English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Many essential functions for life are performed by proteins and the study of their structures yields the ability to elucidate these functions in terms of a molecular view. (Creighton, 1992; Devlin, 1997) The interest in discovering a methodology for protein structure prediction (PSP) is of great interesti on many fields including drug design and carriers, disease mechanisms, and the food industry. In this context, several in vitro methods have been applied, as X-ray crystallography and nuclear magnetic resonance. Despite their relative success, both methods have their limitations. Conversely, the knowledge of the primary sequence of the amino acids of a protein can be achieved by a relatively simpler experimental measurement. From this information, one can in principle predict the three dimensional arrangement of its atoms, which has motivated the investigation of ab initio methods combining such initial knowledge with effective models (force fields) in order to predict the spatial structure of a protein (Bonneau & Baker, 2001; Hardin et al., 2002). In fact, several computational methods for PSP are semi ab initio methodologies in the sense that they also use prior knowledge from both the sequence homology and the statistics found on protein databases [see e.g. (Miyazawa & Jernigan, 1985; Poole & Ranganathan, 2006)]. However, the use of these additional information restrict the search of protein structures that could be correctly predicted from the vast universe of proteins. This chapter focuses on the development of a pure ab initio approach for PSP, not using prior information. In this context, evolutionary algorithms (EAs) have been investigated as a search method due to their flexibility to solve complex optimization problems. Our researches on EAs applied to PSP are twofold: 1) the investigation of more appropriate modeling of the physical and chemical interactions of a protein for the purpose of an optimization algorithm; 2) the development of computationally efficient EAs for PSP. Two important modeling issues have been poorly investigated in the literature related to the optimization techniques for PSP: a) the combined effects of the effective Hamiltonians based on force fields and the solvation free energy contribution (Section 3), and b) the use of O pe n A cc es s D at ab as e w w w .in te ch w eb .o rg

Some Modeling Issues for Protein Structure Prediction Using Evolutionary Algorithms