ADP-glucose pyrophosphorylase (AGPase), a key allosteric enzyme involved in higher plant starch biosynthesis, is composed of pairs of large (LS) and small subunits (SS). Current evidence indicates that the two subunit types play distinct roles in enzyme function. Recently the heterotetrameric structure of potato AGPase has been modeled. In the current study, we have applied the molecular mechanics generalized born surface area (MM-GBSA) method and identified critical amino acids of the potato AGPase LS and SS subunits that interact with each other during the native heterotetrameric structure formation. We have further shown the role of the LS amino acids in subunit-subunit interaction by yeast two-hybrid, bacterial complementation assay and native gel. Comparison of the computational results with the experiments has indicated that the backbone energy contribution (rather than the side chain energies) of the interface residues is more important in identifying critical residues. We have found that lateral interaction of the LS-SS is much stronger than the longitudinal one, and it is mainly mediated by hydrophobic interactions. This study will not only enhance our understanding of the interaction between the SS and the LS of AGPase, but will also enable us to engineer proteins to obtain better assembled variants of AGPase which can be used for the improvement of plant yield. ADP-glucose pyrophosphorylase (AGPase) is a key heterotetrameric allosteric enzyme involved in plant starch biosynthesis. In this study, we have applied computational and experimental methods to identify critical amino acids of the AGPase large and small subunits that interact with each other during the heterotetrameric structure formation. During the comparison of the computational with the experimental results we also noted that the backbone energy contribution of the interface residues is more important in identifying critical residues. This study will enable us to use a rational approach to obtain better assembled mutant AGPase variants and use them for the improvement of the plant yield. These two subunits are encoded by two distinct genes ADP-glucose pyrophosphorylase (AGPase) is a key regulatory allosteric enzyme involved in starch biosynthesis in higher plants. It catalyzes the rate limiting reversible reaction and controls the carbon-flux in the α-glucan pathway by converting Glucose-1-phosphate and ATP to ADP-glucose and pyrophosphate using Mg2β2) structure have been solved yet. This is due to the difficulty of obtaining AGPase in stable form. However, it is critical to elucidate the native heterotetrameric AGPase structure and identify the key residues taking place in subunit-subunit interactions to obtain a more detailed picture of the enzyme. Understanding the structure and the hot spot residues in the subunit interface will enable us to manipulate the native enzyme to get a stable form which can be utilized for improving the yield of crops. The feasibility of such an approach has been shown previously 97, Pro327, Ile330, Ile335, Ile339, Ile340, and His342 are involved in lateral interaction with the potato AGPase SS whereas residues Arg45, Arg88, Arg92, and Trp135 are involved in longitudinal interaction with the potato AGPase SS. The effect of these mutations on the interactions of the LS and the SS of potato AGPase were further characterized in vivo using the bacterial complementation and the yeast two-hybrid methods. Also, experimental results indicated that the backbone bindingΔG energy of the interface amino acids is a decisive parameter for the subunit-subunit interaction rather than side chain bindingΔG or total bindingΔG energies. This study will highlight the important structural aspects of AGPase structure and provide insights for further attempts to engineer a more functional form of the enzyme.Recently crystal structure of SS was found in a homotetrameric form by Jin et al. elec terms are compensated by unfavorable ΔGpolar terms. Hence, total electrostatic interactions ΔGelec, favor binding of subunits. Contributions from van der Waals and non-polar solvation energies also favor interactions thus being the major forces that drive the association of subunits. These results are in agreement with our previous work To determine the critical amino acid residues of the potato AGPase LS that interact with potato AGPase SS, we performed MM-GBSA method which calculates the binding free energy and decomposes the energy at the amino acid level. The binding free energy differences for the longitudinal (D2) and lateral (D1) dimers of the modeled heterotetramer binding|>3.0 kcal/mol), then it is considered as a hot spot. Hot-spot residues for D1 and D2 and their binding free energy components together with the standard deviations are shown in 2 upon subunit complexation and it must satisfy this condition for at least 160 of the snapshots. Based on these requirements, a total of 79  residues in D1 were classified to be part of interfaces. A total of 19 out of 79 interface residues (8 in LS and 11 in SS) in D1, are hot-spots. The hot-spot residues in LS are mostly non-polar in general with the exception of Asn97, Thr328, and His342. Seven of the hot-spots in SS for D1 are also non-polar, too. Residues SSLys288, SSTyr308, SSLys313 and SSThr320 make up the polar region in this interface. Overall interaction in the lateral dimer is mediated by amino acids that have hydrophobic side chain  in this interface. The remaining two residues are Trp135 in LS and Trp120 in SS  as interface residues. Number of hot-spots (five) in D2 is relatively less than the residues in D1. In contrast to D1 hot-spots, which are generally non-polar, there are three basic hot spot amino acids (Arg20 in SS  in conco308 in SS (in D1) shows the highest free energy difference with a |ΔGbinding| value of 6.75 kcal/mol upon complexation. We see that favorable contributions to ΔGbinding for this residue are dominated by Eele  and Evdw . Indeed, several H-bonds are formed by Tyr308 and several polar residues . Tyr308 is also in close contact with non-polar residues, such as Pro322 in LS which account for the favorable van der Waals interactions. The unfavorable contribution of polar solvation energy  comes from these interactions and it is observed to be compensated by the favorable electrostatic term. The backbone and side chain contributions to the total free energy are −1.99 and −4.75 kcal/mol, respectively for this residue. Pro327 in LS has the second highest |ΔGbinding| energy difference with a value of 5.03 kcal/mol. It should be noted that this residue is highly conserved and makes van der Waals contacts with Gly40, Ala41, Ile285, Ile324 and the aromatic ring of Tyr43 in SS. These interactions explain the hydrophobic contribution of Pro327 to the total |ΔGbinding|. The backbone and side chain contributions to the total free energy are −1.80 and −3.24 kcal/mol, respectively for Pro327.As can be seen from 330, Ile335 and Ile340 that constitute a hydrophobic core at the inner layer of β-helix domain. The bulky side-chain groups of these residues make strong hydrophobic interactions with each other as well as their counterparts in SS. In fact, favorable ΔGbinding for these amino acids are mainly driven by the van der Waals forces . This is especially important to decide whether the side chains or backbone interactions are important to define critical residues, hot spots, in AGPase complex.Lysvdw term has no contribution for Arg45 stabilization in LS during dimerization  and Trp120 in D4 were also classified as hot-spots in our AGPase model. All the other residues, except for the Glu94, also have negative ΔGbinding values which mean that they are stabilized upon complex formation. However, they were not considered as hot-spots since their change in ΔGbinding values according to free energy decomposition are higher than our cutoff value . We see that while the important amino acids reported by Jin et al. binding energy in our model, they are less stabilized in the homotetrameric SS with a ΔGbinding value of −29.03 kcal/mol  structural gene of Escherichia coliE. coli glgC− (containing pML10). The ability of LS mutants to form a functional heterotetrameric AGPase was assessed by exposing mutant colonies to I2 vapor to monitor the glycogen accumulation. The residues of the LS listed in and334  and Lys336  adjacent to the Ile335 were replaced with Ala. These LS mutants were transformed into E. coli glgC− cell lines containing the WT SS. Cells were exposed to iodine staining to see the effect of mutation on the heterotetrameric assemblies. As seen in E. coli. Our results are in agreement with previously reported data where they showed that lateral interaction is mainly mediated by the hydrophobic amino acids in homotetrameric enzymes of the potato SS and Agrobacterium AGPases within the αβ domain of AGPase E. coli. For example when Ile339, and Ile340 were changed to Ala , there were no heterotetrameric assemblies between the potato LS and SS AGPase subunits in E. coli  of the potato AGPase LS in longitudinal interaction with the potato AGPase SS by bacterial complementation and yeast two hybrid assays. Residues, Arg45, Arg88, and Arg92 were mutated to the Ala whereas Trp135 was mutated to Arg by site-directed mutagenesis in pML7 vector. Mutants were transformed into E. coli glgC− (with the pML10). Only the LSArg88Ala mutants have glycogen deficient phenotype and they were unable to complement glgC− gene compared to cells containing wildtype AGPase genes  and His89  to Ala. Bacterial complementation result indicated that cells harboring mutant LS constructs can complement glgC− in E. coli  were expressed with the WT SS in acid see . Then, c45, Arg88, Arg92, and Trp135 of the LS with interaction of the SS, Arg88 had the highest backbone energy ( and E. coli glgC− containing the pML10 (WT SS). As seen in