GenTHREADER: an efficient and reliable protein fold recognition method for genomic sequences

Genetic algorithm with alternating selection pressure for protein side-chain packing and pK(a) prediction

The prediction of protein side-chain conformation is central for understanding protein functions. Side-chain packing is a sub-problem of protein folding and its computational complexity has been shown to be NP-hard. We investigated the capabilities of a hybrid (genetic algorithm/simulated annealing) technique for side-chain packing and for the generation of an ensemble of low energy side-chain conformations. Our method first relies on obtaining a near-optimal low energy protein conformation by optimizing its amino-acid side-chains. Upon convergence, the genetic algorithm is allowed to undergo forward and "backward" evolution by alternating selection pressures between minimal and higher energy setpoints. We show that this technique is very efficient for obtaining distributions of solutions centered at any desired energy from the minimum. We outline the general concepts of our evolutionary sampling methodology using three different alternating selective pressure schemes. Quality of the method was assessed by using it for protein pK(a) prediction.

Unique aliphatic amidase from a psychrotrophic and haloalkaliphilic nesterenkonia isolate

Nesterenkonia strain AN1 was isolated from a screening program for nitrile- and amide-hydrolyzing microorganisms in Antarctic desert soil samples. Strain AN1 showed significant 16S rRNA sequence identity to known members of the genus. Like known Nesterenkonia species, strain AN1 was obligately alkaliphilic (optimum environmental pH, 9 to 10) and halotolerant (optimum environmental Na(+) content, 0 to 15% [wt/vol]) but was also shown to be an obligate psychrophile with optimum growth at approximately 21°C. The partially sequenced genome of AN1 revealed an open reading frame (ORF) encoding a putative protein member of the nitrilase superfamily, referred to as NitN (264 amino acids). The protein crystallized readily as a dimer and the atomic structure of all but 10 amino acids of the protein was determined, confirming that the enzyme had an active site and a fold characteristic of the nitrilase superfamily. The protein was screened for activity against a variety of nitrile, carbamoyl, and amide substrates and was found to have only amidase activity. It had highest affinity for propionamide but demonstrated a low catalytic rate. NitN had maximal activity at 30°C and between pH 6.5 and 7.5, conditions which are outside the optimum growth range for the organism.

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.

Novel nonlinear knowledge-based mean force potentials based on machine learning

The prediction of 3D structures of proteins from amino acid sequences is one of the most challenging problems in molecular biology. An essential task for solving this problem with coarse-grained models is to deduce effective interaction potentials. The development and evaluation of new energy functions is critical to accurately modeling the properties of biological macromolecules. Knowledge-based mean force potentials are derived from statistical analysis of proteins of known structures. Current knowledge-based potentials are almost in the form of weighted linear sum of interaction pairs. In this study, a class of novel nonlinear knowledge-based mean force potentials is presented. The potential parameters are obtained by nonlinear classifiers, instead of relative frequencies of interaction pairs against a reference state or linear classifiers. The support vector machine is used to derive the potential parameters on data sets that contain both native structures and decoy structures. Five knowledge-based mean force Boltzmann-based or linear potentials are introduced and their corresponding nonlinear potentials are implemented. They are the DIH potential (single-body residue-level Boltzmann-based potential), the DFIRE-SCM potential (two-body residue-level Boltzmann-based potential), the FS potential (two-body atom-level Boltzmann-based potential), the HR potential (two-body residue-level linear potential), and the T32S3 potential (two-body atom-level linear potential). Experiments are performed on well-established decoy sets, including the LKF data set, the CASP7 data set, and the Decoys “R”Us data set. The evaluation metrics include the energy Z score and the ability of each potential to discriminate native structures from a set of decoy structures. Experimental results show that all nonlinear potentials significantly outperform the corresponding Boltzmann-based or linear potentials, and the proposed discriminative framework is effective in developing knowledge-based mean force potentials. The nonlinear potentials can be widely used for ab initio protein structure prediction, model quality assessment, protein docking, and other challenging problems in computational biology.

Incorporation of local structural preference potential improves fold recognition

Fold recognition, or threading, is a popular protein structure modeling approach that uses known structure templates to build structures for those of unknown. The key to the success of fold recognition methods lies in the proper integration of sequence, physiochemical and structural information. Here we introduce another type of information, local structural preference potentials of 3-residue and 9-residue fragments, for fold recognition. By combining the two local structural preference potentials with the widely used sequence profile, secondary structure information and hydrophobic score, we have developed a new threading method called FR-t5 (fold recognition by use of 5 terms). In benchmark testings, we have found the consideration of local structural preference potentials in FR-t5 not only greatly enhances the alignment accuracy and recognition sensitivity, but also significantly improves the quality of prediction models.

Insight into the mode of action of the LRRK2 Y1699C pathogenic mutant

Mutations in the leucine-rich repeat kinase 2 (LRRK2) gene are the most prevalent known cause of autosomal dominant Parkinson's disease. The LRRK2 gene encodes a Roco protein featuring a Ras of complex proteins (ROC) GTPase and a kinase domain linked by the C-terminal of ROC (COR) domain. Here, we explored the effects of the Y1699C pathogenic LRRK2 mutation in the COR domain on GTPase activity and interactions within the catalytic core of LRRK2. We observed a decrease in GTPase activity for LRRK2 Y1699C comparable to the decrease observed for the R1441C pathogenic mutant and the T1348N dysfunctional mutant. To study the underlying mechanism, we explored the dimerization in the catalytic core of LRRK2. ROC-COR dimerization was significantly weakened by the Y1699C or R1441C/G mutation. Using a competition assay, we demonstrated that the intra-molecular ROC : COR interaction is favoured over ROC : ROC dimerization. Interestingly, the intra-molecular ROC : COR interaction was strengthened by the Y1699C mutation. This is supported by a 3D homology model of the ROC-COR tandem of LRRK2, showing that Y1699 is positioned at the intra-molecular ROC : COR interface. In conclusion, our data provides mechanistic insight into the mode of action of the Y1699C LRRK2 mutant: the Y1699C substitution, situated at the intra-molecular ROC : COR interface, strengthens the intra-molecular ROC : COR interaction, thereby locally weakening the dimerization of LRRK2 at the ROC-COR tandem domain resulting in decreased GTPase activity.

