In this method, we use a modified D domain from staphylococcal protein A molecule that is fused with an N-terminus polyhistidine tag (H12-D-domain) to tether IgG surrogate antigens on Ni2+-containing PLB membranes. We systematically assessed the specificity and capability of H12-D-domain construct to capture IgG molecules from different species through live cell and single molecule TIRFM imaging. We find that these IgG surrogate antigens tethered by H12-D-domain show better lateral mobility and are more uniformly distributed on PLB membranes than the ones tethered by streptavidin. Neither IgM molecules, nor Fab or F(ab’)2 fragments of IgG molecules can be tethered on PLB membranes by H12-D-domain construct. These tethered IgG surrogate antigens strongly induce the formation and accumulation of signaling active antigen receptor microclusters within the immunological synapse in B or T lymphocyte cells. Thus our method provides a new and robust method to tether IgG surrogate antigens or other molecules fused with IgG Fc portion on PLB membranes for TIRFM based molecule imaging experiments.Our understanding of cell-cell interactions has been significantly improved in the past years with the help of Total Internal Reflection Fluorescence Microscope (TIRFM) in combination with an antigen presenting system supported by planar lipid bilayer (PLB) membranes, which are used to mimic the extensive receptor and ligand interactions within cell-cell contact interface. In TIRFM experiments, it is a challenge to uniformly present ligand molecules in monomeric format on the surface of PLB membranes. Here, we introduce a new and robust method of tethering IgG surrogate antigen ligands on the surface of Ni Cell-cell contact based information exchanges play key roles in maintaining the function of various types of organismal systems, for instance, the neurological system and the immunological system. Within cell-cell contact interfaces, extensive receptor and ligand interactions are established. It is not completely clear how these interactions mediate the formation and stability of cell to cell adhesion focal planes. Neither clear is how these interactions initiate and modulate cross membrane signal transduction. These types of questions have been attracting the research interests of many laboratories. To visualize these receptor and ligand interactions within such cell-cell contact interface, a variety of advanced fluorescence microscope techniques have been applied to the related studies In TIRFM imaging experiments, an antigen presentation system supported by fluid planar lipid bilayer (PLB) membranes has been widely used to mimic the cell-cell contact interface as recently reviewed by Dustin and colleagues For these TIRFM imaging experiments, in many cases, the investigators need to tether IgG molecules as surrogate antigens on the surface of PLB membranes to mimic the function of physiological antigens. For example, in T cell studies, anti-CD3 or anti-CD28 antibodies are widely used as surrogate antigens to activate the T cells; similarly, in B cell studies, anti-IgM, anti-IgD and anti-IgG antibodies are widely used as surrogate antigens to activate the B cells. The current solution is to have these antibodies biotinylated and then have them tethered on PLB membranes containing biotinylated lipid using streptavidin as a linker molecule. Although this solution is widely used by others and us, it is not very convenient as the biotin-conjugated IgG molecule instead of intact IgG needs to be used. When biotinylating the IgG molecule, it is usually difficult to control where on the surface of IgG molecules to conjugate the biotin molecules, thus the biotin molecules can be covalently conjugated to either the variable region or the constant region of the IgG molecules. If the biotin is conjugated to the light chain or the variable region of IgG heavy chain, it is highly possible that the biotinylated IgG molecules are tethered on biotin-containing PLB membranes in an “upside down” manner. In this case, the “upside down” format of IgG surrogate antigens on PLB membranes is not easily accessible to the receptors.2+-containing PLB membrane which is also commonly used in TIRFM imaging experiments 2+-containing PLB membranes. The D domain of staphylococcal protein A molecule has been well studied to show the capability to bind the constant region of IgG molecule in a 1 to 1 ratio 2 fragments of IgG molecules can be tethered on PLB membranes by H12-D-domain construct. More importantly, we determined that these IgG surrogate antigens well maintained their stimuli characteristics. Upon encountering these IgG surrogate antigen-containing PLB membranes, B lymphocyte cells showed the formation and accumulation of signaling active antigen receptor microclusters within the immunological synapse, and efficiently up-regulated the cell surface activation maker CD69. In summary, our reported method using H12-D-domain provides a new way to tether the IgG surrogate antigens or other recombinant proteins fused with IgG Fc portion on PLB membranes for TIRFM based molecule imaging experiments.In this report, we introduce an alternative method to tether IgG molecules or other recombinant proteins fused with an IgG Fc portion on the surface of PLB membranes. In our method, instead of using PLB membranes containing biotinylated lipids, we used NiHuman Ramos B cells and mouse A20 B cells were cultured in RPMI 1640 medium containing 10% FBS, penicillin and streptomycin antibiotics (Invitrogen) as described 2 fragment of Goat IgG, Alexa Fluor 488-conjugated Fab fragment of Goat IgG, Mouse IgM anti-Chicken IgM, Alexa Fluor 405-conjugated Rabbit IgG anti-mouse IgG. Alexa Fluor 568-conjugated Streptavidin, Alexa Fluor 647-conjugated F(ab’)2 fragment of Goat IgG anti-Rabbit IgG were purchased from Invitrogen. FITC-conjugated Hamster IgG anti-Mouse CD69 (H1-2F3) was purchased from BD. We conjugated both biotin and Alexa 568 to goat whole IgG anti-human IgM antibodies following our published protocol Most of the antibodies used in this paper were purchased from Jackson ImmunoResearch, Rockland Immunochemicals or SouthernBiotech including Alexa Fluor 647-conjugated Goat IgG anti-human IgM, FITC-conjugated Mouse IgG anti-Chicken IgM, Alexa Fluor 647-conjugated Donkey IgG anti-mouse IgM, FITC-conjugated Rabbit IgG anti-mouse IgG, FITC-conjugated Rat anti-mouse CD3 molecular complex, Alexa Flour 647-conjugated Goat IgG anti-human IgM, Alexa Fluor 568-conjugated F(ab’)2+-containing lipids, 1,2-dioleoyl-sn-glycero-3-phosphocholine (DOPC) and 1,2-dioleoyl-sn-glycero-3-[N(5-amino-1-carboxypentyl) iminodiacetic acid]-succinyl  were mixed at a 9∶1 ratio of DOPC:DOGS–Ni-NTA. For biotin-containing lipids, lipids were made by mixing 99% 1,2-dioleoyl-sn-glycero-3-phosphocholine lipid and 1% 1,2-Dioleoyl-sn-Glycero-3-phosphoethanolamine-cap-biotin (Avanti Polar Lipids). The mixed lipids were sonicated to form unilamellar vesicles. After overnight ultra-centrifuge and filtration, small unilamellar vesicles (SUV) were acquired and used as stock SUV resolution following our published protocol Planar lipid bilayer membranes were prepared following a well-established method 2 fragment of Goat IgG, or 100 nM Alexa Flour 488 conjugated-Fab fragment of Goat IgG, or 100 nM biotin-conjugated mouse IgM molecules were loaded to PLB membranes tethering 50 nM H12-D-domain construct for 20 min at RT and washed clearly; in the case of IgM group, 50 nM Alexa568 streptavidin was further added for another 30 min at 37°C and washed thoroughly. The binding of these molecules was detected by TIRFM imaging subsequently. To test the binding capability of IgG molecules on PLB membranes mediated through H12-D-domain constructs, Alexa Fluor 647-conjugated goat IgG anti-Human IgM , or FITC-conjugated Mouse IgG anti-Chicken IgM  were incubated with the PLB membranes tethering H12-D-domain for 20 min at RT and washed thoroughly. The binding was detected by TIRFM imaging.To test the binding specificity of IgG molecules on PLB membranes mediated through H12-D-domain constructs, 100 nM Alexa Fluor 647 Goat IgG anti human IgM, or 100 nM Alexa Fluor 647-conjugated Donkey IgG anti-mouse IgM, or 100 nM FITC-conjugated Rabbit IgG anti-mouse IgG were loaded to PLB membranes tethering 50 nM or 0 nM (a negative control) H12-D-domain construct for 20 min at RT and washed thoroughly. The binding of these fluorophore conjugated IgG surrogate molecules were detected by TIRFM imaging subsequently. To further detect the binding specificity of IgG molecules on PLB membranes tethering H12-D-domain construct, 100 nM Alexa Fluor 568-conjugated F(ab’)Molecule images were acquired by an Olympus IX-81 microscope supported by a TIRF port, ANDOR iXon+ DU-897D electron-multiplying EMCCD camera, Olympus 100x 1.45 NA objective TIRF lens, a 488 nm, a 561 nm and a 633 nm laser . The exposure time was 100 ms unless specially indicated. Acquisition was controlled by Metamorph software . Images were analyzed by Image Pro Plus (Media Cybernetics), Image J , or Matlab (Mathworks) software following our published protocol To check the biological activity of these IgG surrogate antigens tethered on PLB membranes, live B cells or T cells were placed on these IgG molecules-containing PLB membranes, and the responses of these lymphocytes cells were examined by TIRFM as reported earlier 2 goat antibodies specific for rabbit IgG (Invitrogen) staining as previously described Recruitment of pSyk into the immunological synapse of human Ramos B cells upon encountering IgG surrogate antigens tethered on PLB membranes was imaged by TIRFM following our published protocol with modifications 6 cells/ml in RPMI 1640 containing 10% FBS, then loaded into PLB membranes containing IgG surrogate antigen molecules. After culturing for 24 hours at 37°C with 5% CO2, cells were harvested. Before harvesting the cells, we will recommend including a step to have the H12-D-domain constructs on PLB membranes blocked with high concentration of purified goat IgG molecules. The harvested B cells were stained with FITC-conjugated Hamster anti-Mouse CD69 for 15 min on ice, then washed twice and accessed by flow cytometry.Primary B cells were isolated from spleens of BALB/c mice as reported To check the mobility of IgG surrogate molecules tethered on PLB membranes, we did single molecule imaging experiment by TIRFM following our published protocol The animal work and the collection of spleen samples were following an animal protocol that was reviewed and approved by the Institutional Animal Care and Use Committee (IACUC) of Tsinghua University. The Assurance Identification Number is 12-LWL1 and was issued by Dr. Zai Chang, the vice chair of IACUC of Tsinghua University, Beijing, China.Staphylococcus aureusThe core purpose of this paper is to introduce a molecule that specifically binds the intact whole IgG molecule with high affinity in a 1 to 1 ratio. We draw our attention to the various types of gram-positive bacteria IgG-binding receptor proteins showing the well-established binding feature to mammalian immunoglobulins (Igs) 2+-containing PLB membranes, we fused a 12 aa polyhistidine tag at the N-terminus of the modified D domain, showing the following amino acid sequence as shown in E. coli cells, we choose to chemically synthesize the H12-D-domain construct