glgC− gene in E. coli and in turn glycogen production. These results point out that the backbone energy of these residues showed an additive effect when they combined and caused disruption of the heterotetrameric assemblies.When we analyzed the backbone energy contribution of Argrgy  and 4. ThenAgrobacterium AGPase have been reported 340 and SS-Ile324 provided by two water molecules in D1. These water molecules form several hydrogen bonds with the Ile residues separately during the simulation and the same network as in ele and ΔGpolar values for Lys288, Lys313 in SS (D1) and Arg45, Arg88 and Arg92 in LS (D2). Based on these considerations, although our MM-GBSA calculations may not be perfect, they are fairly consistent with the experimental results. Total of 79 (38 in LS and 41 in SS) residues in lateral interaction and 53 residues (27 in LS and 26 in SS) in longitudinal interactions were classified to be part of interfaces. Free energy decomposition scheme was applied to identify the critical residues in the LS-SS interfaces. In both cases, residues that showed 3.0 kcal/mol energy drop upon complexation of the LS and the SS were defined as hot-spots. A total of 19 out of 79 interface residues (8 in LS and 11 in SS) and 5 of 41 residues (4 in LS and one in SS) were accepted as hot-spots. Interestingly, the identified hot-spot residues in LS are highly conserved among different species (333) from the maize endosperm LS AGPase that participates in interactions with the SS. Our analysis of interface residues of potato LS indicated that Tyr275 (corresponding to maize His333 LS AGPase) is not involved in interaction. This specific residue may be solely responsible for heat stability rather than any interaction between the subunits. There are many studies performed to understand the subunit-subunit interaction of the AGPase subunits mainly carrying out domain swap between the different species subunits Plant AGPases contain two different subunits encoded by two different genes 97, Pro327, Thr328, Ile330, Ile335, Ile339, Ile340, and His342, were involved in lateral LS-SS interaction whereas the LS residues of Arg45, Arg88, Arg92, and Trp135 were involved in longitudinal LS-SS association. These residues were mutated and the effect of these mutations on the interactions of LS and SS were characterized in vivo using the yeast two-hybrid method and the bacterial complementation assay. Mutating the LS residues at position 88, 327, 330, 335 and 339/340 abolished the interaction between the LS and the SS subunits. On the other hand, the LS residues at position 45, 92, 97, 328 and 342 have no significant contribution to the subunit-subunit interactions. When we compared the MM-GBSA results with the experimental data, we made a very interesting and insightful observation: Residues with favorable backbone free energy terms actually correspond to critical residues  and Asn97  residues have an insignificant effect in the lateral interaction. Changing the LS residues at position 327, 330, 335 and 339/340 abolished the interaction between LS and SS and inhibited the glycogen synthesis; however, His342Ala mutants were able to synthesize glycogen. Experimental results confirmed the computational analysis using MM-GBSA method, and exhibited a remarkable concordance with backbone bindingΔG energy values instead of side chain or total bindingΔG.As discussed above, the heterotetrameric structure consists of lateral and longitudinal interactions. The LS residues, Asnresidues Table 6.339 side-chain is excluded from the inner part of the β-helix domain and van der Waals energies  have a dominant contribution to the favorable ΔGbinding of this residue which is mainly due to the interactions with Ile338 of the SS. Mutation of this residue to Ala will not only decrease the Evdw term, but may also force the side-chain of alanine to be included in the inner layer of β-helix domain. This is highly possible since Ile340, whose side-chain is involved in the inner layer, is mutated to alanine at the same time and the residue at position 321 of the SS is also an alanine. Such an inclusion will certainly result in steric clashes between the side-chains and disturb the interface structure of β-helix domain where it makes important interactions with SS. In other words, this mutation can affect the interactions both energetically and structurally.We have previously mentioned that Ile88 residue has the highest value and mutant cells were defective in glycogen synthesis. The backbone free energy decomposition of Trp135  residue is less favorable than Arg88  but more than Arg45 , and Arg92 . Consistently, glycogen synthesis was reduced in cells expressing Trp135Arg when compared with Arg45Ala and Arg92Ala. Although mutation at residues of Arg45 and Arg92 displayed no effect on the subunit-subunit interactions, the coupling of these residues with Trp135 abolished the heterotetrameric structure formation, hence, inhibited the glycogen synthesis suggesting the importance of each identified residue and cooperative effect of these residues.Among the four candidate hotspot residues involved in longitudinal LS-SS association, the backbone free energy decomposition of Arg328 and Ile330 double mutation to alanine seems to have an effect on D1 formation according to yeast two hybrid result (data is not shown) because these residues account for a total of −8.17 kcal/mol. We see in 328 has a minor contribution  to ΔGbinding compared to its backbone . Having high backbone energy increases the chance of an alanine mutation to disturb the interactions between the subunits. In addition, an alanine mutation at this position might result in a decrease in the electrostatic interactions, which is −4.21 kcal/mol. Thus, these balancing changes decrease the overall effect of the alanine mutation. It is obvious that an alanine mutation at Ile330 position will certainly decrease the van der Waals effects  of this residue. As previously mentioned Ile330 makes two H-bonds with Ala317 in SS which accounts for the −2.99 kcal/mol electrostatic interactions.Thr97, Pro327, Ile330, Ile335, Ile339, Ile340, and His342 are critical for the interaction with the SS of AGPase. Longitudinal interaction by the LS AGPase with the SS subunit is mediated by the Arg42, Arg88, Arg92, and Trp135. Second, we found that dimer 1 is much more stable compared with dimer 2 due to the hydrophobic interaction in dimer 1. Finally, backbone energy is an important deterministic parameter for the protein-protein interaction.The data presented in this paper allow us to reach the following conclusions. First, critical amino acid residues of the potato LS AGPase subunit that interact with SS subunit were identified using MM-GBSA and experimental methods. Lateral interaction between the LS and SS subunits was mainly mediated by the hydrophobic amino acids as shown previously for homotetrameric AGPase . For the first time we have shown the amino acids of the LS subunit that are important for such interactions The amino acids AsnPotato tuber AGPase large and small subunits share 53% sequence identity according to the CLUSTALW + atoms, in order to neutralize the systems. All the histidine residues were charged as +1 at their Nε atoms in order to establish unity. Particle Mesh Ewald (PME) method 4 steps using conjugate gradient method and keeping the backbone atoms of the solute atoms fixed. Minimization was completed by an additional 104 steps with all the atoms relaxed to remove the bad contacts. The systems were then gradually heated from 0 K to 300 K in 150 ps using canonical ensembles (NVT) during which the Cα atoms of the solutes were restrained by applying 2 kcal mol−1 Å−2 force constants. Subsequent shift into isothermal-isobaric (NPT) ensembles was done and harmonic restraints on the Cα atoms were gradually removed in 80 ps after which the systems were equilibrated with an additional 100 ps. NPT simulations were performed for 8 ns at 300 K from which the last 4 ns was used to extract the snapshots with 20 ps time intervals. The 200 snapshots were then used for interface residue identification and binding free energy calculations together with free energy decomposition scheme  were treated as the true interface amino acids.Snapshots are taken from the last 4 ns of the simulations . Interface residues were determined using NACCESS complex, Greceptor, Gligand are the energies of the complex, receptor and ligand respectively. Each term on the right hand side of Eq 1 can be represented as shown in the following equation:MM is the total mechanical energy of the molecule in gas phase, Gsol is the solvation free energy and TS is the entropic term. Each term in Eq (2) can be written as follows:MM represents the bonded and non-bonded interactions as a sum of electrostatic (columbic), van der Waals (Lennard-Jones) and internal strain  energies. This term is calculated by classical molecular-mechanics methods using standard force fields such as parm96 force field polar) is computed in a continuum solvent environment by using the GBSA method. Non-polar solvation energy (Gnon-polar), which is considered to be the sum of a solute-solvent van der Waals interaction and solvent-solvent cavity formation energy, is approximated by using an empirical formula such as Gnon-polar = α×SASA. According to this formula, non-polar solvation energy of a molecule is proportional to the solvent accessible surface area (SASA) of that molecule in a solvent, where α was taken as 0.005 kcal•Å−2relative binding free energies see refIn this study, MM-GBSA MM) of the proteins were calculated by the SANDER module applying no cutoff value for non-bonded interactions. Dielectric constants for the solute and solvent were taken as 1 and 80, respectively; and the solvent probe radius was adjusted to 1.4 Å. Residues in interfaces of the subunits that showed at least 3 kcal/mol energy decrease, upon