Kevin Bryson

The PSIPRED protein structure prediction server

SUMMARY The PSIPRED protein structure prediction server allows users to submit a protein sequence, perform a prediction of their choice and receive the results of the prediction both textually via e-mail and graphically via the web. The user may select one of three prediction methods to apply to their sequence: PSIPRED, a highly accurate secondary structure prediction method; MEMSAT 2, a new version of a widely used transmembrane topology prediction method; or GenTHREADER, a sequence profile based fold recognition method. AVAILABILITY Freely available to non-commercial users at http://globin.bio.warwick.ac.uk/psipred/

Protein structure prediction servers at University College London

A number of state-of-the-art protein structure prediction servers have been developed by researchers working in the Bioinformatics Unit at University College London. The popular PSIPRED server allows users to perform secondary structure prediction, transmembrane topology prediction and protein fold recognition. More recent servers include DISOPRED for the prediction of protein dynamic disorder and DomPred for domain boundary prediction. These servers are available from our software home page at .

Scalable web services for the PSIPRED Protein Analysis Workbench

Here, we present the new UCL Bioinformatics Group’s PSIPRED Protein Analysis Workbench. The Workbench unites all of our previously available analysis methods into a single web-based framework. The new web portal provides a greatly streamlined user interface with a number of new features to allow users to better explore their results. We offer a number of additional services to enable computationally scalable execution of our prediction methods; these include SOAP and XML-RPC web server access and new HADOOP packages. All software and services are available via the UCL Bioinformatics Group website at http://bioinf.cs.ucl.ac.uk/.

The DISOPRED server for the prediction of protein disorder

UNLABELLED Dynamically disordered regions appear to be relatively abundant in eukaryotic proteomes. The DISOPRED server allows users to submit a protein sequence, and returns a probability estimate of each residue in the sequence being disordered. The results are sent in both plain text and graphical formats, and the server can also supply predictions of secondary structure to provide further structural information. AVAILABILITY The server can be accessed by non-commercial users at http://bioinf.cs.ucl.ac.uk/disopred/

Protein annotation and modelling servers at University College London

The UCL Bioinformatics Group web portal offers several high quality protein structure prediction and function annotation algorithms including PSIPRED, pGenTHREADER, pDomTHREADER, MEMSAT, MetSite, DISOPRED2, DomPred and FFPred for the prediction of secondary structure, protein fold, protein structural domain, transmembrane helix topology, metal binding sites, regions of protein disorder, protein domain boundaries and protein function, respectively. We also now offer a fully automated 3D modelling pipeline: BioSerf, which performed well in CASP8 and uses a fragment-assembly approach which placed it in the top five servers in the de novo modelling category. The servers are available via the group web site at http://bioinf.cs.ucl.ac.uk/.

CAFASP-1: Critical Assessment of Fully Automated Structure Prediction Methods

The results of the first Critical Assessment of Fully Automated Structure Prediction (CAFASP‐1) are presented. The objective was to evaluate the success rates of fully automatic web servers for fold recognition which are available to the community. This study was based on the targets used in the third meeting on the Critical Assessment of Techniques for Protein Structure Prediction (CASP‐3). However, unlike CASP‐3, the study was not a blind trial, as it was held after the structures of the targets were known. The aim was to assess the performance of methods without the user intervention that several groups used in their CASP‐3 submissions. Although it is clear that “human plus machine” predictions are superior to automated ones, this CAFASP‐1 experiment is extremely valuable for users of our methods; it provides an indication of the performance of the methods alone, and not of the “human plus machine” performance assessed in CASP. This information may aid users in choosing which programs they wish to use and in evaluating the reliability of the programs when applied to their specific prediction targets. In addition, evaluation of fully automated methods is particularly important to assess their applicability at genomic scales. For each target, groups submitted the top‐ranking folds generated from their servers. In CAFASP‐1 we concentrated on fold‐recognition web servers only and evaluated only recognition of the correct fold, and not, as in CASP‐3, alignment accuracy. Although some performance differences appeared within each of the four target categories used here, overall, no single server has proved markedly superior to the others. The results showed that current fully automated fold recognition servers can often identify remote similarities when pairwise sequence search methods fail. Nevertheless, in only a few cases outside the family‐level targets has the score of the top‐ranking fold been significant enough to allow for a confident fully automated prediction.Because the goals, rules, and procedures of CAFASP‐1 were different from those used at CASP‐3, the results reported here are not comparable with those reported in CASP‐3. Nevertheless, it is clear that current automated fold recognition methods can not yet compete with “human‐expert plus machine” predictions. Finally, CAFASP‐1 has been useful in identifying the requirements for a future blind trial of automated served‐based protein structure prediction. Proteins Suppl 1999;3:209–217.

