps with the structural domains assigned by SD searches. DICHOT admits all ID regions predicted by DISOPRED2 and CLADIST if they fall outside of structural domains assigned by SD searches. If ID regions predicted by DISOPRED2 overlap with the structural domains predicted by CLADIST, they are classified as ID regions. Due to interruption by an ID region, the left structural domain predicted by CLADIST (yellowish green bar) is divided into two enclaves (hatched boxes), both of which await classification in later steps.In step 3, DICHOT examines the short structural domains left unclassified in step 2. A short structural domain found to coincide with a structural domain predicted by CLADIST is regarded as a known domain. Otherwise, it is classified as an ID region, as in the case of the right-most hatched rectangle in step 2. This additional requirement for short structural domain was introduced to prevent functional sites in ID regions from erroneously assigned as structural domains. For example, the 3D structures of TADs complexed with partner proteins have been determined and registered in the PDB. As they are classified as ID regions by the CLADIST program, they are correctly classified as ID regions by this step. Structural domains predicted by CLADIST whose lengths are longer than or equal to 30 amino acid residues are considered as candidates for cryptic domains. The middle hatched box in step 2 is shorter than 30 residues and therefore discarded at this step, while the left-most one is long enough to be left as a cryptic domain candidate.In the fourth and final step, overlaps of cryptic domain candidates with known domains are checked. This step is necessary because some known domains arise at step 3 and they had not been subjected to the overlap check at step 2. If less than half of the regions overlap with any of the known domains, the regions are classified as cryptic domains (orange region). Any query sequence is thereby entirely classified into two categories, i.e., structural domains consisting of known and cryptic domains, and ID regions.E. coli TFs were taken from the GTOP-TF database  to constitute the reference set.The dataset of 401 human TFs used in the present study is identical to that in the previous study : data haSF developed the CLADIST program and the DICHOT system, and applied it to human TFs. SF also drafted the manuscript. KH assessed the results and participated in drafting the manuscript. YM provided the human TF dataset. TG supervised the research project. KN coordinated the study, assessed the results, and drafted the manuscript.List of proteins with cryptic domains. The protein names, swiss-prot IDs, ENSEMBL IDs, ID regions, structural domain regions, and cryptic domain regions are listed.Click here for file


Plasmodium falciparum in the intervillous blood spaces of the placenta. Sequestration of malaria parasite in the human placenta is mediated by interactions between chondroitin sulphate A (CSA) on the syncytiotrophoblasts and proteins expressed on the surface of infected human erythrocytes. Plasmodium falciparum Erythrocyte Membrane Protein 1 (PfEMP1) encoded by the var2CSA gene is believed to be the main parasite ligand for CSA-mediated placental binding.Placental malaria is typified by selective clustering of var2CSA gene from A4 and 3D7 strains of P. falciparum were performed. Three-dimensional structural models of various DBL domains were built and analysed with a view to assessing conservation of CSA interaction sites across various DBL domains.Extensive sequence and structure comparisons of the various CSA-binding and non-binding DBL domains from the var2CSA are likely to retain the disulfide linkages evident from previously published DBL domain crystal structures. The number of disulfide linkages between the various DBL domains analysed varies from three to seven, of which two are conserved across all DBL domains. The conserved disulfide linkages are distributed within the respective three sub-domains and only one linkage is shared by sub-domains I and II. Major differences between CSA-binding DBL domains are in the loop regions, which tie the alpha helices together, and in variable length terminal extensions. Intriguingly, a crucial loop from A4 DBL 3X which provides the important Gly and Lys residues that chelate the bound sulphate is missing or significantly altered in all other DBL domains that interact with CSA. Further analysis of the proposed sulphate and predicted CSA-binding site indicates either none or very low level of conservation among the critical interacting residues.Each of the six DBL domains from var2CSA. Therefore, the 4 CSA-binding DBL domains encoded by var2CSA are unlikely to have common architectures to their CSA recognition sites. These structural insights have clear implications in using CSA-binding DBL domains for vaccines against placental malaria as it is proposed that the various CSA-binding DBL domains on var2CSA will recognize their CSA ligands differently.Structural comparisons of the three-dimensional structures of CSA-binding DBL domains indicates that the proposed CSA interaction site on A4 DBL 3X is unlikely to be conserved across the other CSA-binding DBL domains from  It has long been observed that there is an increase in the severity of malaria during pregnancy, resulting in such negative outcomes as maternal anaemia and reduction in birth weight, which in turn lead to increase in maternal and infant mortality . AdversePlasmodium falciparum ligands that mediate adhesion to CSA [Pregnancy-associated malaria is coupled with massive accumulation of parasitized erythrocytes (PE) and monocytes in the placental intervillous blood spaces. The basis for the amassing of parasitized erythrocytes (PEs) in the placenta was unknown until it was shown that PEs from placenta primarily bind to chondroitin sulfate A (CSA) . Signifin to CSA .var2CSA gene, which is a member of the P. falciparum Erythrocyte Membrane Protein 1 (PfEMP1) family, may have an important role in PAM disease and immunity. This gene encodes a large protein with an estimated molecular weight of 350 kDa, and can