complexation, according to the per-residue free energy decomposition were considered as hot-spots.In all the calculations the LS was treated as the receptor and the SS as the ligand. Gas phase energies .Site-directed mutations of the specified hot spot residues were introduced to potato AGPase LS by PCR. Plasmids pML7, pGBT7K-LS, or pGAD-SS were used as template. PCR reaction was performed in a total volume of 50 µl containing approximately 50 ng of plasmid samples, 20 pmol of each primer, 0.2 mM dNTPs, and 2.5 unit Dream Taq DNA polymerase (MBI Fermentas) with appropriate primers indicated at Table I. Conditions for the 18 cycles of amplification reaction were 95°C for 30 s, 50°C for 30 s and 68°C for 14 min. Before the first cycle reaction mixtures were kept at 95°C for 4 min and at the end of the 18Yeast-two hybrid assays were performed as described previously E. coli AC70R1–504 (glgC−), carrying the SS cDNA expression plasmid pML10. The particular contribution of each mutant to the LS-SS interaction was evaluated by their ability to complement the glgC− mutation and synthesize glycogen on Kornberg medium enriched with 2% glucose. Glycogen accumulation phenotypes was detected by iodine staining The WT or mutant LS cDNA containing pML7 plasmids were sequentially transformed into glgC−) cells were grown in 25 ml of LB medium and then induced with 10 mg/L of nalidixic acid and 200 µM isopropyl- b-D-thiogalactopyranoside (IPTG) at room temperature for 20 h when the culture OD600 reached 1–1.2. The cells were harvested by centrifugation and disrupted by sonication in 1 ml lysis buffer . The crude homogenate was centrifuged at 14,000 g for 10 min. The resulting supernatant was used. Protein levels were determined by Bradford assay (Bradford 1976) according to the manufacturer's (Bio-Rad) instructions .AC70R1–504  with a Mini-Trans-Blot electrophoretic transfer cell (Bio-Rad) at 90 V for 1 hr. After pre-blocking with 5% BSA dissolved in Tris-buffered saline (TBS), the membrane was incubated with anti-LS or anti-SS primary antibodies (1∶2000 diluted in 0.15% Tween20/TBS) for 1 hr at room temperature. After a series of washes the membrane was subsequently incubated with HRP-conjugated secondary anti-rabbit IgG antibody (1∶5000 diluted in 0.15% Tween20/TBS)  for 1 hr. Proteins were visualized by Amersham ECL plus western blotting detection system . The blot was exposed to autoradiography film.Native-PAGE was performed using a Bio-Rad Mini-PROTEAN III electrophoresis cell. Cell lysates containing 10 µg total protein was mixed with Laemmli's sample loading buffer except β-mercaptoethanol and reducing agent. Samples were electrophoresed on 3–13% polyacrylamide gradient gel (pH 7.0) with 1X running buffer  at constant 100 V at 4°C for 2 hrs. Western blotting and protein visualization were performed as described above. The observed position of protein complexes was compared with BSA oligomer running pattern.


Biological evolution conserves protein residues that are important for structure and function. Both protein stability and function often require a certain degree of structural co-operativity between spatially neighboring residues and it has previously been shown that conserved residues occur clustered together in protein tertiary structures, enzyme active sites and protein-DNA interfaces. Residues comprising protein interfaces are often more conserved compared to those occurring elsewhere on the protein surface. We investigate the extent to which conserved residues within protein-protein interfaces are clustered together in three-dimensions.Out of 121 and 392 interfaces in homodimers and heterocomplexes, 96.7 and 86.7%, respectively, have the conserved positions clustered within the overall interface region. The significance of this clustering was established in comparison to what is seen for the subsets of the same size of randomly selected residues from the interface. Conserved residues occurring in larger interfaces could often be sub-divided into two or more distinct sub-clusters. These structural cluster(s) comprising conserved residues indicate functionally important regions within the protein-protein interface that can be targeted for further structural and energetic analysis by experimental scanning mutagenesis. Almost 60% of experimental hot spot residues  were localized to these conserved residue clusters. An analysis of the residue types that are enriched within these conserved subsets compared to the overall interface showed that hydrophobic and aromatic residues are favored, but charged residues (both positive and negative) are less common. The potential use of this method for discriminating binding sites (interfaces) versus random surface patches was explored by comparing the clustering of conserved residues within each of these regions - in about 50% cases the true interface is ranked among the top 10% of all surface patches.Protein-protein interaction sites are much larger than small molecule biding sites, but still conserved residues are not randomly distributed over the whole interface and are distinctly clustered. The clustered nature of evolutionarily conserved residues within interfaces as compared to those within other surface patches not involved in binding has important implications for the identification of protein-protein binding sites and would have applications in docking studies. The analysis of sequence conservation in a protein family is a useful method for identifying residues that are functionally important - for catalytic activity or binding, or responsible for providing stability to the folded structure -10. ResiThe question addressed in this paper is whether the subset of conserved residues in a protein-protein interface occurs scattered across the interface, or cluster together in three-dimension? It is possible that the conserved residues would form one or more localized clusters within the interface as it would enable the formation of "functional motifs". It has recently been shown in protein-DNA interfaces that the most stabilizing residues (putative 'hotspots') are those that form clusters of conserved residues at the interface . The resOf the large number of residues comprising a protein-protein interface, only a few contribute significantly to the free energy of binding. These "hot spot" residues are generally occluded from bulk solvent, being surrounded by other less important residues . It is p2 of surface area upon complexation are considered as belonging to the interface [The sets of interfaces used were 122 homodimers  and 204 nterface .The sequence variablility at each interface residue position is calculated as the Shannon entropy (s) in sets of homologous protein sequences :(1)i(k) is the probability that the ith position in the multiple sequence alignment is occupied by a residue of class 'k', and s(i) is the sequence entropy of that position. A low value of sequence entropy, s(i) implies that the position has been subjected to relatively higher evolutionary pressure than another position in the same alignment having a higher sequence entropy value. Multiple sequence alignments were obtained from the Homology-Derived Secondary Structure of Proteins (HSSP) database [where, pdatabase . The datdatabase . The amiEq. 1 makes use of the probability (or frequency) of occurrence of each residue class in a given aligned position. However, it does not take into account the "background" frequencies of these amino acids. It has been shown previously that the use of background frequency information significantly improves entropy-based functional site prediction within protein structures . In ordeback(k) denotes the background frequencies of the amino acids in group 'k', and the remaining terms are the same as in Eq. 1. This relative entropy measure  is similar to the one used by Wang and Samudrala [where pamudrala . A higheamudrala . We alsoFor each interface with 'n' residues an average value of sequence entropy was calculated:int) were assumed to constitute the conserved residues. (2) We also selected the subset of conserved residues with sequence entropy lower than the average less the standard deviation (< s>int - σ). It may be mentioned that the values of the mean and standard deviation used for selecting the set of conserved interface residues were calculated for each individual interface. (3) Finally, we also used only those residues with the sequence entropy value of 0.0, i.e., the fully conserved residues.We used three different criteria with increasing levels of stringency to identify the conserved interface residues, and compared the results. (1) Interface residues with sequence entropy values lower than the average s is the number of residues in the set, Npairs is the number of different pairs of residues in the set given by: Npairs = (Ns-1).Ns/2; and, rij is the distance between the centers-of-mass of the two residues in question, i and j. Greater the value of Ms, greater is the degree of spatial clustering of the residues in the set. The advantage of this inverse-distance based formula is that one or a few outlier positions are unable to significantly influence the overall value of Ms for the entire set. The values of Ms that are obtained are continuous and can be used in ranking different sets of residues.where Ns,cons) and then for the whole interface . The contrast between the spread of inter-residue distances between the two sets , ρ, is an indicator of the extent of clustering of evolutionary conserved residues,For each interface Eq. 3 was employed twice, once for the subset of conserved residues  the conserved residues are clustered within the interface . However, the occurrence of isolated conserved residues has been dealt with while considering cluster size.s,cons) was compared to Ms values obtained for 1000 random subsets of interface residues of the same size in each structure. The