Adam Zemla

A phylogeny-driven genomic encyclopaedia of Bacteria and Archaea.

Sequencing of bacterial and archaeal genomes has revolutionized our understanding of the many roles played by microorganisms. There are now nearly 1,000 completed bacterial and archaeal genomes available, most of which were chosen for sequencing on the basis of their physiology. As a result, the perspective provided by the currently available genomes is limited by a highly biased phylogenetic distribution. To explore the value added by choosing microbial genomes for sequencing on the basis of their evolutionary relationships, we have sequenced and analysed the genomes of 56 culturable species of Bacteria and Archaea selected to maximize phylogenetic coverage. Analysis of these genomes demonstrated pronounced benefits (compared to an equivalent set of genomes randomly selected from the existing database) in diverse areas including the reconstruction of phylogenetic history, the discovery of new protein families and biological properties, and the prediction of functions for known genes from other organisms. Our results strongly support the need for systematic ‘phylogenomic’ efforts to compile a phylogeny-driven ‘Genomic Encyclopedia of Bacteria and Archaea’ in order to derive maximum knowledge from existing microbial genome data as well as from genome sequences to come.

A modified definition of Sov, a segment‐based measure for protein secondary structure prediction assessment

We present a measure for the evaluation of secondary structure prediction methods that is based on secondary structure segments rather than individual residues. The algorithm is an extension of the segment overlap measure Sov, originally defined by Rost et al. (J Mol Biol 1994;235:13–26). The new definition of Sov corrects the normalization procedure and improves Sovs ability to discriminate between similar and dissimilar segment distributions. The method has been comprehensively tested during the second Critical Assessment of Techniques for Protein Structure Prediction (CASP2). Here, we describe the underlying concepts, modifications to the original definition, and their significance. Proteins 1999;34:220–223. Published 1999 Wiley‐Liss, Inc.

Processing and Analysis of CASP3 Protein Structure Predictions

Livermore Prediction Center provides basic infrastructure for the CASP (Critical Assessment of Structure Prediction) experiments, including prediction processing and verification servers, a system of prediction evaluation tools, and interactive numerical and graphical displays. Here we outline the essentials of our approach, with discussion of the superposition procedures, definitions of basic measures, and descriptions of new methods developed to analyze predictions. Our primary focus is on the evaluation of threedimensional models and secondary structure predictions. To put the results of the three prediction experiments held to date on the same footing, the latest CASP3 evaluation criteria were retrospectively applied to both CASP1 and CASP2 predictions. Finally, we give an overview of our website (http://PredictionCenter.llnl.gov), which makes the target structures, predictions, and the evaluation system accessible to the community. Proteins Suppl 1999;3:22–29. Published 1999 Wiley‐Liss, Inc.

A study of quality measures for protein threading models

BackgroundPrediction of protein structures is one of the fundamental challenges in biology today. To fully understand how well different prediction methods perform, it is necessary to use measures that evaluate their performance. Every two years, starting in 1994, the CASP (Critical Assessment of protein Structure Prediction) process has been organized to evaluate the ability of different predictors to blindly predict the structure of proteins. To capture different features of the models, several measures have been developed during the CASP processes. However, these measures have not been examined in detail before. In an attempt to develop fully automatic measures that can be used in CASP, as well as in other type of benchmarking experiments, we have compared twenty-one measures. These measures include the measures used in CASP3 and CASP2 as well as have measures introduced later. We have studied their ability to distinguish between the better and worse models submitted to CASP3 and the correlation between them.ResultsUsing a small set of 1340 models for 23 different targets we show that most methods correlate with each other. Most pairs of measures show a correlation coefficient of about 0.5. The correlation is slightly higher for measures of similar types. We found that a significant problem when developing automatic measures is how to deal with proteins of different length. Also the comparisons between different measures is complicated as many measures are dependent on the size of the target. We show that the manual assessment can be reproduced to about 70% using automatic measures. Alignment independent measures, detects slightly more of the models with the correct fold, while alignment dependent measures agree better when selecting the best models for each target. Finally we show that using automatic measures would, to a large extent, reproduce the assessors ranking of the predictors at CASP3.ConclusionsWe show that given a sufficient number of targets the manual and automatic measures would have given almost identical results at CASP3. If the intent is to reproduce the type of scoring done by the manual assessor in in CASP3, the best approach might be to use a combination of alignment independent and alignment dependent measures, as used in several recent studies.

