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Lackner, Peter; Koppensteiner, Walter A.; Domingues, Francisco S.; Sippl, Manfred J.
1999 Proteins: Structure Function and Bioinformatics
doi: 10.1002/(SICI)1097-0134(1999)37:3+<7::AID-PROT3>3.3.CO;2-Mpmid: 10526347
Evaluation and assessment are critical issues in CASP experiments. Automated procedures are necessary to compare a large number of predictions with the target folds. The evaluation has to reveal the maximum extent of similarity between predictions and targets, it should be applicable across prediction categories, and it should be transparent and accessible to a wide community. Here we present an automated evaluation scheme which is an attempt to meet these requirements. In the implementation and execution of this scheme we had to solve or circumvent problems of convergence, where algorithms fail to find optimum solutions, problems of ambiguity where no unique optimum solution exists, and problems in ranking and interpretation. Key features of this implementation are (1) the root mean square deviation of structure superimposition is kept close to a constant value throughout the evaluation and (2) all structural matches found between two folds are taken into account. We discuss these points in detail and describe the numerical criteria used in the CASP3 evaluation. Proteins Suppl 1999;3:7–14. © 1999 Wiley‐Liss, Inc.
Lackner, Peter; Koppensteiner, Walter A.; Domingues, Francisco S.; Sippl, Manfred J.
1999 Proteins: Structure Function and Bioinformatics
doi: 10.1002/(SICI)1097-0134(1999)37:3+<7::AID-PROT3>3.0.CO;2-V
Evaluation and assessment are critical issues in CASP experiments. Automated procedures are necessary to compare a large number of predictions with the target folds. The evaluation has to reveal the maximum extent of similarity between predictions and targets, it should be applicable across prediction categories, and it should be transparent and accessible to a wide community. Here we present an automated evaluation scheme which is an attempt to meet these requirements. In the implementation and execution of this scheme we had to solve or circumvent problems of convergence, where algorithms fail to find optimum solutions, problems of ambiguity where no unique optimum solution exists, and problems in ranking and interpretation. Key features of this implementation are (1) the root mean square deviation of structure superimposition is kept close to a constant value throughout the evaluation and (2) all structural matches found between two folds are taken into account. We discuss these points in detail and describe the numerical criteria used in the CASP3 evaluation. Proteins Suppl 1999;3:7–14. © 1999 Wiley‐Liss, Inc.
1999 Proteins: Structure Function and Bioinformatics
doi: 10.1002/(SICI)1097-0134(1999)37:3+<15::AID-PROT4>3.3.CO;2-Qpmid: 10526348
Evaluating a set of protein structure predictions is difficult as each prediction may omit different residues and different parts of the structure may have different accuracies. A method is described that captures the best results from a large number of alternative sequence‐dependent structural superpositions between a prediction and the experimental structure and represents them as a single line on a graph. Applied to CASP2 and CASP3 data the best predictions stand out visually in most cases, as judged by manual inspection. The results from this method applied to CASP data are available from the URLs http://PredictionCenter.llnl.gov/casp3/results/th/ and http://www.sanger.ac.uk/∼th/casp/. Proteins Suppl 1999;3:15–21. © 1999 Wiley‐Liss, Inc.
1999 Proteins: Structure Function and Bioinformatics
doi: 10.1002/(SICI)1097-0134(1999)37:3+<15::AID-PROT4>3.0.CO;2-Z
Evaluating a set of protein structure predictions is difficult as each prediction may omit different residues and different parts of the structure may have different accuracies. A method is described that captures the best results from a large number of alternative sequence‐dependent structural superpositions between a prediction and the experimental structure and represents them as a single line on a graph. Applied to CASP2 and CASP3 data the best predictions stand out visually in most cases, as judged by manual inspection. The results from this method applied to CASP data are available from the URLs http://PredictionCenter.llnl.gov/casp3/results/th/ and http://www.sanger.ac.uk/∼th/casp/. Proteins Suppl 1999;3:15–21. © 1999 Wiley‐Liss, Inc.
Zemla, Adam; Venclovas, Česlovas; Moult, John; Fidelis, Krzysztof
1999 Proteins: Structure Function and Bioinformatics
doi: 10.1002/(SICI)1097-0134(1999)37:3+<22::AID-PROT5>3.3.CO;2-Npmid: 10526349
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.
Zemla, Adam; Venclovas, Česlovas; Moult, John; Fidelis, Krzysztof
1999 Proteins: Structure Function and Bioinformatics
doi: 10.1002/(SICI)1097-0134(1999)37:3+<22::AID-PROT5>3.0.CO;2-W
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.
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