Unusual commonality in active site structural features of substrate promiscuous and specialist enzymes.

Enzyme promiscuity is the ability of (some) enzymes to perform alternate reactions or catalyze non-cognate substrate(s). The latter is referred to as substrate promiscuity, widely studied for its biotechnological applications and understanding enzyme evolution. Insights into the structural basis of substrate promiscuity would greatly benefit the design and engineering of enzymes. Previous studies on some enzymes have suggested that flexibility, hydrophobicity, and active site protonation state could play an important role in enzyme promiscuity. However, it is not known yet whether substrate promiscuous enzymes have distinctive structural characteristics compared to specialist enzymes, which are specific for a substrate. In pursuit to address this, we have systematically compared substrate/catalytic binding site structural features of substrate promiscuous with those of specialist enzymes. For this, we have carefully constructed dataset of substrate promiscuous and specialist enzymes. On careful analysis, surprisingly, we found that substrate promiscuous and specialist enzymes are similar in various binding/catalytic site structural features such as flexibility, surface area, hydrophobicity, depth, and secondary structures. Recent studies have also alluded that promiscuity is widespread among enzymes. Based on these observations, we propose that substrate promiscuity could be defined as a continuum feature that varies from narrow (specialist) to broad range of substrate preferences. Moreover, diversity of conformational states of an enzyme accessible for ligand binding may possibly regulate its substrate preferences.

Optimal Protein Sequence Design Mitigates Mechanical Failure in Silk ß-Sheet Nanocrystals.

The excellent mechanical strength and toughness of spider silk are well characterized experimentally and understood atomistically using computational simulations. However, little attention has been focused on understanding whether the amino acid sequence of ß-sheet nanocrystals, which is the key to rendering strength to silk fiber, is optimally chosen to mitigate molecular-scale failure mechanisms. To investigate this, we modeled ß-sheet nanocrystals of various representative small/polar/hydrophobic amino acid repeats for determining the sequence motif having superior nanomechanical tensile strength and toughness. The constant velocity pulling of the central ß-strand in the nanocrystal, using steered molecular dynamics, showed that homopolymers of small amino acid (alanine/alanine-glycine) sequence motifs, occurring in natural silk fibroin, have better nanomechanical properties than other modeled structures. Further, we analyzed the hydrogen bond (HB) and ß-strand pull dynamics of modeled nanocrystals to understand the variation in their rupture mechanisms and explore sequence-dependent mitigating factors contributing to their superior mechanical properties. Surprisingly, the enhanced side-chain interactions in homopoly-polar/hydrophobic amino acid models are unable to augment backbone HB cooperativity to increase mechanical strength. Our analyses suggest that nanocrystals of pristine silk sequences most likely achieve superior mechanical strength by optimizing side-chain interaction, packing, and main-chain HB interactions. Thus, this study suggests that the nanocrystal ß-sheet sequence plays a crucial role in determining the nanomechanical properties of silk, and the evolutionary process has optimized it in natural silk. This study provides insight into the molecular design principle of silk with implications in the genetically modified artificial synthesis of silk-like biomaterials.

Curcumin Inhibits Membrane-Damaging Pore-Forming Function of the ß-Barrel Pore-Forming Toxin Vibrio cholerae Cytolysin.

