Novel matrix descriptor for secondary structure segments in proteins: demonstration of predictability from circular dichroism spectra. 1999

P Pancoska, and V Janota, and T A Keiderling
Department of Chemistry, University of Illinois at Chicago, 845 West Taylor Street, M/C 111, Chicago, Illinois, 60607-7061, USA.

An extension to standard protein secondary structure predictions using optical spectra that encompasses the number and average lengths of segments of uniform secondary structure in the sequence is demonstrated. The connectivity and numbers of segments can be described by a matrix descriptor [sij] (i, j representing segment types such as helix and beta-sheet strands). Independent knowledge of the fractional concentration of each secondary structure type and of the total number of residues in the protein then with [sij] yields the average segment length of each type. The physical background for prediction of this extended structural descriptor from spectral data is summarized, rules for its generation from reference X-ray structures are defined, and formal variants of its form are discussed. Using a novel neural network approach to analyze a training set of electronic circular dichroism (ECD) and vibrational circular dichroism (VCD) spectra for 23 proteins, matrix descriptors encompassing helix, sheet, and other forms are predicted. The results show that the matrix descriptor can be predicted to an accuracy comparable to that of conventionally predicted average fractional secondary structures. In this respect the ECD predictions of [sij] were significantly more accurate than the VCD ones, which may result from the longer range length dependence of the ECD bandshape and intensity. Summary results for a parallel analysis using Fourier transform infrared spectra indicate somewhat lower reliability than those for VCD.

UI MeSH Term Description Entries
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
D002942 Circular Dichroism A change from planar to elliptic polarization when an initially plane-polarized light wave traverses an optically active medium. (McGraw-Hill Dictionary of Scientific and Technical Terms, 4th ed) Circular Dichroism, Vibrational,Dichroism, Circular,Vibrational Circular Dichroism
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
D017550 Spectroscopy, Fourier Transform Infrared A spectroscopic technique in which a range of wavelengths is presented simultaneously with an interferometer and the spectrum is mathematically derived from the pattern thus obtained. FTIR,Fourier Transform Infrared Spectroscopy,Spectroscopy, Infrared, Fourier Transform
D018360 Crystallography, X-Ray The study of crystal structure using X-RAY DIFFRACTION techniques. (McGraw-Hill Dictionary of Scientific and Technical Terms, 4th ed) X-Ray Crystallography,Crystallography, X Ray,Crystallography, Xray,X Ray Crystallography,Xray Crystallography,Crystallographies, X Ray,X Ray Crystallographies

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