EVAisaweb-basedserverthat evaluatesautomaticstructurepredictionservers continuouslyandobjectively.SinceJune2000,EVA collectedmorethan20,000secondarystructurepredictions. TheEVAsetssufficedtoconcludethatthe fieldofsecondarystructurepredictionhasadvancedagain. Accuracyincreasedsubstantiallyin the1990sthroughusingevolutionaryinformation takenfromthedivergenceofproteinsinthesame structuralfamily.Recently,theevolutionaryinformationresultingfromimprovedsearchesandlarger databaseshasagainboostedpredictionaccuracyby morethan4%toitscurrentheightaround76%ofall residuespredictedcorrectlyinoneofthethree states:helix,strand,orother.Thebestcurrent methodssolvedmostoftheproblemsraisedat earlierCASPmeetings:Allgoodmethodsnowget segmentsrightandperformwellonstrands.Isthe recentincreaseinaccuracysignificantenoughto makepredictionsevenmoreuseful?Webelievethe answerisaffirmative.Whatisthelimitofprediction accuracy?Weshallsee.Alldataareavailable throughtheEVAwebsiteat{cubic.bioc.columbia. edu/eva/}.Therawdatafortheresultspresentedare availableat{eva}/sec/bup_common/2001_02_22/. Proteins2001;Suppl5:192--199.2002Wiley-Liss,Inc. Keywords:automaticevaluation;large-scaleassessment; proteinstructureprediction

Introduction The sequence information generated by genome sequencing projects offers opportunities for understanding biology at an unprecedented fine level of detail. At the same time, the biomedical literature provides a record of high level biological phenomena as observed and reported over many decades. There is an opportunity to combine the power of the genome sequence information with the published biological record to accelerate progress and gain insight. Here we show that including literature to tailor homology searches against sequence databases can improve performance. The concept of homology between two protein or nucleotide sequences is often used to infer that two genes or their protein products are related by evolution. Divergence between the two entities may have occurred when two species evolved from a single ancestor (orthologs) or when gene duplication occurs within a species (paralogs). We usually expect that homologous sequences have common functional roles

TMPDB is a database of experimentally-characterized transmembrane (TM) topologies. TMPDB release 6.2 contains a total of 302TM protein sequences, in which 276 are a-helical sequences, 17 b-stranded, and 9 a-helical sequences with short pore-forming helices buried in the membrane. The TM topologies in TMPDB were determined experimentally by means of X-ray crystallography, NMR, gene fusion technique, substituted cysteine accessibility method, N-linked glycosylation experiment and other biochemical methods. TMPDB would be useful as a test and/or training dataset in improving the proposed TM topology prediction methods or developing novel methods with higher performance, and as a guide for both the bioinformaticians and biologists to better understand TM proteins. TMPDB and its subsets are freely available at the following web site:

BASys (Bacterial Annotation System) is a web server that supports automated, in-depth annotation of bacterial genomic (chromosomal and plasmid) sequences. It accepts raw DNA sequence data and an optional list of gene identification information and provides extensive textual annotation and hyperlinked image output. BASys uses .30 programs to determine annotation subfields for each gene, including gene/protein name,GO function, COG function, possible paraloguesand orthologues, molecular weight, isoelectric point, operon structure, subcellular localization, signal peptides, transmembrane regions, secondary structure, 3D structure, reactions and pathways. The depth and detail of a BASys annotation matches or exceeds that found in a standard SwissProt entry. BASys also generates colorful, clickable and fully zoomable maps of each query chromosome to permit rapid navigation and detailed visual analysis of all resulting gene annotations. The textual annotations and images that are provided by BASys can be generated in h for an average bacterial chromosome (5 Mb). BASys annotations maybe viewed and downloaded anonymously or through a password protected access system. The BASys server and databases can also be downloaded and run locally. BASys is accessible at http://wishart.biology. ualberta.ca/basys.

