Topic
Global distance test
About: Global distance test is a research topic. Over the lifetime, 180 publications have been published within this topic receiving 38771 citations.
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TL;DR: A comparative protein modelling method designed to find the most probable structure for a sequence given its alignment with related structures, which is automated and illustrated by the modelling of trypsin from two other serine proteinases.
12,386 citations
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TL;DR: A two-stage neural network has been used to predict protein secondary structure based on the position specific scoring matrices generated by PSI-BLAST and achieved an average Q3 score of between 76.5% to 78.3% depending on the precise definition of observed secondary structure used, which is the highest published score for any method to date.
5,512 citations
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TL;DR: The algorithm is shown to be at least as good as, and usually superior to, the reported prediction methods assessed in the same way and the implication in protein folding is discussed.
4,360 citations
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TL;DR: A two-layered feed-forward neural network is trained on a non-redundant data base to predict the secondary structure of water-soluble proteins with a new key aspect is the use of evolutionary information in the form of multiple sequence alignments that are used as input in place of single sequences.
2,977 citations