Vibrio cholerae cytolysin (VCC) is a ß-barrel pore-forming toxin (ß-PFT). Upon encountering the target cells, VCC forms heptameric ß-barrel pores and permeabilizes the cell membranes. Structure-function mechanisms of VCC have been extensively studied in the past. However, the existence of any natural inhibitor for VCC has not been reported yet. In the present study, we show that curcumin can compromise the membrane-damaging activity of VCC. Curcumin is known to modulate a wide variety of biological processes and functions. However, the application of curcumin in the physiological scenario often gets limited due to its extremely poor solubility in the aqueous environment. Interestingly, we find that VCC can associate with the insoluble fraction of curcumin in the aqueous medium and thus gets separated from the solution phase. This, in turn, reduces the availability of VCC to attack the target membranes and thus blocks the membrane-damaging action of the toxin. We also observe that the soluble aqueous extract of curcumin, generated by the heat treatment, compromises the pore-forming activity of VCC. Interestingly, in the presence of such soluble extract of curcumin, VCC binds to the target membranes and forms the oligomeric assembly. However, such oligomers appear to be non-functional, devoid of the pore-forming activity. The ability of curcumin to bind to VCC and neutralize its membrane-damaging activity suggests that curcumin has the potential to act as an inhibitor of this potent bacterial ß-PFT.

Tyrosine in the hinge region of the pore-forming motif regulates oligomeric ß-barrel pore formation by Vibrio cholerae cytolysin.

ß-barrel pore-forming toxins perforate cell membranes by forming oligomeric ß-barrel pores. The most crucial step is the membrane-insertion of the pore-forming motifs that create the transmembrane ß-barrel scaffold. Molecular mechanism that regulates structural reorganization of these pore-forming motifs during ß-barrel pore-formation still remains elusive. Using Vibrio cholerae cytolysin as an archetypical example of the ß-barrel pore-forming toxin, we show that a key tyrosine residue (Y321) in the hinge region of the pore-forming motif plays crucial role in this process. Mutation of Y321 abrogates oligomerization of the membrane-bound toxin protomers, and blocks subsequent steps of pore-formation. Our study suggests that the presence of Y321 in the hinge region of the pore-forming motif is crucial for the toxin molecule to sense membrane-binding, and to trigger essential structural rearrangements required for the subsequent oligomerization and pore-formation process. Such a regulatory mechanism of pore-formation by V. cholerae cytolysin has not been documented earlier in the structurally related ß-barrel pore-forming toxins.

An inquiry-based approach in large undergraduate labs: Learning, by doing it the "wrong" way.

Undergraduate laboratory courses, owing to their larger sizes and shorter time slots, are often conducted in highly structured modes. However, this approach is known to interfere with students' engagement in the experiments. To enhance students' engagement, we propose an alternative mode of running laboratory courses by creating some "disorder" in a previously adopted structure. After performing an experiment in the right way, the students were asked to repeat the experiment but with a variation at certain steps leading to the experiment being done the "wrong" way. Although this approach led to fewer experiments being conducted in a semester, it significantly enhanced the students' involvement. This was also reflected in the students' feedback. The majority of students preferred repeating an experiment with a variant protocol than performing a new experiment. Although we have tested this inquiry-based approach only for an undergraduate laboratory course in molecular biology, we believe such an approach could also be extended to undergraduate laboratory courses of other subjects.

Unraveling the structural landscape of intra-chain domain interfaces: Implication in the evolution of domain-domain interactions.

Intra-chain domain interactions are known to play a significant role in the function and stability of multidomain proteins. These interactions are mediated through a physical interaction at domain-domain interfaces (DDIs). With a motivation to understand evolution of interfaces, we have investigated similarities among DDIs. Even though interfaces of protein-protein interactions (PPIs) have been previously studied by structurally aligning interfaces, similar analyses have not yet been performed on DDIs of either multidomain proteins or PPIs. For studying the structural landscape of DDIs, we have used iAlign to structurally align intra-chain domain interfaces of domains. The interface alignment of spatially constrained domains (due to inter-domain linkers) showed that ~88% of these could identify a structural matching interface having similar C-alpha geometry and contact pattern despite that aligned domain pairs are not structurally related. Moreover, the mean interface similarity score (IS-score) is 0.307, which is higher compared to the average random IS-score (0.207) suggesting domain interfaces are not random. The structural space of DDIs is highly connected as ~84% of all possible directed edges among interfaces are found to have at most path length of 8 when 0.26 is IS-score threshold. At this threshold, ~83% of interfaces form the largest strongly connected component. Thus, suggesting that structural space of intra-chain domain interfaces is degenerate and highly connected, as has been found in PPI interfaces. Interestingly, searching for structural neighbors of inter-chain interfaces among intra-chain interfaces showed that ~86% could find a statistically significant match to intra-chain interface with a mean IS-score of 0.311. This implies that domain interfaces are degenerate whether formed within a protein or between proteins. The interface degeneracy is most likely due to limited possible ways of packing secondary structures. In principle, interface similarities can be exploited to accurately model domain interfaces in structure prediction of multidomain proteins.

