Biomaterial Database

Prediction of protein folding class using global description of amino acid sequence. 1995

I Dubchak, and I Muchnik, and S R Holbrook, and S H Kim

Department of Chemistry, University of California, Berkeley 94720, USA.

We present a method for predicting protein folding class based on global protein chain description and a voting process. Selection of the best descriptors was achieved by a computer-simulated neural network trained on a data base consisting of 83 folding classes. Protein-chain descriptors include overall composition, transition, and distribution of amino acid attributes, such as relative hydrophobicity, predicted secondary structure, and predicted solvent exposure. Cross-validation testing was performed on 15 of the largest classes. The test shows that proteins were assigned to the correct class (correct positive prediction) with an average accuracy of 71.7%, whereas the inverse prediction of proteins as not belonging to a particular class (correct negative prediction) was 90-95% accurate. When tested on 254 structures used in this study, the top two predictions contained the correct class in 91% of the cases.

UI	MeSH Term	Description	Entries
D008956	Models, Chemical	Theoretical representations that simulate the behavior or activity of chemical processes or phenomena; includes the use of mathematical equations, computers, and other electronic equipment.	Chemical Models,Chemical Model,Model, Chemical
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
D003198	Computer Simulation	Computer-based representation of physical systems and phenomena such as chemical processes.	Computational Modeling,Computational Modelling,Computer Models,In silico Modeling,In silico Models,In silico Simulation,Models, Computer,Computerized Models,Computer Model,Computer Simulations,Computerized Model,In silico Model,Model, Computer,Model, Computerized,Model, In silico,Modeling, Computational,Modeling, In silico,Modelling, Computational,Simulation, Computer,Simulation, In silico,Simulations, Computer
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
D000596	Amino Acids	Organic compounds that generally contain an amino (-NH2) and a carboxyl (-COOH) group. Twenty alpha-amino acids are the subunits which are polymerized to form proteins.	Amino Acid,Acid, Amino,Acids, Amino
D012997	Solvents	Liquids that dissolve other substances (solutes), generally solids, without any change in chemical composition, as, water containing sugar. (Grant & Hackh's Chemical Dictionary, 5th ed)	Solvent
D015203	Reproducibility of Results	The statistical reproducibility of measurements (often in a clinical context), including the testing of instrumentation or techniques to obtain reproducible results. The concept includes reproducibility of physiological measurements, which may be used to develop rules to assess probability or prognosis, or response to a stimulus; reproducibility of occurrence of a condition; and reproducibility of experimental results.	Reliability and Validity,Reliability of Result,Reproducibility Of Result,Reproducibility of Finding,Validity of Result,Validity of Results,Face Validity,Reliability (Epidemiology),Reliability of Results,Reproducibility of Findings,Test-Retest Reliability,Validity (Epidemiology),Finding Reproducibilities,Finding Reproducibility,Of Result, Reproducibility,Of Results, Reproducibility,Reliabilities, Test-Retest,Reliability, Test-Retest,Result Reliabilities,Result Reliability,Result Validities,Result Validity,Result, Reproducibility Of,Results, Reproducibility Of,Test Retest Reliability,Validity and Reliability,Validity, Face
D016208	Databases, Factual	Extensive collections, reputedly complete, of facts and data garnered from material of a specialized subject area and made available for analysis and application. The collection can be automated by various contemporary methods for retrieval. The concept should be differentiated from DATABASES, BIBLIOGRAPHIC which is restricted to collections of bibliographic references.	Databanks, Factual,Data Banks, Factual,Data Bases, Factual,Data Bank, Factual,Data Base, Factual,Databank, Factual,Database, Factual,Factual Data Bank,Factual Data Banks,Factual Data Base,Factual Data Bases,Factual Databank,Factual Databanks,Factual Database,Factual Databases
D016571	Neural Networks, Computer	A computer architecture, implementable in either hardware or software, modeled after biological neural networks. Like the biological system in which the processing capability is a result of the interconnection strengths between arrays of nonlinear processing nodes, computerized neural networks, often called perceptrons or multilayer connectionist models, consist of neuron-like units. A homogeneous group of units makes up a layer. These networks are good at pattern recognition. They are adaptive, performing tasks by example, and thus are better for decision-making than are linear learning machines or cluster analysis. They do not require explicit programming.	Computational Neural Networks,Connectionist Models,Models, Neural Network,Neural Network Models,Neural Networks (Computer),Perceptrons,Computational Neural Network,Computer Neural Network,Computer Neural Networks,Connectionist Model,Model, Connectionist,Model, Neural Network,Models, Connectionist,Network Model, Neural,Network Models, Neural,Network, Computational Neural,Network, Computer Neural,Network, Neural (Computer),Networks, Computational Neural,Networks, Computer Neural,Networks, Neural (Computer),Neural Network (Computer),Neural Network Model,Neural Network, Computational,Neural Network, Computer,Neural Networks, Computational,Perceptron
D017433	Protein Structure, Secondary	The level of protein structure in which regular hydrogen-bond interactions within contiguous stretches of polypeptide chain give rise to ALPHA-HELICES; BETA-STRANDS (which align to form BETA-SHEETS), or other types of coils. This is the first folding level of protein conformation.	Secondary Protein Structure,Protein Structures, Secondary,Secondary Protein Structures,Structure, Secondary Protein,Structures, Secondary Protein