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008			170417s2017 ne ob 001 0 eng d
020			_a9402410694 _q(electronic bk.)
020			_a9789402410693 _q(electronic bk.)
020			_z9402410678
020			_z9789402410679
040			_aN$T _cN$T _dN$T _dGW5XE _dEBLCP _dYDX _dUAB _dMERER _dESU _dOCLCQ _dNJR _dVT2 _dOTZ _dIOG _dJG0 _dU3W _dES-MaUEC _bspa
050		4	_aQP551 _b.F766 2017 EB
245	0	0	_aFrom protein structure to function with bioinformatics _cDaniel J. Rigden, editor.
250			_aSecond edition.
264		1	_aThe Netherlands _bSpringer _c2017.
300			_a1 recurso en línea
336			_aTexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
500			_aSpringerLink
504			_aIncluye referencias bibliográficas e índice
505	0		_aPreface -- Ab Initio Protein Structure Prediction -- Protein structures, interactions and function from evolutionary couplings.-Fold Recognition -- Comparative protein structure modelling -- Advances in computational methods for transmembrane protein structure prediction -- Bioinformatics approaches to the structure and function of intrinsically disordered proteins -- Prediction of Protein Aggregation and Amyloid Formation -- Prediction of biomolecular complexes -- Function Diversity within Folds and Superfamilies -- Function prediction using patches, pockets and other surface properties -- 3D Motifs -- Protein Dynamics: From Structure to Function -- Integrated servers for structure-informed function prediction -- Case Studies: Function Predictions Of Structural Genomics Results -- Prediction of protein function from theoretical models -- Index.
520	3		_aThis book is about protein structural bioinformatics and how it can help understand and predict protein function. It covers structure-based methods that can assign and explain protein function based on overall folds, characteristics of protein surfaces, occurrence of small 3D motifs, protein-protein interactions and on dynamic properties. Such methods help extract maximum value from new experimental structures, but can often be applied to protein models. The book also, therefore, provides comprehensive coverage of methods for predicting or inferring protein structure, covering all structural classes from globular proteins and their membrane-resident counterparts to amyloid structures and intrinsically disordered proteins. The book is split into two broad sections, the first covering methods to generate or infer protein structure, the second dealing with structure-based function annotation. Each chapter is written by world experts in the field. The first section covers methods ranging from traditional homology modelling and fold recognition to fragment-based ab initio methods, and includes a chapter, new for the second edition, on structure prediction using evolutionary covariance. Membrane proteins and intrinsically disordered proteins are each assigned chapters, while two new chapters deal with amyloid structures and means to predict modes of protein-protein interaction. The second section includes chapters covering functional diversity within protein folds and means to assign function based on surface properties and recurring motifs. Further chapters cover the key roles of protein dynamics in protein function and use of automated servers for function inference. The book concludes with two chapters covering case studies of structure prediction, based respectively on crystal structures and protein models, providing numerous examples of real-world usage of the methods mentioned previously. This book is targeted at postgraduate students and academic researchers. It is most obviously of interest to protein bioinformaticians and structural biologists, but should also serve as a guide to biologists more broadly by highlighting the insights that structural bioinformatics can provide into proteins of their interest.
588	0		_aVendor-supplied metadata.
988			_aEBOOK, EBSPRINGER_2017C
650		7	_2embne _aProteínas _9139861
700	1		_aRigden, Daniel John, _eeditor literario
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=http://link.springer.com/10.1007/978-94-024-1069-3 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b02/2018 _dz _e- _zSI