An aspartate residue in the external vestibule of GLYT2 (glycine transporter 2) controls cation access and transport coupling.

Synaptic glycine levels are controlled by GLYTs (glycine transporters). GLYT1 is the main regulator of synaptic glycine concentrations and catalyses Na+-Cl--glycine co-transport with a 2:1:1 stoichiometry. In contrast, neuronal GLYT2 supplies glycine to the presynaptic terminal with a 3:1:1 stoichiometry. We subjected homology models of GLYT1 and GLYT2 to molecular dynamics simulations in the presence of Na+. Using molecular interaction potential maps and in silico mutagenesis, we identified a conserved region in the GLYT2 external vestibule likely to be involved in Na+ interactions. Replacement of Asp471 in this region reduced Na+ affinity and Na+ co-operativity of transport, an effect not produced in the homologous position (Asp295) in GLYT1. Unlike the GLYT1-Asp295 mutation, this Asp471 mutant increased sodium leakage and non-stoichiometric uncoupled ion movements through GLYT2, as determined by simultaneously measuring current and [3H]glycine accumulation. The homologous Asp471 and Asp295 positions exhibited distinct cation-sensitive external accessibility, and they were involved in Na+ and Li+-induced conformational changes. Although these two cations had opposite effects on GLYT1, they had comparable effects on accessibility in GLYT2, explaining the inhibitory and stimulatory responses to lithium exhibited by the two transporters. On the basis of these findings, we propose a role for Asp471 in controlling cation access to GLYT2 Na+ sites, ion coupling during transport and the subsequent conformational changes.

Molecular basis of the differential interaction with lithium of glycine transporters GLYT1 and GLYT2.

Glycine synaptic levels are controlled by glycine transporters (GLYTs) catalyzing Na(+)/Cl(-)/glycine cotransport. GLYT1 displays a 2:1 :1 stoichiometry and is the main regulator of extracellular glycine concentrations. The neuronal GLYT2, with higher sodium coupling (3:1 :1), supplies glycine to the pre-synaptic terminal to refill synaptic vesicles. In this work, using structural homology modelling and molecular dynamics simulations of GLYTs, we predict the conservation of the two sodium sites present in the template (leucine transporter from Aquifex aeolicus), and confirm its use by mutagenesis and functional analysis. GLYTs Na1 and Na2 sites show differential cation selectivity, as inferred from the action of lithium, a non-transport-supporting ion, on Na(+)-site mutants. GLYTs lithium responses were unchanged in Na1-site mutants, but abolished or inverted in mutants of Na2 site, which binds lithium in the presence of low sodium concentrations and therefore, controls lithium responses. Here, we report, for the first time, that lithium exerts opposite actions on GLYTs isoforms. Glycine transport by GLYT1 is inhibited by lithium whereas GLYT2 transport is stimulated, and this effect is more evident at increased glycine concentrations. In contrast to GLYT1, high and low affinity lithium-binding processes were detected in GLYT2.

Cross-over between discrete and continuous protein structure space: insights into automatic classification and networks of protein structures.