be divided into six Duffy-binding-like domains (DBL 1–6) based on several conserved cysteines. The DBL domains are cysteine-rich modules which recognize diverse host cell-surface receptors during pathogenesis. The gene var2CSA is found in all parasite isolates and is transcriptionally up-regulated in both placental isolates and laboratory parasites selected to bind CSA [var2CSA knock-out parasites revealed that no other parasite ligand can promote adhesion to CSA [var2CSA encoded protein contains multiple CSA-binding DBL domains and is the target of maternal antibodies, making it the leading candidate for malaria vaccine in pregnancy. [The ns DBL 1– based onbind CSA . Signifins DBL 1– based onegnancy. -9.P. falciparum were modelled and analysed by predicting the disulfide linkages using the crystal structures of A4 DBL 3X domain , Plasmodium knowlesi DBL domain Pkα-DBL and P. falciparum EBA-175 DBL domains F1 and F2 [In the present study, three-dimensional structures of all CSA-binding and non-binding DBL domains from A4 and 3D7 strains of 1 and F2 -13. An o1 and F2 ,11) showvar2CSA were modelled using HHpred server with default settings [var2CSA DBL domains were modelled for 3D7  and for A4 strains (accession code AY372123). The PFL0030c sequence was split into separate domains . The A4 accession code AY372123 sequence was also split into separate domains . All HMM databases available in web-server were used for template structure search, including the Protein Data Bank. Multi-template alignments proposed by the HHpred method were used to generate 3D models by using HHpred server toolkit protocol for MODELLER [All sequence alignments were done using ClustalW2 online program. Structures of the DBL domains from A4 and 3D7 settings . The HHpsettings . The varMODELLER . All strP. falciparum have been recently determined with a single bound sulphate ion [var2CSA encoded DBL domains from the A4 and 3D7 strains of P. falciparum  that bind CSA and also two remaining non CSA-binding domains (1X and 4ε) were aligned with the A4 DBL 3X crystal structure sequence. Since all conserved cysteines in DBL domains where structures have been determined to date [var2CSA DBL domains. Subsequently, predictions were made for the disulfide linkages of all DBL domains from A4 and 3D7. Based upon these predictions, final sequence boundaries for DBL domains were defined   and two non-binding domains (3D7 var2CSA DBL 1X and A4 var2CSA DBL 4ε) were generated. The structures for DBL domains 3D7 DBL 1X and 3X; A4 and 3D7 DBL 5ε and A4 and 3D7 DBL 6ε were modelled successfully in the first run. However, for 3D7 and A4 DBL 2X domains, the program inserted a loop region in the middle of a crucial and conserved α-helix 2 in sub-domain II – a result which is unlikely. Hence, to successfully generate correct models for 2X domain, the input sequence length for the program was altered while keeping conserved disulfides intact in this altered 2X sequence length   Figure . A struc) Figure .P. falciparum A4 DBL 3X, P. falciparum EBA-175 DBL domains F1 and F2 and P. knowlesi DBL domain Pkα-DBL [var2CSA domains are the non-DBL domain stretches from A4 at the start (aa 1–45), between DBL 1X and 2X (aa 345–535), between 2X and 3X (aa 931–1217), between 5ε and 6ε domains (aa 2277–2322) and in the TM/ATS region (aa 2632–3064)  has a corresponding Lys in 3D7 DBL 3X, 2X and the non-binding DBL 1X domains  in these DBL domains shows that the sulphate binding site is only very partially conserved in the seven CSA-binding DBL domains from A Figure ; Table 3A Figure ; Table 3var2CSA gene is intuitively, and from the presented data evidentially, unfeasible and unlikely. Instead, it is likely that CSA-binding DBL domains have evolved to differ in their regions which recognize CSA – a notable feature of significance if the parasite needs to evade immune responses efficiently. As an extension of this argument, it is proposed that it may be unlikely that a common inhibitor/drug will bind to the CSA-recognition pocket in all CSA-recognizing DBL domains. In summary, it is plausible that CSA makes different  binding footprints on various CSA-binding DBL domains.Conservation in the CSA-binding sites within all four DBL domains of a given To investigate if there are other regions on these DBL domains which have identical/conserved and exposed residues, the surfaces of DBL domains were inspected . These In an earlier study, antibody binding regions have been predicted on DBL domains by identifying linear epitopes and mapping them onto the modelled DBL structures . For thevar2CSA – the main parasite ligand for human placental CSA – were predicted and analysed. The analysed DBL domains have a variable number of disulfide linkages out of which three to seven are conserved in accordance with the A4 DBL 3X crystal structure. The described detailed sequence and structural analysis suggests that the CSA-binding DBL domains from var2CSA and var1CSA are unlikely to retain similar/identical CSA recognition surfaces. This crucial observation seems consistent with the need for P. falciparum to constantly evade immune responses against its CSA recognizing DBL domains. Maintenance of identical CSA interacting sites on the four DBL domains from var2CSA will provide an easier immune target than a scenario where the four DBL domains use different and variable surfaces for CSA chelation. It is more evident than before that a complete description of CSA-binding footprints on CSA-binding DBL domains will be of tremendous value as a platform to further guide efforts at drug/vaccine development against placental malaria.In the present study, three-dimensional structures of CSA-binding DBL domains from The authors declare that they have no competing interests.JG and AS designed the study. JG conducted all experiments and performed the analyses. JG wrote the manuscript in consultation with CC and AS.Sequence alignment of A4 DBL 3X (PDB code: 3BQK) and 3D7 DBL 3X domains. The data provided shows sequence alignment of A4 DBL 3X (PDB code: 3BQK) and 3D7 DBL 3X domains. The disulfide linkages of A4 DBL 3X structure are colored in pairs.Click here for filevar2CSA CSA-binding DBL domainsFinal sequence boundaries defined for the . The data provided shows final sequence boundaries defined for all the CSA-binding DBL domains from A4 and 3D7 strains.Click here for filevar2CSAStructure-based sequence alignment of CSA-binding DBL domains from A4 and 3D7 . The data provided shows structure-based sequence alignment of CSA-binding DBL domains from A4 and 3D7 var2CSA highlighting the surface identical/conserved residues.Click here for filevar2CSA.Structure-based sequence alignment of CSA-binding DBL domains from A4 and 3D7  The data provided shows structure-based sequence alignment of CSA-binding DBL domains from A4 and 3D7 var2CSA highlighting the conserved and predicted disulfide linkages in accordance with the A4 DBL 3X crystal structure.Click here for file


