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GLASS: A tool to visualize protein structure prediction data in three dimensions and evaluate their consistency
Author(s) -
Leplae Raphael,
Hubbard Tim,
Tramontano Anna
Publication year - 1998
Publication title -
proteins: structure, function, and bioinformatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.699
H-Index - 191
eISSN - 1097-0134
pISSN - 0887-3585
DOI - 10.1002/(sici)1097-0134(19980301)30:4<339::aid-prot1>3.0.co;2-e
Subject(s) - consistency (knowledge bases) , casp , computer science , a priori and a posteriori , protein structure prediction , workbench , correctness , data mining , similarity (geometry) , sequence (biology) , reliability (semiconductor) , machine learning , visualization , protein structure , artificial intelligence , algorithm , philosophy , power (physics) , physics , image (mathematics) , genetics , epistemology , nuclear magnetic resonance , quantum mechanics , biology
When a protein sequence does not share any significant sequence similarity with a protein of known structure, homology modeling cannot be applied. However, many novel and interesting methods, such as secondary structure prediction, fold recognition, and prediction of long‐range interactions, are being developed and have been shown to be reasonably successful in predicting protein structures from sequence data and evolutionary information. The a priori evaluation of the correctness of a prediction obtained by one of these methods is however often problematic. Consequently, it is important to use all available information provided by as many different methods as possible and all the available experimental data about the protein of interest, since the consistency of the results is indicative of the reliability of the prediction. Hence the need has arisen for suitable tools able to compare results provided by different methods and evaluate their consistency. We have therefore constructed GLASS, a general platform to read, visualize, compare, and evaluate prediction results from many different sources and to project these prediction results into three dimensions. In addition, GLASS allows the comparison of selected parameters calculated for a model with the distribution observed in real protein structures, thus providing an easy way to test new methods for evaluating the likelihood of different structural models. GLASS can be considered as a “workbench” for structural predictions useful to both experimentalists and theoreticians. Proteins 30:339–351, 1998. © 1998 Wiley‐Liss, Inc.