CSmetaPred: a consensus method for prediction of catalytic residues.

BACKGROUND: Knowledge of catalytic residues can play an essential role in elucidating mechanistic details of an enzyme. However, experimental identification of catalytic residues is a tedious and time-consuming task, which can be expedited by computational predictions. Despite significant development in active-site prediction methods, one of the remaining issues is ranked positions of putative catalytic residues among all ranked residues. In order to improve ranking of catalytic residues and their prediction accuracy, we have developed a meta-approach based method CSmetaPred. In this approach, residues are ranked based on the mean of normalized residue scores derived from four well-known catalytic residue predictors. The mean residue score of CSmetaPred is combined with predicted pocket information to improve prediction performance in meta-predictor, CSmetaPred_poc. RESULTS: Both meta-predictors are evaluated on two comprehensive benchmark datasets and three legacy datasets using Receiver Operating Characteristic (ROC) and Precision Recall (PR) curves. The visual and quantitative analysis of ROC and PR curves shows that meta-predictors outperform their constituent methods and CSmetaPred_poc is the best of evaluated methods. For instance, on CSAMAC dataset CSmetaPred_poc (CSmetaPred) achieves highest Mean Average Specificity (MAS), a scalar measure for ROC curve, of 0.97 (0.96). Importantly, median predicted rank of catalytic residues is the lowest (best) for CSmetaPred_poc. Considering residues ranked ≤20 classified as true positive in binary classification, CSmetaPred_poc achieves prediction accuracy of 0.94 on CSAMAC dataset. Moreover, on the same dataset CSmetaPred_poc predicts all catalytic residues within top 20 ranks for ~73% of enzymes. Furthermore, benchmarking of prediction on comparative modelled structures showed that models result in better prediction than only sequence based predictions. These analyses suggest that CSmetaPred_poc is able to rank putative catalytic residues at lower (better) ranked positions, which can facilitate and expedite their experimental characterization. CONCLUSIONS: The benchmarking studies showed that employing meta-approach in combining residue-level scores derived from well-known catalytic residue predictors can improve prediction accuracy as well as provide improved ranked positions of known catalytic residues. Hence, such predictions can assist experimentalist to prioritize residues for mutational studies in their efforts to characterize catalytic residues. Both meta-predictors are available as webserver at: http://14.139.227.206/csmetapred/ .

Disulphide bond restrains the C-terminal region of thermostable direct hemolysin during folding to promote oligomerization.

Pore-forming toxins (PFTs) are typically produced as water-soluble monomers, which upon interacting with target cells assemble into transmembrane oligomeric pores. Vibrio parahaemolyticus thermostable direct hemolysin (TDH) is an atypical PFT that exists as a tetramer in solution, prior to membrane binding. The TDH structure highlights a core ß-sandwich domain similar to those found in the eukaryotic actinoporin family of PFTs. However, the TDH structure harbors an extended C-terminal region (CTR) that is not documented in the actinoporins. This CTR remains tethered to the ß-sandwich domain through an intra-molecular disulphide bond. Part of the CTR is positioned at the inter-protomer interface in the TDH tetramer. Here we show that the truncation, as well as mutation, of the CTR compromise tetrameric assembly, and the membrane-damaging activity of TDH. Our study also reveals that intra-protomer disulphide bond formation during the folding/assembly process of TDH restrains the CTR to mediate its participation in the formation of inter-protomer contact, thus facilitating TDH oligomerization. However, once tetramerization is achieved, disruption of the disulphide bond does not affect oligomeric assembly. Our study provides critical insights regarding the regulation of the oligomerization mechanism of TDH, which has not been previously documented in the PFT family.