Protein domains coordinate to perform multifaceted cellular functions, and domain combinations serve as the functional building blocks of the cell. The available methods to identify functional domain combinations are limited in their scope, e.g. to the identification of combinations falling within individual proteins or within specific regions in a translated genome. Further effort is needed to identify groups of domains that span across two or more proteins and are linked by a cooperative function. Such functional domain combinations can be useful for protein annotation.Saccharomyces cerevisiae. The units participate in many important cellular processes such as transcription regulation, translation initiation, and mRNA splicing. Within the units the domains were found to function in a cooperative manner; and each domain contributed to a different aspect of the unit's overall function. The member domains of DASSEM units were found to be significantly enriched among proteins contained in transcription modules, defined as genes sharing similar expression profiles and presumably similar functions. The observation further confirmed the functional coherence of DASSEM units. The functional linkages of units were found in both functionally characterized and uncharacterized proteins, which enabled the assessment of protein function based on domain composition.Using a new computational method, we have identified 114 groups of domains, referred to as domain assembly units (DASSEM units), in the proteome of budding yeast Saccharomyces cerevisiae. These groups can either lie within individual proteins or span across different proteins. We propose that the functional linkages among the domains within the DASSEM units can be used as a non-homology based tool to annotate uncharacterized proteins.A new computational method was developed to identify groups of domains that are linked by a common function in the proteome of  Protein domains are sequential, structural, and functional units . They peThere are a variety of methods available for the identification of functional domain combinations based on protein sequence information, and these methods vary in both the scope of the combinations identified and in their applications. As examples, domain combinations can be identified by finding domain fusion pairs ,11, prevFor the present study, a systematic method was developed to automatically identify functionally coherent groups of protein domains, referred to as domain assembly units or DASSEM units, and their corresponding functions. The method employed a soft-margin clustering technique that was guided by singular value decomposition (SVD). SVD is often used to capture the significant variance in a large dataset, and here it was used to retrieve the highly prevalent domain combinations found in an adjacency matrix of proteins versus domains. The prevalent domain combinations were clustered such that, when necessary, a domain was assigned to multiple groups in order to reflect the fact it can participate in different functions. Note that the clustering method is similar in spirit to the fuzzy k-means clustering method used for the extraction of coherent expression patterns from microarray experiments .Saccharomyces cerevisiae, and 114 functionally coherent groups of domains, referred to as domain assembly units (DASSEM units), were identified. The functions of the units included a broad range of cellular tasks such as chromatin modification, carbohydrate transport, translation, and ubiquitin-dependent protein catabolism. Within the units, the functional linkages among the domains were demonstrated in three ways. First, there was a significant enrichment of Gene Ontology (GO) terms in proteins contributing domains to the units, which suggested that the domains were used in a functionally coherent manner. Second, the domains of DASSEM units were shown by manual review to be utilized in a rational way to facilitate particular cellular processes. Third, DASSEM units overlapped significantly with transcription modules, defined as groups of genes that share the same expression pattern under a set of cellular conditions. Such overlap further confirmed the functional coherence of DASSEM units.The current method was applied to the protein/domain complement of We found that the functional linkages within DASSEM units can allow for the prediction of protein function based on domain composition. Since the transfer of annotation from a DASSEM unit to a protein of unknown function does not require high sequence homology between the unknown protein and an annotated one , the metA domain assembly unit can be viewed as a group of domains that are linked together by domain fusion events or events that cause domains that function together to be placed in the same protein. For example, if domain A is fused with domain B in one protein while domain B is fused with domain C in a second protein, domains A and C can be functionally linked. If multiple instances are found where A is fused to B and B is fused to C, the functional inference between A and C can be strengthened, and as a consequence the two proteins containing domains A and C can be found to participate in the same biological process (see below for examples). The cycle can continue with more domain fusions and