average (and SD) of the Ms values calculated for the 1000 random subsets  was compared to Ms,cons obtained for each interface.The degree of clustering of conserved interface residues . To ideThe clustering analysis was also carried out on a set of 26 protein-protein complexes for which experimental alanine scanning mutagenesis on the interface residues has been carried out. The list of complexes used has been described in our earlier paper . Interfas values (Eq. 3) for both the conserved and the overall residues in the patch were computed. The procedure was repeated for each patch. Finally, the surface patches were arranged in descending order of ρ (Eq. 4) and the rank of the true interface in relation to all the other surface patches was found out.Three different procedures were used for the identification of surface patches. Method 1: NACCESS  was run Two variations were also explored in the algorithm used to generate surface patches. Method 2: Instead of using standard cutoffs for all the proteins in the dataset, individual cutoffs were used for each protein depending on the size of the particular interface. For each interface the maximum distance between any two atoms was found out and the radial cutoff was set as half that value. This step is likely to generate surface patches of a size which will more closely approximate the size of the true interface, than a cutoff based on the average value calculated over the whole database. Method 3: In addition to using individual cutoffs for each protein, vector constraints were used while selecting surface neighbors around each central residue . This stAll three definitions of the surface patches result in approximately contiguous, circular regions of the protein surface which overlap each other. We also evaluated how the generated patches sampled the true interface region by calculating a percentage overlap - fraction of residues common between the real interface and the surface patch relative to the total number in the interface:where NrI is the number of residues in the true interface patch, and NrC is the number of residues in the generated surface patch. The numerator defines the set of residue common between the real interface and the calculated patch.s (Eq. 3) is a simple but useful measure for assessing the degree of spatial clustering of a group of points (residues in this case) in space [s uses an inverse distance relationship, residues that are close together will mainly influence its value and one or a few outliers will not unduly affect it. A high value of Ms indicates that the set of residues under consideration are mostly clustered together. Ms is calculated both for the subset of conserved residues as well as for the whole set of interface residues. The ratio (ρ) of Ms for the conserved subset to that for the entire interface gives an indication of the clustered (or dispersed) nature of the distribution of the evolutionary conserved subset. ρ > 1.0 indicates that the conserved residues are relatively more clustered compared to the whole interface. A few representative examples of interfaces where the evolutionary conserved residues are clearly clustered together are shown in Additional file s,cons and Ms,int for each interface are plotted. A point lying above the diagonal indicates that Ms,cons is greater than Ms,int , implying that the conserved residue subset is more clustered in space relative to the overall interface . We repeated the clustering analysis but this time the calculation of sequence entropies was carried out using Eq. 1a (which takes amino acid background frequencies into account). However, this additional step did not affect the clustering results significantly . A fewer number of residues from each interface are labeled conserved, but the conclusion that the conserved residues are clustered within the interface remains the same . From the same interface, subsets of residues (of the same size as the number of conserved residues) were selected randomly and their Ms values were found out. The average Ms,random value of 1000 such random subsets were computed for each interface and compared against Ms,cons. In 96.7% (117/121) homodimeric and 87.7% (341/389) protein complex interfaces, we found that the randomly selected groups of residues were indeed less clustered than the conserved residue subset in the interface, and this difference was statistically significant at the 1% level (P < 0.01).We also found that subsets of evolutionary conserved interface residues are significantly more clustered than what is observed in subsets of the same size consisting of randomly selected interface residues  per chain, which on average possesses an interface area of 1000 ± 422 Å2. Since however, on average, homodimer interfaces are almost twice the size of protein complex interfaces [2 of the interface area were 13.9 and 14.9 for homodimers and complexes, respectively. The number of conserved interface residues  per subunit (or chain) as a function of the interface size has been plotted in Additional file On average, the homodimer interfaces contain 27 ± 16) conserved interface residues per subunit (comprising 52 ± 29 interface residues), with the average interface area being 1941.2 (± 1108.2) Återfaces , the num6 conserv2 and all but one of the interfaces with 3 or more sub-clusters possess interfaces larger than 2000 Å2. A similar observation can also be drawn from the complexes - out of the 193 single-cluster interfaces, 94.3% (182 cases) have interface areas within 1200 Å2 whereas 54 of the 69 (~ 80%) interfaces with 3 or more sub-clusters have interfaces > 1200 Å2.The larger interfaces often comprise of multiple distinct clusters of evolutionary conserved residues. The number of sub-clusters formed by the subset of conserved residues can be easily identified by a simple geometric clustering algorithm which uses the average linkage method    of a particular amino acid (X) is defined as the ratio of the frequency of occurrence of that amino acid in the conserved residue subset compared to its frequency in the whole of the interface region, i.e.,Certain amino acid types are enriched in the conserved residue clusters. The relative enrichment of each of the 20 amino acid types in the conserved subsets compared to the overall interface has been calculated Figure . The enrThe same types of residues are found to be preferred in conserved residue clusters in both homodimeric and protein complex interfaces, namely, hydrophobic , Cys, Gly, and the aromatic residues . Except for Gly the observed preference matches with the propensities of residues to occur in interface core . The onlResidues targeted for alanine scanning mutagenesis are distributed over all the residue classes and have a wide range of sequence conservation  was then used to sort the surface patches (in descending order of ρ). A ranking of the true interface patch relative to all the other surface patches was then calculated . A rank of 1 indicates that the true interface is present in the top 10% of all surface patches and a rank of 10 indicates a location in the lowest 10% range in the distribution of ρ for all surface patches. Figure To study if the results depend on the nature of the complex we made a functional classification of heterocomplexes as interfaces belonging to enzyme-inhibitor, antigen-antibody, signaling complexes, and Others. For each of these four types, we found out the prediction accuracy separately Table . In all int - < ρ>)/σ, where ρint is the value (Eq. 4) for the real interface, and < ρ> is the average vale for all surface patches in the protein, σ being the standard deviation. For the homodimers, about 40% (49/121) interfaces contain conserved residues which are significantly more clustered compared to conserved residues present within other surface patches . For the complexes, such significant clustering of conserved residues within the interface was observed in 38% (148/389) cases. Hence, for these interfaces, the clustered nature of the conserved residues alone is sufficient to distinguish the true interface from remaining surface patches.We further examined the statistical significance of the degree of clustering of conserved residues within true interfaces as compared to that in random regions of the protein surface. The Z test was used for this purpose, defined as Z =  the distribution of conserved residues in interfaces, (2) the degree of overlap between the subset of conserved residue positions and experimentally determined binding hot spots, and, (3) the prediction of the interface using the distribution of conserved residues.s,cons > Ms,int) can be seen in Figures A ρ value of > 1.0 indicating the clustering of conserved residues relative to all the residues in the interface  [et al. [Experimental approaches for the identification of functionally important residues on protein surface involve mutagenesis of a large number of residues and recoding the change in activity or binding to other proteins. However, considering the large size of the protein-protein interfaces and without hod (ET)  searches [et al.  also com [et al. ,26,45,46 [et al. ,23,24,47 [et al. ,48 and tIt is known that interface hot spot residues form clusters within densely packed 'hot regions', where they form networks of interactions contributing cooperatively to the stability of the complex . TherefoWe investigated the potential use of the clustering of conserved residues for the identification of the binding site by comparing this feature in the real interface against all other surface patches Figure . In abouA question may be asked if a direct assessment by first identifying conserved residues on the protein surface and then searching for spatial clusters could have been performed . Methods like the Evolutionary Trace (ET)  use the Recently, machine learning techniques, such as Support Vector Machines and Neural