XTMS: pathway design in an eXTended metabolic space.

As metabolic engineering and synthetic biology progress toward reaching the goal of a more sustainable use of biological resources, the need of increasing the number of value-added chemicals that can be produced in industrial organisms becomes more imperative. Exploring, however, the vast possibility of pathways amenable to engineering through heterologous genes expression in a chassis organism is complex and unattainable manually. Here, we present XTMS, a web-based pathway analysis platform available at http://xtms.issb.genopole.fr, which provides full access to the set of pathways that can be imported into a chassis organism such as Escherichia coli through the application of an Extended Metabolic Space modeling framework. The XTMS approach consists on determining the set of biochemical transformations that can potentially be processed in vivo as modeled by molecular signatures, a specific coding system for derivation of reaction rules for metabolic reactions and enumeration of all the corresponding substrates and products. Most promising routes are described in terms of metabolite exchange, maximum allowable pathway yield, toxicity and enzyme efficiency. By answering such critical design points, XTMS not only paves the road toward the rationalization of metabolic engineering, but also opens new processing possibilities for non-natural metabolites and novel enzymatic transformations.

Evolutionarily conserved and conformationally constrained short peptides might serve as DNA recognition elements in intrinsically disordered regions.

Despite recent advances, it is yet not clear how intrinsically disordered regions in proteins recognize their targets without any defined structures. Short linear motifs had been proposed to mediate molecular recognition by disordered regions; however, the underlying structural prerequisite remains elusive. Moreover, the role of short linear motifs in DNA recognition has not been studied. We report a repertoire of short evolutionarily Conserved Recognition Elements (CoREs) in long intrinsically disordered regions, which have very distinct amino-acid propensities from those of known motifs, and exhibit a strong tendency to retain their three-dimensional conformations compared to adjacent regions. The majority of CoREs directly interact with the DNA in the available 3D structures, which is further supported by literature evidence, analyses of ΔΔG values of DNA-binding energies and threading-based prediction of DNA binding potential. CoREs were enriched in cancer-associated missense mutations, further strengthening their functional nature. Significant enrichment of glycines in CoREs and the preference of glycyl ϕ-Ψ values within the left-handed bridge range in the l-disallowed region of the Ramachandran plot suggest that Gly-to-nonGly mutations within CoREs might alter the backbone conformation and consequently the function, a hypothesis that we reconciled using available mutation data. We conclude that CoREs might serve as bait for DNA recognition by long disordered regions and that certain mutations in these peptides can disrupt their DNA binding potential and consequently the protein function. We further hypothesize that the preferred conformations of CoREs and of glycyl residues therein might play an important role in DNA binding. The highly ordered nature of CoREs hints at a therapeutic strategy to inhibit malicious molecular interactions using small molecules mimicking CoRE conformations.

Enumerating metabolic pathways for the production of heterologous target chemicals in chassis organisms.

BACKGROUND: We consider the possibility of engineering metabolic pathways in a chassis organism in order to synthesize novel target compounds that are heterologous to the chassis. For this purpose, we model metabolic networks through hypergraphs where reactions are represented by hyperarcs. Each hyperarc represents an enzyme-catalyzed reaction that transforms set of substrates compounds into product compounds. We follow a retrosynthetic approach in order to search in the metabolic space (hypergraphs) for pathways (hyperpaths) linking the target compounds to a source set of compounds. RESULTS: To select the best pathways to engineer, we have developed an objective function that computes the cost of inserting a heterologous pathway in a given chassis organism. In order to find minimum-cost pathways, we propose in this paper two methods based on steady state analysis and network topology that are to the best of our knowledge, the first to enumerate all possible heterologous pathways linking a target compounds to a source set of compounds. In the context of metabolic engineering, the source set is composed of all naturally produced chassis compounds (endogenuous chassis metabolites) and the target set can be any compound of the chemical space. We also provide an algorithm for identifying precursors which can be supplied to the growth media in order to increase the number of ways to synthesize specific target compounds. CONCLUSIONS: We find the topological approach to be faster by several orders of magnitude than the steady state approach. Yet both methods are generally scalable in time with the number of pathways in the metabolic network. Therefore this work provides a powerful tool for pathway enumeration with direct application to biosynthetic pathway design.

