Establishment of a Quantitative Method for the Extraction of Nicotine and Cotinine in Gingival Tissue and Relationship Between Gingival Intoxication With Conventional Smoking Biomarkers: A Pilot Study.

Objectives: Smoking is considered a major risk factor for periodontitis genesis and progression. In clinical studies, specific indicators have been used to characterize the smoking status of the patient as the number of cigarettes consumed (NCC), the pack-years (PY), or Fagerström Test for Nicotine Dependence (FTND). However, available literature is missing on the relationship between cotinine gingival intoxication and smoking indicators.

Analyzing the sequence-structure relationship of a library of local structural prototypes.

We present a thorough analysis of the relation between amino acid sequence and local three-dimensional structure in proteins. A library of overlapping local structural prototypes was built using an unsupervised clustering approach called "hybrid protein model" (HPM). The HPM carries out a multiple structural alignment of local folds from a non-redundant protein structure databank encoded into a structural alphabet composed of 16 protein blocks (PBs).

"Pinning strategy": a novel approach for predicting the backbone structure in terms of protein blocks from sequence.

The description of protein 3D structures can be performed through a library of 3D fragments, named a structural alphabet. Our structural alphabet is composed of 16 small protein fragments of 5 C alpha in length, called protein blocks (PBs). It allows an efficient approximation of the 3D protein structures and a correct prediction of the local structure.

Protein Peeling 2: a web server to convert protein structures into series of protein units.

Protein Peeling 2 (PP2) is a web server for the automatic identification of protein units (PUs) given the 3D coordinates of a protein. PUs are an intermediate level of protein structure description between protein domains and secondary structures. It is a new tool to better understand and analyze the organization of protein structures.

Comparing gene expression networks in a multi-dimensional space to extract similarities and differences between organisms.

Motivation: Molecular evolution, which is classically assessed by comparison of individual proteins or genes between species, can now be studied by comparing co-expressed functional groups of genes. This approach, which better reflects the functional constraints on the evolution of organisms, can exploit the large amount of data generated by genome-wide expression analyses. However, it requires new methodologies to represent the data in a more accessible way for cross-species comparisons.