lead to a larger group of functionally linked domains/proteins. These groups are required for higher order cellular functions.Shown in Figure Saccharomyces cerevisiae. Shown in Table We derived 114 DASSEM units from the domain content of protein sequences within the proteome of -7 for the GO term biological process category, 4.1*10-03 for the component category, 9.9*10-13 for the molecular function category  terms that were enriched  across the proteins associated with the unit Figure  32]. In. In32]. The domains within the DASSEM units cooperate with one another in order to achieve a particular function, which was made apparent through an examination of the functional roles of proteins associated with the units. Three example DASSEM units are described in following in order to show how such cooperation may occur.Saccharomyces Genome Database (SGD) , the Fungal specific transcription factor domain [PFAM:PF04082], the Gal4-like dimerization domain [PFAM:PF03902], and the PAS domain [PFAM:PF00989]. The first three domains are the components of the GAL4 family transcription factors . Th. Th57]. To further demonstrate the overlap between of the DASSEM units with the transcription modules, the DASSEM units with the highest overlap scores with the transcription modules were examined. The distribution of the overlap scores for the highest overlapping DASSEM units with the modules is shown in Figure t-test that compared the averages of the distributions was significant with a p-value of 4.6*10-3. The test statistically validated that DASSEM units are utilized in the transcription modules. Further, the test demonstrated that DASSEM units represent a level of domain organization that can go beyond the level of protein domain architecture found with individual proteins. That is because the average overlap scores would not be different between the original versus the randomized set if the functional modules of the domain combinations did not extend beyond individual proteins. Further, subsequent Student t-tests that compared the second, third, fourth, and fifth DASSEM units that overlapped with the transcription modules versus the randomized modules were significant with p-values of 2.9*10-3, 3.4*10-3, 3.6*10-3, and 4.6*10-2, respectively. Since each DASSEM unit contributes a distinct function, the result validates that multiple DASSEM units can be engaged by a transcription module.The difference in the overlap between the original versus the randomized modules shown in Figure Saccharomyces cerevisiae proteome, and all the domains within the pair were designated as a group of transitively related domains. 1,615 such groups were identified, and 114 of these were selected so that they had the same number of domains as the original DASSEM units. The overlap scores (using domains as the overlap medium) of the DASSEM units with the transcription modules was then compared to the overlap of the transitively related domains with the transcription modules. The overlap was significantly higher for the DASSEM units than the arbitrary chosen groups of transitively related domains, and that result was validated with five bootstrap samples taken, with replacement, from set of transitively related groups of domains. The result indicates that functional coherence was imparted in the DASSEM units constructed from prevalent domain combinations, and that coherence was higher than that found for arbitrary groups of transitively related domains.As illustrated in Figure -4), 3346 for the transcription modules  and 240 for the randomized modules . The plot shown in Figure To further validate the functional coherence of the DASSEM units, a comparison of the GO term enrichment for the DASSEM units, the transcription modules and the randomized modules was made. The number of terms found below a p-value threshold of 0.05 with all GO term categories were considered was 2295 for the DASSEM units -Cys(6) binuclear cluster domain [PFAM:PF00172]. According to its associated DASSEM unit  algorithm to find combinations in biological data . The algSaccharomyces cerevisea, was analyzed; and that may limit the number of the functional groups identified. However, the use of data only from S. cerevisea ensured that relevant domain groups were found and that these groups were not contaminated by outside domains.There are a number of limitations of the current analysis. For example, the ability of DASSEM units to predict protein function of individual proteins was manual assessed and requires further validation. That validation is complicated by the fact that the function of DASSEM units can be distributed across more than one protein and may not yet be annotated at the individual protein level. In addition, for the current study a single proteome, A method was developed to identify groups of functionally linked domains. Knowledge of the groups furthers our understanding of what domains are utilized in a cooperative manner to perform a variety of cellular tasks. The groups can also provide a means to annotate uncharacterized proteins.Saccharomyces cerevisiae were retrieved from the UniProt database [A matrix, was then created consisting of proteins versus domains: n = 3,781 proteins by m = 1,753 domains. Within the matrix, if a protein contained one or more copies of a domain, the corresponding matrix entry was one, otherwise it was zero. Each row vector of the A matrix lists the domains contained by a protein and each column vector of the A matrix lists the proteins in which a domain is present.The protein sequences and open reading frame (ORF) designations from proteins in database , which cdatabase . An addidatabase . An adjaEach domain can be considered as a vector in the protein space (each dimension is a protein) and the coordinates of a domain in this space are decided by its occurrence in each protein. A domain combination, referred to as domain assembly unit (DASSEM unit) is a cluster of domains in the protein space. Because of the large number of proteins, identification of domain clusters in such a high-dimension space is a challenge. We employed a soft-margin clustering method that was