because of its relative small size with only 69 aa residues  from Wang and his colleagues As mentioned above, the PLB membrane based antigen presentation system has broad applications in B and T lymphocyte cell studies through TIRFM imaging techniques. Having shown the application of the new method for B cells, we further tested the application of this new method on T cells. To do so, we similarly tethered the FITC-conjugated rat IgG anti-mouse CD3 molecular complex surrogate antigens on PLB membranes through H12-D-domain construct, and imaged the formation of antigen microclusters within T cell immunological synapse by TIRFM. Indeed, we found that the H12-D-domain-containing PLB membranes specifically tethered FITC-conjugated rat IgG anti-mouse CD3 surrogate antigens on the surfaces . When en2 fragments of goat IgG molecules. The results suggested that only whole IgG, but not Fab, nor F(ab’)2 fragment of IgG molecules were efficiently tethered on the PLB membranes through the H12-D-domain construct onstruct , suggestembranes . Since w2+-containing PLB membranes by H12-D-domain or biotin-containing PLB membranes by streptavidin. Then, we performed the single molecule tracking experiments using TIRFM imaging experiment at RT, and analyzed these TIRFM images by a Matlab driven algorithm for fluorescence intensity (FI) and instant diffusion coefficient of each IgG molecules following our published protocol A unique feature of molecules tethered on PLB membranes are that all these molecules show mobile Brownian diffusion feature, mimicking the situation of the antigens tethered on the plasma membrane of antigen presenting cells. In our new method, the IgG arrogate molecule is bound to the H12-D-domain construct on PLB membrane in an exact 1 to 1 ratio, thus we assume that the IgG molecules tethered by H12-D-domain will show more uniform distribution and better mobility feature than the IgG molecules tethered by streptavidin, which can bind up to four molecules of biotinylated IgGs. To test that, we conjugated both biotin and Alexa 568 to goat whole IgG anti-human IgM antibodies and have these antibodies tethered on either NiNext we tested the potency of recruiting BCRs and pSyk molecules into B cell immunological synapse by the same batch of IgG surrogate antigens that are tethered on the PLB membranes by either H12-D-domain or streptavidin. Since it is well documented the antigen concentration on the PLB membranes significantly affects the formation of B cell immunological synapse 2+-containing PLB membranes. In this method, the polyhistidine tagged molecules of interest can be easily tethered. For example, in our earlier studies, we have been able to tether the polyhistidine tagged hapten molecule 4-hydroxy-3-iodo-5-nitrophenyl (NIP) on Ni2+-containing PLB membranes to address the activation of B lymphocyte cells In this report, we introduced a new and robust method of tethering IgG surrogate antigens on the surface of planar lipid bilayer (PLB) membranes. The significantly increased needs of TIRFM based live cell and single molecule imaging applications require the development of new and robust method, as an alternative choice, to tether IgG surrogate antigens or molecules containing IgG Fc portion  on PLB membranes. Currently, there are generally two methods to tether molecules of interest on PLB membranes through specific affinity tags. The first method is to use NiThe second method is to use the biotin-containing PLB membranes. In this case, streptavidin molecules are commonly used as a mediator to tether biotin-conjugated IgG molecules on PLB membranes. Although this method has been widely used by others and us 2+-containing PLB membranes. To identify an appropriate bridge molecule to tether the intact IgG surrogate antigens on PLB membranes, we targeted to the well-studied IgG receptor from microbial proteins that are expressed by certain types of the gram-positive bacteria. Such proteins are known for their capability to bind to the constant fragment (Fc portion) of IgG molecules with high affinity and specificity, like Protein G from group G and C strains of streptococci Streptococcus pyogenesStaphylococcus aureusIn our new method, we combined these two different approaches and provide an alternative choice for the tethering of intact IgG surrogate antigens on NiH-3 region of both IgG and IgM molecules was well studied We focused on Protein A as it has been extensively studied. It is known that one protein A molecule has four or five 56 to 61 aa residue Ig binding domains  H-3 region of both IgG and IgM molecules H-3 region of both IgG and IgM molecules, and among them, the Aspartate (D) 36 and 37 between the linker region of H2 and H3 helical bundle are the most critical residues H-3 region of both IgG and IgM molecules. Based on the elegant early studies as mentioned above, we choose to mutate the DD (aa36–37) to AA (aa36–37) in the original D domain sequences. According to the published studies from others H-3 region of IgG and IgM molecules. In our proposed method, such modification to SpA D-domain is absolutely needed as mutating the DD (aa36–37) to AA (aa36–37) in SpA D-domain can efficiently eliminate the super-antigen ability of SpA D-domain to stimulate the B cell receptors (BCRs) hyper-activation and the subsequent B cell proliferation 2+-containing PLB membranes.The SpA D-domain adopts three alpha-helix bundles 2+-containing PLB membranes. H12-D-domain construct showed potent abilities to tether whole IgG surrogate antigens on PLB membranes. In contrast, Fab and F(ab’)2 fragment of IgG molecules, or IgM molecules cannot be tethered on the PLB membranes by H12-D-domains. It is known that protein A can bind IgG Fc portion with a very high equilibrium association constant (KA) value of 2.8∼2.9×108 M−1 and a very low equilibrium dissociation constant (KD) value of 3.4∼3.6 nM A value of a single IgG binding domain to IgG molecule is 5.9∼10×107 M−1 with a KD value of 10∼16.9 nM D value of IgG and H12-D-domain (10∼16.9 nM) A, 3.97∼5.99×105 M-1) and KD is extremely high at 1670 ∼ 2520 nM We extensively examined the specificity and capability of H12-D-domain construct to tether IgG molecules on NiOur synthesized H12-D-domain has one IgG Fc portion binding region per early structural studies Staphylococcus aureus is a good bridge molecule to tether intact IgG surrogate antigens on the surface of PLB membranes and meanwhile well maintain the biological activity of these IgG surrogate antigens.We used B and T lymphocyte cells and TIRFM based live cell imaging to examine the efficacy of these tethered IgG molecules as surrogate antigens. We found that these IgG surrogate antigens strongly activated both B and T cells by inducing the formation and accumulation of signaling active antigen receptor microclusters, IgG surrogate antigen microclusters and pSyk signaling molecule microclusters within the immunological synapse. All these observations are consistent with the early phase activation events of lymphocyte cells encountering the antigens tethered on PLB membranes using conventional methods This new method of using H12-D-domain to tether IgG surrogate antigens on PLB membranes can be applied to IgG molecules from a diverse range of species. We found that IgG molecules from mouse, rat, rabbit, goat and donkey can be efficiently tethered to PLB membranes through H12-D-domain. More importantly, many recombinant molecules fused with the Fc portion of IgG molecules, like recombinant ICAM-1-Fc molecule, are currently widely used in cell biology studies. We speculate that using this new method these IgG Fc portion fused molecules can be tethered on PLB membranes. Indeed, we showed that recombinant IL-1 receptor protein fused with the Fc portion of human IgG molecule can be efficiently tethered on the surface of PLB membranes through H12-D-domain molecules, suggesting the much broader application of this new method. In this short report, we just tested the responses of B and T lymphocyte cells encountering IgG surrogate antigens tethered on PLB membranes. We guess that antibody and antigen immune complexes tethered on the PLB membrane using our new method can be used as good stimuli to activate the IgG Fc portion receptors on immune cells including macrophages, neutrophils and dendritic cells.Staphylococcus aureus can also bind to von Willebrand factor (vWF) molecule on cell surfaces We do not expect that our new method can fit to all the related applications. We listed below the potential caveats and boundaries of this new method. (1) It’s reported that the D-domain of 2+-containing PLB membranes. This innovative method will be helpful to boost the studies on the complicated receptor and ligand interactions during cell-cell crosstalk by TIRFM based imaging experiments.Overall, we provide a new and robust method to tether IgG surrogate antigens or other recombinant protein molecules fused with IgG Fc portion on the surface of NiFigure S1The new method based on H12-D-domain construct robustly tethers IgG surrogate molecules from different species and other recombinant molecules containing the IgG Fc portion to PLB membranes. (A), (C) and (E) Shown are representative TIRFM images of Alexa647-conjugated donkey IgG anti-mouse IgM surrogate antigens (A), FITC-conjugated rabbit IgG anti-mouse IgM surrogate antigens (C), or Alexa 568-conjugated IL-1R-Fc molecule (E) tethered on the surface of PLB membranes with (left panel) or without (right panel) H12-D-domain molecules. Bar is 1.5 µm. (B), (D) and (F) Statistical quantification for the mFI of Alexa647-conjugated donkey IgG anti-mouse IgM surrogate antigens (B), FITC-conjugated rabbit IgG anti-mouse IgM surrogate antigens (D), or Alexa 568-conjugated IL-1R-Fc molecule (F) tethered on the surface of PLB membranes with (red closed circle) or without (blue closed square) H12-D-domain molecules.(TIF)Click here for additional data file.Figure S2IgG surrogate antigens tethered on PLB membranes induce the formation of antigen microclusters within T cell immunological synapse. (A) Shown are representative TIRFM images of FITC-conjugated rat IgG anti-mouse CD3 molecular complex surrogate antigens tethered on the surface of PLB membranes with (top panel) or without (lower panel) the pre-attached H12-D-domain construct. Bar is 1.5 µm. (B) Statistical quantification for the mFI of FITC-conjugated rat IgG anti-mouse CD3 molecular complex surrogate antigens tethered on the surface of PLB membranes with or without the pre-attached H12-D-domain construct. Each dot represents a single measurement for the mFI of the tethered IgG surrogate antigens by Image J software. Bars represent means ± SD. Two-tailed t tests were performed for statistical comparisons. (C) Shown are representative TIRFM images of IgG surrogate antigen microclusters within the contact interface of mouse EL4 T cells with the PLB membranes tethering FITC-conjugated mouse IgG anti-chicken IgM surrogate antigens with H12-D-domain (top panel) or without (lower panel) the linker H12-D-domain construct. Bar is 1.5 µm. (D) Statistical quantification for the mFI of surrogate antigen microclusters within the T cell immunological synapse. Each dot shows one measurement from a single cell. Bars represent means ± SD. Two-tailed t tests were performed for statistical comparisons.(TIF)Click here for additional data file.


