Proteomic analysis of Plasmodium in the mosquito: progress and pitfalls.

Here we discuss proteomic analyses of whole cell preparations of the mosquito stages of malaria parasite development (i.e. gametocytes, microgamete, ookinete, oocyst and sporozoite) of Plasmodium berghei. We also include critiques of the proteomes of two cell fractions from the purified ookinete, namely the micronemes and cell surface. Whereas we summarise key biological interpretations of the data, we also try to identify key methodological constraints we have met, only some of which we were able to resolve. Recognising the need to translate the potential of current genome sequencing into functional understanding, we report our efforts to develop more powerful combinations of methods for the in silico prediction of protein function and location. We have applied this analysis to the proteome of the male gamete, a cell whose very simple structural organisation facilitated interpretation of data. Some of the in silico predictions made have now been supported by ongoing protein tagging and genetic knockout studies. We hope this discussion may assist future studies.

The flagellum in malarial parasites.

The malarial parasites assemble flagella exclusively during the formation of the male gamete in the midgut of the female mosquito vector. The observation of gamete formation ex vivo reported by Laveran (Laveran MA: De la nature parasitaire des accidents de l'impaludisme. Comptes Rendues De La Societe de Biologie. Paris 1881, 93:627-630) was seminal to the discovery of the parasite itself. Following ingestion of malaria-infected blood by the mosquito, microgamete formation from the terminally arrested gametocytes is exceptionally rapid, completing three mitotic divisions in just a few minutes, and is precisely regulated. This review attempts to draw together the diverse original observations with subsequent electron microscopic studies, and recent work on the signalling pathways regulating sexual development, together with transcriptomic and proteomic studies that are paving the way to new understandings of the molecular mechanisms involved and the potential they offer for effective interventions to block the transmission of the parasites in natural communities.

EMAAS: an extensible grid-based rich internet application for microarray data analysis and management.

BACKGROUND: Microarray experimentation requires the application of complex analysis methods as well as the use of non-trivial computer technologies to manage the resultant large data sets. This, together with the proliferation of tools and techniques for microarray data analysis, makes it very challenging for a laboratory scientist to keep up-to-date with the latest developments in this field. Our aim was to develop a distributed e-support system for microarray data analysis and management. RESULTS: EMAAS (Extensible MicroArray Analysis System) is a multi-user rich internet application (RIA) providing simple, robust access to up-to-date resources for microarray data storage and analysis, combined with integrated tools to optimise real time user support and training. The system leverages the power of distributed computing to perform microarray analyses, and provides seamless access to resources located at various remote facilities. The EMAAS framework allows users to import microarray data from several sources to an underlying database, to pre-process, quality assess and analyse the data, to perform functional analyses, and to track data analysis steps, all through a single easy to use web portal. This interface offers distance support to users both in the form of video tutorials and via live screen feeds using the web conferencing tool EVO. A number of analysis packages, including R-Bioconductor and Affymetrix Power Tools have been integrated on the server side and are available programmatically through the Postgres-PLR library or on grid compute clusters. Integrated distributed resources include the functional annotation tool DAVID, GeneCards and the microarray data repositories GEO, CELSIUS and MiMiR. EMAAS currently supports analysis of Affymetrix 3' and Exon expression arrays, and the system is extensible to cater for other microarray and transcriptomic platforms. CONCLUSION: EMAAS enables users to track and perform microarray data management and analysis tasks through a single easy-to-use web application. The system architecture is flexible and scalable to allow new array types, analysis algorithms and tools to be added with relative ease and to cope with large increases in data volume.

Predicting the sub-cellular location of proteins from text using support vector machines.

We present an automatic method to classify the sub-cellular location of proteins based on the text of relevant medline abstracts. For each protein, a vector of terms is generated from medline abstracts in which the protein/gene's name or synonym occurs. A Support Vector Machine (SVM) is used to automatically partition the term space and to thus discriminate the textual features that define sub-cellular location. The method is benchmarked on a set of proteins of known sub-cellular location from S. cerevisiae. No prior knowledge of the problem domain nor any natural language processing is used at any stage. The method out-performs support vector machines trained on amino acid composition and has comparable performance to rule-based text classifiers. Combining text with protein amino-acid composition improves recall for some sub-cellular locations. We discuss the generality of the method and its potential application to a variety of biological classification problems.

A structural census of metabolic networks for E. coli.