Methods for sequence-structure alignment

Homology modeling is based on the observation that related protein sequences adopt similar three-dimensional structures. Hence, a homology model of a protein can be derived using related protein structure(s) as modeling template(s). A key step in this approach is the establishment of correspondence between residues of the protein to be modeled and those of modeling template(s). This step, often referred to as sequence-structure alignment, is one of the major determinants of the accuracy of a homology model. This chapter gives an overview of methods for deriving sequence-structure alignments and discusses recent methodological developments leading to improved performance. However, no method is perfect. How to find alignment regions that may have errors and how to make improvements? This is another focus of this chapter. Finally, the chapter provides a practical guidance of how to get the most of the available tools in maximizing the accuracy of sequence-structure alignments.

Trends in template/fragment-free protein structure prediction

Predicting the structure of a protein from its amino acid sequence is a long-standing unsolved problem in computational biology. Its solution would be of both fundamental and practical importance as the gap between the number of known sequences and the number of experimentally solved structures widens rapidly. Currently, the most successful approaches are based on fragment/template reassembly. Lacking progress in template-free structure prediction calls for novel ideas and approaches. This article reviews trends in the development of physical and specific knowledge-based energy functions as well as sampling techniques for fragment-free structure prediction. Recent physical- and knowledge-based studies demonstrated that it is possible to sample and predict highly accurate protein structures without borrowing native fragments from known protein structures. These emerging approaches with fully flexible sampling have the potential to move the field forward.

A protein fold classifier formed by fusing different modes of pseudo amino acid composition via PSSM

Protein function is related to its chemical reaction to the surrounding environment including other proteins. On the other hand, this depends on the spatial shape and tertiary structure of protein and folding of its constituent components in space. The correct identification of protein domain fold solely using extracted information from protein sequence is a complicated and controversial task in the current computational biology. In this article a combined classifier based on the information content of extracted features from the primary structure of protein has been introduced to face this challenging problem. In the first stage of our proposed two-tier architecture, there are several classifiers each of which is trained with a different sequence based feature vector. Apart from the application of the predicted secondary structure, hydrophobicity, van der Waals volume, polarity, polarizability, and different dimensions of pseudo-amino acid composition vectors in similar studies, the position specific scoring matrix (PSSM) has also been used to improve the correct classification rate (CCR) in this study. Using K-fold cross validation on training dataset related to 27 famous folds of SCOP, the 28 dimensional probability output vector from each evidence theoretic K-NN classifier is used to determine the information content or expertness of corresponding feature for discrimination in each fold class. In the second stage, the outputs of classifiers for test dataset are fused using Sugeno fuzzy integral operator to make better decision for target fold class. The expertness factor of each classifier in each fold class has been used to calculate the fuzzy integral operator weights. Results make it possible to provide deeper interpretation about the effectiveness of each feature for discrimination in target classes for query proteins.

Quantifying the impact of dependent evolution among sites in phylogenetic inference

Nearly all commonly used methods of phylogenetic inference assume that characters in an alignment evolve independently of one another. This assumption is attractive for simplicity and computational tractability but is not biologically reasonable for RNAs and proteins that have secondary and tertiary structures. Here, we simulate RNA and protein-coding DNA sequence data under a general model of dependence in order to assess the robustness of traditional methods of phylogenetic inference to violation of the assumption of independence among sites. We find that the accuracy of independence-assuming methods is reduced by the dependence among sites; for proteins this reduction is relatively mild, but for RNA this reduction may be substantial. We introduce the concept of effective sequence length and its utility for considering information content in phylogenetics.

Improving threading algorithms for remote homology modeling by combining fragment and template comparisons

In this work, we develop a method called fragment comparison and the template comparison (FTCOM) for assessing the global quality of protein structural models for targets of medium and hard difficulty (remote homology) produced by structure prediction approaches such as threading or ab initio structure prediction. FTCOM requires the C(alpha) coordinates of full length models and assesses model quality based on fragment comparison and a score derived from comparison of the model to top threading templates. On a set of 361 medium/hard targets, FTCOM was applied to and assessed for its ability to improve on the results from the SP(3), SPARKS, PROSPECTOR_3, and PRO-SP(3)-TASSER threading algorithms. The average TM-score improves by 5-10% for the first selected model by the new method over models obtained by the original selection procedure in the respective threading methods. Moreover, the number of foldable targets (TM-score >or= 0.4) increases from least 7.6% for SP(3) to 54% for SPARKS. Thus, FTCOM is a promising approach to template selection. Proteins 2010. (c) 2010 Wiley-Liss, Inc.

Molecular recognition determinants for type IV secretion of diverse families of conjugative relaxases

In preparation for transfer conjugative type IV secretion systems (T4SS) produce a nucleoprotein adduct containing a relaxase enzyme covalently linked to the 5' end of single-stranded plasmid DNA. The bound relaxase is expected to present features necessary for selective recognition by the type IV coupling protein (T4CP), which controls substrate entry to the envelope spanning secretion machinery. We prove that the IncF plasmid R1 relaxase TraI is translocated to the recipient cells. Using a Cre recombinase assay (CRAfT) we mapped two internally positioned translocation signals (TS) on F-like TraI proteins that independently mediate efficient recognition and secretion. Tertiary structure predictions for the TS matched best helicase RecD2 from Deinococcus radiodurans. The TS is widely conserved in MOB(F) and MOB(Q) families of relaxases. Structure/function relationships within the TS were identified by mutation. A key residue in specific recognition by T4CP TraD was revealed by a fidelity switch phenotype for an F to plasmid R1 exchange L626H mutation. Finally, we show that physical linkage of the relaxase catalytic domain to a TraI TS is necessary for efficient conjugative transfer.

A Drosophila model for primary coenzyme Q deficiency and dietary rescue in the developing nervous system

Coenzyme Q (CoQ) or ubiquinone is a lipid component of the electron transport chain required for ATP generation in mitochondria. Mutations in CoQ biosynthetic genes are associated with rare but severe infantile multisystemic diseases. CoQ itself is a popular over-the-counter dietary supplement that some clinical and rodent studies suggest might be beneficial for neurodegenerative diseases. Here, we identify mutations in the Drosophila qless gene, which encodes an orthologue of the human PDSS1 prenyl transferase that synthesizes the isoprenoid side chain of CoQ. We show that neurons lacking qless activity upregulate markers of mitochondrial stress and undergo caspase-dependent apoptosis. Surprisingly, even though experimental inhibition of caspase activity did not prevent mitochondrial disruption, it was sufficient to rescue the size of neural progenitor clones. This demonstrates that, within the developing larval CNS, qless activity is required primarily for cell survival rather than for cell growth and proliferation. Full rescue of the qless neural phenotype was achieved by dietary supplementation with CoQ4, CoQ9 or CoQ10, indicating that a side chain as short as four isoprenoid units can provide in vivo activity. Together, these findings show that Drosophila qless provides a useful model for studying the neural effects of CoQ deficiency and dietary supplementation.