Abstract. Protein domains are the structural and functional units of proteins. The ability to parse protein chains into different domains is important for protein classification and for understanding protein structure, function, and evolution. Here we use machine learning algorithms, in the form of recursive neural networks, to develop a protein domain predictor called DOMpro. DOMpro predicts protein domains using a combination of evolutionary information in the form of profiles, predicted secondary structure, and predicted relative solvent accessibility. DOMpro is trained and tested on a curated dataset derived from the CATH database. DOMpro correctly predicts the number of domains for 69 % of the combined dataset of single and multi-domain chains. DOMpro achieves a sensitivity of 76 % and specificity of 85 % with respect to the single-domain proteins and sensitivity of 59 % and specificity of 38% with respect to the two-domain proteins. DOMpro also achieved a sensitivity and specificity of 71 % and 71 % respectively in the Critical Assessment of Fully Automated Structure Prediction 4 (CAFASP-4) (Fischer et al., 1999; Saini and Fischer, 2005) and was ranked among the top ab initio domain predictors. The DOMpro server, software, and dataset are available at

ABSTRACT The field of computational biology has been revolutionized by recent advances in genomics. The completion of a number of genome projects, including that of the human genome, has paved the way toward a variety of challenges and opportunities in bioinformatics and biological systems engineering. One of the first challenges has been the determination of the structures of proteins encoded by the individual genes. This problem, which represents the progression from sequence to structure (genomics to structural genomics), has been widely known as the structure-prediction-in-protein-folding problem. We present the development and application of ASTRO-FOLD, a novel and complete approach for the ab initio prediction of protein structures given only the amino acid sequences of the proteins. The approach exhibits many novel components and the merits of its application are examined for a suite of protein systems, including a number of targets from several critical-assessment-ofstructure-prediction experiments.

We describe ProteinShop, a new visualization tool that streamlines and simplifies the process of determining optimal protein folds. ProteinShop may be used at different stages of a protein structure prediction process. First, it can create protein configurations containing secondary structures specified by the user. Second, it can interactively manipulate protein fragments to achieve desired folds by adjusting the dihedral angles of selected coil regions using an Inverse Kinematics method. Last, it serves as a visual framework to monitor and steer a protein structure prediction process that may be running on a remote machine. ProteinShop was used to create initial configurations for a protein structure prediction method developed by a team that competed in CASP5. ProteinShop's use accelerated the process of generating initial configurations, reducing the time required from days to hours. This paper describes the structure of ProteinShop and discusses its main features.

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Abstract. The automation of the entire NMR protein structure determination process requires a superior error tolerant backbone resonance assignment method. Although a variety of assignment approaches have been developed, none works well on noisy automatically picked peaks. IPASS is proposed as a novel integer linear programming (ILP) based assignment method. In order to reduce size of the problem, IPASS employs probabilistic spin system typing based on chemical shifts and secondary structure predictions. Furthermore, IPASS extracts connectivity information from the inter-residue information and the 15 N-edited NOESY peaks which are then used to fix reliable fragments. The experimental results demonstrate that IPASS significantly outperforms the previous assignment methods on the synthetic data sets. It achieves an average of 99 % precision and 96 % recall on the synthesized spin systems, and an average of 96 % precision and 90 % recall on the synthesized peak lists. When applied on automatically picked peaks from experimentally derived data sets, it achieves an average precision and recall of 78 % and 67%, respectively. In contrast, the next best method, MARS, achieved an average precision and recall of 50 % and 40%, respectively. Availability: IPASS is available upon request, and the web server for IPASS is under construction.

The Bioinformatics Links Directory is an online community resource that contains a directory of freely available tools, databases, and resources for bioinformatics and molecular biology research. The listing of the servers published in this and previous issues of Nucleic Acids Research together with other useful tools and websites represents a rich repository of resources that are openly provided to the research community using internet technologies. The 166 servers highlighted in the 2005 Web Server Issue are included in the more than 700 links to useful online resources that are currently contained within the descriptive biological categories of the Bioinformatics Links Directory. This curated listing of bioinformaticsresourcesisavailableonlineattheBioinformatics