Computational Methods for Protein Structure Prediction and Modeling. Volume 1, Basic Characterization
Volume one of this two volume sequence focuses on the basic characterization of known protein structures as well as structure prediction from protein sequence information. The 11 chapters provide an overview of the field, covering key topics in modeling, force fields, classification, computational m...
Enregistré dans:
| Autres auteurs: | , , |
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| Format: | Livre numérique |
| Langue: | Anglais |
| Publié: |
New York, NY :
Springer New York : Springer e-books
[20..].
Cham : Springer Nature |
| Collection: | BIOLOGICAL AND MEDICAL PHYSICS BIOMEDICAL ENGINEERING
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| Accès en ligne: | Accès sur la plateforme de l'éditeur Accès sur la plateforme Istex Accès Université d'Orléans Accès INSA CVL |
| Note: |
Archives Springer e-books (Licence nationale) Archives Springer e-books (Licence nationale) |
| Autres localisations: | Voir dans le Sudoc |
| Edition sous un autre format: | • Computational methods for protein structure prediction and modeling, Ying Xu, Dong Xu, and Jie Liang (eds.), New York, N.Y., Springer, 2007, 2 vol. (XX-394, 320 p.), Biological and medical physics, biomedical engineering, 0-387-33319-3 |
| Résumé: | Volume one of this two volume sequence focuses on the basic characterization of known protein structures as well as structure prediction from protein sequence information. The 11 chapters provide an overview of the field, covering key topics in modeling, force fields, classification, computational methods, and struture prediction. Each chapter is a self contained review designed to cover (1) definition of the problem and an historical perspective, (2) mathematical or computational formulation of the problem, (3) computational methods and algorithms, (4) performance results, (5) existing software packages, and (6) strengths, pitfalls, challenges, and future research directions |
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| Description: | Archives Springer e-books (Licence nationale) Archives Springer e-books (Licence nationale) |
| ISBN: | 9780387683720 |
| ISSN: | 1618-7210 |
| Accès: | Accès en ligne pour les établissements français bénéficiaires des licences nationales Accès soumis à abonnement pour tout autre établissement Conditions particulières de réutilisation pour les bénéficiaires des licences nationales. https://www.licencesnationales.fr/springer-nature-ebooks-contrat-licence-ln-2017 |