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Lily Ph. Nizolenko

dblp:77/5456 · DBLP profile ↗
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2ranked-venue papers
0as first author
0since 2021 · last 2000
0000-0002-9647-4969ORCID · reported

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

Applied, interdisciplinary, general and emerging computing · 2

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
2 papers
Bioinformatics and computational biology · 100%

Topics — the 2 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Bioinformatics and computational biology
protein function prediction
0.012000
PROF_ PAT 1.3: Updated database of patterns used to detect local similarities · Bioinform. 2000
Bioinformatics and computational biology › protein analysis › protein bioinformatics
protein function analysis
0.011997
A bank of protein family patterns for rapid identification of possible functions of amino acid sequences · Comput. Appl. Biosci. 1997

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

similarity matrix search · 0.0pattern construction · 0.0profile · 0.0pattern construction from physicochemical properties · 0.0PROSITE · 0.0BLOCKS · 0.0
YearPublicationVenuePosition
2000 PROF_ PAT 1.3: Updated database of patterns used to detect local similarities
abstract
MOTIVATION: When analysing novel protein sequences, it is now essential to extend search strategies to include a range of 'secondary' databases. Pattern databases have become vital tools for identifying distant relationships in sequences, and hence for predicting protein function and structure. The main drawback of such methods is the relatively small representation of proteins in trial samples at the time of their construction. Therefore, a negative result of an amino acid sequence comparison with such a databank forces a researcher to search for similarities in the original protein banks. We developed a database of patterns constructed for groups of related proteins with maximum representation of amino acid sequences of SWISS-PROT in the groups. RESULTS: Software tools and a new method have been designed to construct patterns of protein families. By using such method, a new version of databank of protein family patterns, PROF_ PAT 1.3, is produced. This bank is based on SWISS-PROT (r1.38) and TrEMBL (r1.11), and contains patterns of more than 13 000 groups of related proteins in a format similar to that of the PROSITE. Motifs of patterns, which had the minimum level of probability to be found in random sequences, were selected. Flexible fast search program accompanies the bank. The researcher can specify a similarity matrix (the type PAM, BLOSUM and other). Variable levels of similarity can be set (permitting search strategies ranging from exact matches to increasing levels of 'fuzziness'). AVAILABILITY: The Internet address for comparing sequences with the bank is: http://wwwmgs.bionet.nsc.ru/mgs/programs/prof_pat/. The local version of the bank and search programs (approximately 50 Mb) is available via ftp: ftp://ftp.bionet.nsc. ru/pub/biology/vector/prof_pat/, and ftp://ftp.ebi.ac. uk/pub/databases/prof_pat/. Another appropriate way for its external use is to mail amino acid sequences to [email protected] for comparison with PROF_ PAT 1.3.
Alexander G. Bachinsky, Anatoly S. Frolov, Andrey N. Naumochkin, Lily Ph. Nizolenko, Andrey A. Yarigin
Bioinform.4
1997 A bank of protein family patterns for rapid identification of possible functions of amino acid sequences
abstract
A method and software tool to develop patterns of protein families has been designed. These patterns are intended for the identification of local similarities in arbitrary amino acid sequences with proteins of the SWISS-PROT bank. The method is based on the physical, chemical and structural properties of amino acids. It assembles a 'best set' of elements (a pattern) for a given group of aligned related proteins. These elements provide discrimination between proteins of a family and representatives of other families or random sequences. The method combines the advantages of BLOCKS (automatic generation of multiple elements for protein groups), PROSITE (simplicity of element presentation) and matrices/profiles (different distinctions between amino acids for different positions of aligned sequences). Using our method, a data bank of protein family patterns, PROF_PAT, is produced. This data bank is based on the 27,752 amino acid sequences of SWISS-PROT bank release 24. The characteristics of patterns of 743 related protein groups are described. The results of comparisons of PROF_PAT patterns with the proteins of the SWISS-PROT bank are discussed.
Alexander G. Bachinsky, Andrey A. Yarigin, E. H. Guseva, V. A. Kulichkov, Lily Ph. Nizolenko
Comput. Appl. Biosci.5