guided by singular value decomposition (SVD) . SVD is A matrix using Matlab,SVD was performed on the U (a n by n matrix) and VT  are orthonormal, and S is a n by m diagonal matrix containing singular values S11 > S22 > ...... > Smm, n > m. Analogous to the SVD analyses on gene expression microarray data [ith row of VT, A matrix) and is called eigenprotein. The jth element of jth domain of the eigenprotein jth column of U, A matrix) and is called eigendomain. The ith element of uij, is the coordinate of the ith protein of the eigendomain where ray data ,85, the K was adjusted so that the K giving the maximum Calinski-Harabasz (CH) index, a quantity which describes the degree of inter-cluster separation and intra-cluster homogeneity [th singular value was already quite small, and subsequent clusters became unstable between the K-means and mixture Gaussian fitting steps, with some clusters being produced with zero content.The proteins were clustered in the eigendomain space using the K-means algorithm, and a mixture Gaussian model was built upon these initial clusters in order to assign proteins to multiple clusters . For theogeneity ,88. The After the K-means clustering step, each protein was assigned to only one cluster. Since domains may participate in multiple combinations, it was important to allow flexible assignment of proteins, and their corresponding domains, to multiple clusters. We modeled each cluster obtained from K-means using a Gaussian distribution, centered at the centroid of the cluster. The variance of each Gaussian distribution was determined by maximizing the likelihood of the data,K is the number of clusters, n is the number of proteins considered in the analysis, jth protein in the eigendomain space, σi are the mean and variance of the ith Gaussian distribution respectively. The final assignment of a protein to a given cluster was based on a threshold level of probability, set to be within three standard deviations of the mean of the cluster. A protein could thereby be assigned to multiple clusters. The domain groups associated with the protein clusters were found using the relation V = ATUS-1. These domain groups were referred to as the domain assembly units (DASSEM units). Functional annotation of each DASSEM unit was found by applying the GO::TermFinder tool to the list of proteins associated with the unit [where the unit .To show how DASSEM units were utilized, the content of the units was compared to that found in groups of proteins in transcription modules. From a study by Imhels et al., 86 transcription modules were obtained ,56. An oNc is the number of domains that are in common between the DASSEM unit and the module, NM is the total number of domains in the module, and ND is the number of domains in the DASSEM unit. The overlap score was used to provide an overall measure the degree of functional utilization of a DASSEM unit within a transcription module. It considers the degree to which the function of the DASSEM unit is utilized and the degree to which the function of the unit contributes to the module.where DASSEM units were ranked according to their overlap scores for each module. From the highest ranking DASSEM units, four DASSEM unit collections were generated for each module by combining the domains in units ranked one and two, units ranked one through three, units ranked one through four, and units ranked one through five. The cumulative overlap score of each collection with each module was calculated. The process of finding overlapping DASSEM units and collections was done for each of the 86 co-expression modules.t-tests comparing the average overlap scores of DASSEM units with the original transcription modules versus the random sets of proteins. The other control analyses were fully explained in the results section.A randomization protocol was employed to compare the average overlap scores for the highest overlapping DASSEM units with the original modules versus random sets of proteins. The random sets of proteins were created by randomly redistributing the proteins among the transcription modules while keeping the number of proteins in each module constant. Since the proteins were randomized, the domains within a given protein remained together. Statistical significance of the overlap scores were estimated by the p-values of Student's WAM participated in the design of the study, performed data analysis, interpreted the results and wrote the manuscript. KC participated in the design of the study and wrote the Matlab computer code used for clustering. TH participated in the design of the study and performed data analysis to test the utility of the clustering algorithm. WW participated in the design of the study, interpreted the results and wrote the manuscript.All authors read and approved the final manuscript.


Herein, the β(MT3)-β(MT3) mutant and the hGIF-truncated Δ33-35 mutant were constructed, and their biochemical properties were characterized by pH titration, EDTA, and DTNB reactions. Their inhibitory activity toward neuron survival and neurite extension was also examined. We found that the Δ33-35 mutant α-domain containing β-domain-like M3S9 cluster exhibits the function of α-domain with M4S11 cluster in hGIF. These results showed that the stability and solvent accessibility of the metal-thiolate cluster in β-domain is very significant to the neuronal growth inhibitory activity of hGIF and also indicated that the particular primary structure of α-domain is pivotal to domain-domain interaction in hGIF.Neuronal growth inhibitory factor (GIF), also known as metallothionein , impairs the survival and neurite formation of cultured neurons. It is known that the  The pathological pattern of Alzheimer's disease (AD) is characterized by the progressive loss of neurons accompanied by the formation of intraneural neurofibrillary tangles and extracellular amyloid plaques . One hypβ-domain contains a cluster of three metals coordinated with nine cysteines; the C-terminal α-domain contains a cluster of four metals coordinated with eleven cysteines. The cluster structure, where the metal ions are tetrahedrally