A structural survey of the Escherichia coli proteins occurring in metabolic networks in the KEGG database (release 19 of LIGAND) has been carried out. A measure of structural coverage of a network is defined and calculated for each network. Twenty-four networks have 50 % or more of the enzyme steps assigned in E. coli and of these 21 have a structural coverage of 50 % or more. For those proteins that have a region matching a SCOP domain 50 % fall on or below the 30 % sequence identity threshold and represent non-trivial comparative modelling targets highlighting the need for experimental structure determination studies. The survey reveals the predominance of alpha/beta and alpha+beta folds for enzymes involved in metabolic pathways and that this general trend is maintained at the level of each pathway. The most popular superfamilies are coenzyme binding domains and are involved in the supply of energy to reactions. Although a few superfamilies are found in many pathways, in general there is a specificity of a particular superfamily for a particular pathway.

PML bodies associate specifically with the MHC gene cluster in interphase nuclei.

Promyelocytic leukemia (PML) bodies are nuclear multi-protein domains. The observations that viruses transcribe their genomes adjacent to PML bodies and that nascent RNA accumulates at their periphery suggest that PML bodies function in transcription. We have used immuno-FISH in primary human fibroblasts to determine the 3D spatial organisation of gene-rich and gene-poor chromosomal regions relative to PML bodies. We find a highly non-random association of the gene-rich major histocompatibilty complex (MHC) on chromosome 6 with PML bodies. This association is specific for the centromeric end of the MHC and extends over a genomic region of at least 1.6 megabases. We also show that PML association is maintained when a subsection of this region is integrated into another chromosomal location. This is the first demonstration that PML bodies have specific chromosomal associations and supports a model for PML bodies as part of a functional nuclear compartment.

Automated structure-based prediction of functional sites in proteins: applications to assessing the validity of inheriting protein function from homology in genome annotation and to protein docking.

A major problem in genome annotation is whether it is valid to transfer the function from a characterised protein to a homologue of unknown activity. Here, we show that one can employ a strategy that uses a structure-based prediction of protein functional sites to assess the reliability of functional inheritance. We have automated and benchmarked a method based on the evolutionary trace approach. Using a multiple sequence alignment, we identified invariant polar residues, which were then mapped onto the protein structure. Spatial clusters of these invariant residues formed the predicted functional site. For 68 of 86 proteins examined, the method yielded information about the observed functional site. This algorithm for functional site prediction was then used to assess the validity of transferring the function between homologues. This procedure was tested on 18 pairs of homologous proteins with unrelated function and 70 pairs of proteins with related function, and was shown to be 94 % accurate. This automated method could be linked to schemes for genome annotation. Finally, we examined the use of functional site prediction in protein-protein and protein-DNA docking. The use of predicted functional sites was shown to filter putative docked complexes with a discrimination similar to that obtained by manually including biological information about active sites or DNA-binding residues.

An approach to improving multiple alignments of protein sequences using predicted secondary structure.

The object of this work was to improve multiple sequence alignments using public-domain software and methods as far as possible. A method is described where the secondary structure of proteins is predicted and this information, coupled with a simplified description of the amino acids, is used to produce multiple sequence alignments. This method improved the accuracy of the resulting alignments by between 5 and 14% when compared with full sequence profile alignments (as scored against structural alignments). These improved alignments were used to predict the secondary structure of the sequences they contain. The resultant predictions were more accurate than those produced from less optimal alignments. An improvement of 6% for a three-state (helix, sheet and coil) prediction was observed when using the best alignment from the method presented here and the alignment obtained using sequence only. The method makes use of public domain software and all the associated files required to repeat the work are available from the primary author.

Automated discovery of structural signatures of protein fold and function.

There are constraints on a protein sequence/structure for it to adopt a particular fold. These constraints could be either a local signature involving particular sequences or arrangements of secondary structure or a global signature involving features along the entire chain. To search systematically for protein fold signatures, we have explored the use of Inductive Logic Programming (ILP). ILP is a machine learning technique which derives rules from observation and encoded principles. The derived rules are readily interpreted in terms of concepts used by experts. For 20 populated folds in SCOP, 59 rules were found automatically. The accuracy of these rules, which is defined as the number of true positive plus true negative over the total number of examples, is 74% (cross-validated value). Further analysis was carried out for 23 signatures covering 30% or more positive examples of a particular fold. The work showed that signatures of protein folds exist, about half of rules discovered automatically coincide with the level of fold in the SCOP classification. Other signatures correspond to homologous family and may be the consequence of a functional requirement. Examination of the rules shows that many correspond to established principles published in specific literature. However, in general, the list of signatures is not part of standard biological databases of protein patterns. We find that the length of the loops makes an important contribution to the signatures, suggesting that this is an important determinant of the identity of protein folds. With the expansion in the number of determined protein structures, stimulated by structural genomics initiatives, there will be an increased need for automated methods to extract principles of protein folding from coordinates.