Networks have also incorporated the use of sequence conservation metrics to enhance the likelihood of predicting which surface residues of a given protein form an interface -58. In oPC conceptualized the work that was carried out by MG. MG and PC participated in interpretation of the data and writing the manuscript. Both the authors have read and accepted the final version of the manuscript.The file contains three tables (numbered S1 to S3), and nine figures (numbered S1 to S9).Table S1. Values of the parameters indicating the clustering of conserved residues in individual interfaces.Table S2. Location of experimental hot spots within the conserved residue clusters in the interface.Table S3. Distribution of 462 alanine scanned interface residues among the seven residue classes.Figure S1. Representative examples of interfaces showing the clustered nature of evolutionarily conserved residues.Figure S2. Plots of Ms,cons versus Ms,int.s,cons versus < Ms,random>.Figure S3. Plots of MFigure S4. Number of interface residues and conserved residues as a function of interface area.Figure S5. Multiple clusters of evolutionary conserved residues in protein interfaces.Figure S6. Distribution of cluster size.Figure S7. The level of sequence conservation of residues subjected to alanine scanning experiments.Figure S8. Comparison of the clustering of conserved residues within the subunit interface and other surface patches.Figure S9. Plot of Ms,cons versus Ms,int for interfaces from the bound forms 124 protein complexes described in the Protein-protein Docking Benchmark version 3.0.Click here for file


The number of available structures of large multi-protein assemblies is quite small. Such structures provide phenomenal insights on the organization, mechanism of formation and functional properties of the assembly. Hence detailed analysis of such structures is highly rewarding. However, the common problem in such analyses is the low resolution of these structures. In the recent times a number of attempts that combine low resolution cryo-EM data with higher resolution structures determined using X-ray analysis or NMR or generated using comparative modeling have been reported. Even in such attempts the best result one arrives at is the very course idea about the assembly structure in terms of trace of the Cα atoms which are modeled with modest accuracy.In this paper first we present an objective approach to identify potentially solvent exposed and buried residues solely from the position of Cα atoms and amino acid sequence using residue type-dependent thresholds for accessible surface areas of Cα. We extend the method further to recognize potential protein-protein interface residues.Our approach to identify buried and exposed residues solely from the positions of Cα atoms resulted in an accuracy of 84%, sensitivity of 83–89% and specificity of 67–94% while recognition of interfacial residues corresponded to an accuracy of 94%, sensitivity of 70–96% and specificity of 58–94%. Interestingly, detailed analysis of cases of mismatch between recognition of interface residues from Cα positions and all-atom models suggested that, recognition of interfacial residues using Cα atoms only correspond better with intuitive notion of what is an interfacial residue. Our method should be useful in the objective analysis of structures of protein assemblies when positions of only Cα positions are available as, for example, in the cases of integration of cryo-EM data and high resolution structures of the components of the assembly. Chemical nature and structural context of residues in a protein generate diversity in the contribution of residues towards stability and function of the protein in vitro from the purified components. In the recent times cryo-electron microscopy has emerged as a very important technique to obtain structural information about these assemblies Owing to the advent of high throughput proteomic studies in combination with the computational methods, a vast amount of information is becoming available on the protein assemblies and protein-protein interaction networks In the present study we first present an objective method to recognize the buried and exposed residues in the structures of proteins with positions of Cα atoms alone available. Given the reasonable success of this approach and given the importance of interactions between proteins in an assembly Interestingly in-depth assessment of our approach to identification of interaction interface residues solely from Cα positions points to structural contexts where the proposed approach identifies interface residues more effectively than the traditional approaches which use positions of other atoms such as those in the sidechains.The general approach to recognize protein-protein interaction interfacial residues solely from the positions of Cα atoms mimics the popular approach used for protein-protein complex structures with all the atomic positions available and using the solvent accessibility calculations. Though there are a few criteria for identifying interfacial residues in complex structures with all the atomic positions available, in our approach based solely on Cα positions we mimic the following criterion which has been used commonly in the literature For a residue to be considered in a protein-protein interface solvent accessibility of the residue in the complex should be ≤7% and in the absence of interacting subunit the accessibility should be ≥10%.The primary challenge in using an alteration of this criterion for complex structures with positions of only the Cα atoms available is to identify the equivalence of 7% and 10% sidechain accessibility for accessible surface area of Cα atoms as a function of the residue type.Sidechain orientation is a key factor that determines extent of solvent accessibility. Absence of sidechain positions in low resolution structures with only Cα positions available makes recognition of solvent exposed and buried residues non-trivial. However relative orientation of virtual bonds connecting contiguous Cα atoms gives a rough indication of sidechain orientation.Our approach to recognize solvent exposed and buried residues based solely on Cα positions involves calculation of accessible surface area values of Cα using a probe sphere of appropriate radius. In this analysis we have used 1464 high resolution (≤2Å) crystal structures of proteins which are largely non-homologous with positions of all the non-hydrogen atoms available. Solvent accessibilities of all the residues in these proteins employing the standard probe radius of 1.4Å, which is commonly used for all-atom models, have been calculated. We have generated a separate coordinate dataset of only Cα atoms in these protein structures consciously deleting the coordinate data for all non-Cα atom types. We refer this dataset as “Cα-only structures”. This dataset is not entirely equivalent to a dataset of low resolution structures with only Cα positions available as the accuracy associated with Cα positions in the dataset of Cα-only structures is expected to be higher (owing to the higher resolution) than that of true low-resolution structures. However, as shown earlier In order to recognize the radius of the probe sphere that is appropriate for the structures with only Cα positions available we have calculated accessible surface area values of Cα atoms for the entries in the dataset of Cα-only structures using a series of probe of radii namely (in Å), 2.1, 2.5, 3.0, 3.2, 3.4, 3.5, 3.6, 3.8, 4.0. Accessible surface area (expressed in square Angstroms) of a Cα atom corresponding to a specific residue, calculated using a specific probe radius in a given protein structure, is compared to accessibility value (expressed as %) of the same residue calculated using all the available atomic positions and using a probe radius of 1.4Å. Two measures have been employed to assess the correspondence between the accessibility values and accessible surface area values.A simple correlation coefficient has been calculated corresponding to a specific probe radius for every protein structure in the dataset of Cα-only structures. Distribution of correlation coefficients has been studied for the range of probe radii for every structure in the data set. We seek to choose the probe radius that generally provides highest correlation coefficient for most of the structures in the data set.per defines the deviation in the rank correlation between the two distributions for a given probe radii:Rank order of the buried residue positions corresponding to the increasing order of accessible surface area of the Cα atoms for a specific probe radius is compared to the rank order of the buried residues in the same protein using all-atom model and the probe radius of 1.4Å. The parameter iAll and RiCα correspond to accessibility rank of a buried residue (characterized by ≤7% solvent accessibility) from full-atom structures and ASA rank of the same residue in the Cα-only structure calculated for a specific probe radius. N corresponds to the number of buried residues.Here RNo standard cut-off values in terms of ASA values are available to determine the buried residues solely from the positions of Cα atoms. Hence, we identified residue type dependent cut-off for accessible surface area values of Cα atoms corresponding to 7% and 10% solvent accessibility. Towards this, correlation between surface area values of Cα atoms from Cα-only records, obtained for each one of 20 residue types and the accessibility values for the same residue as obtained using the whole atom record and 1.4Å probe radius. The value of Cα accessible surface area corresponding to the 7% and 10% accessibility was then calculated from the regression lines. The ASA values obtained in such a way were then used as cut-offs to identify the residues with ≤7% accessibility and ≥10% accessibility from the Cα-only