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  • 1
    Keywords: Life sciences ; Computer science ; Bioinformatics ; Biological models ; Biomedical Engineering ; Life sciences ; Systems Biology ; Computational Biology/Bioinformatics ; Computer Science, general ; Biomedical Engineering ; Springer eBooks
    Pages: : digital
    ISBN: 9781461403203
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  • 2
    ISSN: 0885-6125
    Keywords: domain knowledge ; change of representation ; theory revision ; protein structure prediction ; homology modeling ; amino acid properties
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract Predicting the fold, or approximate 3D structure, of a protein from its amino acid sequence is an important problem in biology. The homology modeling approach uses a protein database to identify fold-class relationships by sequence similarity. The main limitation of this method is that some proteins with similar structures appear to have very different sequences, which we call the “hidden-homology problem.” As in other real-world domains for machine learning, this difficulty may be caused by a low-level representation. Learning in such domains can be improved by using domain knowledge to search for representations that better match the inductive bias of a preferred algorithm. In this domain, knowledge of amino acid properties can be used to construct higher-level representations of protein sequences. In one experiment using a 179-protein data set, the accuracy of fold-class prediction was increased from 77.7% to 81.0%. The search results are analyzed to refine the grouping of small residues suggested by Dayhoff. Finally, an extension to the representation incorporates sequential context directly into the representation, which can express finer relationships among the amino acids. The methods developed in this domain are generalized into a framework that suggests several systematic roles for domain knowledge in machine learning. Knowledge may define both a space of alternative representations, as well as a strategy for searching this space. The search results may be summarized to extract feedback for revising the domain knowledge.
    Type of Medium: Electronic Resource
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  • 3
    ISSN: 1546-1696
    Source: Nature Archives 1869 - 2009
    Topics: Biology , Process Engineering, Biotechnology, Nutrition Technology
    Notes: [Auszug] Immunoglobulin (Ig) amino acid sequences are highly conserved and often have sequence homology ranging from 70 to 95%. Antigen binding fragments (Fab), variable region fragments (Fv), and single chain Fv (scFv) of more than 50 myeloma proteins and monoclonal antibodies (mAb) have been crystallized ...
    Type of Medium: Electronic Resource
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  • 4
    Source: ACS Legacy Archives
    Topics: Chemistry and Pharmacology , Physics
    Type of Medium: Electronic Resource
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  • 5
    ISSN: 1476-4687
    Source: Nature Archives 1869 - 2009
    Topics: Biology , Chemistry and Pharmacology , Medicine , Natural Sciences in General , Physics
    Notes: [Auszug] The Alliance for Cellular Signaling (AfCS)–Nature Molecule Pages will be a comprehensive database of key facts about more than 3,000 proteins involved in cell signalling. Each entry will be created by invited experts and be peer-reviewed. Alongside the large-scale experiments being conducted ...
    Type of Medium: Electronic Resource
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  • 6
    ISSN: 0887-3585
    Keywords: nonlinear elliptic equations ; nonlinear multigrid ; inexact Newton methods ; damped Newton methods ; crambin ; BPTI ; HyHEL-5 ; superoxide dismutase ; rhinovirus ; tryptophan synthase ; electrostatic steering ; Brownian dynamics ; antibody-antigen complex ; Chemistry ; Biochemistry and Biotechnology
    Source: Wiley InterScience Backfile Collection 1832-2000
    Topics: Medicine
    Notes: The nonlinear Poisson-Boltzmann equation (NPBE) provides a continuum description of the electrostatic field in an ionic medium around a macromolecule. Here, a novel approach to the solution of the full NPBE is developed. This robust and efficient algorithm combines multilevel techniques with a damped inexact Newton's method. The CPU time required for solution of the full NPBE, which is less than that for standard single-grid approaches in solving the corresponding linearized equation, is proportional to the number of unknowns enabling applications to very large macromolecular systems. Convergence of the method is demonstrated for a variety of protein systems. Comparison of the solutions to the linearized Poisson-Boltzmann equation shows that the damping of the electrostatic field around the charge is increased and that the potential scales logarithmically with charge. The inclusion of the full nonlinearity thus reduces the impact of highly charged residues on protein surfaces and provides a more realistic representation of electrostatic effects. This is demonstrated through calculation of potential around the active site regions of the 1,266-residue tryptophan synthase dimer and in the computation of rate constants from Brownian dynamics calculations in the superoxide dismutase-superoxide and antibody-antigen systems. © 1994 John Wiley & Sons, Inc.