Structural classifications of proteins assume the existence of the fold, which is an intrinsic equivalence class of protein domains. Here, we test in which conditions such an equivalence class is compatible with objective similarity measures. We base our analysis on the transitive property of the equivalence relationship, requiring that similarity of A with B and B with C implies that A and C are also similar. Divergent gene evolution leads us to expect that the transitive property should approximately hold. However, if protein domains are a combination of recurrent short polypeptide fragments, as proposed by several authors, then similarity of partial fragments may violate the transitive property, favouring the continuous view of the protein structure space. We propose a measure to quantify the violations of the transitive property when a clustering algorithm joins elements into clusters, and we find out that such violations present a well defined and detectable cross-over point, from an approximately transitive regime at high structure similarity to a regime with large transitivity violations and large differences in length at low similarity. We argue that protein structure space is discrete and hierarchic classification is justified up to this cross-over point, whereas at lower similarities the structure space is continuous and it should be represented as a network. We have tested the qualitative behaviour of this measure, varying all the choices involved in the automatic classification procedure, i.e., domain decomposition, alignment algorithm, similarity score, and clustering algorithm, and we have found out that this behaviour is quite robust. The final classification depends on the chosen algorithms. We used the values of the clustering coefficient and the transitivity violations to select the optimal choices among those that we tested. Interestingly, this criterion also favours the agreement between automatic and expert classifications. As a domain set, we have selected a consensus set of 2,890 domains decomposed very similarly in SCOP and CATH. As an alignment algorithm, we used a global version of MAMMOTH developed in our group, which is both rapid and accurate. As a similarity measure, we used the size-normalized contact overlap, and as a clustering algorithm, we used average linkage. The resulting automatic classification at the cross-over point was more consistent than expert ones with respect to the structure similarity measure, with 86% of the clusters corresponding to subsets of either SCOP or CATH superfamilies and fewer than 5% containing domains in distinct folds according to both SCOP and CATH. Almost 15% of SCOP superfamilies and 10% of CATH superfamilies were split, consistent with the notion of fold change in protein evolution. These results were qualitatively robust for all choices that we tested, although we did not try to use alignment algorithms developed by other groups. Folds defined in SCOP and CATH would be completely joined in the regime of large transitivity violations where clustering is more arbitrary. Consistently, the agreement between SCOP and CATH at fold level was lower than their agreement with the automatic classification obtained using as a clustering algorithm, respectively, average linkage (for SCOP) or single linkage (for CATH). The networks representing significant evolutionary and structural relationships between clusters beyond the cross-over point may allow us to perform evolutionary, structural, or functional analyses beyond the limits of classification schemes. These networks and the underlying clusters are available at http://ub.cbm.uam.es/research/ProtNet.php.

In vivo assembling of bacterial ribosomal protein L11 into yeast ribosomes makes the particles sensitive to the prokaryotic specific antibiotic thiostrepton.

Eukaryotic ribosomal stalk protein L12 and its bacterial orthologue L11 play a central role on ribosomal conformational changes during translocation. Deletion of the two genes encoding L12 in Saccharomyces cerevisiae resulted in a very slow-growth phenotype. Gene RPL12B, but not the RPL12A, cloned in centromeric plasmids fully restored control protein level and the growth rate when expressed in a L12-deprived strain. The same strain has been transformed to express Escherichia coli protein EcL11 under the control of yeast RPL12B promoter. The bacterial protein has been found in similar amounts in washed ribosomes from the transformed yeast strain and from control E. coli cells, however, EcL11 was unable to restore the defective acidic protein stalk composition caused by the absence of ScL12 in the yeast ribosome. Protein EcL11 induced a 10% increase in L12-defective cell growth rate, although the in vitro polymerizing capacity of the EcL11-containing ribosomes is restored in a higher proportion, and, moreover, the particles became partially sensitive to the prokaryotic specific antibiotic thiostrepton. Molecular dynamic simulations using modelled complexes support the correct assembly of bacterial L11 into the yeast ribosome and confirm its direct implication of its CTD in the binding of thiostrepton to ribosomes.

Local interactions in protein folding determined through an inverse folding model.

We adopt a model of inverse folding in which folding stability results from the combination of the hydrophobic effect with local interactions responsible for secondary structure preferences. Site-specific amino acid distributions can be calculated analytically for this model. We determine optimal parameters for the local interactions by fitting the complete inverse folding model to the site-specific amino acid distributions found in the Protein Data Bank. This procedure reduces drastically the influence on the derived parameters of the preference of different secondary structures for buriedness, which affects local interaction parameters determined through the standard approach based on amino acid propensities. The quality of the fit is evaluated through the likelihood of the observed amino acid distributions given the model and the Bayesian Information Criterion, which indicate that the model with optimal local interaction parameters is strongly preferable to the model where local interaction parameters are determined through propensities. The optimal model yields a mean correlation coefficient r = 0.96 between observed and predicted amino acid distributions. The local interaction parameters are then tested in threading experiments, in combination with contact interactions, for their capacity to recognize the native structure and structures similar to the native against unrelated ones. In a challenging test, proteins structurally aligned with the Mammoth algorithm are scored with the effective free energy function. The native structure gets the highest stability score in 100% of the cases, a high recognition rate comparable to that achieved against easier decoys generated by gapless threading. We then examine proteins for which at least one highly similar template exists. In 61% of the cases, the structure with the highest stability score excluding the native belongs to the native fold, compared to 60% if we use local interaction parameters derived from the usual amino acid propensities and 52% if we use only contact interactions. A highly similar structure is present within the five best stability scores in 82%, 81%, and 76% of the cases, for local interactions determined through inverse folding, through propensity, and set to zero, respectively. These results indicate that local interactions improve substantially the performances of contact free energy functions in fold recognition, and that similar structures tend to get high stability scores, although they are often not high enough to discriminate them from unrelated structures. This work highlights the importance to apply more challenging tests, as the recognition of homologous structures, for testing stability scores for protein folding.