Learning Protein Folding Energy Functions.

A critical open problem in ab initio protein folding is protein energy function design, which pertains to defining the energy of protein conformations in a way that makes folding most efficient and reliable. In this paper, we address this issue as a weight optimization problem and utilize a machine learning approach, learning-to-rank, to solve this problem. We investigate the ranking-via-classification approach, especially the RankingSVM method and compare it with the state-of-the-art approach to the problem using the MINUIT optimization package. To maintain the physicality of the results, we impose non-negativity constraints on the weights. For this we develop two efficient non-negative support vector machine (NNSVM) methods, derived from L2-norm SVM and L1-norm SVMs, respectively. We demonstrate an energy function which maintains the correct ordering with respect to structure dissimilarity to the native state more often, is more efficient and reliable for learning on large protein sets, and is qualitatively superior to the current state-of-the-art energy function.

TASSER_low-zsc: an approach to improve structure prediction using low z-score-ranked templates.

In a variety of threading methods, often poorly ranked (low z-score) templates have good alignments. Here, a new method, TASSER_low-zsc that identifies these low z-score-ranked templates to improve protein structure prediction accuracy, is described. The approach consists of clustering of threading templates by affinity propagation on the basis of structural similarity (thread_cluster) followed by TASSER modeling, with final models selected by using a TASSER_QA variant. To establish the generality of the approach, templates provided by two threading methods, SP(3) and SPARKS(2), are examined. The SP(3) and SPARKS(2) benchmark datasets consist of 351 and 357 medium/hard proteins (those with moderate to poor quality templates and/or alignments) of length < or =250 residues, respectively. For SP(3) medium and hard targets, using thread_cluster, the TM-scores of the best template improve by approximately 4 and 9% over the original set (without low z-score templates) respectively; after TASSER modeling/refinement and ranking, the best model improves by approximately 7 and 9% over the best model generated with the original template set. Moreover, TASSER_low-zsc generates 22% (43%) more foldable medium (hard) targets. Similar improvements are observed with low-ranked templates from SPARKS(2). The template clustering approach could be applied to other modeling methods that utilize multiple templates to improve structure prediction.

PSiFR: an integrated resource for prediction of protein structure and function.

UNLABELLED: In the post-genomic era, the annotation of protein function facilitates the understanding of various biological processes. To extend the range of function annotation methods to the twilight zone of sequence identity, we have developed approaches that exploit both protein tertiary structure and/or protein sequence evolutionary relationships. To serve the scientific community, we have integrated the structure prediction tools, TASSER, TASSER-Lite and METATASSER, and the functional inference tools, FINDSITE, a structure-based algorithm for binding site prediction, Gene Ontology molecular function inference and ligand screening, EFICAz(2), a sequence-based approach to enzyme function inference and DBD-hunter, an algorithm for predicting DNA-binding proteins and associated DNA-binding residues, into a unified web resource, Protein Structure and Function prediction Resource (PSiFR). AVAILABILITY AND IMPLEMENTATION: PSiFR is freely available for use on the web at http://psifr.cssb.biology.gatech.edu/

Performance of the Pro-sp3-TASSER server in CASP8.