Diversity of glycosyl hydrolases from cellulose-depleting communities enriched from casts of two earthworm species

The guts and casts of earthworms contain microbial assemblages that process large amounts of organic polymeric substrates from plant litter and soil; however, the enzymatic potential of these microbial communities remains largely unexplored. In the present work, we retrieved carbohydrate-modifying enzymes through the activity screening of metagenomic fosmid libraries from cellulose-depleting microbial communities established with the fresh casts of two earthworm species, Aporrectodea caliginosa and Lumbricus terrestris, as inocula. Eight glycosyl hydrolases (GHs) from the A. caliginosa-derived community were multidomain endo-beta-glucanases, beta-glucosidases, beta-cellobiohydrolases, beta-galactosidase, and beta-xylosidases of known GH families. In contrast, two GHs derived from the L. terrestris microbiome had no similarity to any known GHs and represented two novel families of beta-galactosidases/alpha-arabinopyranosidases. Members of these families were annotated in public databases as conserved hypothetical proteins, with one being structurally related to isomerases/dehydratases. This study provides insight into their biochemistry, domain structures, and active-site architecture. The two communities were similar in bacterial composition but significantly different with regard to their eukaryotic inhabitants. Further sequence analysis of fosmids and plasmids bearing the GH-encoding genes, along with oligonucleotide usage pattern analysis, suggested that those apparently originated from Gammaproteobacteria (pseudomonads and Cellvibrio-like organisms), Betaproteobacteria (Comamonadaceae), and Alphaproteobacteria (Rhizobiales).

A synergistic approach to protein crystallization: combination of a fixed-arm carrier with surface entropy reduction

Protein crystallographers are often confronted with recalcitrant proteins not readily crystallizable, or which crystallize in problematic forms. A variety of techniques have been used to surmount such obstacles: crystallization using carrier proteins or antibody complexes, chemical modification, surface entropy reduction, proteolytic digestion, and additive screening. Here we present a synergistic approach for successful crystallization of proteins that do not form diffraction quality crystals using conventional methods. This approach combines favorable aspects of carrier-driven crystallization with surface entropy reduction. We have generated a series of maltose binding protein (MBP) fusion constructs containing different surface mutations designed to reduce surface entropy and encourage crystal lattice formation. The MBP advantageously increases protein expression and solubility, and provides a streamlined purification protocol. Using this technique, we have successfully solved the structures of three unrelated proteins that were previously unattainable. This crystallization technique represents a valuable rescue strategy for protein structure solution when conventional methods fail.

Chondroitin sulfate synthase-2/chondroitin polymerizing factor has two variants with distinct function

Chondroitin sulfate (CS) is a polysaccharide consisting of repeating disaccharide units of N-acetyl-D-galactosamine and d-glucuronic acid residues, modified with sulfated residues at various positions. To date six glycosyltransferases for chondroitin synthesis have been identified, and the complex of chondroitin sulfate synthase-1 (CSS1)/chondroitin synthase-1 (ChSy-1) and chondroitin sulfate synthase-2 (CSS2)/chondroitin polymerizing factor is assumed to play a major role in CS biosynthesis. We found an alternative splice variant of mouse CSS2 in a data base that lacks the N-terminal transmembrane domain, contrasting to the original CSS2. Here, we investigated the roles of CSS2 variants. Both the original enzyme and the splice variant, designated CSS2A and CSS2B, respectively, were expressed at different levels and ratios in tissues. Western blot analysis of cultured mouse embryonic fibroblasts confirmed that both enzymes were actually synthesized as proteins and were localized in both the endoplasmic reticulum and the Golgi apparatus. Pulldown assays revealed that either of CSS2A, CSS2B, and CSS1/ChSy-1 heterogeneously and homogeneously interacts with each other, suggesting that they form a complex of multimers. In vitro glycosyltransferase assays demonstrated a reduced glucuronyltransferase activity in CSS2B and no polymerizing activity in CSS2B co-expressed with CSS1, in contrast to CSS2A co-expressed with CSS1. Radiolabeling analysis of cultured COS-7 cells overexpressing each variant revealed that, whereas CSS2A facilitated CS biosynthesis, CSS2B inhibited it. Molecular modeling of CSS2A and CSS2B provided support for their properties. These findings, implicating regulation of CS chain polymerization by CSS2 variants, provide insight in elucidating the mechanisms of CS biosynthesis.

Examination of apoptosis signaling in pancreatic cancer by computational signal transduction analysis

Background

Pancreatic ductal adenocarcinoma (PDAC) remains an important cause of cancer death. Changes in apoptosis signaling in pancreatic cancer result in chemotherapy resistance and aggressive growth and metastasizing. The aim of this study was to characterize the apoptosis pathway in pancreatic cancer computationally by evaluation of experimental data from high-throughput technologies and public data bases. Therefore, gene expression analysis of microdissected pancreatic tumor tissue was implemented in a model of the apoptosis pathway obtained by computational protein interaction prediction.

Methodology/Principal Findings

Apoptosis pathway related genes were assembled from electronic databases. To assess expression of these genes we constructed a virtual subarray from a whole genome analysis from microdissected native tumor tissue. To obtain a model of the apoptosis pathway, interactions of members of the apoptosis pathway were analysed using public databases and computational prediction of protein interactions. Gene expression data were implemented in the apoptosis pathway model. 19 genes were found differentially expressed and 12 genes had an already known pathophysiological role in PDAC, such as Survivin/BIRC5, BNIP3 and TNF-R1. Furthermore we validated differential expression of IL1R2 and Livin/BIRC7 by RT-PCR and immunohistochemistry. Implementation of the gene expression data in the apoptosis pathway map suggested two higher level defects of the pathway at the level of cell death receptors and within the intrinsic signaling cascade consistent with references on apoptosis in PDAC. Protein interaction prediction further showed possible new interactions between the single pathway members, which demonstrate the complexity of the apoptosis pathway.

Conclusions/Significance

Our data shows that by computational evaluation of public accessible data an acceptable virtual image of the apoptosis pathway might be given. By this approach we could identify two higher level defects of the apoptosis pathway in PDAC. We could further for the first time identify IL1R2 as possible candidate gene in PDAC.

