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Venkatarajan S. Mathura

dblp:05/2325 · DBLP profile ↗
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1ranked-venue papers
1as first author
0since 2021 · last 2003
—ORCID · none

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

Applied, interdisciplinary, general and emerging computing · 1 · 1 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 function prediction › protein classification
protein family classification
0.012003
Identifying Property Based Sequence Motifs in Protein Families and Superfamilies: Application to DNase-1 Related Endonucleases · Bioinform. 2003
Bioinformatics and computational biology
protein sequence analysis
0.012003
Identifying Property Based Sequence Motifs in Protein Families and Superfamilies: Application to DNase-1 Related Endonucleases · Bioinform. 2003
Bioinformatics and computational biology › sequence analysis › motif discovery
sequence motif detection
0.012003
Identifying Property Based Sequence Motifs in Protein Families and Superfamilies: Application to DNase-1 Related Endonucleases · Bioinform. 2003

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

physicochemical property conservation · 0.0bayesian scoring · 0.0
YearPublicationVenuePosition
2003 Identifying Property Based Sequence Motifs in Protein Families and Superfamilies: Application to DNase-1 Related Endonucleases
abstract
MOTIVATION: Identification of short conserved sequence motifs common to a protein family or superfamily can be more useful than overall sequence similarity in suggesting the function of novel gene products. Locating motifs still requires expert knowledge, as automated methods using stringent criteria may not differentiate subtle similarities from statistical noise. RESULTS: We have developed a novel automatic method, based on patterns of conservation of 237 physical-chemical properties of amino acids in aligned protein sequences, to find related motifs in proteins with little or no overall sequence similarity. As an application, our web-server MASIA identified 12 property-based motifs in the apurinic/apyrimidinic endonuclease (APE) family of DNA-repair enzymes of the DNase-I superfamily. Searching with these motifs located distantly related representatives of the DNase-I superfamily, such as Inositol 5'-polyphosphate phosphatases in the ASTRAL40 database, using a Bayesian scoring function. Other proteins containing APE motifs had no overall sequence or structural similarity. However, all were phosphatases and/or had a metal ion binding active site. Thus our automated method can identify discrete elements in distantly related proteins that define local structure and aspects of function. We anticipate that our method will complement existing ones to functionally annotate novel protein sequences from genomic projects. AVAILABILITY: MASIA WEB site: http://www.scsb.utmb.edu/masia/masia.html SUPPLEMENTARY INFORMATION: The dendrogram of 42 APE sequences used to derive motifs is available on http://www.scsb.utmb.edu/comp_biol.html/DNA_repair/publication.html
Venkatarajan S. Mathura, Catherine H. Schein, Werner Braun
Bioinform.1