The SET and MYND (SMYD) family of lysine methyltransferases is defined by a SET domain that is split into two segments by a MYND domain, followed by a cysteine-rich post-SET domain. While members of the SMYD family are important in the SET-mediated regulation of gene transcription, pathological consequences have also been associated with aberrant expression of SMYD proteins. The last decade has witnessed a rapid increase in the studies and corresponding understanding of these highly impactful enzymes. Herein, we review the current body of knowledge related to the SMYD family of lysine methyltransferases and their role in transcriptional regulation, epigenetics, and tumorigenesis. In thisHistone tail alterations encompass the greatest range of variation in epigenetic regulation, encompassing more than 50 known sites of modification ,12. HistWhile acetylation of histone tails is largely ephemeral in nature, histone methylation is widely observed to be a mark that confers long-standing epigenetic memory . MountinMost histone lysine methyltransferases catalyze methyl transfer by way of the SET domain, a module encoded within many proteins that regulate diverse processes, including some critical for development and proper progression of the cell cycle ,23,26. R2.The SMYD family comprises a subset of five SET domain-containing proteins that have unique domain architecture. Specifically, this family of proteins is defined by a SET domain that is split into two segments by a MYND domain, followed by a cysteine-rich post-SET domain  30,31].,31.30,31The MYND domain of SMYD proteins  encompasThorough characterizations have not yet been conducted for SMYDs 4 or 5. Thus the catalytic activity has not been determined for those proteins. In fact, other than their identification  and data3.et al. first identified SMYD1 as a cardiac- and skeletal muscle-specific protein that is critical for cell differentiation and heart morphogenesis during embryonic development [Gottlieb elopment . Targeteelopment . SMYD1 helopment .et al. published the first confirmation that the SET domain of SMYD1 is a catalytically active lysine methyltransferase [In 2006, Tan nsferase . Specifiet al. published the crystal structure for SMYD1 [This year, Sirinupong or SMYD1  providinor SMYD1 –37. Give4.et al. in 2006 [in vitro reporter assays where the effect of chromatin modifications imparted by SMYD2 on the recruitment of other factors could not be observed. Further evidence linking SMYD2 to transcriptional regulation was reported in the observation that SMYD2 interacts with RNA polymerase II and the RNA helicase, HELZ [The identification and characterization of SMYD2 was published by Brown  in 2006 . In that in 2006 ,47 was sse, HELZ .SMYD2 has also been shown to catalyze methylation of non-histone targets. Control of spatial and temporal expression of SMYD2 has been proven critical, as methylation of p53 at lysine 370 by SMYD2 was observed to repress the activity of p53 . Thus, S5.SMYD3 was the first member of the SMYD family for which the catalytic activity of the SET domain was confirmed. SMYD3 methylates histone 3, lysine 4 and has a role in the regulation of transcription through its association with an RNA polymerase complex . SMYD3 iThe findings that Smyd3 is substantially upregulated in most CRCs , HCCs 55, and br6.The SMYD Family is a group of SET and MYND domain-containing transcriptional regulators that function, at least partly, through histone modifications. Future research on SMYD proteins, with strong emphasis on the unique organismal context, will shed light onto the biological functions of SMYD family proteins. Such emphasis may reveal new insights into the relationships between protein modifications and the development and differentiation of tissues and organisms as well as pathways through which aberrant activity of protein modifiers lead to tumorigenesis.


Saccahromyces cerevisiae strains and the experimentally determined protein interaction network of the laboratory strain. We analyze the polymorphism in domain residues involved in interactions at various levels of resolution, depending on their likelihood to be interaction mediators. We find that domains involved in interactions are less polymorphic than other domains. Furthermore, as the likelihood of a residue to be involved in interaction increases, its polymorphism decreases. Our results suggest that purifying selection operates on domains capable of mediating protein interactions to maintain their function.Genome sequencing of various individuals or isolates of the same species allows studying the polymorphism level of specific proteins and protein domains. Here we ask whether domains that are known to be involved in mediating protein-protein interactions show lower polymorphism than other domains. To this end we take advantage of a recent genome sequence dataset of 39  Saccharomyces paradoxus, Vishnoi et al. demonstrated that residues within domains are less polymorphic than residues outside the domains Most proteins are composed of domains, which are their functional and structural units. As such, domains are expected to be less prone to diverge than extra-domain regions. Indeed it was shown that domains are conserved between species et al. have recently provided genome sequence data of 39 strains of Saccharomyces cerevisiaeS. cerevisiae laboratory strain is shared by all other strains.It is widely established that domain-domain interactions play a major role in interactome networks S. cerevisiae residues in domains associated with PPIs . This analysis was carried out for the residues at various resolution levels as in the previous analyses, each time comparing the distribution of pN/pS ratios of the relevant residues in the studied proteins to their distribution in a complementary set of residues in the same proteins . This coS. cerevisiae strains compared to the laboratory strain. We compared the distribution of the substitution scores between interacting and non-interacting residues, defined for different resolution levels as in −3 and 4.6×10−34, applying FDR correction). In all the comparisons the residues in interacting domains have substitutions with higher scores than the residues in the non-interacting domains, implying that they are substituted by similar amino acids that probably preserve their functionality.Next, we investigated the substitutions of amino acids in the 38 Our analysis provides a bird's eye view on the polymorphism in yeast residues involved in protein interactions at various levels of resolution. To obtain a more concrete understanding of our results, we provide as an example a closer look at one protein in our data, DCP1, mRNA-decapping enzyme subunit 1 (Q12517), a single domain protein whose homodimer structure was solved by crystallography et al.In summary, our study shows at the different resolutions that residues in domains associated with PPIs are less polymorphic than in other domains. At the lowest resolution level, our results are consistent with that of Vishnoi We determined positions with non-synonymous substitutions in comparison to the laboratory strain as those in which there was a different amino acid in at least a pre-set number of strains (n). The analysis was repeated for n ranging from 1 to 37. We counted one SNP per a position even if the substitutions differed between the various strains. In each such analysis we counted the fraction of polymorphic positions out of all relevant positions of a protein, and computed the average of these fractions over all proteins in our data.Each protein in our data was labeled by its domains according to Pfam database et al.We used data of yeast protein expression in log-phase growth from Ghaemmaghami Saccharomyces cerevisiae strains compared to the laboratory strain, we used the Blosum100 substitution matrix, which is suitable for pairwise comparisons of closely related proteins In order to examine the substitutions of amino acids in the 38 Figure S1i.e. a position is determined as having a SNP if a mutation occurs in a number of strains that exceeds the threshold).Fraction of non-synonymous mutations in each resolution level, using thresholds for SNP determination ((DOCX)Click here for additional data file.Figure S2Analysis of residue conservation in interacting domains in highly and weakly expressed proteins. Average fractions of non-synonymous mutations in residues determined by the various resolution levels.(DOCX)Click here for additional data file.Figure S3Heat map of the p-values of Kolmogorov-smirnov tests comparing pN/pS between a set of residues defined by a resolution level and a complementary set, when mutations are determined by different thresholds, as in (DOCX)Click here for additional data file.