structures.Having identified residue type-dependent equivalence of 7% and 10% solvent accessibility for Cα only coordinate sets it is a straightforward exercise to use the criteria of ≤7% and ≥10% to recognize interfacial residues in the protein-protein complex structures with only Cα positions available.Protocol section various radii for the probe sphere have been used to calculate accessible surface areas of Cα atoms. Correlation coefficient has been calculated between accessibility values from full-atom models and ASA of Cα atoms in Cα-only structures for various probe radii. For a dataset of 1464 high resolution, largely non-homologous protein structures we had calculated the percentage solvent accessibilities of residues using all atom model and the classical probe radius of 1.4Å. A dataset of Cα-only structures has been formed by deleting the positions of all the non-Cα atoms from the dataset of 1464 proteins and this dataset is referred to as “Cα-only structures”. As mentioned in the Protocol section the parameter per defines the correlation between the ranks of buried residues arranged in the increasing order of percent solvent accessibilities and ranks of same residues arranged according to the ASA of Cα atoms, calculated using various probe radii, from the dataset of CA structures. per values of under 20% as a function of probe radii. It can be seen that at about 3.5Å of probe radius the number of protein structures having a good per value of under 20% reaches almost the maximum. Thus, from two independent analyses we identified 3.5Å as the appropriate probe radius for accessibility calculations of Cα-only structures.We have also used rank correlation of buried residues in identifying, independently, the most suitable probe radius for use with Cα-only structures. As mentioned in the Protocol for each of the 20 residue types we have analyzed the relationship between percentage solvent accessibility calculated from full-atom models using a probe radius of 1.4Å and ASA of Cα atom from Cα-only structures for a probe radius of 3.5Å. As mentioned in the section on Sens_bur) can be defined as the number of buried residues identified out of the total number of actual buried residues while the specificity as the actual number of true buried residues out of the total number of the residues that have been identified as the buried residues. As indicated in the Table, for the heterogeneous dataset that has been used here, the method recognized the buried residues with significantly high accuracy of about 85%. It has covered about 90% of the buried residues out of total number of buried residues. For any method while it is very important to correctly recognize the positives, it is equally important (sometimes even more important) to recognize the negatives correctly. Hence, we defined the sensitivity and specificity values in terms of non-buried (exposed) residues as well. The sensitivity of the exposed residues then can be defined as the number of residues identified as exposed residues from the total number of actual exposed residues. The specificity is defined as the actual number of exposed residues out of the total number of residues identified as exposed residues. As can be seen from the correlation, sensitivity, specificity and accuracy values listed in Using an independent data set of 1100 high resolution protein structures, we have recognized buried and exposed residues using the positions of Cα atoms only and using the thresholds defined for each of the 20 residue types. The buried and exposed residues thus identified were assessed by calculating sensitivity and specificity values for the two classes of the residues namely buried and non-buried (exposed), and the overall accuracy as well as the correlation coefficient using the expressions given in the An alternate approach to identifying solvent exposed and buried residues starting solely from Cα positions is to generate all atom models from Cα trace and employ the traditional solvent accessible surface area calculations on the dataset of coordinates of all the atoms in the proteins. For this purpose we have employed two methods to generate positions of sidechain atoms: the sidechain modeling approach employed by Sali and Blundell in their comparative modeling software MODELER Having obtained these encouraging results, the method was then further extended to recognize the residues in the interface of protein-protein complexes.Interface residues have been recognized for a high resolution dataset of 1100 protein-protein complex structures using the accessibility criteria mentioned in an earlier section. The residues were tagged as the interface residues if the accessibility values in complex form were less than or equal to 7% and in the isolated chain the accessibility value of the same residue increases to greater than or equal to 10%. In case of the Cα-only structures of the protein-protein complexes the ASA cutoff values corresponding to the above mentioned accessibility cutoffs were calculated for each amino acid as mentioned previously . The intAs mentioned previously in case of the buried residues, to validate the results obtained in case of the Cα-only structures the sensitivity and specificity values were calculated for two classes of the residues namely interface and non-interface residues. Also, the accuracy and the correlation coefficient values were calculated using the formulas mentioned in the A few residues were identified as interface residues while apparently they are not interfacial residues. Hence, the apparent false positive residues were further looked at more closely. The visual inspection of these residues in Pymol It is possible that residues in the periphery of the interface with solvent accessibility values greater than 7% even in the complexed form interact with the associated protein. These residues may not be considered as interfacial residues due accessibility values greater than 7% in the complexed form. Our method based solely on Cα positions capture these cases successfully despite the absence of sidechain positions.Further these “false positives” were found to be fairly conserved in the course of evolution (data not shown) reinforcing the important role of these residues in the formation of protein-protein interaction interface.Apart from accessibility based method there are several other methods A set of protein structures at low resolution was considered with only Cα positions available  and the An approach has been developed to identify the buried and exposed residues in proteins solely based on the positions of Cα atoms. As shown using a large number of protein structures with complete atomic positional entries available the method works with very good accuracy, sensitivity and specificity. It is interesting to note that specificity, sensitivity, accuracy and correlation of the results of proposed method is better than that of all-atom models generated starting solely from Cα positions. Aside, the proposed method does not involve the otherwise additional step of sidechain modeling in order to identify solvent exposed and buried residues solely from Cα positions.The approach has been extended to recognize residues in the protein-protein interfaces. Assessment of the performance reveals that the proposed method works well. In fact the structural roles of residues those are recognized as interfacial in our approach, but not in the approach using full-atom model suggest that our approach is useful even if the complex structure has positions of all the atoms available. The proposed approach seeks to mimic the solvent accessibility-based identification of protein-protein interface as applied to all-atom structures. The extent of agreement between the results of proposed approach and inter-subunit distance-based approach is a reflection of difference in perceptions and definition of protein-protein interfacial residues.The proposed method is highly relevant in the analysis of low resolution structures with only the Cα positions available. Our work has a specific impact on the emerging low resolution pictures of fundamentally important protein assemblies obtained by embedding atomic resolution structures in cryo-EM maps. Results of our approaches employed on such structures should highlight the fundamental principles of stability and specificity of multi-protein assemblies and evolution of such complexes.The two different datasets have been used in the present study namely a set of 1464 high resolution structures (comprising monomers) and a set of 1100 structures of protein-protein complexes. These datasets were culled using PISCES NACCESS Performance of the method was measured by calculating the following parameters;Sensitivity (buried) or Sensitivity (interface) = TP/(TP+FN)Specificity (buried) or Specificity (interface) = TP/(TP+FP)Sensitivity (exposed) or Sensitivity (non-interface) = TN/(TN+FP)Specificity (exposed) or Specificity (non-interface) = TN/(TN+FN)Accuracy = (TP+TN)/NCorrelation Coefficient = ((TP*TN-FP*FN)/(sqrt((TP+FN)(TP+FP)(TN+FP)(TN+FN))))Where TP : True positives; FP : False positives; TN : True negatives and FN : False negatives.Figure S1Accessibility plots for Aspargine, Glutamine, Aspartate and Glutamate(101.02 MB TIF)Click here for additional data file.Figure S2Accessibility plots for Alanine, Valine, Leucine and Isoleucine(101.02 MB TIF)Click here for additional data file.Figure S3Accessibility plots for Phenylalanine, Tyrosine, Tryptophan and Methionine(101.86 MB TIF)Click here for additional data file.Figure S4Accessibility plots for Lysine, Arginine, Histidine and Proline(101.02 MB TIF)Click here for additional data file.Figure S5Accessibility plots for Serine, Threonine, Cysteine and Glycine(101.67 MB TIF)Click here for additional data file.Table S1supporting information table(0.04 MB DOC)Click here for additional data file.