Effective connectivity profile: a structural representation that evidences the relationship between protein structures and sequences.

The complexity of protein structures calls for simplified representations of their topology. The simplest possible mathematical description of a protein structure is a one-dimensional profile representing, for instance, buriedness or secondary structure. This kind of representation has been introduced for studying the sequence to structure relationship, with applications to fold recognition. Here we define the effective connectivity profile (EC), a network theoretical profile that self-consistently represents the network structure of the protein contact matrix. The EC profile makes mathematically explicit the relationship between protein structure and protein sequence, because it allows predicting the average hydrophobicity profile (HP) and the distributions of amino acids at each site for families of homologous proteins sharing the same structure. In this sense, the EC provides an analytic solution to the statistical inverse folding problem, which consists in finding the statistical properties of the set of sequences compatible with a given structure. We tested these predictions with simulations of the structurally constrained neutral (SCN) model of protein evolution with structure conservation, for single- and multi-domain proteins, and for a wide range of mutation processes, the latter producing sequences with very different hydrophobicity profiles, finding that the EC-based predictions are accurate even when only one sequence of the family is known. The EC profile is very significantly correlated with the HP for sequence-structure pairs in the PDB as well. The EC profile generalizes the properties of previously introduced structural profiles to modular proteins such as multidomain chains, and its correlation with the sequence profile is substantially improved with respect to the previously defined profiles, particularly for long proteins. Furthermore, the EC profile has a dynamic interpretation, since the EC components are strongly inversely related with the temperature factors measured in X-ray experiments, meaning that positions with large EC component are more strongly constrained in their equilibrium dynamics. Last, the EC profile allows to define a natural measure of modularity that correlates with the number of domains composing the protein, suggesting its application for domain decomposition. Finally, we show that structurally similar proteins have similar EC profiles, so that the similarity between aligned EC profiles can be used as a structure similarity measure, a property that we have recently applied for protein structure alignment. The code for computing the EC profile is available upon request writing to ubastolla@cbm.uam.es, and the structural profiles discussed in this article can be downloaded from the SLOTH webserver http://www.fkp.tu-darmstadt.de/SLOTH/.

An efficient conformational sampling method for homology modeling.

The structural refinement of protein models is a challenging problem in protein structure prediction (Moult et al., Proteins 2003;53(Suppl 6):334-339). Most attempts to refine comparative models lead to degradation rather than improvement in model quality, so most current comparative modeling procedures omit the refinement step. However, it has been shown that even in the absence of alignment errors and using optimal templates, methods based on a single template have intrinsic limitations, and that refinement is needed to improve model accuracy. It is thought that failure of current methods originates on one hand from the inaccuracy of the effective free energy functions adopted, which do not represent properly the energetic balance in the native state, and on the other hand from the difficulty to sample the high dimensional and rugged free energy landscape of protein folding, in the search for the global minimum. Here, we address this second issue. We define the evolutionary and vibrational armonics subspace (EVA), a reduced sampling subspace that consists of a combination of evolutionarily favored directions, defined by the principal components of the structural variation within a homologous family, plus topologically favored directions, derived from the low frequency normal modes of the vibrational dynamics, up to 50 dimensions. This subspace is accurate enough so that the cores of most proteins can be represented within 1 A accuracy, and reduced enough so that Replica Exchange Monte Carlo (Hukushima and Nemoto, J Phys Soc Jpn 1996;65:1604-1608; Hukushima et al., Int J Mod Phys C: Phys Comput 1996;7:337-344; Mitsutake et al., J Chem Phys 2003;118:6664-6675; Mitsutake et al., J Chem Phys 2003;118:6676-6688) (REMC) can be applied. REMC is one of the best sampling methods currently available, but its applicability is restricted to spaces of small dimensionality. We show that the combination of the EVA subspace and REMC can essentially solve the optimization problem for backbone atoms in the reduced sampling subspace, even for rather rugged free energy landscapes. Applications and limitations of this methodology are finally discussed.