Successful recognition of protein folds using threading methods biased by sequence similarity and predicted secondary structure.

Analysis of our fold recognition results in the 3rd Critical Assessment in Structure Prediction (CASP3) experiment, using the programs THREADER 2 and GenTHREADER, shows an encouraging level of overall success. Of the 23 submitted predictions, 20 targets showed no clear sequence similarity to proteins of known 3D structure. These 20 targets can be divided into 22 domains, of which, 20 domains either entirely match a previously known fold, or partially match a substantial region of a known fold. Of these 20 domains, we correctly assigned the folds in 10 cases. Proteins Suppl 1999:3:104–111.

Prediction of novel and analogous folds using fragment assembly and fold recognition

A number of new and newly improved methods for predicting protein structure developed by the Jones–University College London group were used to make predictions for the CASP6 experiment. Structures were predicted with a combination of fold recognition methods (mGenTHREADER, nFOLD, and THREADER) and a substantially enhanced version of FRAGFOLD, our fragment assembly method. Attempts at automatic domain parsing were made using DomPred and DomSSEA, which are based on a secondary structure parsing algorithm and additionally for DomPred, a simple local sequence alignment scoring function. Disorder prediction was carried out using a new SVM‐based version of DISOPRED. Attempts were also made at domain docking and “microdomain” folding in order to build complete chain models for some targets. Proteins 2005;Suppl 7:143–151.

What are the baselines for protein fold recognition

MOTIVATION What constitutes a baseline level of success for protein fold recognition methods? As fold recognition benchmarks are often presented without any thought to the results that might be expected from a purely random set of predictions, an analysis of fold recognition baselines is long overdue. Given varying amounts of basic information about a protein-ranging from the length of the sequence to a knowledge of its secondary structure-to what extent can the fold be determined by intelligent guesswork? Can simple methods that make use of secondary structure information assign folds more accurately than purely random methods and could these methods be used to construct viable hierarchical classifications? EXPERIMENTS PERFORMED: A number of rapid automatic methods which score similarities between protein domains were devised and tested. These methods ranged from those that incorporated no secondary structure information, such as measuring absolute differences in sequence lengths, to more complex alignments of secondary structure elements. Each method was assessed for accuracy by comparison with the Class Architecture Topology Homology (CATH) classification. Methods were rated against both a random baseline fold assignment method as a lower control and FSSP as an upper control. Similarity trees were constructed in order to evaluate the accuracy of optimum methods at producing a classification of structure. RESULTS Using a rigorous comparison of methods with CATH, the random fold assignment method set a lower baseline of 11% true positives allowing for 3% false positives and FSSP set an upper benchmark of 47% true positives at 3% false positives. The optimum secondary structure alignment method used here achieved 27% true positives at 3% false positives. Using a less rigorous Critical Assessment of Structure Prediction (CASP)-like sensitivity measurement the random assignment achieved 6%, FSSP-59% and the optimum secondary structure alignment method-32%. Similarity trees produced by the optimum method illustrate that these methods cannot be used alone to produce a viable protein structural classification system. CONCLUSIONS Simple methods that use perfect secondary structure information to assign folds cannot produce an accurate protein taxonomy, however they do provide useful baselines for fold recognition. In terms of a typical CASP assessment our results suggest that approximately 6% of targets with folds in the databases could be assigned correctly by randomly guessing, and as many as 32% could be recognised by trivial secondary structure comparison methods, given knowledge of their correct secondary structures.

Applying Agents to Bioinformatics in GeneWeaver

Recent years have seen dramatic and sustained growth in the amount of genomic data being generated, including in late 1999 the first complete sequence of a human chromosome. The challenge now faced by biological scientists is to make sense of this vast amount of accumulated and accumulating data. Fortunately, numerous databases are provided as resources containing relevant data, and there are similarly many available programs that analyse this data and attempt to understand it. However, the key problem in analyzing this genomic data is how to integrate the software and primary databases in a flexible and robust way. The wide range of available programs conform to very different input, output and processing requirements, typically with little consideration given to issues of integration, and in many cases with only token efforts made in the direction of usability. In this paper, we introduce the problem domain and describe GeneWeaver, a multi-agent system for genome analysis. We explain the suitability of the information agent paradigm to the problem domain, focus on the problem of incorporating different existing analysis tools, and describe progress to date.