coordinated by bridging and terminal cysteines, is important for the function of MTs [4Zn3-hGIF and recombinant Zn7-hGIF [β-domain, whereas the C-terminal α-domain alone is found to be inactive [β-domain, and so forth. Metallothioneins (MTs) are low molecular weight, cysteinerich, and metal ion binding proteins. In mammalians, four MT isoforms  have been identified . The twon of MTs . Unlike n of MTs –13. Thesn of MTs , 15, andZn7-hGIF . Furtherinactive , 18. Accinactive . The CPCinactive –19, the inactive –23. Moreα-domain is very significant to the biological function of the protein. We proposed that the α-domain has a stabilizing effect to β-domain in hGIF. However, the mechanism of this stabilizing effect remains unclear. In 2003, Dr. Wenhao Yu in our lab constructed C34M and C35S mutants of monkey metallothionein and discovered that the newly formed α-domain (residue 32 to 61) was able to combine with up to fourfold of metal ions, although the domain had only nine cysteines [α-domain of sheep metallothionein (sGIF), which was found in 2002, is nine, two less than other species which contain eleven cysteines. Hence, by comparing the primary structure of hGIF with sGIF, we constructed an hGIF Δ33-35 mutant  and a β(MT3)-β(MT3) mutant. The biochemical properties of the mutants were characterized by pH titration, EDTA, and DTNB reaction, and their bioactivity toward neuron survival and neurite extension was also examined. We found that in hGIF Δ33-35 mutant the truncated α-domain  contains M3S9 cluster which is similar to the wild type hGIF β-domain, while it exhibits the functions of α-domain with M4S11 cluster in human growth inhibitory factor. The experimental results showed that the stability and solvent accessibility of the metal-thiolate cluster in the β-domain is very significant to the neuronal growth inhibitory activity of hGIF and also indicated that the particular primary structure of the α-domain of hGIF is pivotal to domain-domain interaction in hGIF.  The earlier research showed that ysteines . BesidesBamH I and EcoR I, were purchased from New England Biolabs. Pfu DNA polymerase, cell culture reagents, isopropyl β-D-thiogalactoside (IPTG), and Triton-100 were purchased from Sangon . The DNA gel extraction kit was purchased from Qiagen. 2,2′-dithiodipyridine, 5,5′-dithiobis-(1-nitrobenzoic acid) (DTNB), ethylenediamine tetraacetic acid (EDTA), and thrombin were from Sigma . Neurobasal-A medium and B27 serum-free supplements were purchased from GIBCO-BRL . The other reagents were of analytic grade. Fusion expression vector pGEX-4T-2, Escherichia coli strain BL21, glutathione Sepharose 4B, Superdex-75, and Sephadex G-25 were purchased from Amersham Pharmacia Biotech. The (deoxy-ribonucleoside triphosphate) dNTP, T4 DNA ligase, and restriction enzymes, β(MT3)-β(MT3) mutant, the hGIF Δ33-35 mutant, and the single β-domain of hMT3 were amplified by PCR method [BamH I/EcoR I fragment and verified by DNA sequencing. The expression and purification procedure for hMT3 and its mutants were carried out as described in the instructions for Glutathione-Sepharose 4B  with some modifications. Briefly, cultures of cells (50 mL) were grown overnight in LB medium containing 100 mg mL−1 of ampicillin at 37°C. This start culture was diluted 100-fold into 500 mL of fresh 2·YTA, and the cells were induced with 0.1 mM IPTG when an absorbance of 0.6–0.8 at 600 nm was attained, followed by 1-hour growth before addition of 0.2 mM ZnSO4. The cells were harvested at OD600 value of 2.5, which was generally reached 3-4 hours after induction. Then, under protection of mercaptoethanol, the GST fusion protein was separated by affinity column of Glutathione Sepharose 4B, which was washed by PBS until the absorbance at 280 nm was less than 0.02. Then the fusion protein was digested by thrombin for 14 hours at 25°C in a cleavage buffer . The elute containing thrombin and recombinant hMT3 (tagged Gly-Ser in the N-terminus) cleaved from the fusion protein was pooled, concentrated, and further separated by a Superdex-75 gel filtration column equilibrated with 10 mM Tris-HCl, 50 mM NaCl, pH 8.0. The main eluted peak was concentrated, desalted, lyophilized, and stored at −80°C. Human MT3 (hMT3) cDNA was prepared from cells by reverse transcription followed by polymerase chain reaction (PCR). The genes of the R method . Each se2+- or Zn2+-loaded proteins were prepared by reconstitution under the protection of nitrogen [−1 SDS, 1 mM EDTA, and 12 mL L−1 acetate (pH 4.0), using ε324 = 19,800 M−1cm−1 [−1. The measurement was carried out on a Bruker Esquire 3000 electrospray mass spectrometer . The instrumental conditions were capillary volt, 4 kV; dry gas, 5 L min −1; nebulizer gas, 15 PSI; and infusion flow rate, 3 μL min −1.The apo-form of hMT3 and its variants were generated according to literature  and fullnitrogen . The con M−1cm−1 . The con The UV-vis absorption spectra were scanned from 200 to 400 nm on a HP8453 UV-vis spectrophotometer  at room temperature using a 1.0 cm quartz cuvette. CD spectra were measured in the range of 200–300 nm on a Jasco J-715 spectropolarimeter at room temperature in phosphate buffer .μM Cd2+-reconstituted proteins were dissolved in 10 mM Tris-HCl, pH 8.0, containing 100 mM KCl and titrated with increasing amounts of 1 M HCl. The progress of acidification was monitored at 250 nm on the HP8453 UV spectrophotometer. The spectrophotometric pH titration was performed according to the method of Winge and Miklossy . Briefly2+-reconstituted hMT3 and its mutants with EDTA were investigated at 25°C according to the method of Li et al. [μM protein was reacted with 1.2 mM EDTA in 10 mM Tris-HCl, pH 8.0, containing 100 mM KCl. To avoid the absorbance of EDTA, the reaction was monitored at 265 nm on the HP8453 UV spectrophotometer at 20-seocnd intervals for 100 minutes. The reactions of Cd2+-reconstituted hMT3 and its mutants with DTNB were studied according to the method of Shaw et al. [μM protein was reacted with 1 mM DTNB in 10 mM Tris-HCl, pH 8.0, containing 100 mM KCl at 25°C. The reaction was monitored at 412 nm  on an HP8453 UV spectrophotometer at 