Purification and functional characterisation of rhiminopeptidase A, a novel aminopeptidase from the venom of Bitis gabonica rhinoceros

Background

Snake bite is a major neglected public health issue within poor communities living in the rural areas of several countries throughout the world. An estimated 2.5 million people are bitten by snakes each year and the cost and lack of efficacy of current anti-venom therapy, together with the lack of detailed knowledge about toxic components of venom and their modes of action, and the unavailability of treatments in rural areas mean that annually there are around 125,000 deaths worldwide. In order to develop cheaper and more effective therapeutics, the toxic components of snake venom and their modes of action need to be clearly understood. One particularly poorly understood component of snake venom is aminopeptidases. These are exo-metalloproteases, which, in mammals, are involved in important physiological functions such as the maintenance of blood pressure and brain function. Although aminopeptidase activities have been reported in some snake venoms, no detailed analysis of any individual snake venom aminopeptidases has been performed so far. As is the case for mammals, snake venom aminopeptidases may also play important roles in altering the physiological functions of victims during envenomation. In order to further understand this important group of snake venom enzymes we have isolated, functionally characterised and analysed the sequence-structure relationships of an aminopeptidase from the venom of the large, highly venomous West African gaboon viper, Bitis gabonica rhinoceros.

Methodology and Principal Findings

The venom of B. g. rhinoceros was fractionated by size exclusion chromatography and fractions with aminopeptidase activities were isolated. Fractions with aminopeptidase activities showed a pure protein with a molecular weight of 150 kDa on SDS-PAGE. In the absence of calcium, this purified protein had broad aminopeptidase activities against acidic, basic and neutral amino acids but in the presence of calcium, it had only acidic aminopeptidase activity (APA). Together with the functional data, mass spectrometry analysis of the purified protein confirmed this as an aminopeptidase A and thus this has been named as rhiminopeptidase A. The complete gene sequence of rhiminopeptidase A was obtained by sequencing the PCR amplified aminopeptidase A gene from the venom gland cDNA of B. g. rhinoceros. The gene codes for a predicted protein of 955 amino acids (110 kDa), which contains the key amino acids necessary for functioning as an aminopeptidase A. A structural model of rhiminopeptidase A shows the structure to consist of 4 domains: an N-terminal saddle-shaped β domain, a mixed α and β catalytic domain, a β-sandwich domain and a C-terminal α helical domain.

Conclusions

This study describes the discovery and characterisation of a novel aminopeptidase A from the venom of B. g. rhinoceros and highlights its potential biological importance. Similar to mammalian aminopeptidases, rhiminopeptidase A might be capable of playing roles in altering the blood pressure and brain function of victims. Furthermore, it could have additional effects on the biological functions of other host proteins by cleaving their N-terminal amino acids. This study points towards the importance of complete analysis of individual components of snake venom in order to develop effective therapies for snake bites.

Snake bite is a major neglected public health issue causing an estimated 125,000 deaths each year, predominantly within poor communities living in rural areas of countries in South East Asia and Africa. Current treatments for snake bites are costly and have limited effectiveness, thus there is a need to develop novel therapeutics. In order to do this the toxic components of snake venom need to be clearly understood. Enzymes called aminopeptidases have been noticed in several snake venoms, but their functions have not been characterised. Related enzymes are also present in mammals, where they are involved in the maintenance of blood pressure and brain function. To further understand this important group of enzymes within snake venom we have purified and analysed the function and structure of an aminopeptidase from the venom of the West African gaboon viper. Our results suggest that this enzyme could also affect the maintenance of blood pressure and brain function in victims of snake bites. Along with other snake venom components, aminopeptidases might be a potential therapeutic target for developing novel treatments for snake bites.

Structural and biochemical characterization of a nitrilase from the thermophilic bacterium, Geobacillus pallidus RAPc8

Geobacillus pallidus RAPc8 (NRRL: B-59396) is a moderately thermophilic gram-positive bacterium, originally isolated from Australian lake sediment. The G. pallidus RAPc8 gene encoding an inducible nitrilase was located and cloned using degenerate primers coding for well-conserved nitrilase sequences, coupled with inverse PCR. The nitrilase open reading frame was cloned into an expression plasmid and the expressed recombinant enzyme purified and characterized. The protein had a monomer molecular weight of 35,790 Da, and the purified functional enzyme had an apparent molecular weight of approximately 600 kDa by size exclusion chromatography. Similar to several plant nitrilases and some bacterial nitrilases, the recombinant G. pallidus RAPc8 enzyme produced both acid and amide products from nitrile substrates. The ratios of acid to amide produced from the substrates we tested are significantly different to those reported for other enzymes, and this has implications for our understanding of the mechanism of the nitrilases which may assist with rational design of these enzymes. Electron microscopy and image classification showed complexes having crescent-like, "c-shaped", circular and "figure-8" shapes. Protein models suggested that the various complexes were composed of 6, 8, 10 and 20 subunits, respectively.

BCL::contact-low confidence fold recognition hits boost protein contact prediction and de novo structure determination

Knowledge of all residue-residue contacts within a protein allows determination of the protein fold. Accurate prediction of even a subset of long-range contacts (contacts between amino acids far apart in sequence) can be instrumental for determining tertiary structure. Here we present BCL::Contact, a novel contact prediction method that utilizes artificial neural networks (ANNs) and specializes in the prediction of medium to long-range contacts. BCL::Contact comes in two modes: sequence-based and structure-based. The sequence-based mode uses only sequence information and has individual ANNs specialized for helix-helix, helix-strand, strand-helix, strand-strand, and sheet-sheet contacts. The structure-based mode combines results from 32-fold recognition methods with sequence information to a consensus prediction. The two methods were presented in the 6(th) and 7(th) Critical Assessment of Techniques for Protein Structure Prediction (CASP) experiments. The present work focuses on elucidating the impact of fold recognition results onto contact prediction via a direct comparison of both methods on a joined benchmark set of proteins. The sequence-based mode predicted contacts with 42% accuracy (7% false positive rate), while the structure-based mode achieved 45% accuracy (2% false positive rate). Predictions by both modes of BCL::Contact were supplied as input to the protein tertiary structure prediction program Rosetta for a benchmark of 17 proteins with no close sequence homologs in the protein data bank (PDB). Rosetta created higher accuracy models, signified by an improvement of 1.3 A on average root mean square deviation (RMSD), when driven by the predicted contacts. Further, filtering Rosetta models by agreement with the predicted contacts enriches for native-like fold topologies.