MvirDB—a microbial database of protein toxins, virulence factors and antibiotic resistance genes for bio-defence applications

Knowledge of toxins, virulence factors and antibiotic resistance genes is essential for bio-defense applications aimed at identifying ‘functional’ signatures for characterizing emerging or engineered pathogens. Whereas genetic signatures identify a pathogen, functional signatures identify what a pathogen is capable of. To facilitate rapid identification of sequences and characterization of genes for signature discovery, we have collected all publicly available (as of this writing), organized sequences representing known toxins, virulence factors, and antibiotic resistance genes in one convenient database, which we believe will be of use to the bio-defense research community. MvirDB integrates DNA and protein sequence information from Tox-Prot, SCORPION, the PRINTS virulence factors, VFDB, TVFac, Islander, ARGO and a subset of VIDA. Entries in MvirDB are hyperlinked back to their original sources. A blast tool allows the user to blast against all DNA or protein sequences in MvirDB, and a browser tool allows the user to search the database to retrieve virulence factor descriptions, sequences, and classifications, and to download sequences of interest. MvirDB has an automated weekly update mechanism. Each protein sequence in MvirDB is annotated using our fully automated protein annotation system and is linked to that systems browser tool. MvirDB can be accessed at .

Community Genomic and Proteomic Analyses of Chemoautotrophic Iron-Oxidizing “Leptospirillum rubarum” (Group II) and “Leptospirillum ferrodiazotrophum” (Group III) Bacteria in Acid Mine Drainage Biofilms

ABSTRACT We analyzed near-complete population (composite) genomic sequences for coexisting acidophilic iron-oxidizing Leptospirillum group II and III bacteria (phylum Nitrospirae) and an extrachromosomal plasmid from a Richmond Mine, Iron Mountain, CA, acid mine drainage biofilm. Community proteomic analysis of the genomically characterized sample and two other biofilms identified 64.6% and 44.9% of the predicted proteins of Leptospirillum groups II and III, respectively, and 20% of the predicted plasmid proteins. The bacteria share 92% 16S rRNA gene sequence identity and >60% of their genes, including integrated plasmid-like regions. The extrachromosomal plasmid carries conjugation genes with detectable sequence similarity to genes in the integrated conjugative plasmid, but only those on the extrachromosomal element were identified by proteomics. Both bacterial groups have genes for community-essential functions, including carbon fixation and biosynthesis of vitamins, fatty acids, and biopolymers (including cellulose); proteomic analyses reveal these activities. Both Leptospirillum types have multiple pathways for osmotic protection. Although both are motile, signal transduction and methyl-accepting chemotaxis proteins are more abundant in Leptospirillum group III, consistent with its distribution in gradients within biofilms. Interestingly, Leptospirillum group II uses a methyl-dependent and Leptospirillum group III a methyl-independent response pathway. Although only Leptospirillum group III can fix nitrogen, these proteins were not identified by proteomics. The abundances of core proteins are similar in all communities, but the abundance levels of unique and shared proteins of unknown function vary. Some proteins unique to one organism were highly expressed and may be key to the functional and ecological differentiation of Leptospirillum groups II and III.