Co-regulation analysis of closely linked genes identifies a highly recurrent gain on chromosome 17q25.3 in prostate cancer.

BACKGROUND: Transcriptional profiling of prostate cancer (PC) has unveiled new markers of neoplasia and allowed insights into mechanisms underlying this disease. Genomewide analyses have also identified new chromosomal abnormalities associated with PC. The combination of both classes of data for the same sample cohort might provide better criteria for identifying relevant factors involved in neoplasia. Here we describe transcriptional signatures identifying distinct normal and tumoral prostate tissue compartments, and the inference and demonstration of a new, highly recurrent copy number gain on chromosome 17q25.3. METHODS: We have applied transcriptional profiling to tumoral and non-tumoral prostate samples with relatively homogeneous epithelial representations as well as pure stromal tissue from peripheral prostate and cultured cell lines, followed by quantitative RT-PCR validations and immunohistochemical analysis. In addition, we have performed in silico colocalization analysis of co-regulated genes and validation by fluorescent in situ hybridization (FISH). RESULTS: The transcriptomic analysis has allowed us to identify signatures corresponding to non-tumoral luminal and tumoral epithelium, basal epithelial cells, and prostate stromal tissue. In addition, in silico analysis of co-regulated expression of physically linked genes has allowed us to predict the occurrence of a copy number gain at chromosomal region 17q25.3. This computational inference was validated by fluorescent in situ hybridization, which showed gains in this region in over 65% of primary and metastatic tumoral samples. CONCLUSION: Our approach permits to directly link gene copy number variations with transcript co-regulation in association with neoplastic states. Therefore, transcriptomic studies of carefully selected samples can unveil new diagnostic markers and transcriptional signatures highly specific of PC, and lead to the discovery of novel genomic abnormalities that may provide additional insights into the causes and mechanisms of prostate cancer.

Conformational model.

The failure to identify changes in the crystal structure of the T-cell antigen receptor (TCR) alpha/beta ectodomains beyond the ligand-binding complementarity-determining region loops is most probably responsible for conformational changes having been relegated to a second plane as a mechanism of signal transduction. However, there is strong biochemical and spectroscopic evidence that the cytoplasmic tails of the tcr and the B-cell antigen receptor undergo conformational changes upon stimulation. This suggests that in the context of the whole TCR complex, including both the TCRalpha/beta ectodomains and the complete CD3 subunits with their transmembrane and cytoplasmic tails, the conformational change has to be transmitted from the ectodomains to the cytoplasmic rails upon ligand binding. While the mechanism of transmission and the importance of conformational changes in T- and B-cell activation are still being elucidated, there are already functional correlates that establish a link between full T-cell activation and this conformational change.

Solution structure of the hypothetical protein TA0095 from Thermoplasma acidophilum: a novel superfamily with a two-layer sandwich architecture.

TA0095 is a 96-residue hypothetical protein from Thermoplasma acidophilum that exhibits no sequence similarity to any protein of known structure. Also, TA0095 is a member of the COG4004 orthologous group of unknown function found in Archaea bacteria. We determined its three-dimensional structure by NMR methods. The structure displays an alpha/beta two-layer sandwich architecture formed by three alpha-helices and five beta-strands following the order beta1-alpha1-beta2-beta3-beta4-beta5-alpha2-alpha3. Searches for structural homologs indicate that the TA0095 structure belongs to the TBP-like fold, constituting a novel superfamily characterized by an additional C-terminal helix. The TA0095 structure provides a fold common to the COG4004 proteins that will obviously belong to this new superfamily. Most hydrophobic residues conserved in the COG4004 proteins are buried in the structure determined herein, thus underlying their importance for structure stability. Considering that the TA0095 surface shows a large positively charged patch with a high degree of residue conservation within the COG4004 domain, the biological function of TA0095 and the rest of COG4004 proteins might occur through binding a negatively charged molecule. Like other TBP-like fold proteins, the COG4004 proteins might be DNA-binding proteins. The fact that TA0095 is shown to interact with large DNA fragments is in favor of this hypothesis, although nonspecific DNA binding cannot be ruled out.

