Protein structures are valuable tools to understand protein function. Nonetheless, proteins are often considered as rigid macromolecules while their structures exhibit specific flexibility, which is essential to complete their functions. Analyses of protein structures and dynamics are often performed with a simplified three-state description, i.e., the classical secondary structures. More precise and complete description of protein backbone conformation can be obtained using libraries of small protein fragments that are able to approximate every part of protein structures. These libraries, called structural alphabets (SAs), have been widely used in structure analysis field, from definition of ligand binding sites to superimposition of protein structures. SAs are also well suited to analyze the dynamics of protein structures. Here, we review innovative approaches that investigate protein flexibility based on SAs description. Coupled to various sources of experimental data (e.g., B-factor) and computational methodology (e.g., Molecular Dynamic simulation), SAs turn out to be powerful tools to analyze protein dynamics, e.g., to examine allosteric mechanisms in large set of structures in complexes, to identify order/disorder transition. SAs were also shown to be quite efficient to predict protein flexibility from amino-acid sequence. Finally, in this review, we exemplify the interest of SAs for studying flexibility with different cases of proteins implicated in pathologies and diseases.

Conversion of local structural state of a protein from an α-helix to a β-strand is usually associated with a major change in the tertiary structure. Similar changes were observed during the self assembly of amyloidogenic proteins to form fibrils, which are implicated in severe diseases conditions, e.g., Alzheimer disease. Studies have emphasized that certain protein sequence fragments known as chameleon sequences do not have a strong preference for either helical or the extended conformations. Surprisingly, the information on the local sequence neighborhood can be used to predict their secondary at a high accuracy level. Here we report a large scale-analysis of chameleon sequences to estimate their propensities to be associated with different local structural states such as α -helices, β-strands and coils. With the help of the propensity information derived from the amino acid composition, we underline their complexity, as more than one quarter of them prefers coil state over to the regular secondary structures. About half of them show preference for both α-helix and β-sheet conformations and either of these two states is favored by the rest.

Trypanosoma brucei is a protozoan parasite of major of interest in discovering new genes for drug targets. This parasite alternates its life cycle between the mammal host(s) (bloodstream form) and the insect vector (procyclic form), with two divergent glucose metabolism amenable to in vitro culture. While the metabolic network of the bloodstream forms has been well characterized, the flux distribution between the different branches of the glucose metabolic network in the procyclic form has not been addressed so far. We present a computational analysis (called Metaboflux) that exploits the metabolic topology of the procyclic form, and allows the incorporation of multipurpose experimental data to increase the biological relevance of the model. The alternatives resulting from the structural complexity of networks are formulated as an optimization problem solved by a metaheuristic where experimental data are modeled in a multiobjective function. Our results show that the current metabolic model is in agreement with experimental data and confirms the observed high metabolic flexibility of glucose metabolism. In addition, Metaboflux offers a rational explanation for the high flexibility in the ratio between final products from glucose metabolism, thsat is, flux redistribution through the malic enzyme steps.

The central dogma in molecular biology postulated that 'DNA makes RNA makes protein', however this dogma has been recently extended to integrate new biological activities involving small bacterial noncoding RNAs, called sRNAs. Accordingly, increasing attention has been given to these molecules over the last decade and related experimental works have shown a wide range of functional activities for these molecules. In this paper, we present rNAV (for rna NAVigator), a new tool for the visual exploration and analysis of bacterial sRNA-mediated regulatory networks. rNAV has been designed to help bioinformaticians and biologists to identify, from lists of thousands of predictions, pertinent and reasonable sRNA target candidates for carrying out experimental validations. We propose a list of dedicated algorithms and interaction tools that facilitate the exploration of such networks. These algorithms can be gathered into pipelines which can then be saved and reused over several sessions. To support exploration awareness, rNAV also provides an exploration tree view that allows to navigate through the steps of the analysis but also to select the sub-networks to visualize and compare. These comparisons are facilitated by the integration of multiple and fully linked views. We demonstrate the usefulness of our approach by a case study on Escherichia coli bacteria performed by domain experts.