The performance of the protein structure prediction server pro-sp3-TASSER in CASP8 is described. Compared to CASP7, the major improvement in prediction is in the quality of input models to TASSER. These improvements are due to the PRO-SP(3) threading method, the improved quality of contact predictions provided by TASSER_2.0, multiple short TASSER simulations for building the full-length model, and the accuracy of model selection using the TASSER-QA quality assessment method. Finally, we analyze the overall performance and highlight some successful predictions of the pro-sp3-TASSER server.

Fr-TM-align: a new protein structural alignment method based on fragment alignments and the TM-score.

BACKGROUND: Protein tertiary structure comparisons are employed in various fields of contemporary structural biology. Most structure comparison methods involve generation of an initial seed alignment, which is extended and/or refined to provide the best structural superposition between a pair of protein structures as assessed by a structure comparison metric. One such metric, the TM-score, was recently introduced to provide a combined structure quality measure of the coordinate root mean square deviation between a pair of structures and coverage. Using the TM-score, the TM-align structure alignment algorithm was developed that was often found to have better accuracy and coverage than the most commonly used structural alignment programs; however, there were a number of situations when this was not true. RESULTS: To further improve structure alignment quality, the Fr-TM-align algorithm has been developed where aligned fragment pairs are used to generate the initial seed alignments that are then refined using dynamic programming to maximize the TM-score. For the assessment of the structural alignment quality from Fr-TM-align in comparison to other programs such as CE and TM-align, we examined various alignment quality assessment scores such as PSI and TM-score. The assessment showed that the structural alignment quality from Fr-TM-align is better in comparison to both CE and TM-align. On average, the structural alignments generated using Fr-TM-align have a higher TM-score (~9%) and coverage (~7%) in comparison to those generated by TM-align. Fr-TM-align uses an exhaustive procedure to generate initial seed alignments. Hence, the algorithm is computationally more expensive than TM-align. CONCLUSION: Fr-TM-align, a new algorithm that employs fragment alignment and assembly provides better structural alignments in comparison to TM-align. The source code and executables of Fr-TM-align are freely downloadable at: http://cssb.biology.gatech.edu/skolnick/files/FrTMalign/.

Analysis of TASSER-based CASP7 protein structure prediction results.

An improved TASSER (Threading/ASSEmbly/Refinement) methodology is applied to predict the tertiary structure for all CASP7 targets. TASSER employs template identification by threading, followed by tertiary structure assembly by rearranging continuous template fragments, where conformational space is searched via Parallel Hyperbolic Monte Carlo sampling with an optimized force-field that includes knowledge-based statistical potentials and restraints derived from threading templates. The final models are selected by clustering structures from the low temperature replicas. Improvements in TASSER over CASP6 involve use of better templates from 3D-jury applied to three threading programs, PROSPECTOR_3, SP(3), and SPARKS, and a fragment comparison method for better model ranking. For targets with no reliable templates, a variant of TASSER (chunk-TASSER) is also applied with potentials and restraints extracted from ab initio folded supersecondary chunks of the target to build full-length models. For all 124 CASP targets/domains, the average root-mean-square-deviation (RMSD) from native and alignment coverage of the best initial threading models from 3D-jury are 6.2 A and 93%, respectively. Following TASSER reassembly, the average RMSD of the best model in the template aligned region decreases to 4.9 A and the average TM-score increases from 0.617 for the template to 0.678 for the best full-length model. Based on target difficulty, the average TM-scores of the final model to native are 0.904, 0.671, and 0.307 for high-accuracy template-based modeling, template-based modeling, and free modeling targets/domains, respectively. For the more difficult targets, TASSER with modest human intervention performed better in comparison to its server counterpart, MetaTASSER, which used a limited time simulation.

Phosphoprotein and phosphopeptide interactions with the FHA domain from Arabidopsis kinase-associated protein phosphatase.

