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008			161027s2017 xxua o |||| 0|eng d
020			_a9781493964062
024	7		_a10.1007/978-1-4939-6406-2 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aQD431.25 .S85 _b2017 EB
245	0	0	_aPrediction of Protein Secondary Structure _cedited by Yaoqi Zhou, Andrzej Kloczkowski, Eshel Faraggi, Yuedong Yang
250			_a1st edition 2017
264		1	_aNew York, NY _bSpringer International Publishing _c2017
300			_a1 recurso en línea (XI, 313 páginas) _b67 ilustraciones, 56 ilustraciones a color
336			_atexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
347			_aarchivo de texto _bPDF
490	0		_aMethods in Molecular Biology _x1940-6029 _v1484
505	0		_aWhere the Name "GOR" Originates: A Story -- The GOR Method of Protein Secondary Structure Prediction and Its Application as a Protein Aggregation Prediction Tool -- Consensus Prediction of Charged Single Alpha-Helices with CSAHserver -- Predicting Protein Secondary Structure Using Consensus Data Mining (CDM) Based on Empirical Statistics and Evolutionary Information -- Accurate Prediction of One-Dimensional Protein Structure Features Using SPINE-X -- SPIDER2: A Package to Predict Secondary Structure, Accessible Surface Area, and Main-Chain Torsional Angles by Deep Neural Networks -- Backbone Dihedral Angle Prediction -- One-Dimensional Structural Properties of Proteins in the Coarse-Grained CABS Model -- Assessing Predicted Contacts for Building Protein Three-Dimensional Models -- Fast and Accurate Accessible Surface Area Prediction Without a Sequence Profile -- How to Predict Disorder in a Protein of Interest -- Intrinsic Disorder and Semi-Disorder Prediction by SPINE-D -- Predicting Real-Valued Protein Residue Fluctuation Using FlexPred -- Prediction of Disordered RNA, DNA, and Protein Binding Regions Using DisoRDPbind -- Sequence-Based Prediction of RNA-Binding Residues in Proteins -- Computational Approaches for Predicting Binding Partners, Interface Residues, and Binding Affinity of Protein-Protein Complexes -- In Silico Prediction of Linear B-Cell Epitopes on Proteins -- Prediction of Protein Phosphorylation Sites by Integrating Secondary Structure Information and Other One-Dimensional Structural Properties -- Predicting Post-Translational Modifications from Local Sequence Fragments Using Machine Learning Algorithms: Overview and Best Practices -- CX, DPX, and PCW: Web Servers for the Visualization of Interior and Protruding Regions of Protein Structures in 3D and 1D.
520			_aThis thorough volume explores predicting one-dimensional functional properties, functional sites in particular, from protein sequences, an area which is getting more and more attention. Beginning with secondary structure prediction based on sequence only, the book continues by exploring secondary structure prediction based on evolution information, prediction of solvent accessible surface areas and backbone torsion angles, model building, global structural properties, functional properties, as well as visualizing interior and protruding regions in proteins. Written for the highly successful Methods in Molecular Biology series, the chapters include the kind of detail and implementation advice to ensure success in the laboratory. Practical and authoritative, Prediction of Protein Secondary Structure serves as a vital guide to numerous state-of-the-art techniques that are useful for computational and experimental biologists.
988			_aSpringer_Protocols_2017
650		7	_2embne _9669891 _aProteínas _xEstructura
776	0	8	_iPrinted edition: _z9781493964048
776	0	8	_iPrinted edition: _z9781493964055
776	0	8	_iPrinted edition: _z9781493981892
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-1-4939-6406-2 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b10/2023 _dz _eb _zSI