A localized multimeric anchor attaches the Caulobacter holdfast to the cell pole

Caulobacter crescentus attachment is mediated by the holdfast, a complex of polysaccharide anchored to the cell by HfaA, HfaB and HfaD. We show that all three proteins are surface exposed outer membrane (OM) proteins. HfaA is similar to fimbrial proteins and assembles into a high molecular weight (HMW) form requiring HfaD, but not holdfast polysaccharide. The HfaD HMW form is dependent on HfaA but not on holdfast polysaccharide. We show that HfaA and HfaD form homomultimers and that they require HfaB for stability and OM translocation. All three proteins localize to the late pre-divisional flagellar pole, remain at this pole in swarmer cells, and localize at the stalk tip after the stalk is synthesized at the same pole. Hfa protein localization requires the holdfast polysaccharide secretion proteins and the polar localization factor PodJ. An hfaB mutant is much more severely deficient in adherence and holdfast attachment than hfaA and hfaD mutants. An hfaA, hfaD double mutant phenocopies either single mutant, suggesting that HfaB is involved in holdfast attachment beyond secretion of HfaA and HfaD. We hypothesize that HfaB secretes HfaA and HfaD across the outer membrane, and the three proteins form a complex anchoring the holdfast to the stalk.

A new group of aromatic prenyltransferases in fungi, catalyzing a 2,7-dihydroxynaphthalene 3-dimethylallyl-transferase reaction

Five fungal genomes from the Ascomycota (sac fungi) were found to contain a gene with sequence similarity to a recently discovered small group of bacterial prenyltransferases that catalyze the C-prenylation of aromatic substrates in secondary metabolism. The genes from Aspergillus terreus NIH2624, Botryotinia fuckeliana B05.10 and Sclerotinia sclerotiorum 1980 were expressed in Escherichia coli, and the resulting His(8)-tagged proteins were purified and investigated biochemically. Their substrate specificity was found to be different from that of any other prenyltransferase investigated previously. Using 2,7-dihydroxynaphthalene (2,7-DHN) and dimethylallyl diphosphate as substrates, they catalyzed a regiospecific Friedel-Crafts alkylation of 2,7-DHN at position 3. Using the enzyme of A. terreus, the K(m) values for 2,7-DHN and dimethylallyl diphosphate were determined as 324 +/- 25 microM and 325 +/- 35 microM, respectively, and k(cat) as 0.026 +/- 0.001 s(-1). A significantly lower level of prenylation activity was found using dihydrophenazine-1-carboxylic acid as aromatic substrate, and only traces of products were detected with aspulvinone E, flaviolin, or 4-hydroxybenzoic acid. No product was formed with l-tryptophan, l-tyrosine, or 4-hydroxyphenylpyruvate. The genes for these fungal prenyltransferases are not located within recognizable secondary metabolic gene clusters. Their physiological function is yet unknown.

Why hypothetical protein KPN00728 of Klebsiella pneumoniae should be classified as chain C of succinate dehydrogenase?

Twenty percent of genes that encode for hypothetical proteins from Klebsiella pneumoniae MGH78578 were identified, leading to KPN00728 and KPN00729 after bioinformatics analysis. Both open reading frames showed high sequence homology to Succinate dehydrogenase Chain C (SdhC) and D (SdhD) from Escherichia coli. Recently, KPN00729 was assigned as SdhD. KPN00728 thus remains of particular interest as no annotated genes from the complete genome sequence encode for SdhC. We discovered KPN00728 has a missing region with conserved residues important for ubiquinone (UQ) and heme group binding. Structure and function prediction of KPN00728 coupled with secondary structure analysis and transmembrane topology showed KPN00728 adopts SDH-(subunit C)-like structure. To further probe its functionality, UQ was docked on the built model (consisting KPN00728 and KPN00729) and formation of hydrogen bonds between UQ and Ser27, Arg31 (KPN00728) and Tyr84 (KPN00729) further reinforces and supports that KPN00728 is indeed SDH. This is the first report on the structural and function prediction of KPN00728 of K. pneumoniae MGH78578 as SdhC.

On-line hierarchy of general linear models for selecting and ranking the best predicted protein structures

To predict the three dimensional structure of proteins, many computational methods sample the conformational space, generating a large number of candidate structures. Subsequently, such methods rank the generated structures using a variety of model quality assessment programs in order to obtain a small set of structures that are most likely to resemble the unknown experimentally determined structure. Model quality assessment programs suffer from two main limitations: (i) the rank-one structure is not always the best predicted structure; in other words, the best predicted structure could be ranked as the 10th structure (ii) no single assessment method can correctly rank the predicted structures for all target proteins. However, because often at least some of the methods achieve a good ranking, a model quality assessment method that is based on a consensus of a number of model quality assessment methods is likely to perform better. We have devised the STPdata algorithm, a consensus method based on five model quality assessment programs. We have applied it to build an on-line "custom-trained" hierarchy of general linear models to select and rank the best predicted structures. By "custom-trained", we mean for each target protein the STPdata algorithm trains a unique model on data related to the input target protein. To evaluate our method we participated in CASP8 as human predictors. In CASP8, the STPdata algorithm has trained 128 hierarchical models for each of the 128 target proteins. Based on the official results of CASP8 our method outperformed the best server by 6% and won the fourth position among human predictors. Our CASP results are purely based on computational methods without any human intervention.

[Prediction of the spatial structure of proteins: emphasis on membrane targets]

Knowledge of the spatial structure of proteins is a prerequisite for both awareness of their functional mechanisms and the framework for rational drug discovery and design. Meanwhile, direct structural determination is often hampered or impractical due to the complexity, expensiveness, and limited capabilities of experimental techniques. These issues are especially pronounced for integral membrane proteins. On numerous occasions, the theoretical prediction of protein structures may facilitate the process by exploiting physical or empirical principles. This paper surveys modern techniques for the prediction of the spatial structure of proteins using computer algorithms, and the main emphasis is placed on the most "complex" targets - membrane proteins (MPs). The first part of the review describes de novo methods based on empirical physical principles; in the second part, a comparative modeling philosophy, which accounts for the structure of related proteins, is described. Special focus is made regarding pharmacologically relevant classes of G-coupled receptors, receptor tyrosine ki-nases, and other MPs. Algorithms for the assessment of the models quality and potential fields of application of computer models are discussed.

