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We report the latest release (version 3.0) of the CATH protein domain database (http://www.cathdb.info). There has been a 20% increase in the number of structural domains classified in CATH, up to 86 151 domains. Release 3.0 comprises 1110 fold groups and 2147 homologous superfamilies. To cope with the increases in diverse structural homologues being(More)
CATH version 3.5 (Class, Architecture, Topology, Homology, available at http://www.cathdb.info/) contains 173 536 domains, 2626 homologous superfamilies and 1313 fold groups. When focusing on structural genomics (SG) structures, we observe that the number of new folds for CATH v3.5 is slightly less than for previous releases, and this observation suggests(More)
The latest version of the CATH-Gene3D protein structure classification database (4.0, http://www.cathdb.info) provides annotations for over 235,000 protein domain structures and includes 25 million domain predictions. This article provides an update on the major developments in the 2 years since the last publication in this journal including: significant(More)
In this paper, we describe our work to investigate how much cyclic graph based Genetic Programming (GP) can be accelerated on one machine using currently available mid-range Graphics Processing Units (GPUs). Cyclic graphs pose different problems for evaluation than do trees and we describe how our CUDA based, "population parallel" evaluator tackles these(More)
Genome3D, available at http://www.genome3d.eu, is a new collaborative project that integrates UK-based structural resources to provide a unique perspective on sequence-structure-function relationships. Leading structure prediction resources (DomSerf, FUGUE, Gene3D, pDomTHREADER, Phyre and SUPERFAMILY) provide annotations for UniProt sequences to indicate(More)
Many of the most effective attempts to harness the power of the Graphics Processing Unit (GPU) to accelerate Genetic Programming (GP) have dynamically compiled code for individuals as they are to be evaluated. This approach executes very quickly on the GPU but is slow to compile, hence only vast data-sets fully reap its rewards. To reduce compilation time,(More)
Several forms of computer program (or representation) have been proposed for Genetic Programming (GP) systems to evolve, such as linear, tree based or graph based. Typically, GP representations are highly effective during the initial search phases of evolution but stagnate before deep levels of complexity are acquired. A new representation, TREAD, is(More)
— If a population of programs evolved not for a few hundred generations but for a few hundred thousand or more, could it generate more interesting behaviours and tackle more complex problems? We begin to investigate this question by introducing Tweak-ing Mutation Behaviour Learning (TMBL), a form of evolutionary computation designed to meet this challenge.(More)
Genome3D (http://www.genome3d.eu) is a collaborative resource that provides predicted domain annotations and structural models for key sequences. Since introducing Genome3D in a previous NAR paper, we have substantially extended and improved the resource. We have annotated representatives from Pfam families to improve coverage of diverse sequences and added(More)
The most impressive accelerations of Genetic Programming (GP) using the Graphics Processing Unit (GPU) have been achieved by dynamically compiling new GPU code for each batch of individuals to be evaluated. This approach suffers an overhead in compilation time. We aim to reduce this penalty by pre-processing the individuals to identify and draw out their(More)
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