    Additional Material: 8 Ill.
    Type of Medium: Electronic Resource
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  • 7
    ISSN: 0887-3585
    Keywords: Chemistry ; Biochemistry and Biotechnology
    Source: Wiley InterScience Backfile Collection 1832-2000
    Topics: Medicine
    Notes: No abstract.
    Type of Medium: Electronic Resource
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  • 8
    ISSN: 0887-3585
    Keywords: solvent accessible surface ; molecular surface ; area and volume ; Delaunay complex ; alpha shape ; Chemistry ; Biochemistry and Biotechnology
    Source: Wiley InterScience Backfile Collection 1832-2000
    Topics: Medicine
    Notes: The size and shape of macromolecules such as proteins and nucleic acids play an important role in their functions. Prior efforts to quantify these properties have been based on various discretization or tessellation procedures involving analytical or numerical computations. In this article, we present an analytically exact method for computing the metric properties of macromolecules based on the alpha shape theory. This method uses the duality between alpha complex and the weighted Voronoi decomposition of a molecule. We describe the intuitive ideas and concepts behind the alpha shape theory and the algorithm for computing areas and volumes of macromolecules. We apply our method to compute areas and volumes of a number of protein systems. We also discuss several difficulties commonly encountered in molecular shape computations and outline methods to overcome these problems. Proteins 33:1-17, 1998. © 1998 Wiley-Liss, Inc.
    Additional Material: 9 Ill.
    Type of Medium: Electronic Resource
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  • 9
    ISSN: 0887-3585
    Keywords: molecular cavities ; packing defects ; Delaunay complex ; alpha shape ; structural solvent in proteins ; Chemistry ; Biochemistry and Biotechnology
    Source: Wiley InterScience Backfile Collection 1832-2000
    Topics: Medicine
    Notes: The structures of proteins are well-packed, yet they contain numerous cavities which play key roles in accommodating small molecules, or enabling conformational changes. From high-resolution structures it is possible to identify these cavities. We have developed a precise algorithm based on alpha shapes for measuring space-filling-based molecular models (such as van der Waals, solvent accessible, and molecular surface descriptions). We applied this method for accurate computation of the surface area and volume of cavities in several proteins. In addition, all of the atoms/residues lining the cavities are identified. We use this method to study the structure and the stability of proteins, as well as to locate cavities that could contain structural water molecules in the proton transport pathway in the membrane protein bacteriorhodopsin. Proteins 33:18-29, 1998. © 1998 Wiley-Liss, Inc.
    Additional Material: 5 Ill.
    Type of Medium: Electronic Resource
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  • 10
    ISSN: 0885-6125
    Keywords: domain knowledge ; change of representation ; theory revision ; protein structure prediction ; homology modeling ; amino acid properties
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract Predicting the fold, or approximate 3D structure, of a protein from its amino acid sequence is an important problem in biology. The homology modeling approach uses a protein database to identify fold-class relationships by sequence similarity. The main limitation of this method is that some proteins with similar structures appear to have very different sequences, which we call the “hidden-homology problem.” As in other real-world domains for machine learning, this difficulty may be caused by a low-level representation. Learning in such domains can be improved by using domain knowledge to search for representations that better match the inductive bias of a preferred algorithm. In this domain, knowledge of amino acid properties can be used to construct higher-level representations of protein sequences. In one experiment using a 179-protein data set, the accuracy of fold-class prediction was increased from 77.7% to 81.0%. The search results are analyzed to refine the grouping of small residues suggested by Dayhoff. Finally, an extension to the representation incorporates sequential context directly into the representation, which can express finer relationships among the amino acids. The methods developed in this domain are generalized into a framework that suggests several systematic roles for domain knowledge in machine learning. Knowledge may define both a space of alternative representations, as well as a strategy for searching this space. The search results may be summarized to extract feedback for revising the domain knowledge.
    Type of Medium: Electronic Resource
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