An improved hybrid global optimization method for protein tertiary structure prediction

First principles approaches to the protein structure prediction problem must search through an enormous conformational space to identify low-energy, near-native structures. In this paper, we describe the formulation of the tertiary structure prediction problem as a nonlinear constrained minimization problem, where the goal is to minimize the energy of a protein conformation subject to constraints on torsion angles and interatomic distances. The core of the proposed algorithm is a hybrid global optimization method that combines the benefits of the αBB deterministic global optimization approach with conformational space annealing. These global optimization techniques employ a local minimization strategy that combines torsion angle dynamics and rotamer optimization to identify and improve the selection of initial conformations and then applies a sequential quadratic programming approach to further minimize the energy of the protein conformations subject to constraints. The proposed algorithm demonstrates the ability to identify both lower energy protein structures, as well as larger ensembles of low-energy conformations.

Genome scale prediction of substrate specificity for acyl adenylate superfamily of enzymes based on active site residue profiles

Background

Enzymes belonging to acyl:CoA synthetase (ACS) superfamily activate wide variety of substrates and play major role in increasing the structural and functional diversity of various secondary metabolites in microbes and plants. However, due to the large sequence divergence within the superfamily, it is difficult to predict their substrate preference by annotation transfer from the closest homolog. Therefore, a large number of ACS sequences present in public databases lack any functional annotation at the level of substrate specificity. Recently, several examples have been reported where the enzymes showing high sequence similarity to luciferases or coumarate:CoA ligases have been surprisingly found to activate fatty acyl substrates in experimental studies. In this work, we have investigated the relationship between the substrate specificity of ACS and their sequence/structural features, and developed a novel computational protocol for in silico assignment of substrate preference.

Results

We have used a knowledge-based approach which involves compilation of substrate specificity information for various experimentally characterized ACS and derivation of profile HMMs for each subfamily. These HMM profiles can accurately differentiate probable cognate substrates from non-cognate possibilities with high specificity (Sp) and sensitivity (Sn) (Sn = 0.91-1.0, Sp = 0.96-1.0) values. Using homologous crystal structures, we identified a limited number of contact residues crucial for substrate recognition i.e. specificity determining residues (SDRs). Patterns of SDRs from different subfamilies have been used to derive predictive rules for correlating them to substrate preference. The power of the SDR approach has been demonstrated by correct prediction of substrates for enzymes which show apparently anomalous substrate preference. Furthermore, molecular modeling of the substrates in the active site has been carried out to understand the structural basis of substrate selection. A web based prediction tool http://www.nii.res.in/pred_acs_substr.html has been developed for automated functional classification of ACS enzymes.

Conclusions

We have developed a novel computational protocol for predicting substrate preference for ACS superfamily of enzymes using a limited number of SDRs. Using this approach substrate preference can be assigned to a large number of ACS enzymes present in various genomes. It can potentially help in rational design of novel proteins with altered substrate specificities.

A pairwise residue contact area-based mean force potential for discrimination of native protein structure

Background

Considering energy function to detect a correct protein fold from incorrect ones is very important for protein structure prediction and protein folding. Knowledge-based mean force potentials are certainly the most popular type of interaction function for protein threading. They are derived from statistical analyses of interacting groups in experimentally determined protein structures. These potentials are developed at the atom or the amino acid level. Based on orientation dependent contact area, a new type of knowledge-based mean force potential has been developed.

Results

We developed a new approach to calculate a knowledge-based potential of mean-force, using pairwise residue contact area. To test the performance of our approach, we performed it on several decoy sets to measure its ability to discriminate native structure from decoys. This potential has been able to distinguish native structures from the decoys in the most cases. Further, the calculated Z-scores were quite high for all protein datasets.

Conclusions

This knowledge-based potential of mean force can be used in protein structure prediction, fold recognition, comparative modelling and molecular recognition. The program is available at http://www.bioinf.cs.ipm.ac.ir/softwares/surfield

Template-based protein structure modeling

Functional characterization of a protein is often facilitated by its 3D structure. However, the fraction of experimentally known 3D models is currently less than 1% due to the inherently time-consuming and complicated nature of structure determination techniques. Computational approaches are employed to bridge the gap between the number of known sequences and that of 3D models. Template-based protein structure modeling techniques rely on the study of principles that dictate the 3D structure of natural proteins from the theory of evolution viewpoint. Strategies for template-based structure modeling will be discussed with a focus on comparative modeling, by reviewing techniques available for all the major steps involved in the comparative modeling pipeline.

Protein secondary structure prediction

While the prediction of a native protein structure from sequence continues to remain a challenging problem, over the past decades computational methods have become quite successful in exploiting the mechanisms behind secondary structure formation. The great effort expended in this area has resulted in the development of a vast number of secondary structure prediction methods. Especially the combination of well-optimized/sensitive machine-learning algorithms and inclusion of homologous sequence information has led to increased prediction accuracies of up to 80%. In this chapter, we will first introduce some basic notions and provide a brief history of secondary structure prediction advances. Then a comprehensive overview of state-of-the-art prediction methods will be given. Finally, we will discuss open questions and challenges in this field and provide some practical recommendations for the user.

An N-terminal region of Lassa virus L protein plays a critical role in transcription but not replication of the virus genome

The central domain of the 200-kDa Lassa virus L protein is a putative RNA-dependent RNA polymerase. N- and C-terminal domains may harbor enzymatic functions important for viral mRNA synthesis, including capping enzymes or cap-snatching endoribonucleases. In the present study, we have employed a large-scale mutagenesis approach to map functionally relevant residues in these regions. The main targets were acidic (Asp and Glu) and basic residues (Lys and Arg) known to form catalytic and binding sites of capping enzymes and endoribonucleases. A total of 149 different mutants were generated and tested in the Lassa virus replicon system. Nearly 25% of evolutionarily highly conserved acidic and basic side chains were dispensable for function of L protein in the replicon context. The vast majority of the remaining mutants had defects in both transcription and replication. Seven residues (Asp-89, Glu-102, Asp-119, Lys-122, Asp-129, Glu-180, and Arg-185) were selectively important for mRNA synthesis. The phenotype was particularly pronounced for Asp-89, Glu-102, and Asp-129, which were indispensable for transcription but could be replaced by a variety of amino acid residues without affecting genome replication. Bioinformatics disclosed the remote similarity of this region to type IIs endonucleases. The mutagenesis was complemented by experiments with the RNA polymerase II inhibitor alpha-amanitin, demonstrating dependence of viral transcription from the cellular mRNA pool. In conclusion, this paper describes an N-terminal region in L protein being important for mRNA, but not genome synthesis. Bioinformatics and cell biological experiments lend support to the hypothesis that this region could be part of a cap-snatching enzyme.