A new implicit solvent model for protein-ligand docking.

A new implicit solvent model for computing the electrostatics binding free energy in protein-ligand docking is proposed. The new method is based on an adaptation of the screening coulombic potentials proposed originally by Hassan et al. (J Phys Chem B 2000;104:6490-6498). In essence, it relies on two basic assumptions; (i) solvent screening can be accounted for by means of radially dependent sigmoidal dielectric functions and; (ii) the effective atom Born radii can be expressed only as a function of the exposed atom surface. Parameters of the model other than radii and charges are generic. These were optimized for a dataset of 826 protein-ligand complexes, comprising both X-ray complexes for 23 receptors as well as decoys generated by docking computations. We show that the new model provides satisfactory results when benchmarked against reference values based on the numerical solution of the Poisson equation, with a root mean square error of 4.2 kcal/mol over a range of approximately 40 kcal/mol in electrostatics binding free energies, a cross-validated r2 of 0.81, a slope of 0.97, and an intercept of 1.06 kcal/mol. We show that the model is appropriate for ligands of different sizes, polarities, overall charge, and chemical composition. Furthermore, not only the total value of the electrostatic contribution to the binding free energy, but also its components (coulombic term, receptor desolvation, and ligand desolvation) are reasonably well reproduced. Computation times of approximately 0.030 s per pose are obtained on a single processor desktop workstation.

Microarray-based identification of antigenic variants of foot-and-mouth disease virus: a bioinformatics quality assessment.

BACKGROUND: The evolution of viral quasispecies can influence viral pathogenesis and the response to antiviral treatments. Mutant clouds in infected organisms represent the first stage in the genetic and antigenic diversification of RNA viruses, such as foot and mouth disease virus (FMDV), an important animal pathogen. Antigenic variants of FMDV have been classically diagnosed by immunological or RT-PCR-based methods. DNA microarrays are becoming increasingly useful for the analysis of gene expression and single nucleotide polymorphisms (SNPs). Recently, a FMDV microarray was described to detect simultaneously the seven FMDV serotypes. These results encourage the development of new oligonucleotide microarrays to probe the fine genetic and antigenic composition of FMDV for diagnosis, vaccine design, and to gain insight into the molecular epidemiology of this pathogen. RESULTS: A FMDV microarray was designed and optimized to detect SNPs at a major antigenic site of the virus. A screening of point mutants of the genomic region encoding antigenic site A of FMDV C-S8c1 was achieved. The hybridization pattern of a mutant includes specific positive and negative signals as well as crosshybridization signals, which are of different intensity depending on the thermodynamic stability of each probe-target pair. Moreover, an array bioinformatic classification method was developed to evaluate the hybridization signals. This statistical analysis shows that the procedure allows a very accurate classification per variant genome. CONCLUSION: A specific approach based on a microarray platform aimed at distinguishing point mutants within an important determinant of antigenicity and host cell tropism, namely the G-H loop of capsid protein VP1, was developed. The procedure is of general applicability as a test for specificity and discriminatory power of microarray-based diagnostic procedures using multiple oligonucleotide probes.

Computational approaches to model ligand selectivity in drug design.

To be effective, a designed drug must discriminate successfully the macromolecular target from alternative structures present in the organism. The last few years have witnessed the emergence of different computational tools aimed to the understanding and modeling of this process at molecular level. Although still rudimentary, these methods are shaping a coherent approach to help in the design of molecules with high affinity and specificity, both in lead discovery and in lead optimization. It is the purpose of this review to illustrate the array of computational tools available to consider selectivity in the design process, to summarize the most relevant applications, and to sketch the challenges ahead.

Comparative binding energy analysis considering multiple receptors: a step toward 3D-QSAR models for multiple targets.

