Biomaterial Database

PrDOS: prediction of disordered protein regions from amino acid sequence. 2007

Takashi Ishida, and Kengo Kinoshita

Institute of Medical Science, The University of Tokyo, 4-6-1 Shirokanedai, Minato-ku, Tokyo, 108-8639, Japan. t-ishida@hgc.jp

PrDOS is a server that predicts the disordered regions of a protein from its amino acid sequence (http://prdos.hgc.jp). The server accepts a single protein amino acid sequence, in either plain text or FASTA format. The prediction system is composed of two predictors: a predictor based on local amino acid sequence information and one based on template proteins. The server combines the results of the two predictors and returns a two-state prediction (order/disorder) and a disorder probability for each residue. The prediction results are sent by e-mail, and the server also provides a web-interface to check the results.

UI	MeSH Term	Description	Entries
D008969	Molecular Sequence Data	Descriptions of specific amino acid, carbohydrate, or nucleotide sequences which have appeared in the published literature and/or are deposited in and maintained by databanks such as GENBANK, European Molecular Biology Laboratory (EMBL), National Biomedical Research Foundation (NBRF), or other sequence repositories.	Sequence Data, Molecular,Molecular Sequencing Data,Data, Molecular Sequence,Data, Molecular Sequencing,Sequencing Data, Molecular
D010363	Pattern Recognition, Automated	In INFORMATION RETRIEVAL, machine-sensing or identification of visible patterns (shapes, forms, and configurations). (Harrod's Librarians' Glossary, 7th ed)	Automated Pattern Recognition,Pattern Recognition System,Pattern Recognition Systems
D011487	Protein Conformation	The characteristic 3-dimensional shape of a protein, including the secondary, supersecondary (motifs), tertiary (domains) and quaternary structure of the peptide chain. PROTEIN STRUCTURE, QUATERNARY describes the conformation assumed by multimeric proteins (aggregates of more than one polypeptide chain).	Conformation, Protein,Conformations, Protein,Protein Conformations
D011506	Proteins	Linear POLYPEPTIDES that are synthesized on RIBOSOMES and may be further modified, crosslinked, cleaved, or assembled into complex proteins with several subunits. The specific sequence of AMINO ACIDS determines the shape the polypeptide will take, during PROTEIN FOLDING, and the function of the protein.	Gene Products, Protein,Gene Proteins,Protein,Protein Gene Products,Proteins, Gene
D003205	Computing Methodologies	Computer-assisted analysis and processing of problems in a particular area.	High Performance Computing,Methodologies, Computing,Computing Methodology,Computing, High Performance,Methodology, Computing,Performance Computing, High
D000465	Algorithms	A procedure consisting of a sequence of algebraic formulas and/or logical steps to calculate or determine a given task.	Algorithm
D000595	Amino Acid Sequence	The order of amino acids as they occur in a polypeptide chain. This is referred to as the primary structure of proteins. It is of fundamental importance in determining PROTEIN CONFORMATION.	Protein Structure, Primary,Amino Acid Sequences,Sequence, Amino Acid,Sequences, Amino Acid,Primary Protein Structure,Primary Protein Structures,Protein Structures, Primary,Structure, Primary Protein,Structures, Primary Protein
D001185	Artificial Intelligence	Theory and development of COMPUTER SYSTEMS which perform tasks that normally require human intelligence. Such tasks may include speech recognition, LEARNING; VISUAL PERCEPTION; MATHEMATICAL COMPUTING; reasoning, PROBLEM SOLVING, DECISION-MAKING, and translation of language.	AI (Artificial Intelligence),Computer Reasoning,Computer Vision Systems,Knowledge Acquisition (Computer),Knowledge Representation (Computer),Machine Intelligence,Computational Intelligence,Acquisition, Knowledge (Computer),Computer Vision System,Intelligence, Artificial,Intelligence, Computational,Intelligence, Machine,Knowledge Representations (Computer),Reasoning, Computer,Representation, Knowledge (Computer),System, Computer Vision,Systems, Computer Vision,Vision System, Computer,Vision Systems, Computer
D015233	Models, Statistical	Statistical formulations or analyses which, when applied to data and found to fit the data, are then used to verify the assumptions and parameters used in the analysis. Examples of statistical models are the linear model, binomial model, polynomial model, two-parameter model, etc.	Probabilistic Models,Statistical Models,Two-Parameter Models,Model, Statistical,Models, Binomial,Models, Polynomial,Statistical Model,Binomial Model,Binomial Models,Model, Binomial,Model, Polynomial,Model, Probabilistic,Model, Two-Parameter,Models, Probabilistic,Models, Two-Parameter,Polynomial Model,Polynomial Models,Probabilistic Model,Two Parameter Models,Two-Parameter Model
D015693	Gene Products, nef	Products of the retroviral NEF GENE. They play a role as accessory proteins that influence the rate of viral infectivity and the destruction of the host immune system. nef gene products were originally found as factors that trans-suppress viral replication and function as negative regulators of transcription. nef stands for negative factor.	nef Gene Products,nef Protein,3'-orf Protein,Gene Product, nef,3' orf Protein,nef Gene Product