F-spondin is a multi-domain extracellular matrix (ECM) protein and a contact-repellent molecule that directs axon outgrowth and cell migration during development. The reelin_N domain and the F-spondin domain (FS domain) comprise a proteolytic fragment that interacts with the cell membrane and guides the projection of commissural axons to floor plate. The FS domain is found in F-spondins, mindins, M-spondin and amphiF-spondin.2+-binding C2 domain variant with an 8-stranded antiparallel β-sandwich fold. Though the primary sequences of the FS domains of F-spondin and mindin are less than 36% identical, their overall structures are very similar. The unique feature of F-spondin FS domain is the presence of three disulfide bonds associated with the N- and C-termini of the domain and a highly conserved N-linked glycosylation site. The integrin-binding motif found in mindin is not conserved in the F-spondin FS domain.We present the crystal structure of human F-spondin FS domain at 1.95Å resolution. The structure reveals a Ca2+- and lipid-binding C2 domain suggests that the F-spondin FS domain may be responsible for part of the membrane targeting of F-spondin in its regulation of axon development. The structural properties of the FS domain revealed in this study pave the way for further exploration into the functions of F-spondin.The structure of the F-spondin FS domain completes the structural studies of the multiple-domain ECM molecule. The homology of its core structure to a common Ca F-spondin was initially identified in the rat embryo floor plate, a ventrIn vitro experiments suggested that F-spondin may also contribute to the inhibition of angiogenesis via the functional blockade of endothelial integrin αvβ3, which plays important roles in angiogenesis[The expression of F-spondin indicates that it also functions outside the nervous system. F-spondin is expressed in various organs including the ovary, lung, kidney, small intestine, prostate and testiogenesis. It has iogenesis. The moliogenesis.The first identification of ligands for F-spondin came from the studies of the pathogenesis of Alzheimer's disease,14. AmylRecently, the structure of mindin FS domain has been reported. Mindin The core of the FS domain has an 8-stranded β-sandwich fold, with strands β4, β1, β8, and β7 forming one β-sheet and strands β3, β2, β5 and β6 forming the other sheet  before the β1 strands form three disulfide bonds with C336 and C340 from the β6-β7 loop, and C415 after the β8 strand at the C-terminal region, respectively  at the end of the β1 strand in the FS domain Figure . The comprocessed,24. The  at the e2+-binding site, formed from D325 (from β5-β6 loop), D354 (from β7' short strand) and D358 (right after β7' short strand). The site also contains three coordinating water molecules in an approximately octahedral coordination geometry  though the residue is part of a highly conserved motif. All three aspartic acids and three coordinating waters  are part of an extensive hydrogen bond network. The geometry of the metal-binding site, including bond distances, coordination[2+ ion (16.2 Å2) and its surround protein atoms (~15.5 Å2), support the metal ion assignment.Associated with the short strands at the top of the FS domain, there is a solvent-accessible Ca4(1) and SO4(2) in Figure 4(1) occupies a special position with 2-fold crystallographic symmetry, and binds the residues R226 and R227, and the same residues from an adjacent, symmetry-related FS molecule.Three sulfate groups from the crystallization buffer that included 0.2M lithium sulfate monohydrate have also been identified in the structure Figure . One of The primary sequence identity of the FS domains between F-spondin  and mindin  is 35.9%. A secondary-structure matching (SSM) superposition[http://www2.ebi.ac.uk/dali/) using the F-spondin FS domain found a large number of fits (Z ≤ 5.8 and rmsd ≥ 3.3 Å), including the N-terminal Ca2+-dependent lipid-binding/C2 domain of cytosolic phospholipase A2 (PDB code:1CJY) [A Dali structural homology search (de:1CJY) . In the 2+- and SO42--binding sites implies potential roles of this domain in the interaction of F-spondin with membrane and/or membrane associated molecules.Membrane targeting is an important part of axon guidance of the proteolytic fragment of F-spondin that includes the FS domain. The unexpected identification of a C2 domain homology with the FS domain and the presence of Ca2+-dependent membrane binding in Ca2+-mediated cell processes.The C2 domain is the second most abundant lipid binding domain behind the pleckstrin homology (PH) domain,29. Thou2+-binding site(s), though the number of Ca2+ binding-sites and their positions at the top of the domains are variable[2+ ions can (1) function as an electrostatic switch for favorable ionic interaction with the anionic membrane, (2) form a bridge between the C2 domain and anionic phospholipids, or (3) induce conformational changes that lead to protein-membrane interactions[2+-binding sites and the conformation of each Ca2+-binding site. Additionally, protein-membrane associations mediated by divalent metals, especially Ca2+ ions, have been reported in many other proteins, including vitamin-K-dependent proteins, annexins and pentraxins[In most cases, the membrane-binding and/or lipid binding of C2 domains is dependent on the Ca variable. Ca2+ ioeractions. The exaentraxins.4(1) at a crystallographic special position, could be an artifact of molecular packing. The heparin-binding, or more generally glycosaminoglycan (GAG)-binding, implies another potential pathway for targeting the membrane through the interaction with membrane-associated GAGs such as those from transmembrane proteoglycans.The presence of a sulfate group from crystallization buffer in a crystal structure could be an indicator of a binding site of an anionic group of a potential ligand, such as heparin (with sulfate group) and lipid (with phosphate head group). The arginines, R226 and R227, on one side of the top of the FS domain could contribute to a binding site for the phosphate head group of a lipid. It is interesting that R226 is conserved between F-spondins and mindins while R227 in mindins is replaced by a proline Figure . Heparin2+- and lipid-binding C2 domain suggests that the F-spondin FS domain may be responsible for part of the membrane targeting of F-spondin in its regulation of axon development. The structure of the F-spondin FS domain completes the structural studies of the multiple domain ECM molecule and its properties revealed in this study pave the way for further exploration into the functions of F-spondin.The structure of F-spondin FS domain, together with mindin FS domain, represents the first examples of C2 domain-like structures in ECM proteins. Although the two FS domains have similar overall structures, they have evolved separately from their common ancestor. The integrin-binding motif found in mindin is not conserved in the F-spondin FS domain. The F-spondin FS domain is unique with a conserved N-glycosylation site and three disulfide bonds, which may play critical roles in protein folding and stability as well as defining domain boundaries. The homology of its core structure to a common CaSmaI and the AgeI sites of the vector pMT/BiP V5-HisA  for expression in Drosophila Schneider 2 (S2) cell. The recombinant proteins include the vector-derived sequence RSPWPG at the N-terminal and the sequence TGHHHHHH at the C-terminal.A cDNA pool was prepared from a mixture of human spleen, placenta and liver poly(A)+ RNA  using a reverse transcription kit . A recombinant version of the human F-spondin FS domain (amino acids V191-K434) was prepared by PCR using the cDNA pool as the template. The FS domain was prepared using the forward primer fs102f (5'-GAT GAT CCC GGG GTG ACT GAC AAA CCC ATC-3') and the reverse primer fs104r (5'-ATG ATG ACC GGT TTT CTC TTC TGG AGC CAG-3'). The PCR products were sequenced and cloned between the The vector transfection and cells selection were performed following the protocol from Invitrogen, catalog no. R690-07. The methods for FS domain expression and purification were described previously. The proThe purified protein was concentrated to about 30 mg/ml. The crystallization was carried out with sitting drop vapor diffusion technique. Protein crystals grew from the buffer containing 0.2 M lithium sulfate monohydrate, 25% (w/v) PEG 3350 and 0.1 M HEPES at pH 7.5. The crystals had a hexagonal form with a dimension of about 60 μm on edge. The crystals were then treated with a cryoprotectant  and flash frozen in liquid nitrogen.One X-ray diffraction data set was collected from a frozen crystal at 100K at the 19ID beamline of the Structure Biology Center at the Advanced Photon Source, Argonne National Laboratory. The data set was processed using the program suite HKL3000 (Table 13D34) was used as a search template. After sequence substitution and manual model building using the program Coot[2+ ion, three sulfate groups from crystallization buffer and 344 water molecules. The quality of the structure was validated with MolProbity[The F-spondin FS domain structure was determined with molecular replacement method using the program MolRep. One mongram Coot Table 13D34 was olProbity (Table 13Q13.The coordinates and structure factors of the human F-spondin FS domain have been deposited in the Protein Data Bank with accession code KT conceived of the study and carried out cloning, cell culture, protein purification and crystallization, X-ray diffraction data collection and structural determination, structural analysis and manuscript preparation. JL participated in the study design and coordination, sequence analysis and construct design, structural interpretation and manuscript preparation. All authors read and approved the final manuscript.


