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David F. Burke

dblp:33/4656 · DBLP profile ↗
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6ranked-venue papers
2as first author
0since 2021 · last 2007
0000-0001-8830-3951ORCID · corroborated

Domains — the database's venue-derived domains; a paper can count in several

Applied, interdisciplinary, general and emerging computing · 6 · 2 first-author

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Interdisciplinary, comprehensive, and emerging computing
4 papers
Bioinformatics and computational biology · 86% Computational science and engineering · 14%

Topics — the 11 heaviest of 12, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Bioinformatics and computational biology › protein structure prediction › template-based modeling
homology modeling
0.112007
Andante: reducing side-chain rotamer search space during comparative modeling using environment-specific substitution probabilities · Bioinform. 2007
Bioinformatics and computational biology › structural bioinformatics
protein structure
0.112007
Andante: reducing side-chain rotamer search space during comparative modeling using environment-specific substitution probabilities · Bioinform. 2007
Bioinformatics and computational biology
protein structure analysis
0.112005
PROVAT: a tool for Voronoi tessellation analysis of protein structures and complexes · Bioinform. 2005
Computational science and engineering › computational geometry
voronoi tessellation
0.112005
PROVAT: a tool for Voronoi tessellation analysis of protein structures and complexes · Bioinform. 2005
Bioinformatics and computational biology
structural bioinformatics
0.022001
HOMSTRAD: adding sequence information to structure-based alignments of homologous protein families · Bioinform. 2001
Browsing the SLoop database of structurally classified loops connecting elements of protein secondary structure · Bioinform. 2000
Bioinformatics and computational biology › protein structure analysis
protein structure alignment
0.012001
HOMSTRAD: adding sequence information to structure-based alignments of homologous protein families · Bioinform. 2001
Bioinformatics and computational biology › structural bioinformatics › sequence-structure relationship
sequence-structure integration
0.012001
HOMSTRAD: adding sequence information to structure-based alignments of homologous protein families · Bioinform. 2001
Bioinformatics and computational biology › protein structure prediction
loop modeling
0.012000
Browsing the SLoop database of structurally classified loops connecting elements of protein secondary structure · Bioinform. 2000
Bioinformatics and computational biology › structural biology
protein structure and function
0.012000
Browsing the SLoop database of structurally classified loops connecting elements of protein secondary structure · Bioinform. 2000
Bioinformatics and computational biology › protein analysis
protein complex analysis
0.012005
PROVAT: a tool for Voronoi tessellation analysis of protein structures and complexes · Bioinform. 2005
Computational science and engineering › scientific software
web server
0.012000
Browsing the SLoop database of structurally classified loops connecting elements of protein secondary structure · Bioinform. 2000

