Database classification

In this section, enzymes in the EC 2.4. class are presented that catalyze valuable and interesting reactions in the field of polymer chemistry. The Enzyme Commission (EC) classification scheme organizes enzymes according to their biochemical function in living systems. Enzymes can, however, also catalyze the reverse reaction, which is very often used in biocatalytic synthesis. Therefore, newer classification systems were developed based on the three-dimensional structure and function of the enzyme, the property of the enzyme, the biotransformation the enzyme catalyzes etc. [88-93]. The Carbohydrate-Active enZYmes Database (CAZy), which is currently the best database/classification system for carbohydrate-active enzymes uses an amino-acid-sequence-based classification and would classify some of the enzymes presented in the following as hydrolases rather than transferases (e.g. branching enzyme, sucrases, and amylomaltase) [91]. Nevertheless, we present these enzymes here because they are transferases according to the EC classification. 

Gracy, J. Argos, P. (1999). Automated protein sequence database classification. I. Integration of compositional similarity search, local similarity search, and multiple sequence alignment. Bioinformatics 14,164-73. 

Database Classification 3-8 aa in length database of 3-8 aa loops 13 CASP3... 

The next question is how to represent the reacting bonds of the reaction center. We wanted to develop a method for reaction classification that can be used for knowledge extraction from reaction databases for the prediction of the products of a reaction. Thus, we could only use physicochemical values of the reactants, because these should tell us what products we obtain. 

A wider variety of reaction types involving reactions at bonds to oxygen atom bearing functional groups was investigated by the same kind of methodology [30]. Reaction classification is an essential step in knowledge extraction from reaction databases. This topic is discussed in Section 10.3.1 of this book. 

Reaction classification is an essential step in knowledge acquisition from reaction databases. 

To become familiar with the classification of chemical databases according to their data content... 

The user is often more interested in the contents than in the technical organization of databases. The wide variety of data allows the classification of databases in chemistry into literature, factual (alphanumeric), and structural types (Figure 5-10) [12, 13). 

A strict separation of these three types of databases is difficult hence most databases contain a mixture of data types. Therefore the classification given here is based on the predominating data type. For example, the major emphasis of a patent database is on hterature, whereas it also comprises numeric and structural data. Another type is the integrated database, which provides a supplement of additional information, especially bibhographic data. Thus, different database types are merged, a textual database and one or more factual databases. 

The protein sequence database is also a text-numeric database with bibliographic links. It is the largest public domain protein sequence database. The current PIR-PSD release 75.04 (March, 2003) contains more than 280 000 entries of partial or complete protein sequences with information on functionalities of the protein, taxonomy (description of the biological source of the protein), sequence properties, experimental analyses, and bibliographic references. Queries can be started as a text-based search or a sequence similarity search. PIR-PSD contains annotated protein sequences with a superfamily/family classification. 

More elaborate scheme.s can he envisaged. Thus, a. self-organizing neural network as obtained by the classification of a set of chemical reactions as outlined in Section 3,5 can be interfaced with the EROS system to select the reaction that acmaliy occurs from among various reaction alternatives. In this way, knowledge extracted from rcaetion databases can be interfaced with a reaction prediction system,... 

SCOP Structural Classification of Proteins. Hierarchical protein structure database... 

CATH, FSSP Sequence-structure classification databases... 

Murzin A G, S E Brenner, T Hubbard and C Chothia 1995. SCOP A Structural Classification of Proteins Database for the Investigation of Sequences and Structures. Journal of Molecular Biology 247 536-540. 

Brugnatellite Bruise energy Bruker s database Brunaner classification... 

U.S. Patents. This file, produced by Derwent, Inc., covers U.S. patents from 1971 to the present. The database iacludes all bibliographic and front page information and the text of all claims. (Prom 1971 to 1974 the claims from many patents were not available from the United States Patent and Trademark Office (USPTO) source tapes, and therefore are not iacluded.) The complete cl aim text can be searched from 1971 but can be ptinted only from 1982. Tides and patentee names are present ia their original form, aeither expanded nor standardized. There is no enhanced iadexiag. Examiner citations are directly searchable, and USPTO classification is updated when the tapes are received from the Patent Office. 

JAPIO. This database is produced by the Japan Patent Information Organization and is based on the Patent Abstracts of Japan provided by the Japanese Patent Office. The database is updated monthly and contains all Kokai Tokyo Kobo (pubUshed unexamined patent appHcations) pubUshed as of October 1976. Records appear ia JAPIO approximately six months after pubhcation of the unexamined patent appHcation. English language abstracts are provided for the majority of appHcations filed by Japanese appHcants. AppHcations by non-Japanese appHcations do not have abstracts, but bibliographic information is iacluded. Searchable fields iaclude the International Patent Office Classification and JAPIO classification (96). 

Searching of one or more on-line databases is a technique increasingly used ia novelty studies. The use of such databases enables the searcher to combine indexing parameters, including national and international classifications natural language words ia the full text of patents, ia their claims, or ia abstracts suppHed by iaventor and by professional documentation services and indexing systems of various sorts. Because the various patent databases have strengths and weaknesses that complement each other, the use of multiple databases is thus pmdent, and is faciUtated by multifile and cross-file techniques provided by the various on-line hosts. 

Patent classification codes are another subject-search parameter available in most patent databases. IPC codes are usually present and U.S. codes exist in a number of files in the case of Japan Patent Information Organization (JAPIO), Japanese codes too are available. It is possible to mimic a hand search by limiting operations to references falling within one class or group of classes. Although such strategies can in some instances be justified, it is usually wiser to treat class codes as just one of the various subject parameters that make up a search strategy. 

Producers who serve as vendors for their own databases are more numerous than vendors that offer services from databases produced by other organizations. More than 650 producer/vendors plus some 270 traditional commercial database vendors are Hsted in CRDB(l). Only those vendors that offer search services or distribute CD-ROMs for databases other than their own come under this vendor classification (1). Vendors that offer services solely from databases they themselves produce are Hsted as database producers. 

The second classification is the physical model. Examples are the rigorous modiiles found in chemical-process simulators. In sequential modular simulators, distillation and kinetic reactors are two important examples. Compared to relational models, physical models purport to represent the ac tual material, energy, equilibrium, and rate processes present in the unit. They rarely, however, include any equipment constraints as part of the model. Despite their complexity, adjustable parameters oearing some relation to theoiy (e.g., tray efficiency) are required such that the output is properly related to the input and specifications. These modds provide more accurate predictions of output based on input and specifications. However, the interactions between the model parameters and database parameters compromise the relationships between input and output. The nonlinearities of equipment performance are not included and, consequently, significant extrapolations result in large errors. Despite their greater complexity, they should be considered to be approximate as well. 

TJP Hubbard, B Alley, SE Brenner, AGMurzm, C Chothia. SCOP A stiaictural classification of proteins database. Nucleic Acids Res 27 254-256, 1999. 

S Henikoff, JG Hemkoff. Pi otem family classification based on searching a database of blocks. Genomics 19 97-107, 1994. 

S Pietrokovski, JG Hemkoff, S Henikoff. The BLOCKS database—A system for protein classification. Nucleic Acids Res 24 197-200, 1996. 

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