Small GTPases comprise a large and heterogeneous group of regulatory proteins that act throughout the cell as on/off switches, orchestrating processes as diverse as endocytosis at the plasma membrane to protein export from the nucleus. GTPases themselves are controlled by proteins called guanine nucleotide exchange factors (GEFs). Upon binding a GEF, the GTPase releases a molecule of GDP and picks up a GTP from the cytosol, inducing a conformational change in the GTPase that allows it to switch on its target protein. GTPases by themselves are relatively indiscriminate, largely relying on GEFs to determine the location and timing of their activity. Thus, understanding the factors that determine where and when a GEF binds to its GTPase is critical for understanding a wide range of transport and signaling activities in the cell.GTPases of the Arf class regulate most intracellular membrane trafficking, and their activity is confined to membranes by the GEFs they bind to. For one group of Arf-GEFs called cytohesins, detailed study has shown that binding specificity is controlled in part by two key factors. First, a positively charged lipid-binding pocket in the so-called PH domain attracts only a small number of specific, negatively charged phosphatidylinositides, a type of membrane lipid. Second, cytohesins in solution are auto-inhibited by an interaction between the PH domain and a catalytic domain called Sec7; membrane binding undoes that interaction, liberating the GEF to act on its target Arf. This process is further amplified by a positive feedback loop whereby freshly produced Arf-GTP binds to the PH domain.But there are other Arf-GEFs about which little is known, including BRAG2, which has critical functions in multiple processes, such as muscle fiber development and breast tumor invasion. In a new study, Kaheina Aizel, Mahel Zeghouf, Jacqueline Cherfils, and colleagues show that the factors that control the activity of this Arf-GEF are quite different from those of the cytohesins, expanding our broader understanding of this important class of regulatory molecules.The authors began by measuring BRAG2's activity in solution, and found that, unlike cytohesin, there was no evidence of auto-inhibition. The explanation came when they determined the crystal structure; an expansion of the PH domain in BRAG2 allowed it to establish contacts elsewhere on the protein surface, and prevented it from blocking the catalytic activity of the Sec7 domain.This change in structure had an impact on BRAG2's exchange efficiency when paired with Arf1. The enlarged non-canonical PH domain increased the exchange activity of the Sec7 domain ten-fold compared to the Sec7 domain alone. And while BRAG2 could operate in solution, its efficiency was increased 160-fold when bound to a membrane. Interestingly, these effects were cumulative, so that the full, membrane-bound BRAG2 was more than 2000 times as efficient at promoting nucleotide exchange compared to the protein in solution without the PH domain. In further contrast, BRAG2 was not regulated by a positive feedback loop.Next, they turned to the amino acid sequence within the catalytic pocket. They found that a positively charged lysine seen in cytohesins had been replaced by a negatively charged glutamic acid, meaning the pocket should not attract phosphatidylinositides. BRAG2 did attract negatively charged lipids, but did so through a much larger positively charged surface around the periphery of the binding pocket. As a consequence, it was much less discriminating in its choice of lipid partners, and attracted a much wider diversity of lipids compared to cytohesins, including multiple phosphatidylinositides as well as phosphatidylserine.In sum, this study demonstrates that the regulation and lipid specificity of BRAG2 differs markedly from cytohesins, at once identifying a new regulatory scheme for GEFs and suggesting there are probably others yet to be discovered. And by shedding light on how this particular GEF is controlled, it may suggest new approaches for disrupting the activity of this protein in its role as a promoter of tumor invasion, or facilitating it in its role as a promoter of muscle repair.Aizel K, Biou V, Navaza J, Duarte LV, Campanacci V, et al. (2013) Integrated Conformational and Lipid-Sensing Regulation of Endosomal ArfGEF BRAG2. doi:10.1371/journal.pbio.1001654


Giant muscle proteins  play a seminal role in muscle elasticity, stretch response, and sarcomeric organization. Each giant protein consists of multiple tandem structural domains, usually arranged in a modular fashion spanning 500 kDa to 4 MDa. Although many of the domains are similar in structure, subtle differences create a unique function of each domain. Recent high and low resolution structural and dynamic studies now suggest more nuanced overall protein structures than previously realized. These findings show that atomic structure, interactions between tandem domains, and intrasarcomeric environment all influence the shape, motion, and therefore function of giant proteins. In this article we will review the current understanding of titin, obscurin, and nebulin structure, from the atomic level through the molecular level. Titin, obscurin, and nebulin are all giant muscle-specific proteins that play key roles in sarcomere organization, strength, and development. The size  and apparent flexibility of these molecules has hindered traditional structure determination. However, through the integration and creative use of multiple structural elucidation tools including crystallography, nuclear magnetic resonance (NMR), small angle x-ray scattering (SAXS), and electron microscopy (EM), a concerted effort to describe fully the structure and dynamics of these systems is underway. Here, we review the current understanding of giant muscle protein structure. Titin and obscurin are primarily composed of related but distinct modular domains, while nebulin has a more repetitive and simple repeating structure.Both titin and obscurin are made up predominantly of either Ig (Immunoglobulin) or FnIII (Fibronectin type III)-like domains. Titin can have close to 300 domains and obscurin can contain almost 70, depending on the isoform [reviewed in Kontrogianni-Konstantopoulos et al. ]. Each oIn both titin and obscurin, the Ig domains have ~40% sequence conservation  and with protein concentrations between 5 and 11 mg/mL, using traditional heteronuclear multidimensional NMR techniques. SAXS experiments, often conducted on tandem-domain constructs, are usually done at pH 7.5 values, 15–20°C using a wide range of salt (50 mM–1 M NaCl). Protein concentrations between 1 and 25 mg/mL are used for these trials. All SAXS experiments are run at multiple concentrations, with no reported systematic errors resulting from protein aggregation.Individual FnIII and Ig domains fold independently, can be easily purified, and are stable in high concentrations at room temperature for months. High-resolution structures of titin and obscurin Ig and FnIII domains show that both crystallography and NMR methods result in reliably accurate structures , which in turn signals the myocytes to recognize and adjust to the changing forces of muscle contraction Linke, . Loss ofKD from 100 nM to 4 μM), part of this Z-insertion forms an α helix , but also contains the PEVK region and the N2A and N2B region. Of these, the structures of many of the Ig regions have been solved, as has that of the PEVK region. Several papers show that various tandem Ig domains in the I band do not move freely relative to each other but, like in the Z-disc structures, are instead relatively fixed in an extended orientation  sequences of a moderately rigid polyproline type II helix, flanked by random coils   and an SH3 domain of obscurin have high resolution structures associated with them  is comprised almost entirely of a simple ~35 residue repeat that tightly associates with the thin filament via the central consensus sequence SDxxYK (Jin and Wang, Nebulin is oriented in the sarcomere so that the N-terminus is in the I-band, closely associated with the thin filament. The C-terminus is embedded into the Z-disc. This configuration both regulates the Z-disc structure and anchors nebulin to the surrounding cytoskeleton (Millevoi et al., Structural homology predictions suggest that these nebulin repeats are helical, and these repeats can bind to other Z-disc structural proteins such as α-actinin, CapZ, archvillin, and desmin (Nave et al., There have been two major breakthroughs in elucidating giant muscle protein structures. First was the realization that individual domains can be studied outside the context of the entire molecule. This allowed for study of multiple X-ray and solution structures of various giant muscle protein segments. Next was the ability to combine high-resolution structures with other structure/dynamics experiments. From these analyses, we now have a more nuanced insight into how the position of one domain relative to its neighbor dictates overall protein shape, flexibility, and target binding. Current questions involve the structure/function/dynamics relationship of giant muscle proteins. For instance, what factors drive target protein binding to one domain but not the neighboring domain? What dictates the properties of the “hinge regions” of titin, and where are these hinges located? Does obscurin also behave like a carpenter's ruler? How does obscurin affect the movement and stretch of muscle? Additionally, many of the enzymatic functions of titin and obscurin are only now beginning to come to light. Elucidating the full molecular mechanism of the titin kinase domain is of key importance for understanding how titin integrates with the rest of the cellular machinery. Likewise, determining the binding partners of obscurin's SH3 domain, fully understanding the downstream effectors of the RhoGEF/PH domain, and studying further the potential mechanosensing roles of the obscurin kinase domains are all areas of interest for understanding the enzymatic roles of giant muscle proteins. Finally, nebulin's role in Z-disc organization and mechanosensing is only now being studied. Answers to these unresolved items will involve a combination of structure and functional studies. The process of more fully understanding such relationships will undoubtedly lead to a more complete view of how these molecular giants intertwine with the rest of the sarcomeric machinery.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


E. coli. Using limited proteolysis followed by mass spectrometry and Edman degradation as well as analytical gel permeation chromatography coupled static light scattering, we experimentally analyzed the structural domain boundaries and determined that the large ectodomain of APP consists of exactly two rigidly folded domains – the E1-domain (Leu18-Ala190) and the E2-domain (Ser295-Asp500). Both, the acidic domain (AcD) connecting E1 and E2 as well as the juxtamembrane region (JMR) connecting E2 to the single transmembrane helix are highly flexible and extended. We identified in-between the E1-domain and the AcD an additional domain of conservation and partial flexibility that we termed extension domain . Using Bio-layer interferometry, pull-down assays and analytical gel filtration experiments we demonstrated that the E1-domain does not tightly interact with the E2-domain, both in the presence and in the absence of heparin. APP hence forms an extended molecule that is flexibly tethered to the membrane. Its multi-domain architecture enables together with the many known functionalities the concomitant performance of several, independent functions, which might be regulated by cellular, compartment specific pH-changes. The amyloid precursor protein (APP) and its processing by the α-, β- and γ-secretases is widely believed to play a central role during the development of Alzheimer´s disease. The three-dimensional structure of the entire protein, its physiologic function and the regulation of its proteolytic processing remain, however, largely unclear to date. To gain a deeper understanding of the structure of APP that underlies all of its functions, we first cloned and recombinantly expressed different constructs in  Alzheimer´s disease (AD) is one of the most common forms of dementia worldwide. One important role plays the amyloid precursor protein (APP) - a type I transmembrane protein that is expressed in a wide range of different cell types including neurons ,2 and be695 splice form)  gav gav695 sIn order to experimentally analyze the exact domain architecture of APP, to determine whether its constituting domains potentially interact with one another within the entire protein and to get a clear picture of the isolated APP, we employed herein a number of biochemical and biophysical methods investigating various recombinantly expressed APP-constructs.695) inclusive a C-terminal His6-Tag was generated from pCK1 [5´ AAAAAAAAAAAACATATGCTGGAGGTACCCACTG 3´ and 5´ AAAAAAAAAAGCTTACGACCTTCGATTTTGTTTGAACCCACATCTTCTGCAAAGAACAC 3´, followed by digestion with NdeI and HindIII and ligation into the vector pET22b(+).The plasmid pIK15 for expression of the entire APP-ectodomain  as well as the extended APP-E1 domain  contain also a C-terminal HisThe constructs APP-E1_ED_AcD (Leu18-R288), APP-E1 (Leu18-Ala190) and APP-E2 (Ser295-D500) were cloned as described before ,20,27. Escherichia coli OrigamiB(DE3), whereas expression of APP-E2_JMR was performed in E. coli BL21(DE3)pRIL. Cells were grown in lysogeny broth (LB) at 25 °C, induced with 1 mM IPTG and harvested after 24 h by centrifugation at 4000 g for 20 min. APP-Ecto, APP-E1_ED_AcD and APP-E1_ED were expressed in 6-tagged proteins were purified using a HisTrap FF Crude affinity