Many protein structures determined in high-throughput structural genomics centers, despite their significant novelty and importance, are available only as PDB depositions and are not accompanied by a peer-reviewed manuscript. Because of this they are not accessible by the standard tools of literature searches, remaining underutilized by the broad biological community.To address this issue we have developed TOPSAN, The Open Protein Structure Annotation Network, a web-based platform that combines the openness of the wiki model with the quality control of scientific communication. TOPSAN enables research collaborations and scientific dialogue among globally distributed participants, the results of which are reviewed by experts and eventually validated by peer review. The immediate goal of TOPSAN is to harness the combined experience, knowledge, and data from such collaborations in order to enhance the impact of the astonishing number and diversity of structures being determined by structural genomics centers and high-throughput structural biology.TOPSAN combines features of automated annotation databases and formal, peer-reviewed scientific research literature, providing an ideal vehicle to bridge a gap between rapidly accumulating data from high-throughput technologies and a much slower pace for its analysis and integration with other, relevant research. Structural biology uses experimental methods, such as X-ray crystallography and NMR spectroscopy, to provide atomic-level information about the three-dimensional shapes of biological macromolecules. Such detailed information often delivers a critical component of the puzzle that has to be solved in order to understand the function of a macromolecule. Information provided by structural biology, while essential, by itself is not enough to decipher protein function without associated information from biochemistry, molecular and cellular biology, genomics, and other fields of biology. Historically, experimental structure determination of a protein was a long and painstaking process, which was generally initiated only after a significant body of biochemical and biological evidence about the function of a specific protein had been assembled. The very length of the structure determination process, with many steps extending into months, offered ample time to perform additional analyses, integrate all available information, and even carry out additional experiments to clarify the most interesting questions that were raised during the investigation. Such a multi-pronged approach to the characterization and analysis of a structure of a single protein was typically conducted by single investigator groups with broad experience or through collaboration among multiple laboratories with synergistic expertise. These collaborations were forged by standard mechanisms of communication in the scientific community.This standard model of a structural biology project is now changing, largely because of the development of technological platforms capable of high-throughput protein structure determination. These platforms have reduced the cost and shortened the time for determination of the structure of a novel protein . A signiAt the same time, the interest, relevant expertise, and resources to complete the characterization and analysis of the proteins determined by SG centers do exist in the broader community, i.e., mostly outside of the structure determination centers. However, the traditional mechanisms of forming collaborations and communicating results cannot keep pace with the high-throughput production of SG centers. Such traditional mechanisms include forming personal networks that arise from extended discussions and interactions at meetings and from local collegial interactions within universities and institutes. These mechanisms have become woefully inadequate as the period for protein structure determination has shrunk to days/weeks rather than months/years. Given the very diverse set of proteins that are worked on by PSI centers, the expertise needed to fully analyze each of them is difficult to find even in the largest labs. Finally, another important difference between an SG center and a standard structural biology lab is the high-throughput aspect of structural genomics. With one structure being determined, on average, each working day, there is simply not enough time to integrate relevant, non-structural data and/or to follow a traditional approach to establishing collaborations with appropriate groups with synergistic experience.Structural biology is not the only field struggling to maintain a balance between high-throughput data accumulation and a much slower pace for its analysis and integration with other, relevant research. The former is driven by the rapid pace of technological development, whereas the latter is limited by the standard methods of data analysis and assimilation. The equivalent amount of time and effort that was once needed to sequence a single gene can now yield the sequence of an entire genome; similarly, the effort needed a few years ago to analyze the expression pattern for a single gene can now yield a genome-size DNA expression array. In contrast, the time needed to research a particular question and look for possible connections to other data and/or experiments, as well as the time to find and consult with a colleague who is knowledgeable in another, often connected field, are not easily changed by technology. As a result, a significant percentage of data obtained by high-throughput techniques remain suspended in the no-man's land of "unpublished results"; for instance, the almost 18,000 microarray experiments in the Stanford microarray database have led to only 449 publications , and almhttp://en.wikipedia.org/w/index.php?title=Wikipedia:No_original_research. On the other hand, creating new knowledge is a critical part of science. Wikipedia's dependence on already verified information allows it to avoid issues such as impartial mechanisms for ensuring the reliability of information  and assigning credit for individual contributions. These problems are addressed by peer-reviewed literature, but at the cost of slower speed of dissemination, as well as other constraints imposed by the inherently inefficient structure of the institutionalized peer-review process.This growing gap between data creation and analysis has led to exploration of new approaches for exchanging and disseminating information, ranging, for example, from blogs to wikis to networking sites. Most of these recent attempts have been enabled by the emergence of the Internet, which has changed, and is still changing, the way people communicate, both in science and beyond. It is interesting to note that the most important developments in the Internet era, from the Internet itself to the concept of the World Wide Web , were drhttp://www.nigms.nih.gov/Initiatives/PSI/psi_biology/. Mechanisms similar to those we outline here could be employed to address similar issues in these fields. By focusing on protein structures, TOPSAN has a particular relationship to the recently developed Proteopedia [Most biology focussed wikis, such as WikiPathways , Proteopteopedia  or PDBWiThe Open Protein Structure Annotation Network http://topsan.org, was developed at one of the PSI high-throughput production centers, the Joint Center for Structural Genomics (JCSG), and has since become a collaborative project involving personnel from other PSI centers, as well as from other institutions. Since its inception, the JCSG has recognized the need for high-throughput annotation and the further analysis of its targets for structure determination and, subsequently, for solved structures; these processes are conducted through a combination of automated annotations, detailed knowledge-based analyses, and follow-up collaborations to advance the functional understanding of the structures being determined. A particularly illustrative example that predates our current instantiation of TOPSAN can be seen as a result of our determination of a novel thymidylate synthase, TM0449 [Thermotoga maritima. Previously known only from genomic complementation studies in Dictyostelium [TOPSAN, , TM0449  from Theostelium , the nonostelium -18.Figure In this article, we describe our ongoing implementation of TOPSAN  and lay Authorship tracking mechanisms ensure both proper assignment of credit and accountability of contributors. Entries are peer-reviewed by users with established records of accomplishment and credentials to ensure content reliability; at this point, the review is largely performed by senior JCSG scientists. All users of the system implicitly accept the collaborative rules, as any TOPSAN entry is, in fact, an open invitation to collaborate with the SG center that solved this particular structure. This invitation is supported by significant preliminary data and results, such as biological material  and sequence and structure analyses that often provide detailed hypotheses on the function of the newly solved protein. Such collaborations can lead to traditional, peer-reviewed papers, and, encouragingly, many of the TOPSAN entries are, in fact, progressing this way.One view of the TOPSAN system then is that of a stepping-stone to peer-reviewed publications, where the openness of our system allows for the immediate establishment of collaborations and TOPSAN pages are treated as a mechanism to link individual collaborators and unpublished results to achieve the ultimate goal of publishing in a, standard, peer-reviewed journal. Another, more far-reaching view of TOPSAN is that of a "live" protein annotation and collaboration platform, i.e., a platform that might lead to novel forms of ongoing, virtually continuous scientific communication and knowledge creation. For collaborative science, this approach offers a new paradigm especially pertinent to the "omics" era. While we personally favor this view, the actual outcome will ultimately be determined by the general community of users and contributors.http://www.topsan.org is a new type of communication platform for annotations and follow-up analysis and research on proteins. At this point, TOPSAN focuses on proteins targeted or solved by structural genomics centers; however, users are free to create pages on any protein they are interested in, and many, actually most, of the TOPSAN pages are compiled for proteins whose structures are not yet determined. Furthermore, many TOPSAN pages increasingly do not focus on individual proteins, but on protein families, specific organisms, or pathways , and several hundred user-created pages that do not fall into the above categories. Over 1,000 pages contain human-curated annotations, ranging from a few sentences to several pages, with a median of over 150 words, and of quality from relatively trivial annotations to publication-quality manuscripts. The content to date has been amassed from over 400 registered users, although the majority has been contributed by a smaller subset of the most active contributors. While most of the TOPSAN pages focus on experimentally determined protein structures, TOPSAN was of great utility in the development of the complete metabolic reconstruction for maritima , in whichttp://creativecommons.org/licenses/by-nc-sa/3.0/legalcode, which allows content to be available for others to build upon and share legally. In addition to open-content licensing, TOPSAN provides an application programming interface (API) that enables external sources to retrieve content easily. API access is available to collaborating sites to retrieve content from TOPSAN protein pages. This has been implemented for PFAM, where the contents of an individual TOPSAN protein page are currently being imported to PFAM by a PFAM server via the TOPSAN API. This feature is standard in the PFAM 24.0 release. In this case, we developed a custom PHP script (files.topsan.org/retrieve.php?uniprotId = xxx) for PFAM servers to readily access TOPSAN page content in real time. A structured XML page is provided for each protein on TOPSAN. A Uniprot ID is passed as an argument to the script, which then queries the TOPSAN MySQL database for the current annotation of the page as well as for any file attachments/images. This information is formatted as an XML document and returned to the requesting client. The API is flexible and can easily be used for third parties to retrieve our content. TOPSAN pages are searchable with all Internet search engines; our internal analysis shows that about 50% of TOPSAN traffic "arrives" from Google and other search engines, and that users directly view specific protein pages that contain keywords that are a subject of an individual search. At the same time, the API access to TOPSAN allows for integration into other resources. For