Ayumi Suzuki

Bio: Ayumi Suzuki is an academic researcher from Tokyo University of Science. The author has contributed to research in topics: Protein Data Bank & Protein structure prediction. The author has an hindex of 2, co-authored 2 publications receiving 6 citations.

FCANAL: Structure based protein function prediction method. Application to enzymes and binding proteins

Abstract: Structural genomics projects are beginning to produce protein structures with unknown functions, thereby creating a need for high-throughput methods to predict functions. Although sequence-based function prediction methods have been used extensively, structure-based prediction is believed to provide higher specificity and sensitivity because functions are closely related to the three-dimensional structures of functional sites, which are more strongly conserved during evolution than sequence. We have developed FCANAL, a method to predict functions using a score matrix obtained from the distances between Cα atoms and frequencies of appearance [1]. The previous report used key residues predicted from sequence comparisons (motifs). In this report, we have expanded the method to include enzymes and binding proteins with key residues predicted on the basis of three-dimensional structures. Using FCANAL, we constructed score matrices for 31 enzymes. When we applied them to all of the structure entries deposited in the Protein Data Bank, FCANAL could detect functional sites with high accuracy. This suggests that FCANAL will help identify the functions of newly determined structures and pinpoint their functionally important regions.

“Bioinformatics” 특집을 내면서

Abstract: BIOE 402. Medical Technology Assessment. 2 or 3 hours. Bioentrepreneur course. Assessment of medical technology in the context of commercialization. Objectives, competition, market share, funding, pricing, manufacturing, growth, and intellectual property; many issues unique to biomedical products. Course Information: 2 undergraduate hours. 3 graduate hours. Prerequisite(s): Junior standing or above and consent of the instructor.

The FEATURE framework for protein function annotation: modeling new functions, improving performance, and extending to novel applications

Abstract: Structural genomics efforts contribute new protein structures that often lack significant sequence and fold similarity to known proteins. Traditional sequence and structure-based methods may not be sufficient to annotate the molecular functions of these structures. Techniques that combine structural and functional modeling can be valuable for functional annotation. FEATURE is a flexible framework for modeling and recognition of functional sites in macromolecular structures. Here, we present an overview of the main components of the FEATURE framework, and describe the recent developments in its use. These include automating training sets selection to increase functional coverage, coupling FEATURE to structural diversity generating methods such as molecular dynamics simulations and loop modeling methods to improve performance, and using FEATURE in large-scale modeling and structure determination efforts.

p-Adic numbers in bioinformatics: from genetic code to PAM-matrix

Abstract: In this paper we denonstrate that the use of the system of 2-adic numbers provides a new insight to some problems of genetics, in particular, generacy of the genetic code and the structure of the PAM matrix in bioinformatics. The 2-adic distance is an ultrametric and applications of ultrametrics in bioinformatics are not surprising. However, by using the 2-adic numbers we match ultrametric with a number theoretic structure. In this way we find new applications of an ultrametric which differ from known up to now in bioinformatics. We obtain the following results. We show that the PAM matrix A allows the expansion into the sum of the two matrices A=A^{(2)}+A^{(\infty)}, where the matrix A^{(2)} is 2-adically regular (i.e. matrix elements of this matrix are close to locally constant with respect to the discussed earlier by the authors 2-adic parametrization of the genetic code), and the matrix A^{(\infty)} is sparse. We discuss the structure of the matrix A^{(\infty)} in relation to the side chain properties of the corresponding amino acids.