column and a HiLoad Superdex 200 26/60 column. For APP-Ecto and APP-E1_ED an additional HiTrap Heparin column  was used. APP-E1 and APP-E2 were expressed and purified as described before [6-tag was cleaved with 5 U/mg Factor Xa (Novagen) within 2 h at 20 °C. Uncleaved protein and Factor Xa were removed using a HisTrap FF Crude affinity column and a HiTrap Heparin column . The Hisd before ,20. For Homo sapiens APP (UP code: P05067-4), Sus scofa APP (UP code: P79307), Bos Taurus APP (UP code: Q08E54), Gallus gallus APP (UP code: F1NDE5), Mus musculus APP (UP code: P12023-2), Rattus norvegicus APP (UP code: P08592), Xenopus laevis APP (UP code: Q98SG0), Danio rerio APPa (UP code: Q6NUZ1), APPb (UP code: Q8UUR9). Sequences of APP were obtained from the UniProt (UP) database and aligned using the program ClustalX2 : Homo saLimited proteolysis experiments were performed in 5 mM Tris pH 8.0, 150 mM NaCl containing 0.5 mg/ml protein and 50 µg/ml of the respective protease. The reactions were incubated at 25 °C and stopped after fixed time points using 10 mM PMSF. Samples were analyzed by SDS-PAGE. All limited proteolysis experiments were repeated in three independent experiments.Limited proteolysis products were separated on a SDS gel and blotted onto a PVDF membrane. The membrane was stained with Coomassie and bands were analyzed using Edman degradation . Limited proteolysis samples were separated on a Superdex 200 5/150 GL column  and the total mass of the fractions were analyzed at the Center for Molecular Medicine Cologne . In addition, protein containing fractions were precipitated with acetone. The pellet was resuspended in ammonium acetate (pH 7.5) and total mass was measured using Ultraflex II, Bruker Daltonics. th) was calculated using the ProtParam tool provided by ExPASy.The theoretical MW (MWrh) was determined using the retention volume of the protein and the calculated calibration curve. Analytical size exclusion chromatography was performed in 5 mM Tris pH 8.0, 150 mM NaCl using a calibrated Superdex 200 5/150 GL column . All runs were repeated in three independent experiments. The column was calibrated using BSA, cytochrome c, carboanhydrase and aprotinin and a calibration curve was calculated using the molecular weight of the proteins as a function of the retention volume. The apparent molecular weight (MWSLS) was determined using the OmniSEC software (Viscotek) provided with the instrument and based on the Rayleigh-Gans-Debye equation. All experiments were performed in 5 mM Tris pH 8.0, 150 mM NaCl using a Superdex 200 10/300  and were done in triplicate.For SLS measurements an Aekta Explorer system  was connected to a VE 3580 RI and 270 Dual detector (Viscotek). The absolute molecular weight (MWCD spectra were measured using a J-710 spectropolarimeter (JASCO Corporation) in 5 mM sodium phosphate buffer pH 7.5. Resulting data were analyzed using Spectra Analysis and CD Spectra Deconvolution 2.1 (JASCO Corporation). All measurements were repeated in three independent experiment. 6-tag at 8 °C for 2 h. Where applicable 50 µM short chain heparin  or long chain heparin  was added to the solution. Samples were centrifuged at 500xg for 1 min and washed with binding buffer. To analyze bound proteins, the beads were mixed with 2x sample buffer , incubated at 95 °C for 5 min and samples were analyzed by SDS-PAGE. All experiments were performed in triplicate.Pull-down experiments were performed in binding buffer  using 80 µl Ni-NTA material (Qiagen). 5 µM His-tagged protein were incubated with 5 µM protein without HisInteraction analysis between APP-E1_ED_AcD and APP-E2_JMR domains was performed on an Octet RED96 instrument (ForteBio) at 28 °C. Biotinylated APP-E1_ED_AcD was prepared by incubating APP-E1_ED_AcD (5 µM) with Sulfo-NHS-LC-Biotin (Thermo) at a molar ratio of 1:1 for 3 hours at 4 °C in PBS, followed by desalting using a PD MiniTrap G25 column  to remove the excess biotin reagent. A column of eight Streptavidin biosensor tips were loaded with biotinylated APP-E1_ED_AcD (0.2 µM) in 1x kinetics buffer  containing 0.1 mg/ml BSA and 0.002% (v/v) Tween20) to a final mean level of 0.74 nm. Loaded biosensors were first washed and transferred to wells containing seven APP-E2_JMR concentrations of a 2-fold dilution series (40 to 0.625 µM) in 1x kinetics buffer. Association and dissociation kinetics were recorded at least three times for 2.5 and 5 minutes at a shake speed of 1000 rpm, respectively. A second column of eight non-coated sensor tips and a 1x kinetics buffer well were used for double referencing of the raw data. Data were processed using Octet Data Analysis Software 7.0 (ForteBio) and were done in triplicate.695 we expressed the entire ectodomain and subjected it to limited proteolysis by V8 endoprotease, trypsin, elastase and thermolysin. The proteases cleave hereby preferentially in regions of higher flexibility but not within compactly structured elements, thereby probing the folding-state of a given protein. The employed proteases gave similar results. The clearest picture was obtained when we incubated APP-Ecto with V8-protease. Potential cleavage sites were hereby distributed throughout the entire protein  in between the acidic region and the E2-domain  are likely to represent structurally independent protein units. In order to test the anticipated multi-domain architecture of APP protein . Upon ex protein . Identif protein  shows th2-domain . BecauseTo determine the exact boundaries of potentially folded and flexible segments within these two protein halves, limited proteolysis experiments were performed with the isolated APP-E1_ED_AcD and APP-E2_JMR constructs, corresponding to the respective complete N- and C-terminal protein segments . In thisLimited proteolysis of APP-E1_ED_AcD with V8‑protease resulted in two major fragments of ~24 kDa and ~20 kDa in size that remained stable after 24 h of digestion . The exaIn summary, these results show that the APP-ectodomain contains two folded domains that are stable against proteolytic degradation. Interestingly, the termini of fragments four and five roughly correspond to the E1- and E2-domains that were initially identified based on homology between APP and members of APP-like family of proteins ,7. rh) and the absolute molecular weight (MWSLS) , showing that APP-E1, APP-E1_ED and APP-E2 are monomeric under the employed experimental conditions. The molecular weights of APP-E1 and APP-E2 determined by their retention volume in the GPC experiments (MWrh) are slightly larger than the theoretical molecular weights. The small difference is, however, readily explained by their non-spherical shape such that both APP-E1 and APP-E2 represent rigidly folded domains without flexible or extended linkers. This is also in line with the observation of these domains as major products in limited proteolysis (see above) and the fact that both domains were crystallized and their structures have been solved by X-ray crystallography [The protein segments corresponding to fragments two and four  were cloned and purified for additional biochemical studies. In addition, we also investigated an already existing construct very similar to fragment five . The co (MWSLS) . For tiglography ,15,16,20rh measured by analytical gel filtration for APP-E1_ED is significantly higher than the absolute molecular weight indicating the presence of a flexible or extended linker region. We also observed repeatedly proteolytic degradation bands in between fragment two (APP-E1_ED) and four (APP-E1) during the preparation of APP-E1_ED (data not shown) further suggesting protease accessibility and hence flexibility of the protein segment at the C‑terminus of APP-E1_ED. In contrast, the MWrh than their theoretical molecular weight (SLS). These data are in excellent agreement with the theoretical molecular weights of the constructs, indicating that both proteins exist as monomer in solution. The difference between MWrh and MWSLS is, however, too large to be explained solely by the non-spherical shape of the respective fragments. Correspondingly, one has to conclude that both, APP-E1_ED_AcD and APP-E2_JMR contain flexible protrusions. As the isolated E1- and E2-domains correspond to rigidly folded domains, we conclude that both, the AcD and the JMR are predominantly flexible without strong secondary structure elements, which is also in excellent agreement with our results from limited proteolysis. In addition, CD experiments of APP-E1_ED_AcD and APP-E2_JMR confirm a higher amount of random coil in comparison to the rigidly folded E1- and E2-domains  . In agreBoth, the E1- and the E2-domains bind to heparin ,35. Ther695 contains two rigidly folded regions – the E1- and E2-domains. Correspondingly, the longer splicing forms APP751 and APP770 contain the KPI-domain as third rigidly folded building block [695-numbering), which fits well to the regions of defined three-dimensional structure seen in the respective structures (Leu28-Ala190 for E1-domain [The amyloid precursor protein (APP) and its proteolytic processing by the α-, β- and γ-secretases are critically involved in the development of Alzheimer’s disease, and highly resolved structural data were obtained by protein crystallography and NMR-spectroscopy for several segments of this multi-domain protein. However, until now, rather little is known about its overall structure, its exact domain architecture, the structure and the extent of flexible linker segments and the possible interaction of its constituting domains. Such knowledge is, however, indispensable to interpret functional data of this protein within the correct three-dimensional, structural framework. Our limited proteolysis experiments together with GPC coupled SLS experimentally demonstrate that the large ectodomain of APPng block ,13 inser1-domain  and His31-domain  as well 1-domain  for E2-d1-domain , and SAX1-domain ,26. Usin1-domain . Our prein vitro [The E1-domain is followed by a protein segment exhibiting a high percentage of acidic amino acid residues (56 %) that is typically referred to as the acidic domain (AcD) and connects the E1- and the E2-domain. Such a negatively charged region is unlikely to form a tightly folded structural entity. Sequence alignments show, however, a certain degree of conservation of this region between vertebrates , suggestin vitro . Directlin vitro  stronglyin vitro . rh might hereby easily be interpreted as indicating dimerization of the APP-ectodomain, underscoring the importance of determining the absolute molecular weight. The folded E2-domain is connected to the single transmembrane helix of APP by the juxtamembrane region (JMR), which represents a second linker and shows less conservation compared to the E1- and E2-domains . Using GInterestingly, the E1- and E2-domains of APP are highly conserved between this protein and the APP-like proteins ,8. OftenInterestingly, the cleavage sites of the β- and the α-secretase, discriminating between the amyloidogenic and the non-amyloidogenic processing of APP, are located at the C-terminus of the JMR next to the single transmembrane helix of APP. The cleavage of Notch, a cell-surface receptor that is processed similar to APP by a ADAM-type metalloprotease at site S2 and afterwards by γ-secretase, is regulated by a conformational change . The higIn conclusion, we could experimentally prove that the multi-domain protein APP has an extended overall topology and consists of two tightly folded segments, the E1- and E2-domains. They are linked to one another by the flexible protein segment AcD, but do not show any direct protein-protein interaction, neither in the presence nor in the absence of heparin. This assembly is connected to the membrane by the again flexible JMR, containing the α- and β-secretase cleavage sites. This work represents a thorough experimental characterization of the overall structure of APP by different methods and will prove very valuable to gain a deeper understanding about the structure-function-relationship of the protein in the future. Several described functions of APP were originally identified with isolated peptides. Only the native arrangement of functional amino acids in space, however, can accommodate their true physiologic function, underlining the importance of the present investigation. On the next level of organization and interaction, APP is probably subject to a number of oligomerization events and additional protein-protein-interactions. Those must build, however, on the here identified three-dimensional structure of the isolated, monomeric protein. Figure S1Sequence alignment of the APP ectodomain among vertebrates. Fully conserved amino acids are highlighted on an orange background. The light yellow and darker yellow background indicate a fully conserved strong group and a conserved weaker group, respectively, according to ClustalX2.1. Potential cleavage sites of trypsin are marked by a vertical arrow. The black and grey (100-300 times slower) arrowheads indicate potential V8 protease cleavage sites. (TIF)Click here for additional data file.Figure S2MS analysis of fragment one, two and four.(TIF)Click here for additional data file.Figure S3MS analysis of fragment five.(TIF)Click here for additional data file.Figure S4MS analysis of fragment six.(TIF)Click here for additional data file.Figure S5MS analysis of fragment two, three and four.(TIF)Click here for additional data file.Figure S6CD-Spectroscopy. APP-E1_ED_AcD and APP-E2_JMR indicated by a blue line show a higher amount of random coil than the proteolysis products APP-E1 and APP-E2 that are shown as a green line. (TIF)Click here for additional data file.Table S1Secondary structure contents measured by CD-spectroscopy.(DOC)Click here for additional data file.