Generating protein three-dimensional fold signatures using inductive logic programming.

Inductive logic programming (ILP) has been applied to automatically discover protein fold signatures. This paper investigates the use of topological information to circumvent problems encountered during previous experiments, namely (1) matching of non-structurally related secondary structures and (2) scaling problems. Cross-validation tests were carried out for 20 folds. The overall estimated accuracy is 73.37+/-0.35%. The new representation allows us to process the complete set of examples, while previously it was necessary to sample the negative examples. Topological information is used in approximately 90% of the rules presented here. Information about the topology of a sheet is present in 63% of the rules. This set of rules presents characteristics of the overall architecture of the fold. In contrast, 26% of the rules contain topological information which is limited to the packing of a restricted number of secondary structures, as such, the later set resembles those found in our previous studies.

Enhancement of protein modeling by human intervention in applying the automatic programs 3D-JIGSAW and 3D-PSSM.

Fourteen models were constructed and analyzed for the comparative modeling section of Critical Assessment of Techniques for Protein Structure Prediction (CASP4). Sequence identity between each target and the best possible parent(s) ranged between 55 and 13%, and the root-mean-square deviation between model and target was from 0.8 to 17.9 A. In the fold recognition section, 10 of the 11 remote homologues were recognized. The modeling protocols are a combination of automated computer algorithms, 3D-JIGSAW (for comparative modeling) and 3D-PSSM (for fold recognition), with human intervention at certain critical stages. In particular, intervention is required to check superfamily assignment, best possible parents from which to model, sequence alignments to those parents and take-off regions for modeling variable regions. There now is a convergence of algorithms for comparative modeling and fold recognition, particularly in the region of remote homology.

The BRCA1 C-terminal domain: structure and function.

The BRCA1 C-terminal region contains a duplicated globular domain termed BRCT that is found within many DNA damage repair and cell cycle checkpoint proteins. The unique diversity of this domain superfamily allows BRCT modules to interact forming homo/hetero BRCT multimers, BRCT-non-BRCT interactions, and interactions with DNA strand breaks. The sequence and functional diversity of the BRCT superfamily suggests that BRCT domains are evolutionarily convenient interaction modules.

SAWTED: structure assignment with text description--enhanced detection of remote homologues with automated SWISS-PROT annotation comparisons.

MOTIVATION: Sequence database search methods often identify putative sub-threshold hits of known function or structure for a given query sequence. It is widespread practice to filter these hits by hand using knowledge of function and other factors; to the expert, some hits may appear more sensible than others. SAWTED (Structure Assignment With Text Description) is an automated solution to this post-filtering problem which will be applicable to large scale genome assignments. RESULTS: A standard document comparison algorithm is applied to text descriptions extracted from SWISS-PROT annotations. The added value of SAWTED in combination with PSI-BLAST has been shown with a benchmark of difficult remote homologues taken from the SCOP structure database. AVAILABILITY: A WAWTED PSI-BLAST Web server is available to perform sensitive searches against the protein structure database (http://www.bmm.icnet.uk/servers/sawted). CONTACT: R.MacCallum@icrf.icnet.uk

Enhanced genome annotation using structural profiles in the program 3D-PSSM.

A method (three-dimensional position-specific scoring matrix, 3D-PSSM) to recognise remote protein sequence homologues is described. The method combines the power of multiple sequence profiles with knowledge of protein structure to provide enhanced recognition and thus functional assignment of newly sequenced genomes. The method uses structural alignments of homologous proteins of similar three-dimensional structure in the structural classification of proteins (SCOP) database to obtain a structural equivalence of residues. These equivalences are used to extend multiply aligned sequences obtained by standard sequence searches. The resulting large superfamily-based multiple alignment is converted into a PSSM. Combined with secondary structure matching and solvation potentials, 3D-PSSM can recognise structural and functional relationships beyond state-of-the-art sequence methods. In a cross-validated benchmark on 136 homologous relationships unambiguously undetectable by position-specific iterated basic local alignment search tool (PSI-Blast), 3D-PSSM can confidently assign 18 %. The method was applied to the remaining unassigned regions of the Mycoplasma genitalium genome and an additional 13 regions were assigned with 95 % confidence. 3D-PSSM is available to the community as a web server: http://www.bmm.icnet.uk/servers/3dpssm

Benchmarking PSI-BLAST in genome annotation.