Comparative binding energy analysis, a technique to derive receptor-based three-dimensional quantitative structure-activity relationships (3D-QSAR), is herein extended to consider both affinity and selectivity in the derivation of the QSAR model. The extension is based on allowing multiple structurally related receptors to enter the X-matrix employed in the derivation of the structure-activity model. As a result, a single model common to all of them is obtained that considers both intra- and inter-receptor affinity differences for a given congeneric series. We applied the technique to a series of 88 3-amidinophenylalanines, binding to thrombin, trypsin, and factor Xa (fXa). A single predictive regression model for the three receptors involving 202 complexes, with a leave-one out (LOO) cross-validated Q(2) of 0.689, was obtained, and selectivity requirements were investigated. We find that total or partial occupancy of any of the three main pockets in the binding site (D-site, P-site, and the rim of the S1-site) leads to higher affinity across the family. However, the fact that thrombin can make stronger interactions in the P-site, as a result of its exclusive 60-loop, makes of this site a specificity pocket for this thrombin. Occupancy of the D-site leads to more active inhibitors toward fXa for the same reason, but the model does not highlight strongly the D-box because inhibitors are too short to fully occupy it. Negative charge density in the neighborhood of position 88 (a Lys insertion in thrombin) is found to be a determinant for thrombin recognition. These results were consistent with previous studies on selectivity in the thrombin/trypsin/fXa system.

Dual activation of pathways regulated by steroid receptors and peptide growth factors in primary prostate cancer revealed by Factor Analysis of microarray data.

BACKGROUND: We use an approach based on Factor Analysis to analyze datasets generated for transcriptional profiling. The method groups samples into biologically relevant categories, and enables the identification of genes and pathways most significantly associated to each phenotypic group, while allowing for the participation of a given gene in more than one cluster. Genes assigned to each cluster are used for the detection of pathways predominantly activated in that cluster by finding statistically significant associated GO terms. We tested the approach with a published dataset of microarray experiments in yeast. Upon validation with the yeast dataset, we applied the technique to a prostate cancer dataset. RESULTS: Two major pathways are shown to be activated in organ-confined, non-metastatic prostate cancer: those regulated by the androgen receptor and by receptor tyrosine kinases. A number of gene markers (HER3, IQGAP2 and POR1) highlighted by the software and related to the later pathway have been validated experimentally a posteriori on independent samples. CONCLUSION: Using a new microarray analysis tool followed by a posteriori experimental validation of the results, we have confirmed several putative markers of malignancy associated with peptide growth factor signalling in prostate cancer and revealed others, most notably ERRB3 (HER3). Our study suggest that, in primary prostate cancer, HER3, together or not with HER4, rather than in receptor complexes involving HER2, could play an important role in the biology of these tumors. These results provide new evidence for the role of receptor tyrosine kinases in the establishment and progression of prostate cancer.

Core deformations in protein families: a physical perspective.

An analysis is presented on how structural cores change shape within protein families, and whether or not there is a relationship between these structural changes and the vibrational modes that proteins experiment due to topological constraints. A set of 13 representative and well-populated protein families are studied. The evolutionary directions of deformation are obtained by applying a new multiple structural alignment technique to superimpose the structures and extract a conserved core, together with Principal Components Analysis (PCA) to extract the main deformation modes. A low-resolution Normal Mode Analysis (NMA) technique is used in parallel to study the properties of the mechanical core plasticity of the same proteins. We find that the evolutionary deformations span a low dimensional space. A statistically significant correspondence exists between these principal deformations and the vibrational modes accessible to a particular topology. We conclude that, to a significant extent, the structures of evolving proteins seem to respond to sequence changes by collective deformations along combinations of low-frequency modes. The findings have implications in structure prediction by homology modeling.

MAMMOTH (matching molecular models obtained from theory): an automated method for model comparison.