20-second intervals for 60 minutes. The reactions of Cdi et al. : 9 μM prw et al. : 3.5 μM μm filter, Millipore, Billerica, MA, USA). The total protein concentration of freshly prepared rat brain extract was determined by the Bradford method, and brain extracts were used immediately at a concentration of 150 μg mL−1 [ Rat brain extracts were prepared as described previously . Briefly μg mL−1 .5 cells/well. Cultures were maintained in a 37°C chamber under an atmosphere of humidified air containing 5% CO2. Four hours later, the culture medium was replaced with fresh medium containing with 150 μg mL−1 rat brain extract and 10–20 μg mL−1 proteins. Cultures were maintained under the same conditions for three days. To visualize neuronal cells, cultures were fixed in 4% paraformaldehyde and then stained with trypan blue. Finally, the neuron neurite length was determined by measuring the distance between the end of the neurite and the cell surface using the Leica Qwin program  and at least 60 neurons with more than 100 neurites were recorded in each individual experiment. Cultures of cerebral cortical cells were prepared as described previously , 31 with−1 culture. Since these proteins were cleaved from GST fusion proteins by thrombin, they had an additional Gly-Ser dipeptide in the N-terminus. However, the existence of the additional dipeptide does not obviously affect the structure and properties of the protein [2+ is considered to be a useful probe of Zn2+ binding sites in MTs [2+-substituted MTs. Furthermore, the Cd2+-substituted MTs show a number of advantages over the Zn2+-substituted MTs, including a tendency toward higher oxidation stability, pronounced and thus easier to detect and quantify LMCT band, as well as the use of the 111Cd or 113Cd isotopes for NMR experiments, and so forth. Thus, Cd2+ has frequently been used as a substitute for Zn2+ in structural studies of hMT3 [2+-reconstituted MTs were adopted in both spectroscopic and metal-binding studies, while Zn2+-reconstituted MTs were only used in bioassays, because Cd2+-MT is cytotoxic to the neurons in the bioassays [DNA sequencing showed that all mutation genes were successfully constructed, and the primary structure of hMT3 and its mutants were shown in  protein . Their ms in MTs , 33, pro of hMT3 , 34, 35.ioassays . The met2+-reconstituted hMT3 and its mutants, dissolved in 10 mM Tris-HCl, pH 8.0, containing 100 mM KCl. All the spectra showed an absorption shoulder at about 250 nm, which is the character of ligand-to-metal charge transfer (LMCT) of Cd-S bond [d-S bond . The abs∆33-35 mutant, the β(MT3)-β(MT3) mutant, and the single β-domain protein. The CD spectrum of hGIF agrees well with reporters [7-MT CD are signs of the formation of metal-thiolate cluster, and the two peaks are very sensitive to the variations of the structure of metal-thiolate cluster [β(MT3)-β(MT3) mutant diminished dramatically, and the wavelength is blue shifted, making the shape of the β(MT3)-β(MT3) mutant similar to that of the single β-domain. It is reasonable by considering the previous research result that α-domain contributes mainly to the CD spectrum of Cd7-MT while β-domain has limited contribution. This is mainly because the three metal atoms of the β-domain locate in the same plane; therefore they are with high symmetry and contribute to a small CD absorption. The N-terminal and C-terminal of the β(MT3)-β(MT3) mutant both formed a three-metal cluster and resulted in the weaker intensity of CD spectrum absorption than that of hGIF. As for the ∆33-35 mutant, the two peaks located in low-energy region blue shifted evidently compared to hGIF. Besides, the Molar ellipticity of CD spectrum of the ∆33-35 mutant decreases significantly, and the shape of CD spectrum is closer to single β-domain. Thus, it is very likely to form two three-metal clusters, M3S9, in the ∆33-35 mutant [∆33-35 mutant and the single β-domain, which demonstrates some discrepancy existed in the β′-domain of the ∆33-35 mutant and the β-domain of hGIF. Maybe the different distribution of the cysteine in the peptide chain leads to the change of protein folding patterns and the structure of metal-thiolate cluster, eventually causing the change of CD spectrum.CD spectrum is a common way to investigate the metal center structure and protein secondary structure. In the study of Metallothionein, CD is applied in the research of the geometry and polypeptide folding secondary structure. eporters . It is k cluster . Compare2+, which leads to the release of Cd2+from the Cd2+-thiolate cluster, causing the declining of the characteristic absorption at 250 nm. Hence, the UV absorbance at 250 nm was recorded during the pH titration in order to investigate the stability of the Cd-S clusters. According to our previous study, the titration curve of hMT3 was not clearly divided into two independent stages as it is difficult to tell one stage from the other in the pH titration plot. It is indicated that there is no sharp stability difference between the two Cd-S clusters. According to β(MT3)-β(MT3) mutant and hGIF. Due to lacking in α-domain in the β(MT3)-β(MT3) mutant, the stability of Cd3S9 in the β(MT3)-β(MT3) mutant begins to decrease from neutral pH, while the stability of Cd3S9 begins to decrease from pH 5 in ∆33-35 mutant and hGIF. This further proves that α-domain can stabilize the β-domain effectively in hGIF. Compared the pH titration curves of the ∆33-35 mutant to the curves of hGIF, we found that the deletion of two cysteines in α-domain did not influence the stability of the Cd3S9 cluster to any obvious degree in hGIF. As we all know, both the ∆33-35 mutant β′-domain and the β(MT3)-β(MT3) mutant β-domain contain a Cd3S9 cluster, while there is a Cd4S11 cluster in the hGIF α-domain. Interestingly, the stabilizing function of Cd3S9 in β′-domain is more similar to the Cd4S11 cluster in hGIF α-domain. This result becomes reasonable when the primary structures of the β′-domain and the hGIF α-domain are considered since they have the same primary structure except for the removed fragment (33SCC35). Thus, we suggest that probably it is due to the particular