Methods — techniques the papers use, named apart from their topics

rotamer search space reduction · 0.1environment-specific substitution probabilities · 0.1qhull · 0.1PyMOL · 0.1
YearPublicationVenuePosition
2007 Andante: reducing side-chain rotamer search space during comparative modeling using environment-specific substitution probabilities
abstract
MOTIVATION: The accurate placement of side chains in computational protein modeling and design involves the searching of vast numbers of rotamer combinations. RESULTS: We have applied the information contained within structurally aligned homologous families, in the form of conserved chi angle conservation rules, to the problem of the comparative modeling. This allows the accurate borrowing of entire side-chain conformations and/or the restriction to high probability rotamer bins. The application of these rules consistently reduces the number of rotamer combinations that need to be searched to trivial values and also reduces the overall side-chain root mean square deviation (rmsd) of the final model. The approach is complementary to current side-chain placement algorithms that use the decomposition of interacting clusters to increase the speed of the placement process.
Richard E. Smith, Simon C. Lovell, David F. Burke, Rinaldo W. Montalvão, Tom L. Blundell
Bioinform.3
2007 Genome bioinformatic analysis of nonsynonymous SNPs
abstract
BACKGROUND: Genome-wide association studies of common diseases for common, low penetrance causal variants are underway. A proportion of these will alter protein sequences, the most common of which is the non-synonymous single nucleotide polymorphism (nsSNP). It would be an advantage if the functional effects of an nsSNP on protein structure and function could be predicted, both for the final identification process of a causal variant in a disease-associated chromosome region, and in further functional analyses of the nsSNP and its disease-associated protein. RESULTS: In the present report we have compared and contrasted structure- and sequence-based methods of prediction to over 5500 genes carrying nearly 24,000 nsSNPs, by employing an automatic comparative modelling procedure to build models for the genes. The nsSNP information came from two sources, the OMIM database which are rare (minor allele frequency, MAF, < 0.01) and are known to cause penetrant, monogenic diseases. Secondly, nsSNP information came from dbSNP125, for which the vast majority of nsSNPs, mostly MAF > 0.05, have no known link to a disease. For over 40% of the nsSNPs, structure-based methods predicted which of these sequence changes are likely to either disrupt the structure of the protein or interfere with the function or interactions of the protein. For the remaining 60%, we generated sequence-based predictions. CONCLUSION: We show that, in general, the prediction tools are able distinguish disease causing mutations from those mutations which are thought to have a neutral affect. We give examples of mutations in genes that are predicted to be deleterious and may have a role in disease. Contrary to previous reports, we also show that rare mutations are consistently predicted to be deleterious as often as commonly occurring nsSNPs.
David F. Burke, Catherine L. Worth, Eva-Maria Priego, Tammy M. K. Cheng, Luc J. Smink, John A. Todd, Tom L. Blundell
BMC Bioinform.1
2005 PROVAT: a tool for Voronoi tessellation analysis of protein structures and complexes
abstract
SUMMARY: Voronoi tessellation has proved to be a useful tool in protein structure analysis. We have developed PROVAT, a versatile public domain software that enables computation and visualization of Voronoi tessellations of proteins and protein complexes. It is a set of Python scripts that integrate freely available specialized software (Qhull, Pymol etc.) into a pipeline. The calculation component of the tool computes Voronoi tessellation of a given protein system in a way described by a user-supplied XML recipe and stores resulting neighbourhood information as text files with various styles. The Python pickle file generated in the process is used by the visualization component, a Pymol plug-in, that offers a GUI to explore the tessellation visually. AVAILABILITY: PROVAT source code can be downloaded from http://raven.bioc.cam.ac.uk/~swanand/Provat1, which also provides a webserver for its calculation component, documentation and examples.
Swanand P. Gore, David F. Burke, Tom L. Blundell
Bioinform.2
2005 PROVAT - a versatile tool for Voronoi tessellation analysis of protein structures and complexes
Swanand P. Gore, David F. Burke, Tom L. Blundell
BMC Bioinform.2
2001 HOMSTRAD: adding sequence information to structure-based alignments of homologous protein families
abstract
summary: We describe an extension to the Homologous Structure Alignment Database (HOMSTRAD; Mizuguchi et al., Protein Sci., 7, 2469-2471, 1998a) to include homologous sequences derived from the protein families database Pfam (Bateman et al., Nucleic Acids Res., 28, 263-266, 2000). HOMSTRAD is integrated with the server FUGUE (Shi et al., submitted, 2001) for recognition and alignment of homologues, benefitting from the combination of abundant sequence information and accurate structure-based alignments. AVAILABILITY The HOMSTRAD database is available at: http://www-cryst.bioc.cam.ac.uk/homstrad/. Query sequences can be submitted to the homology recognition/alignment server FUGUE at: http://www-cryst.bioc.cam.ac.uk/fugue/.
Paul I. W. de Bakker, Alex Bateman, David F. Burke, Ricardo Núñez Miguel, Kenji Mizuguchi, Jiye Shi, Hiroki Shirai, Tom L. Blundell
Bioinform.3
2000 Browsing the SLoop database of structurally classified loops connecting elements of protein secondary structure
abstract
We describe a web server, which provides easy access to the SLoop database of loop conformations connecting elements of protein secondary structure. The loops are classified according to their length, the type of bounding secondary structures and the conformation of the mainchain. The current release of the database consists of over 8000 loops of up to 20 residues in length. A loop prediction method, which selects conformers on the basis of the sequence and the positions of the elements of secondary structure, is also implemented. These web pages are freely accessible over the internet at http://www-cryst.bioc.cam.ac.uk/ approximately sloop.
David F. Burke, Charlotte M. Deane, Tom L. Blundell
Bioinform.1