A distance-dependent atomic knowledge-based potential and force for discrimination of native structures from decoys

The purpose of this article is to introduce a novel model for discriminating correctly folded proteins from well designed decoy structures using mechanical interatomic forces. In our model, we consider a protein as a collection of springs and the force imposed to each atom is calculated. A potential function is obtained from statistical contact preferences within known protein structures. Combining this function with the spring equation, the interatomic forces are calculated. Finally, we consider a structure and define a score function on the 3D structure of a protein. We compare the force imposed to each atom of a protein with the corresponding atom in the other structures. We then assign larger scores to those atoms with lower forces. The total score is the sum of partial scores of atoms. The optimal structure is assumed to be the one with the highest score in the data set. To evaluate the performance of our model, we apply it on several decoy sets.

Splitting statistical potentials into meaningful scoring functions: testing the prediction of near-native structures from decoy conformations

Background

Recent advances on high-throughput technologies have produced a vast amount of protein sequences, while the number of high-resolution structures has seen a limited increase. This has impelled the production of many strategies to built protein structures from its sequence, generating a considerable amount of alternative models. The selection of the closest model to the native conformation has thus become crucial for structure prediction. Several methods have been developed to score protein models by energies, knowledge-based potentials and combination of both.

Results

Here, we present and demonstrate a theory to split the knowledge-based potentials in scoring terms biologically meaningful and to combine them in new scores to predict near-native structures. Our strategy allows circumventing the problem of defining the reference state. In this approach we give the proof for a simple and linear application that can be further improved by optimizing the combination of Zscores. Using the simplest composite score () we obtained predictions similar to state-of-the-art methods. Besides, our approach has the advantage of identifying the most relevant terms involved in the stability of the protein structure. Finally, we also use the composite Zscores to assess the conformation of models and to detect local errors.

Conclusion

We have introduced a method to split knowledge-based potentials and to solve the problem of defining a reference state. The new scores have detected near-native structures as accurately as state-of-art methods and have been successful to identify wrongly modeled regions of many near-native conformations.

Structure-function analysis of IntDOT

CTnDOT integrase (IntDOT) is a member of the tyrosine family of site-specific DNA recombinases. IntDOT is unusual in that it catalyzes recombination between nonidentical sequences. Previous mutational analyses centered on mutants with substitutions of conserved residues in the catalytic (CAT) domain or residues predicted by homology modeling to be close to DNA in the core-binding (CB) domain. That work suggested that a conserved active-site residue (Arg I) of the CAT domain is missing and that some residues in the CB domain are involved in catalysis. Here we used a genetic approach and constructed an Escherichia coli indicator strain to screen for random mutations in IntDOT that disrupt integrative recombination in vivo. Twenty-five IntDOT mutants were isolated and characterized for DNA binding, DNA cleavage, and DNA ligation activities. We found that mutants with substitutions in the amino-terminal (N) domain were catalytically active but defective in forming nucleoprotein complexes, suggesting that they have altered protein-protein interactions or altered interactions with DNA. Replacement of Ala-352 of the CAT domain disrupted DNA cleavage but not DNA ligation, suggesting that Ala-352 may be important for positioning the catalytic tyrosine (Tyr-381) during cleavage. Interestingly, our biochemical data and homology modeling of the CAT domain suggest that Arg-285 is the missing Arg I residue of IntDOT. The predicted position of Arg-285 shows it entering the active site from a position on the polypeptide backbone that is not utilized in other tyrosine recombinases. IntDOT may therefore employ a novel active-site architecture to catalyze recombination.

Evaluating the absolute quality of a single protein model using structural features and support vector machines

Knowing the quality of a protein structure model is important for its appropriate usage. We developed a model evaluation method to assess the absolute quality of a single protein model using only structural features with support vector machine regression. The method assigns an absolute quantitative score (i.e. GDT-TS) to a model by comparing its secondary structure, relative solvent accessibility, contact map, and beta sheet structure with their counterparts predicted from its primary sequence. We trained and tested the method on the CASP6 dataset using cross-validation. The correlation between predicted and true scores is 0.82. On the independent CASP7 dataset, the correlation averaged over 95 protein targets is 0.76; the average correlation for template-based and ab initio targets is 0.82 and 0.50, respectively. Furthermore, the predicted absolute quality scores can be used to rank models effectively. The average difference (or loss) between the scores of the top-ranked models and the best models is 5.70 on the CASP7 targets. This method performs favorably when compared with the other methods used on the same dataset. Moreover, the predicted absolute quality scores are comparable across models for different proteins. These features make the method a valuable tool for model quality assurance and ranking.

Predicting local quality of a sequence-structure alignment

Although protein structure prediction has made great progress in recent years, a protein model derived from automated prediction methods is subject to various errors. As methods for structure prediction develop, a continuing problem is how to evaluate the quality of a protein model, especially to identify some well-predicted regions of the model, so that the structural biology community can benefit from the automated structure prediction. It is also important to identify badly-predicted regions in a model so that some refinement measurements can be applied to it. We present two complementary techniques, FragQA and PosQA, to accurately predict local quality of a sequence-structure (i.e. sequence-template) alignment generated by comparative modeling (i.e. homology modeling and threading). FragQA and PosQA predict local quality from two different perspectives. Different from existing methods, FragQA directly predicts cRMSD between a continuously aligned fragment determined by an alignment and the corresponding fragment in the native structure, while PosQA predicts the quality of an individual aligned position. Both FragQA and PosQA use an SVM (Support Vector Machine) regression method to perform prediction using similar information extracted from a single given alignment. Experimental results demonstrate that FragQA performs well on predicting local fragment quality, and PosQA outperforms two top-notch methods, ProQres and ProQprof. Our results indicate that (1) local quality can be predicted well; (2) local sequence evolutionary information (i.e. sequence similarity) is the major factor in predicting local quality; and (3) structural information such as solvent accessibility and secondary structure helps to improve the prediction performance.

Analyzing the catalytic mechanism of protein tyrosine phosphatase PtpB from Staphylococcus aureus through site-directed mutagenesis

Protein tyrosine phosphatase B (PtpB) from Staphylococcus aureus, MRSA 252, is a low molecular weight protein tyrosine phosphatase involved in its pathogenicity. PtpB has been modeled in silico and site-directed mutagenesis performed to ascertain the importance of active site residues Cys8, Arg14, Ser15 and Asp120 in its catalytic mechanism. Kinetic characterization of wild-type and the mutant PtpBs, C8S, R14A, S15T, S15A, D120A, D120E, D120N revealed the reaction mechanism followed by this LMWPTPase. The mutations caused major changes in the local environment resulting in significant decrease of its catalytic activity. Inhibition kinetics for the wild-type enzyme was performed with maleimide and maleimidobutyric acid.

