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Detalle de: How to guarantee optimal stability for most representative structures in the protein data bank

Title How to guarantee optimal stability for most representative structures in the protein data bank
Abstract

We proposed recently an optimization method to derive energy parameters for simplified models of protein folding. The method is based on the maximization of the thermodynamic average of the overlap between protein native structures and a Boltzmann ensemble of alternative structures. Such a condition enforces protein models whose ground states are most similar to the corresponding native states. We present here an extensive testing of the method for a simple residue‐residue contact energy function and for alternative structures generated by threading. The optimized energy function guarantees high stability and a well‐correlated energy landscape to most representative structures in the PDB database. Failures in the recognition of the native structure can be attributed to the neglect of interactions between different chains in oligomeric proteins or with cofactors. When these are taken into account, only very few X‐ray structures are not recognized. Most of them are short inhibitors or fragments and one is a structure that presents serious inconsistencies. Finally, we discuss the reasons that make NMR structures more difficult to recognize. Proteins 2001;44:79–96. © 2001 Wiley‐Liss, Inc.

Apa Reference Bastolla, U., Farwer, J., Knapp, E. W., & Vendruscolo, M. (2001). How to guarantee optimal stability for most representative structures in the protein data bank. Proteins: Structure, Function, and Bioinformatics, 44(2), 79–96. Portico. 10.1002/prot.1075
Doi 10.1002/prot.1075
Publication Date 14 May 2001
Publication Type Article
Researcher Ugo Bastolla
Ontology Functional annotation, Function prediction, Structural bioinformatics, Molecular flexibility, Molecular evolution, Conservation Analysis, Evolutionary modelling, Ecological modelling, Sequence Alignment, Phylogenetic Analysis, Mutation Analysis and design, Network Biology, Structure-function relationship, Evolutionary rates, Microbial communities, Population dynamics
Research Groups Computational biology and Bioinformatics (CBM): from Proteins to Ecosystems
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