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Jo-Lan Chung

dblp:45/5670 · DBLP profile ↗
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2ranked-venue papers
2as first author
0since 2021 · last 2007
—ORCID · none

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

Applied, interdisciplinary, general and emerging computing · 2 · 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
1 paper
Bioinformatics and computational biology · 100%

Topics — the 3 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Bioinformatics and computational biology
protein structure analysis
0.112007
Con-Struct Map: a comparative contact map analysis tool · Bioinform. 2007
Bioinformatics and computational biology › protein structure analysis › protein structure alignment
multiple structure alignment
0.012007
Con-Struct Map: a comparative contact map analysis tool · Bioinform. 2007
Bioinformatics and computational biology › protein structure analysis
protein structure alignment
0.012007
Con-Struct Map: a comparative contact map analysis tool · Bioinform. 2007

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

contact map superposition · 0.1
YearPublicationVenuePosition
2007 Con-Struct Map: a comparative contact map analysis tool
abstract
UNLABELLED: Con-Struct Map is a graphical tool for the comparative study of protein structures. The tool detects potential conserved residue contacts shared by multiple protein structures by superimposing their contact maps according to a multiple structure alignment. In general, Con-Struct Map allows the study of structural changes resulting from, e.g. sequence substitutions, or alternatively, the study of conserved components of a structure framework across structurally aligned proteins. Specific applications include the study of sequence-structure relationship in distantly related proteins and the comparisons of wild type and mutant proteins. AVAILABILITY: http://pdbrs3.sdsc.edu/ConStructMap/viewer_argument_generator/singleArguments. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Jo-Lan Chung, John E. Beaver, Eric D. Scheeff, Philip E. Bourne
Bioinform.1
2007 High-throughput identification of interacting protein-protein binding sites
abstract
BACKGROUND: With the advent of increasing sequence and structural data, a number of methods have been proposed to locate putative protein binding sites from protein surfaces. Therefore, methods that are able to identify whether these binding sites interact are needed. RESULTS: We have developed a new method using a machine learning approach to detect if protein binding sites, once identified, interact with each other. The method exploits information relating to sequence and structural complementary across protein interfaces and has been tested on a non-redundant data set consisting of 584 homo-dimers and 198 hetero-dimers extracted from the PDB. Results indicate 87.4% of the interacting binding sites and 68.6% non-interacting binding sites were correctly identified. Furthermore, we built a pipeline that links this method to a modified version of our previously developed method that predicts the location of binding sites. CONCLUSION: We have demonstrated that this high-throughput pipeline is capable of identifying binding sites for proteins, their interacting binding sites and, ultimately, their binding partners on a large scale.
Jo-Lan Chung, Wei Wang 0051, Philip E. Bourne
BMC Bioinform.1