Homology modelling and spectroscopy, a never-ending love story

Homology modelling is normally the technique of choice when experimental structure data are not available but three-dimensional coordinates are needed, for example, to aid with detailed interpretation of results of spectroscopic studies. Herein, the state of the art of homology modelling will be described in the light of a series of recent developments, and an overview will be given of the problems and opportunities encountered in this field. The major topic, the accuracy and precision of homology models, will be discussed extensively due to its influence on the reliability of conclusions drawn from the combination of homology models and spectroscopic data. Three real-world examples will illustrate how both homology modelling and spectroscopy can be beneficial for (bio)medical research.

Trm13p, the tRNA:Xm4 modification enzyme from Saccharomyces cerevisiae is a member of the Rossmann-fold MTase superfamily: prediction of structure and active site

2'-O-ribose methylation is one of the most common posttranscriptional modifications in RNA. Methylations at different positions are introduced by enzymes from at least two unrelated superfamilies. Recently, a new family of eukaryotic RNA methyltransferases (MTases) has been identified, and its representative from yeast (Yol125w, renamed as Trm13p) has been shown to 2'-O-methylate position 4 of tRNA. Trm13 is conserved in Eukaryota, but exhibits no sequence similarity to other known MTases. Here, I present the results of bioinformatics analysis which suggest that Trm13 is a strongly diverged member of the Rossmann-fold MTase (RFM) superfamily, and therefore is evolutionarily related to 2'-O-MTases such as Trm7 and fibrillarin. However, the character of conserved residues in the predicted active site of the Trm13 family suggests it may use a different mechanism of ribose methylation than its relatives. A molecular model of the Trm13p structure has been constructed and evaluated for potential accuracy using model quality assessment methods. The predicted structure will facilitate experimental analyses of the Trm13p mechanism of action.

Structural modeling identified the tRNA-binding domain of Utp8p, an essential nucleolar component of the nuclear tRNA export machinery of Saccharomyces cerevisiae

Utp8p is an essential 80 kDa intranuclear tRNA chaperone that transports tRNAs from the nucleolus to the nuclear tRNA export receptors in Saccharomyces cerevisiae. To help understand the mechanism of Utp8p function, predictive tools were used to derive a partial model of the tertiary structure of Utp8p. Secondary structure prediction, supported by circular dichroism measurements, indicated that Utp8p is divided into 2 domains: the N-terminal beta sheet and the C-terminal alpha helical domain. Tertiary structure prediction was more challenging, because the amino acid sequence of Utp8p is not directly homologous to any known protein structure. The tertiary structures predicted by threading and fold recognition had generally modest scores, but for the C-terminal domain, threading and fold recognition consistently pointed to an alpha-alpha superhelix. Because of the sequence diversity of this fold type, no single structural template was an ideal fit to the Utp8p sequence. Instead, a composite template was constructed from 3 different alpha-alpha superhelix structures that gave the best matches to different portions of the C-terminal domain sequence. In the resulting model, the most conserved sequences grouped in a tight cluster of positive charges on a protein that is otherwise predominantly negative, suggesting that the positive-charge cleft may be the tRNA-binding site. Mutations of conserved positive residues in the proposed binding site resulted in a reduction in the affinity of Utp8p for tRNA both in vivo and in vitro. Models were also derived for the 10 fungal homologues of Utp8p, and the localization of the positive charges on the conserved surface was found in all cases. Taken together, these data suggest that the positive-charge cleft of the C-terminal domain of Utp8p is involved in tRNA-binding.

CPDadh: a new peptidase family homologous to the cysteine protease domain in bacterial MARTX toxins

A cysteine protease domain (CPD) has been recently discovered in a group of multifunctional, autoprocessing RTX toxins (MARTX) and Clostridium difficile toxins A and B. These CPDs (referred to as CPDmartx) autocleave the toxins to release domains with toxic effects inside host cells. We report identification and computational analysis of CPDadh, a new cysteine peptidase family homologous to CPDmartx. CPDadh and CPDmartx share a Rossmann-like structural core and conserved catalytic residues. In bacteria, domains of the CPDadh family are present at the N-termini of a diverse group of putative cell-cell interaction proteins and at the C-termini of some RHS (recombination hot spot) proteins. In eukaryotes, catalytically inactive members of the CPDadh family are found in cell surface protein NELF (nasal embryonic LHRH factor) and some putative signaling proteins.

Molecular and biochemical characterization of the parvulin-type PPIases in Lotus japonicus

The cis/trans isomerization of the peptide bond preceding proline is an intrinsically slow process, although important in many biological processes in both prokaryotes and eukaryotes. In vivo, this isomerization is catalyzed by peptidyl-prolyl cis/trans-isomerases (PPIases). Here, we present the molecular and biochemical characterization of parvulin-type PPIase family members of the model legume Lotus japonicus, annotated as LjPar1, LjPar2, and LjPar3. Although LjPar1 and LjPar2 were found to be homologous to PIN1 (Protein Interacting with NIMA)-type parvulins and hPar14 from human, respectively, LjPar3 represents a novel multidomain parvulin, apparently present only in plants, that contains an active carboxyl-terminal sulfurtransferase domain. All Lotus parvulins were heterologously expressed and purified from Escherichia coli, and purified protein verification measurements used a liquid chromatography-mass spectrometry-based proteomic method. The biochemical characterization of the recombinant Lotus parvulins revealed that they possess PPIase activity toward synthetic tetrapeptides, although they exhibited different substrate specificities depending on the amino acid amino terminal to proline. These differences were also studied in a structural context using molecular modeling of the encoded polypeptides. Real-time reverse transcription-polymerase chain reaction revealed that the three parvulin genes of Lotus are ubiquitously expressed in all plant organs. LjPar1 was found to be up-regulated during the later stages of nodule development. Subcellular localization of LjPar-enhanced Yellow Fluorescence Protein (eYFP) fusions expressed in Arabidopsis (Arabidopsis thaliana) leaf epidermal cells revealed that LjPar1- and LjPar2-eYFP fusions were localized in the cytoplasm and in the nucleus, in contrast to LjPar3-eYFP, which was clearly localized in plastids. Divergent substrate specificities, expression profiles, and subcellular localization indicate that plant parvulin-type PPIases are probably involved in a wide range of biochemical and physiological processes.