FHA domains are phosphoThr recognition modules found in diverse signaling proteins, including kinase-associated protein phosphatase (KAPP) from Arabidopsis thaliana. The kinase-interacting FHA domain (KI-FHA) of KAPP targets it to function as a negative regulator of some receptor-like kinase (RLK) signaling pathways important in plant development and environmental responses. To aid in the identification of potential binding sites for the KI-FHA domain, we predicted (i) the structure of a representative KAPP-binding RLK, CLAVATA1, and (ii) the functional surfaces of RLK kinase domains using evolutionary trace analysis. We selected phosphopeptides from KAPP-binding Arabidopsis RLKs for in vitro studies of association with KI-FHA from KAPP. Three phosphoThr peptide fragments from the kinase domain of CLV1 or BAK1 were found to bind KI-FHA with KD values of 8-20 microM, by NMR or titration calorimetry. Their affinity is driven by favorable enthalpy and solvation entropy gain. Mutagenesis of these three threonine sites suggests Thr546 in the C-lobe of the BAK1 kinase domain to be a principal but not sole site of KI-FHA binding in vitro. The brassinosteroid receptor BRI1 and KAPP are shown to associate in vivo and in vitro. Further genetic studies indicate that KAPP may be a negative regulator of the BRI1 signaling transduction pathway. 15N-Labeled KI-FHA was titrated with the GST-BRI1 kinase domain and monitored by NMR. BRI1 interacts with the same 3/4, 4/5, 6/7, 8/9, and 10/11 recognition loops of KI-FHA, with similar affinity as the phosphoThr peptides.

TASSER-Lite: an automated tool for protein comparative modeling.

This study involves the development of a rapid comparative modeling tool for homologous sequences by extension of the TASSER methodology, developed for tertiary structure prediction. This comparative modeling procedure was validated on a representative benchmark set of proteins in the Protein Data Bank composed of 901 single domain proteins (41-200 residues) having sequence identities between 35-90% with respect to the template. Using a Monte Carlo search scheme with the length of runs optimized for weakly/nonhomologous proteins, TASSER often provides appreciable improvement in structure quality over the initial template. However, on average, this requires approximately 29 h of CPU time per sequence. Since homologous proteins are unlikely to require the extent of conformational search as weakly/nonhomologous proteins, TASSER's parameters were optimized to reduce the required CPU time to approximately 17 min, while retaining TASSER's ability to improve structure quality. Using this optimized TASSER (TASSER-Lite), we find an average improvement in the aligned region of approximately 10% in root mean-square deviation from native over the initial template. Comparison of TASSER-Lite with the widely used comparative modeling tool MODELLER showed that TASSER-Lite yields final models that are closer to the native. TASSER-Lite is provided on the web at (http://cssb.biology.gatech.edu/skolnick/webservice/tasserlite/index.html).

A new domain family in the superfamily of alkaline phosphatases.

During the course of our large-scale genome analysis a conserved domain, currently detectable only in the genomes of Drosophila melanogaster, Caenorhabditis elegans and Anopheles gambiae, has been identified. The function of this domain is currently unknown and no function annotation is provided for this domain in the publicly available genomic, protein family and sequence databases. The search for the homologues of this domain in the non-redundant sequence database using PSI-BLAST, resulted in identification of distant relationship between this family and the alkaline phosphatase-like superfamily, which includes families of aryl sulfatase, N-acetylgalactosomine-4-sulfatase, alkaline phosphatase and 2,3-bisphosphoglycerate-independent phosphoglycerate mutase (iPGM). The fold recognition procedures showed that this new domain could adopt a similar 3-D fold as for this superfamily. Most of the phosphatases and sulfatases of this superfamily are characterized by functional residues Ser and Cys respectively in the topologically equivalent positions. This functionally important site aligns with Ser/Thr in the members of the new family. Additionally, set of residues responsible for a metal binding site in phosphatases and sulphtases are conserved in the new family. The in-depth analysis suggests that the new family could possess phosphatase activity.