Advances in structural genomics and protein structure prediction require the design of automatic, fast, objective, and well benchmarked methods capable of comparing and assessing the similarity of low-resolution three-dimensional structures, via experimental or theoretical approaches. Here, a new method for sequence-independent structural alignment is presented that allows comparison of an experimental protein structure with an arbitrary low-resolution protein tertiary model. The heuristic algorithm is given and then used to show that it can describe random structural alignments of proteins with different folds with good accuracy by an extreme value distribution. From this observation, a structural similarity score between two proteins or two different conformations of the same protein is derived from the likelihood of obtaining a given structural alignment by chance. The performance of the derived score is then compared with well established, consensus manual-based scores and data sets. We found that the new approach correlates better than other tools with the gold standard provided by a human evaluator. Timings indicate that the algorithm is fast enough for routine use with large databases of protein models. Overall, our results indicate that the new program (MAMMOTH) will be a good tool for protein structure comparisons in structural genomics applications. MAMMOTH is available from our web site at http://physbio.mssm.edu/~ortizg/.

Solution structure of the hypothetical protein Mth677 from Methanobacterium thermoautotrophicum: a novel alpha+beta fold.

The structure of Mth677, a hypothetical protein from Methanobacterium thermoautotrophicum (Mth), has been determined by using heteronuclear nuclear magnetic resonance (NMR) methods on a double-labeled (15)N-(13)C sample. Mth677 adopts a novel alpha+beta fold, consisting of two alpha-helices (one N terminal and one C terminal) packed on the same side of a central beta-hairpin. This structure is likely shared by its three orthologs, detected in three other Archaebacteria. There are no clear features in the sequences of these proteins or in the genome organization of Mth to make a reliable functional assignment to this protein. However, the structural similarity to Escherichia coli MinE, the protein which controls that division occurs at the midcell site, lends support to the proposal that Mth677 might be, in Mth, the counterpart of the topological specificity domain of MinE in E. coli.

CAFASP3: the third critical assessment of fully automated structure prediction methods.

We present the results of the fully automated CAFASP3 experiment, which was carried out in parallel with CASP5, using the same set of prediction targets. CAFASP participation is restricted to fully automatic structure prediction servers. The servers' performance is evaluated by using previously announced, objective, reproducible and fully automated evaluation methods. More than 60 servers participated in CAFASP3, covering all categories of structure prediction. As in the previous CAFASP2 experiment, it was possible to identify a group of 5-10 top performing independent servers. This group of top performing independent servers produced relatively accurate models for all the 32 "Homology Modeling" targets, and for up to 43% of the 30 "Fold Recognition" targets. One of the most important results of CAFASP3 was the realization of the value of all the independent servers as a group, as evidenced by the superior performance of "meta-predictors" (defined here as predictors that make use of the output of other CAFASP servers). The performance of the best automated meta-predictors was roughly 30% higher than that of the best independent server. More significantly, the performance of the best automated meta-predictors was comparable with that of the best 5-10 human CASP predictors. This result shows that significant progress has been achieved in automatic structure prediction and has important implications to the prospects of automated structure modeling in the context of structural genomics.

Virtual screening with flexible docking and COMBINE-based models. Application to a series of factor Xa inhibitors.

A two-step, fully automatic virtual screening procedure consisting of flexible docking followed by activity prediction by COMparative BINding Energy (COMBINE) analysis is presented. This novel approach has been successfully applied, as an example with medicinal chemistry interest, to a recently reported series of 133 factor Xa (fXa)(1) inhibitors whose activities encompass 4 orders of magnitude. The docking algorithm is linked to the COMBINE analysis program and used to derive independent regression models of the 133 inhibitors docked within three different fXa structures (PDB entries 1fjs, 1f0r, and 1xka), so as to explore the effect of receptor conformation on the overall results. Reliable docking conformations and predictive regression models requiring eight latent variables could be derived for two of the fXa structures, with the best model achieving a Q(2) of 0.63 and a standard deviation of errors of prediction (SDEP) of 0.51 (leave-one-out). The two-step procedure was then employed to screen a designed virtual library of 112 ligands, containing both active and inactive compounds. While docking energies alone could show a good performance for selecting hits, including structurally diverse ones, inclusion of COMBINE analysis regression models provided improved rankings for the identification of structurally related molecules in external sets. In our best case, a recognition rate of approximately 80% of known binders at approximately 15% false positives rate was achieved, corresponding to an enrichment factor of approximately 450% over random.