The recognition of remote protein homologies is a major aspect of the structural and functional annotation of newly determined genomes. Here we benchmark the coverage and error rate of genome annotation using the widely used homology-searching program PSI-BLAST (position-specific iterated basic local alignment search tool). This study evaluates the one-to-many success rate for recognition, as often there are several homologues in the database and only one needs to be identified for annotating the sequence. In contrast, previous benchmarks considered one-to-one recognition in which a single query was required to find a particular target. The benchmark constructs a model genome from the full sequences of the structural classification of protein (SCOP) database and searches against a target library of remote homologous domains (<20 % identity). The structural benchmark provides a reliable list of correct and false homology assignments. PSI-BLAST successfully annotated 40 % of the domains in the model genome that had at least one homologue in the target library. This coverage is more than three times that if one-to-one recognition is evaluated (11 % coverage of domains). Although a structural benchmark was used, the results equally apply to just sequence homology searches. Accordingly, structural and sequence assignments were made to the sequences of Mycoplasma genitalium and Mycobacterium tuberculosis (see http://www.bmm.icnet. uk). The extent of missed assignments and of new superfamilies can be estimated for these genomes for both structural and functional annotations.

Model building by comparison at CASP3: using expert knowledge and computer automation.

Ten models were constructed for the comparative modeling section of the Critical Assessment of Techniques for Protein Structure Prediction-3 (CASP3). Sequence identity between each target and the best possible parent(s) ranged between 12% and 64%. The modeling protocol is a mixture of automated computer algorithms with human intervention at certain critical stages. In particular, intervention is required to check sequence alignments and the selection of parameters for various computer programs. Seven of the targets were constructed from single-parent templates, and three were constructed from multiple parents. The reasons for such a high ratio of modeling from single parents only are discussed. Models constructed from multiple parents were found to be more accurate than models constructed from single parents only. A novel loop-modeling algorithm is presented that consists of fragment database searches, several fragment libraries, and mean-field calculations on representative fragment candidates.

Progress in protein structure prediction: assessment of CASP3.

The third comparative assessment of techniques of protein structure prediction (CASP3) was held during 1998. This is a blind trial in which structures are predicted prior to having knowledge of the coordinates, which are then revealed to enable the assessment. Three sections at the meeting evaluated different methodologies - comparative modelling, fold recognition and ab initio methods. For some, but not all of the target coordinates, high quality models were submitted in each of these sections. There have been improvements in prediction techniques since CASP2 in 1996, most notably for ab initio methods.

Use of pair potentials across protein interfaces in screening predicted docked complexes.

Empirical residue-residue pair potentials are used to screen possible complexes for protein-protein dockings. A correct docking is defined as a complex with not more than 2.5 A root-mean-square distance from the known experimental structure. The complexes were generated by "ftdock" (Gabb et al. J Mol Biol 1997;272:106-120) that ranks using shape complementarity. The complexes studied were 5 enzyme-inhibitors and 2 antibody-antigens, starting from the unbound crystallographic coordinates, with a further 2 antibody-antigens where the antibody was from the bound crystallographic complex. The pair potential functions tested were derived both from observed intramolecular pairings in a database of nonhomologous protein domains, and from observed intermolecular pairings across the interfaces in sets of nonhomologous heterodimers and homodimers. Out of various alternate strategies, we found the optimal method used a mole-fraction calculated random model from the intramolecular pairings. For all the systems, a correct docking was placed within the top 12% of the pair potential score ranked complexes. A combined strategy was developed that incorporated "multidock," a side-chain refinement algorithm (Jackson et al. J Mol Biol 1998;276:265-285). This placed a correct docking within the top 5 complexes for enzyme-inhibitor systems, and within the top 40 complexes for antibody-antigen systems.

An analysis of conformational changes on protein-protein association: implications for predictive docking.

Conformational changes on complex formation have been measured for 39 pairs of structures of complexed proteins and unbound equivalents, averaged over interface and non-interface regions and for individual residues. We evaluate their significance by comparison with the differences seen in 12 pairs of independently solved structures of identical proteins, and find that just over half have some substantial overall movement. Movements involve main chains as well as side chains, and large changes in the interface are closely involved with complex formation, while those of exposed non-interface residues are caused by flexibility and disorder. Interface movements in enzymes are similar in extent to those of inhibitors. All eight of the complexes (six enzyme-inhibitor and two antibody-antigen) that have structures of both components in an unbound form available show some significant interface movement. However, predictive docking is successful even when some of the largest changes occur. We note however that the situation may be different in systems other than the enzyme-inhibitors which dominate this study. Thus the general model is induced fit but, because there is only limited conformational change in many systems, recognition can be treated as lock and key to a first approximation.