The description is discrete yet gives rise to a 2-dimensional mathematical surface. Thus, each protein domain corresponds to a particular mathematical surface with characteristic topological invariants, such as the genus (number of holes) and the number of boundary components. Both invariants are global fatgraph features reflecting the interconnectivity of the domain by hydrogen bonds. We introduce the notion of robust variables, that is variables that are robust towards minor changes in the structure/fatgraph, and show that the genus and the number of boundary components are robust. Further, we invesigate the distribution of different fatgraph variables and show how only four variables are capable of distinguishing different folds. We use local (secondary) and global (tertiary) fatgraph features to describe domain structures and illustrate that they are useful for classification of domains in CATH. In addition, we combine our method with two other methods thereby using primary, secondary, and tertiary structure information, and show that we can identify a large percentage of new and unclassified structures in CATH.The space of possible protein structures appears vast and continuous, and the relationship between primary, secondary and tertiary structure levels is complex. Protein structure comparison and classification is therefore a difficult but important task since structure is a determinant for molecular interaction and function. We introduce a novel mathematical abstraction based on geometric topology to describe protein domain structure. Using the locations of the backbone atoms and the hydrogen bonds, we build a combinatorial object – a so-called  Protein domains are protein subsequences that may fold and function independently of the rest of the protein Using techniques from geometric topology, we propose a novel mathematical abstraction for studying protein domain structures alpha carbon linkage) is considered as twisted or not twisted (untwisted) depending on the relative orientations of the peptide units in physical space . The Euler characteristic, defined for any surface as In general, the surface corresponding to a fatgraph is homeomorphic to a particular The surface in OB fold and has An example of a surface corresponding to a domain fatgraph is shown in global variables We demonstrate that the For each domain we compute the corresponding fatgraph and calculate four robust variables derived from it . Robust We represent a domain by four robust variables, among these the genus de units . Recall  for in  .mainly alpha, mainly beta, and mixed alpha-beta. The last category contains domains with only very few secondary structures. The A-level groups domains according to the general orientations of their secondary structures, and at the T-level the connectivity (the order) of the secondary structures is taken into account. The grouping of domains at the H-level is based on a combination of both sequence similarity and a measure of structural similarity. Below the four main lavels, CATH has an additional five layers called S, O, L, I, and D. The first four group domains according to increasing sequence overlap and similarity, and the D-level assigns a unique identifier to every domain thus ensuring that no two domains have the exact same CATHSOLID classification.The CATH database classifies domains in a hierarchical scheme with four main levels (listed from the top and down) called class (C), architecture (A), topology (T), and homologous superfamily (H), hence the name CATH Pectate Lyase C-like (CATH classification 2.160.20) with one family  rather than robust variables (tertiary level). To do so, we randomly selected 2/3 of all domains in v3.2.0  of the average crossing number.For each t scores . In the Overall the results are comparable  for S95;We combined all three tests into one to achieve higher performance . For S95We discuss a new representation of protein structure and show how local and global variables vary across domains and structural classification. The description makes use of concepts from geometric topology and represents a domain structure by a fatgraph that in turn can be interpreted as a 2-dimensional surface. The structure of the fatgraph relies on atomic coordinates of the protein and its hydrogen bonds but not on primary sequence information.The representation provides a complementary and alternative view to structures as purely 3D geometric objects The invariants We showed that using secondary structure information we could classify a large percentage of domains in S95 (and SAll) correctly. However, correct (and better) classification could also be achieved using other methods using primary or tertiary structure information. We combined all three methods and achieved a method with higher performance as well as sensitivity and specificity. The combined method is also able to identify the majority of unknown or unclassified domains.de novo classification and compare this to existing classification. Given the observation that classification based on primary structures is best at reproducing the CATH database, it is conceivable that a de novo classification based on structural properties alone would lead to a different hierarchy. However, a de novo classification would require further investigations to e.g. determine the number of classes needed, and this is beyond the scope of this paper. The lack of agreement between the two most widely used databases, CATH and SCOP, certainly indicates that more analyses are needed in order to fully comprehend the universe of domain structures We used the CATH database as a gold standard, but using appropriate clustering algorithms it would be possible to make a Automated approaches, including ours, benefit from not relying on human judgment. However, a key difficulty is that structural and evolutionary homology does not always go hand in hand, and different protein families show a wide spectrum in sequence, structural and functional similarities a priori way of choosing such orientations. Other extensions could be the inclusion of sulphur bridges, or extending the two types of edges (backbone and hydrogen bonds) to multiple types reflecting more accurately the twisting of the backbone.Various extension of the fatgraph model are conceivable. For example, one approach could be to include bifurcating hydrogen bonds in the model, as CO and NH groups of the protein backbone engage in two or more hydrogen bonds The fatgraph is constructed as explained in the main text, We infer hydrogen bonds using the DSSP algorithm For integers The invariants, including whether the fatgraph is orientable, can be calculated computationally efficiently and quickly truction , a fatgrA function All modified structures were generated using the CONCOORD algorithm www.cathdb.info). CATH is hierarchical; we focus on the class (C), architecture (A), topology (T) and homologous superfamily (H), and sequence (S) level.The newest version of CATH (3.3.0) contains only including sequences with less than 95% similarity are denoted by S95 (this does not apply to the unclassified set).We operate with the following basic data sets: A) CATH 3.2.0, B) the domains in v3.3.0, but not in v3.2.0 , and C) CATH 3.3.0. In addition, CATH lists There are wer-laws .The algorithm Random Forests Classification by random guessing is done by assigning family levels to domains according to family sizes, that is, the average success rate is the sum over For flip sequences, alignments were made using the Smith-Waterman algorithm with mismatch and gap penalties set to For a given a domain, denote by The three methods were combined into one method. Domains were classified according to the majority rule. If none of the methods agree, the method with the highest Figure S1Each domain in the Pectate Lyase C-like topology was subjected to  independent modifications using the CONCOORD algorithm. In the figure each column is a domain and the distribution of the normalized number of hydrogen in the modified structures is shown. The number is normalized relatively to the number observed in the original (unmodified) domain. The values corresponding to the eight outliers in (TIF)Click here for additional data file.Figure S2The deviation of  and  from the observed values for eight randomly selected domains subjected to  modifications using the CONCOORD algorithm. In general the modified values are centered around the observed values, though in some cases the distribution is biased to the left or right.(TIF)Click here for additional data file.Figure S3Distributions of the three quantities genus , number of boundary components , and number of twisted alpha carbon linkages  for all domains in v3.3.0. Mainly beta and mixed alpha-beta have very similar distributions of (TIF)Click here for additional data file.Figure S4Pairwise scatter plots of the five variables: the genus , the number of boundary components , the number of twisted alpha carbon linkages , the number of residues  and the Euler characteristic  for all domains in v3.3.0. The variables (TIF)Click here for additional data file.Figure S5Wilcoxon plot corresponding to pairwise comparisons of the 1,161 H-levels comprising 10 or more domains with significance level   and  . Each row and column correspond to a H-level, and these are ordered by size in decreasing order. Colors indicate the number of variables ((TIF)Click here for additional data file.Figure S6Plots of the variables , , and , versus the number of alpha helices and beta sheets, respectively, for all domains in v3.3.0. Separation of the mainly alpha and mainly beta classes with the mixed alpha-beta class falling somewhere between is observed. The higher genera observed in the mainly beta and mixed alpha-beta classes are mainly caused by beta sheets. Separation between classes is harder to spot on the plots with beta sheet counts.(TIF)Click here for additional data file.Figure S7Boxplots summarizing the success rates obtained on v3.3.0 using different subsets of variables for classification. For all plots, an energy cut-off at (TIF)Click here for additional data file.Figure S8Standard deviations of the genus and the number of boundary components for each H-level in v3.3.0 (SAll). The standard deviations are generally not increasing with increasing H-level size, indicating that even large families are homogeneous. There is, however, more variation in the number of boundary components than in the genus.(TIF)Click here for additional data file.Figure S9Correlation plots illustrating the difference between v3.2.0 (SAll) and the newly added domains in v3.3.0. The left plot shows the sizes of the families in v3.2.0 test set versus the family sizes among the newly added domains and the right plot shows the corresponding performance rates. The new domains in v3.3.0 evidently have lower performance while family sizes roughly are proportional to those in v3.2.0.(TIF)Click here for additional data file.Figure S10The distribution of H-level sizes in CATH 3.3.0 exhibits power-law behavior with many small levels and a few very large levels.(TIF)Click here for additional data file.Figure S11The distributions of normalized votes for all methods on the S95 training set.(TIF)Click here for additional data file.Table S1Comparison of all three classifiers on the non-redundant S95 subset of CATH (first three tables) as well as the entire CATH . At each level  we split CATH v3.2.0 into two sets: For a level with N members, we used Top: Performance, sensitivity, specificity, and unknown domains flagged as novel/problematic (in percent and in that order) on the CATH v3.2.0 training set. Middle: Similarly on the set of new domains in CATH v3.3.0 with classifications existing in CATH v3.2.0. Bottom: Some domains in CATH v3.3.0 have novel classifications not existing in CATH v3.2.0. This table summarized how many of these are flagged as novel/problematic by the three classifiers. Finally, the percentage of unclassified domains flagged by each method is shown. Note that there is only one set of unclassified domains, and this is used in both the S95 and the SAll case.(TIF)Click here for additional data file.


