Fold Predictions for Bacterial Genomes

Pawłowski, Krzysztof; Rychlewski, Leszek; Zhang, Baohong; Godzik, Adam

doi:10.1006/jsbi.2001.4394

Cited by 24 publications

(10 citation statements)

References 78 publications

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“…We notice that five of the top ranks are now occupied by newly developed servers (BASD, BASP, MBAS, SFST, and STMP). The other ranks are occupied by servers that have also ranked among the top performers in previous experiments: SHGU (Fischer 2003), ORF2 , FFA3 (Pawlowski et al 2001), and ORF-s . Because of the excellent performance of the new servers, other older servers that had ranked among the top performers in previous experiments; e.g., 3DPS (Kelley et al 2000), INBG (Fischer 2000), MGTH (McGuffin and Jones 2003), ST99 (Karplus and Hu 2001), and the two versions of FUGUE (Shi et al 2001), now occupy lower ranks.…”

Section: Sensitivitymentioning

confidence: 92%

LiveBench‐8: The large‐scale, continuous assessment of automated protein structure prediction

Rychlewski

Fischer

2005

Protein Science

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We present the results of the evaluation of the latest LiveBench-8 experiment. These results provide a snapshot view of the state of the art in automated protein structure prediction, just before the 2004 CAFASP-4/CASP-6 experiments begin. The last CAFASP/CASP experiments demonstrated that automated meta-predictors entail a significant advance in the field, already challenging most human expert predictors. LiveBench-8 corroborates the superior performance of meta-predictors, which are able to produce useful predictions for over one-half of the test targets. More importantly, LiveBench-8 identifies a handful of recently developed autonomous (nonmeta) servers that perform at the very top, suggesting that further progress in the individual methods has recently been obtained.

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Section: Sensitivitymentioning

confidence: 92%

LiveBench‐8: The large‐scale, continuous assessment of automated protein structure prediction

Rychlewski

Fischer

2005

Protein Science

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“…In order to understand higher level functions four major studies have been undertaken: (i) automated reconstruction and comparison of metabolic pathways [12,14,18,38,49,58,59,65], (ii) study of protein-protein and protein-DNA interactions to understand regulatory pathways [2,7,15,27,28,30,42-44,47,55,60,61,66], (iii) modeling 2D and 3D structure of proteins [10,31,52,57,67], and (iv) modeling the docking of 3D models of proteins with drugs [34]. Understanding 3D structure of proteins has a major impact in understanding protein-protein interactions.…”

Section: Reviewmentioning

confidence: 99%

Bioinformatics in microbial biotechnology – a mini review

Bansal

2005

Microb Cell Fact

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The revolutionary growth in the computation speed and memory storage capability has fueled a new era in the analysis of biological data. Hundreds of microbial genomes and many eukaryotic genomes including a cleaner draft of human genome have been sequenced raising the expectation of better control of microorganisms. The goals are as lofty as the development of rational drugs and antimicrobial agents, development of new enhanced bacterial strains for bioremediation and pollution control, development of better and easy to administer vaccines, the development of protein biomarkers for various bacterial diseases, and better understanding of host-bacteria interaction to prevent bacterial infections. In the last decade the development of many new bioinformatics techniques and integrated databases has facilitated the realization of these goals. Current research in bioinformatics can be classified into: (i) genomics -sequencing and comparative study of genomes to identify gene and genome functionality, (ii) proteomics -identification and characterization of protein related properties and reconstruction of metabolic and regulatory pathways, (iii) cell visualization and simulation to study and model cell behavior, and (iv) application to the development of drugs and anti-microbial agents. In this article, we will focus on the techniques and their limitations in genomics and proteomics. Bioinformatics research can be classified under three major approaches: (1) analysis based upon the available experimental wet-lab data, (2) the use of mathematical modeling to derive new information, and (3) an integrated approach that integrates search techniques with mathematical modeling. The major impact of bioinformatics research has been to automate the genome sequencing, automated development of integrated genomics and proteomics databases, automated genome comparisons to identify the genome function, automated derivation of metabolic pathways, gene expression analysis to derive regulatory pathways, the development of statistical techniques, clustering techniques and data mining techniques to derive protein-protein and protein-DNA interactions, and modeling of 3D structure of proteins and 3D docking between proteins and biochemicals for rational drug design, difference analysis between pathogenic and non-pathogenic strains to identify candidate genes for vaccines and anti-microbial agents, and the whole genome comparison to understand the microbial evolution. The development of bioinformatics techniques has enhanced the pace of biological discovery by automated analysis of large number of microbial genomes. We are on the verge of using all this knowledge to understand cellular mechanisms at the systemic level. The developed bioinformatics techniques have potential to facilitate (i) the discovery of causes of diseases, (ii) vaccine and rational drug design, and (iii) improved cost effective agents for bioremediation by pruning out the dead ends. Despite the fast paced global effort, the current analysis is limited by the lack...

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“…Assessment of several widely used homolog-detection methods (such as PSI-BLAST and hidden Markov models, HMMs) using structurally validated homologs has shown significant increases in performance accompanying expansions in the sequence and structure databases [32]. …”

Section: Estimations Of the Number Of Foldsmentioning

confidence: 99%