example, the Calit2 Visualization Lab at UCSD displays TOPSAN protein annotations from within their structure gallery CAVE 3D wall display. This protein structure display is utilized in courses both at UCSD and at The Scripps Research Institute.TOPSAN aims not only to collect information about proteins whose structures have been determined, but also to share and disseminate this information. TOPSAN pages are free to view without registration, and TOPSAN content is licensed under the Creative Commons share-alike license The current implementation of TOPSAN was developed using MindTouch, an enterprise open-source collaboration and integration platform, which provides access to tools and scripting that can be utilized to develop a customized, interactive website. Scripts and customized templates defining TOPSAN-specific protein pages integrate several sources and types of information; namely, existing annotation automatically parsed from established resources and user-created content that can be edited by all registered users. The MindTouch platform offers some core features, such as open-source code; user account handling, including advanced security features; and extensive backup and version tracking capabilities that make it an excellent platform choice on which to build TOPSAN. At the same time, MindTouch is comparable in many ways to other platforms. For instance, the initial version of TOPSAN, which was in use until late 2008, was developed using the now-defunct JOTSPOT platform; the TOPSAN server was then migrated to the MindTouch platform, maintaining its overall look, feel, and functionality.A remote application (TopsanApp) is used to retrieve protein information from external resources and to create and store pages for specified proteins on the platform through an API. This application is coded in C# built on the .NET framework Figure  and firsAccess to view or edit a page can be set for all protein pages to be at the individual or group level. As part of our customization, we have developed a registration process that allows for some level of initiation and management of new users. All unregistered guests have access to view all protein pages on TOPSAN. Upon registration, a profile page, which may be edited by the new user, is created. It is here that they may enter scientific interests, upload profile images, and add their publications. A user's profile page is linked to every protein page contribution thereafter. After registration, the site administrator conducts a basic verification procedure that includes checking the user's e-mail address and cursory qualifications to ensure a level of validity of the user and protect the system from abuse and spam. Upon successful verification, a user is given "Contributor"-level permissions that allow him/her to edit all protein pages and respective discussion pages.A new protein page can be created on TOPSAN when requested by a user, but can be also created automatically by the system. At this point, pages are created automatically for all JCSG targets when they pass the crystallization stage and for all targets from other PSI centers when structures are deposited in PDB. At any moment, two fields--protein summary and ligand summary--are available for editing to registered users. Other fields on the page, including links to precalculated analyses, are prepared automatically and placed there by annotation scripts based on parsing of input from several databases, such as targetDB or PDB, and annotation/analysis servers, such as PFAM , SCOP 2, PDBSum An important part of studying a protein is comparing it to other proteins. TOPSAN allows users to create groups of proteins using a tagging system. This feature gives the user the ability to create customized higher-order groupings based on their own criteria, expanding on the predefined classification systems already in use in the scientific community, which were mapped onto TOPSAN proteins using a similar system. For instance, SCOP hierarchical classification protein systems were mapped onto TOPSAN, where each protein that has a SCOP entry can be annotated at its respective Class, Fold, Superfamily, or Family level, as defined in SCOP. In addition to allowing users to describe features at the higher order of the individual structure, community-based structure classification to the SCOP hierarchy could be propagated back to SCOP. Utilizing pre-defined classification systems on TOPSAN to create groupings of proteins for higher-order annotations is currently being expanded to use CATH , PFAM 2, and metNitrosomonas europaea, solved by the JCSG in 2006 and deposited in PDB with code 2ich, was the first structure for the PFAM09410 protein family (unpublished observation). This family, called DUF2006 (Domain of Unknown Function #2006) has over 400 homologs in bacteria, archaea, and fungi. The PFAM DUF2006 family overlaps with the COG5621 family of predicted secreted hydrolases (COG5621) and is distantly related to two other PFAM families of hydroxyneurosporene synthases (PFAM0743) and Svf1-like proteins (PF08622). While this information was available prior to structure determination, structure analysis of NE1406 subsequently revealed that it consists of two repeats of a novel variant of an up-and-down β-barrel structure and is structurally similar to proteins from the calycin superfamily of integral transmembrane proteins in mitochondria and in the outer membrane of Gram-negative bacteria. This structural similarity, accompanied by very low sequence similarity  led to an ongoing debate about possible homology and functional similarity between these two groups of proteins. On one hand, the characteristic sequence signature of the calycin superfamily was present in the N-terminal half of NE1406  and, in addition, two (out of three) short conserved regions (SCR), characteristic of the lipocalin family, were also present in NE1406. On the other hand, analysis of the structural superposition of NE1406 with members of the calycin superfamily revealed a number of systematic differences; the β-sheets forming the NE1406 barrel were both longer and flatter than those in calycins, resulting in a narrower opening at the bottom of the barrel, at the site of the binding site in calycins. Secondary structure elements, such as the long C-terminal α-helix characteristic of lipocalins , which represent a structurally and functionally distinct subclass [A TOPSAN page usually starts from a simple summary of the structure and (if known) functional annotations of the family to which it belongs. Very often, distant structural similarity provides additional hints, leading to further questions and hypotheses. For instance, the crystal structure of the protein NE1406 from subclass  of the cFigure http://www.topsan.org/explore?pdbid=2ich and has contributions from Alexey Murzin, Arne Skerra, Andrei L. Lomize, Darren Flower, and members of the JCSG team. This type of interaction involving scientists in several locations around the globe could have happened naturally over a longer period of time using standard interaction mechanisms, such as chance encounters in scientific meetings.  However, the TOPSAN platform allowed this interaction to happen in cyberspace over the course of a few days among participants who never met in person and some of whom did not even know each other.Experimental verification of the NE1406 connection to calycins or lipocalins would present the first evidence of a lipocalin-related protein in the archaea domain and would settle the question of whether this family may have arisen via horizontal transfer to eukaryotic cells from the endosymbiotic alpha-proteobacterial ancestor of the mitochondrion . This anhttp://topsan.org, a collaborative environment developed by the Bioinformatics Core of the Joint Center for Structural Genomics to facilitate annotations and collaborative research in order to characterize protein structures solved by Protein Structure Initiative production centers and other structural genomics groups, and in turn, to facilitate the integration of these structures with research in the broad biological and biochemical community. Structural genomics, in its quest to provide broad coverage of protein space, frequently targets uncharacterized proteins, whose specific functions are unknown. Sometimes, an analysis of distant homology relationships or of the data from expression arrays suggest a possible relation to another, better-characterized protein family, but the reliability of such predictions and the extent of possible functional similarity varies from case to case and needs expert analysis. For other structures, features that are novel and impossible to predict from the sequence can be gleaned from the structure and hint at possible, previously unknown twists in the evolution of some members of otherwise, well-characterized protein families. In many such cases, structure analysis may lead to a new structural or functional hypothesis or an interesting speculation, which while very intriguing, would likely remain unpublished using standard criteria for peer-review publications. TOPSAN opens up a venue for such information to become available to a wider audience, increasing the chance that such partial information would be debated, discussed, and eventually combined with other supporting information from experimental work in another lab or, encouragingly, would prompt another researcher to perform a critical experiment to evaluate and test the function or the hypothesis. TOPSAN strives to maintain quality that approaches peer-reviewed publications by ensuring the contributions come from registered users, who during registration have to present credentials as to their expertise in biology, as well as by monitoring and evaluating contributions to identify, on one hand, spammers or abusers of the system, and on the other, expert users who are ready to assist in ensuring quality by providing oversight of other users. With a mechanism of tracking authorship of contributions, TOPSAN offers great possibility that disparate collaborators, often unknown to one other, can pool their information and resources to arrive at a body of significant new knowledge. With these goals, TOPSAN aims to occupy a niche different from that of the Wikipedia-type scientific wikis and that of databases or depository sites. Nevertheless, the primary goal of both types of resources is to provide easier access to already existing and validated information. At the same time, with its emphasis on ease of use and its lack of requirements for a minimal contribution size, TOPSAN is different from the peer-reviewed, standard scientific literature and allows more spontaneous, rapid communication and quick interaction.In this article, we describe TOPSAN, The Open Protein Structure Annotation Network The TOPSAN project evolved from an internal JCSG effort to annotate and more fully characterize the proteins that were determined in our center. This history is responsible for the current implementation being strongly focused on protein structure, but the TOPSAN concept can be readily generalized to any high-throughput project, such as PSI:Biology, to novel structures of other macromolecules determined by a structural biology lab, and more broadly, to any research domain where there is a need for a wider collaboration and a chance that critical pieces of information or expertise for any given project or research area are already known or available from members of the scientific community. The essence of the TOPSAN approach is to encourage new collaborations and explore the use of diverse, often disparate data to find new, integrative views on important biological problems. Therefore, while at this point TOPSAN is an experiment in the annotation and analysis of proteins targeted by structural genomics, it is also a model for collaborations in the potentially much larger and more complex research fields that are emerging in biology and other research disciplines.DW developed the TOPSAN website, including page scripting and wrote sections of the paper; SSK developed the core concepts of TOPSAN, planned and supervised the development of TOPSAN implementation, and wrote sections of the paper; CB developed sample TOPSAN pages and edited numerous specific pages, contributed to the discussions about the TOPSAN concept, and wrote sections of the paper; IAW helped interconnect the TOPSAN concept with the research of the JCSG and provided input to the shape and form of TOPSAN pages; AG helped develop the TOPSAN concept, supervised the TOPSAN project, and wrote the paper; JW helped develop the TOPSAN concept, supervised the TOPSAN project, and wrote the paperAll authors participated in discussions and revisions of the paper. All authors read and approved the manuscript.