primary structure of the α-domain they contain, which stabilized the β-domain through the hydrogen bond or hydrophobic interaction, that play an important role in the domain-domain interaction as we described in the previous study [In the experiment of pH titration, the concentration of protons would increase with the decrease of pH. So, protons would compete with thiolate ligand for binding to Cdus study .At − A∞) versus time and are listed in β(MT3)-β(MT3) two-phase reaction increased obviously, while that of the ∆33-35 mutant and hGIF is similar in magnitude, which means that the stability of the metal-thiolate cluster of the ∆33-35 mutant has no considerable change compared to that of hGIF. This result is consistent with the pH titration experiment result. Unlike EDTA, DTNB can react with the nucleophilic sulfhydryl groups in MTs. This reaction is closely related to the solvent accessibility of the metal-thiolate cluster and usually is biphasic for MTs [β-domain and the α-domain of MTs, respectively [A∞ − At) versus time and these are also listed in β(MT3)-β(MT3) mutant was almost twice that of hMT3, showing that the solvent accessibility of the Cd3S9 cluster in the β-domain of the β(MT3)-β(MT3) mutant was greatly enhanced. However, the fast reaction velocity of the ∆33-35 mutant with DTNB is closer to that of hGIF, as well as the slow reaction velocity. This means that the solvent accessibility of the two Cd3S9 clusters in the ∆33-35 mutant is similar to its counterpart in hGIF. In regards with the results of EDTA and DTNB reactions, the Cd3S9 cluster in the β′-domain of the ∆33-35 mutant, has the same stability and solvent accessibility as the Cd4S11 cluster in the hGIF α-domain. That means the structure of metal-thiolate cluster in hGIF α-domain will not change dramatically after removing two cysteines. Besides, compared to hGIF, the metal-thiolate cluster in β(MT3)-β(MT3) mutant shows lower stability and higher solvent accessibility. The most conspicuous difference between the β(MT3)-β(MT3) mutant and the ∆33-35 mutant is that there is no particular amino acids sequence in the β(MT3)-β(MT3) mutant as that in hGIF α-domain. Thus, it once again proved that the primary structure of its hGIF α-domain is important to the function, which is quite in consistence with our pH titration experiment. The reaction of MTs with EDTA reflects competition between the sulfhydryl group and the exogenous ligand in the binding of metal ions and is also used to investigate the stabilities of metal-thiolate cluster . Under p for MTs . Accordiectively . The obs2+ recombinant protein is toxic to nerve cells. Thus, Zn2+-recombinant protein is adopted to test the physiological function. The nerve grow inhibiting function of hMT3, the β(MT3)-β(MT3) mutant, and ∆33-35 mutant was tested. As shown in μm. hMT3 significantly reduced the average neurite length to 94 ± 4 μm. The average neurite lengths of β(MT3)-β(MT3) and ∆33-35 mutant-treated neurons were 150 ± 5 and 92 ± 3 μm, respectively . The neuronal growth inhibitory activity of hMT1g and hMT3 well agrees with that reported previously [β(MT3)-β(MT3) mutant has apparent lower inhibiting activity than that of hGIF. It would be much lower, if taking the concentration of β-domain into account that, the β(MT3)-β(MT3) mutant protein is two times more active than that of equal molar hMT3 in the nerve growth inhibition. However, the ∆33-35 mutant is similar to the hGIF in neuronal growth inhibitory activity.As mentioned above, Cdeviously , 35. Morβ-domain, while the α-domain is not directly involved in neuronal growth inhibitory activity [α-domain can affect the function of β-domain though domain-domain interactions do exist in MTs [β-domain are in good line with the bioactivity of the whole protein; so they may be very important to the neuronal growth inhibitory activity of hMT3. Probably, α-domain can influence the stability and solvent accessibility of the metal-thiolate cluster in β-domain through domain-domain interaction, eventfully effecting the bioactivity of the whole protein. It has been reported that the neuronal growth inhibitory activity of hMT3 mainly arises from its activity . Howevert in MTs , 37–39, t in MTs , 37. Takβ-domain is very significant to the neuronal growth inhibitory activity in hGIF. Comparing the β-domain of the β(MT3)-β(MT3) mutant and the β-domain of the ∆33-35 mutant, these two mutants have different biological activities. It is precisely because the ∆33-35 mutant has the β′-domain which is similar to hGIF α-domain. This further demonstrates that α-domain stabilizes β-domain via the domain-domain interaction in hGIF, thus to regulate the physiological activity. Moreover, in the present study, we found that there was an obvious difference between the ∆33-35 mutant and the β(MT3)-β(MT3) in spectroscopy and biochemical properties. The β′-domain of the ∆33-35 mutant, which contains M3S9 cluster, has the same metal-thiolate cluster as the β-domain of β(MT3)-β(MT3) mutant and still reserves the function of the M4S11 cluster of the hGIF α-domain. This indicated that the removing of two conservative cysteines does not lead to great changes of the metal-thiolate cluster in the hGIF α-domain. Meanwhile, the β′-domain of the ∆33-35 mutant is obviously different from the β-domain of the β(MT3)-β(MT3) mutant in the stabilizing effect, of which the most conspicuous difference is that the β′-domain of the ∆33-35 mutant contains the primary structure of the hGIF α-domain. But the β-domain of β(MT3)-β(MT3) mutant contains the primary structure of the hGIF β-domain. That indicated that the particular primary structure of α-domain is pivotal to domain-domain interaction in hGIF. We believe that our results will provide a step further toward better understanding of the relationship between the structure of metal-thiolate cluster and the bioactivity of this group of fascinating molecules.Considering the experimental result of EDTA, DTNB reaction, and pH titration together with the neuronal growth inhibitory activity, we proposed that the stability and solvent accessibility of the metal-thiolate cluster in