Assessment of Progress Over the CASP Experiments

The quality of structure models produced in the CASP5 experiment has been compared with that in earlier CASPs. The most significant progress is in the fold recognition regime, where the development of meta‐servers has allowed more accurate consensus models to be generated. In contrast to this, there is little evidence of progress in producing more accurate comparative models, particularly those based on sequence identities > 30%. For comparative models based on low‐sequence identity and for fold recognition models, accuracy depends primarily on the fraction of the target structure that is similar to an available template, and the quality of the alignment. Overall, these results indicate that there are still no effective methods of improving model quality beyond that obtained by successfully copying a template structure. For models of proteins with previously unknown folds, there appears to be a pause in the previous consistent improvement. There is some evidence that more groups are producing top‐quality models, however. Although specific progress between successive experiments is sometimes difficulty to identify, over the history of all the CASPs there has been steady, if sometimes slow, progress in all modeling regimes. Proteins 2003;53:585–595.

Processing and evaluation of predictions in CASP4

The Livermore Prediction Center conducted the target collection and prediction submission processes for Critical Assessment of Protein Structure Prediction (CASP4) and Critical Assessment of Fully Automated Structure Prediction Methods (CAFASP2). We have also evaluated all the submitted predictions using criteria and methods developed during the course of three previous CASP experiments and preparation for CASP4. We present an overview of the implemented system. Particular attention is paid to newly developed evaluation techniques and data presentation schemes. With the rapid increase in CASP participation and in the number of submitted predictions, special emphasis is placed on methods allowing reliable pre‐classification of submissions and on techniques useful in automated evaluation of predictions. We also present an overview of our website, including target structures, predictions, and their evaluations ( http://predictioncenter.llnl.gov). Proteins 2001;Suppl 5:13–21.

Comparison of performance in successive CASP experiments

As the number of completed CASP (Critical Assessment of Protein Structure Prediction) experiments grows, so does the need for stable, standard methods for comparing performance in successive experiments. It is critical to develop methods for determining the areas in which there is progress and in which areas are static. We have added an analysis of the CASP4 results to that previously published for CASPs 1, 2, and 3. We again use a unified difficulty scale to permit comparison of performance as a function of target difficulty in the different CASPs. The scale is used to compare performance in aligning target sequences to a structural template. There was a clear improvement in alignment quality between CASP1 (1994) and CASP2 (1996). No change is apparent between CASP2 and CASP3 (1998). There is a small barely detectable improvement between CASP3 and the latest experiment (CASP4, 2000). Alignment remains the major source of error in all models based on less than about 30% sequence identity. Comparison of performance in the new fold modeling regime is complicated by issues in devising an objective target difficulty scale. We have found limited numerical support for significant progress between CASP3 and CASP4 in this area. More subjectively, most observers are convinced that there has been substantial progress. Progress is dominated by a single group. Proteins 2001;Suppl 5:163–170.

The other 90% of the protein: Assessment beyond the Cαs for CASP8 template-based and high-accuracy models†‡

For template‐based modeling in the CASP8 Critical Assessment of Techniques for Protein Structure Prediction, this work develops and applies six new full‐model metrics. They are designed to complement and add value to the traditional template‐based assessment by the global distance test (GDT) and related scores (based on multiple superpositions of Cα atoms between target structure and predictions labeled “Model 1”). The new metrics evaluate each predictor group on each target, using all atoms of their best model with above‐average GDT. Two metrics evaluate how “protein‐like” the predicted model is: the MolProbity score used for validating experimental structures, and a mainchain reality score using all‐atom steric clashes, bond length and angle outliers, and backbone dihedrals. Four other new metrics evaluate match of model to target for mainchain and sidechain hydrogen bonds, sidechain end positioning, and sidechain rotamers. Group‐average Z‐score across the six full‐model measures is averaged with group‐average GDT Z‐score to produce the overall ranking for full‐model, high‐accuracy performance. Separate assessments are reported for specific aspects of predictor‐group performance, such as robustness of approximately correct template or fold identification, and self‐scoring ability at identifying the best of their models. Fold identification is distinct from but correlated with group‐average GDT Z‐score if target difficulty is taken into account, whereas self‐scoring is done best by servers and is uncorrelated with GDT performance. Outstanding individual models on specific targets are identified and discussed. Predictor groups excelled at different aspects, highlighting the diversity of current methodologies. However, good full‐model scores correlate robustly with high Cα accuracy. Proteins 2009.