Predicting the function of newly sequenced proteins is crucial due to the pace at which these raw sequences are being obtained. Almost all resources for predicting protein function assign functional terms to whole chains, and do not distinguish which particular domain is responsible for the allocated function. This is not a limitation of the methodologies themselves but it is due to the fact that in the databases of functional annotations these methods use for transferring functional terms to new proteins, these annotations are done on a whole-chain basis. Nevertheless, domains are the basic evolutionary and often functional units of proteins. In many cases, the domains of a protein chain have distinct molecular functions, independent from each other. For that reason resources with functional annotations at the domain level, as well as methodologies for predicting function for individual domains adapted to these resources are required.We present a methodology for predicting the molecular function of individual domains, based on a previously developed database of functional annotations at the domain level. The approach, which we show outperforms a standard method based on sequence searches in assigning function, concomitantly predicts the structural fold of the domains and can give hints on the functionally important residues associated to the predicted function. Proteins are the key players of the cellular processes. Obtaining information on the structure, function and important residues for the protein repertory of a given organism (proteome) is crucial not only for getting insight into its biology, but also to foresee possible ways for modifying it in our benefit. Nevertheless, obtaining experimentally this kind of information is very slow and expensive. On the contrary, obtaining the raw sequences of complete proteomes or part of them is nowadays relatively fast and inexpensive, and this is getting even better with "next generation sequencing" technologies . For theMethods for predicting protein three-dimensional structure from sequence generally are based on the known relationship between sequence similarity and structural similarity. Most of these methods look for homologous proteins of known structure and model the problem sequence based on them. This search is either based on simple sequence matching methods for cases of close homology, or profile-based methods for remote homology.Similarly, most methods for computationally assigning function to proteins ("annotation") are also based on the observed relationship between sequence similarity and functional similarity -4. FunctThe basic concepts and methodologies for transferring function from homologous sequences have evolved with time and, at the same time, adapted to these new structured vocabularies (for recent reviews describing in detail the field see -10). TheMost of these methods, specially those based on global sequence matches against individual proteins or profiles, are intended to assign function at the whole chain level, without distinguishing which individual domain is associated to a given GO:MF term. In most cases, this is not a problem of the methodologies themselves but of the annotations contained in the resources they search against. In these resources, functions are associated to whole chains, not to particular protein domains, and as such they are transferred to the problem sequences. Nevertheless, domains are the structural, evolutionary, and often functional units of proteins. In many cases, individual molecular functions can be assigned to them. Even the functional annotations in domain-oriented databases such as Pfam or Intepro suffer from this problem when these annotations are interpreted in terms of physical domains .In this work, we present a method for annotating proteins with GO:MF terms at the domain level. The method is based on matching against a library of "position specific scoring matrix" (PSSM) profiles  derived Figure The idea is that each entry in this library represents an alignment of all domain sequences known to have a given GO:MF function  that can be related in an alignment, i.e. belonging to the same fold and hence amenable of structural alignment.The starting point is the SCOP2GO resource, which contains GO:MF annotations at the structural domain level . SCOP2GOX Function (GO:MF)  Figure . Folds a) Figure . Most prThe current version of the library contains 338 entries covering 115 different GO:MF terms and 150 SCOP folds.Since each entry in the library is associated to a fold and a GO:MF term, querying a whole-length sequence against the alignments/profiles within this library with "reverse psi-blast" (rpsblast) produces a list of hits each representing a concomitant prediction of fold and function for a particular segment (i.e. a domain) of the query sequence Figure . AdditioOne of the added values of the method presented here is that the profiles are constructed "informed" by GO:MF annotations, instead of relying on the domain groupings that would result from sequence relationships alone (e.g. families and superfamilies) To evaluate the effect of this, we compared the results of searches against this database of pre-computed profiles, with those obtained by the same method (psi-blast) against an equivalent database with exactly the same domain sequences but not grouped according with GO:MF terms. In order to do that, all the domain sequences after the 40% ID filtering  are mixed together in a large database which is formatted for psi-blast Figure . Each seWe constructed a test set for evaluating the performance of these two resources form the entire PDB clustered at 30% ID downloaded form the RCSB site . The tesFor each chain in our test set, we carry out the following procedure. First we re-construct the two databases as described before but removing from the very beginning any domain corresponding to a chain within the same 30% ID PDB cluster as the test chain Figure . This isSo, for a given chain in the test set we obtain two sorted lists of hits, one for each method/resource, called "GO_PROFILE" and "PSI_BLAST" hereafter Figure . Each hiA ROC (receiver operator characteristics) analysis  is perfowhere Tp, Fn, Fp and Tn are the "true positives, "false negatives", "false positives" and "true negatives" resulting from a given threshold.In the first part of this section we show the results of the large-scale evaluation of performances for GO_PROFILE and PSI_BLAST based on a test set of 1017 protein chains as explained in "Methods". In the second part, we show some examples of cases where one of the methods finds a right match while the other fails and vice versa, and where these failures are due to different reasons, to illustrate the advantage and drawbacks of this methodology as well as its complementarity with others. Another example allows to illustrate an additional advantage of this method: the possibility of obtaining a prediction of functionally important residues associated to the predicted GO:MF term.Figure It can be seen that both approaches present a very good discriminative power. Nevertheless, GO_PROFILE outperforms PSI_BLAST in assigning the right functions to the right domains Figures  and 2b. The ROC plots in the Additional File For the case of fold prediction, it can be seen that the performance of both approaches is very high and very similar Figure .1w0k]D). The top hit of our method is the profile GO:0005524/c.37 (function "ATP binding" in fold "P-loop nucleoside phosphate hydrolases"), matched against the central domain of that protein. Nevertheless, an equivalent search with psi-blast finds as top hit a domain with function GO:0004156  and fold c.1 ("TIM-barrel").The first example is the mitochondrial precursor of the ATP-synthase beta chain ([PDB:2mta]C). While GO_PROFILES correctly predicts GO:0020037 ("heme binding") in fold a.3 ("cytochrome C fold"), psi-blast's top hit is a DNA-binding protein (GO:0003677) with fold d.218.Another example is the periplasmic cytochrome C551I  are frequently linked to the ATP-binding domains of these proteins, and there are many instances of them crystallized in isolation (as fragments) in PDB. The problem arises because these fragments are annotated with the function of the complete chain (ATP-binding) and consequently confound the methodology used in SCOP2GO . Nevertheless, this protein is annotated with a less specific term in GO . For this reason this counts as a failure in the automatic large-scale evaluation discussed in the previous point, even if our method is providing a more detailed (and correct) annotation. In this case, psi-blast matches against a protein with that less specific annotation (GO:0004721) and hence it counts as a true match.In the case of the mono-domain tyrosine phosphatase [PDB:2csn]A) is correctly matched against the GO:0004672/d.144 profile ("protein kinase activity" in fold "protein kinase like"). Figure The last example illustrates an additional advantage of this method: the fact that it can provide clues about possible functional sites, concomitantly with the prediction of fold and function. The casein kinase 1  in order to illustrate the added value of these novelties. Actually, only the added value of the GO-informed profiles, since the GO:MF annotations at the domain level are also provided to psi-blast in this benchmark. The large-scale evaluation based on GO annotations is not perfect due to many reasons , some of them illustrated in the examples shown. Nevertheless, all these factors affect both methodologies due to the parallel evaluation procedure followed, based on the same dataset. An exhaustive comparison with more sophisticated methods for function prediction is outside the scope of this work due to the different functional vocabularies and databases used and, more importantly, the domain orientation of this method: almost all other existing resources for function prediction assign function at the whole-chain level. We also show some examples to illustrate the advantages of this method in particular situations and highlight its complementariness with existing approaches. Indeed, the method presented here is not intended to compete with the plethora of methods designed for predicting function at the whole chain level, but to fill a very particular niche: function prediction at the domain level. Nevertheless, this method can be also used to infer molecular functions for whole chains in two ways: i) although excluded from the benchmark presented here for simplicity, the SCOP2GO resource also contain functional assignments for groups of domains, and ii) the function of the whole chain can be manually inferred from the annotations of the individual domains although this requires some expert knowledge.The domain orientation of this methodology also makes that it can be only applied to the "molecular function" category of GO (GO:MF) and not to the "biological process" category (GO:BP). As commented above, only the molecular functions can be differentially assigned to particular domains, while biological processes are properties of whole chains.This resource will be improved as the GO:MF annotations at the domain level it is based on, which right now are generated with an automatic procedure, are extended and manually curated. Moreover, the method presented here can be implemented with any other domain-based functional annotation.We present here a method and a resource for the concomitant prediction of fold and molecular function at the domain level, using a single sequence as input. The method outperforms standard sequence-based methods. Functionally important sites may also be identified although this feature has not been exhaustively benchmarked so far and we only show illustrative examples.The authors declare that they have no competing interests.FP conceived the original idea. FP and DL designed the experiments. DL implemented and performed all the experiments. FP and DL contributed to the writing of the manuscript.Additional results of the large-scale evaluation. Additional ROC plots for other levels of functional specificity (distance to the root of the GO:MF graph) including also the results of HMM searches against the profiles.Click here for file