In vitro and in silico processes to identify differentially expressed proteins.

We present an integrated proteomics platform designed for performing differential analyses. Since reproducible results are essential for comparative studies, we explain how we improved reproducibility at every step of our laboratory processes, e.g. by taking advantage of the powerful laboratory information management system we developed. The differential capacity of our platform is validated by detecting known markers in a real sample and by a spiking experiment. We introduce an innovative two-dimensional (2-D) plot for displaying identification results combined with chromatographic data. This 2-D plot is very convenient for detecting differential proteins. We also adapt standard multivariate statistical techniques to show that peptide identification scores can be used for reliable and sensitive differential studies. The interest of the protein separation approach we generally apply is justified by numerous statistics, complemented by a comparison with a simple shotgun analysis performed on a small volume sample. By introducing an automatic integration step after mass spectrometry data identification, we are able to search numerous databases systematically, including the human genome and expressed sequence tags. Finally, we explain how rigorous data processing can be combined with the work of human experts to set high quality standards, and hence obtain reliable (false positive < 0.35%) and nonredundant protein identifications.

Neighborhood behavior of in silico structural spaces with respect to in vitro activity spaces-a novel understanding of the molecular similarity principle in the context of multiple receptor binding profiles.

As a consequence of recent advances in the field of High Throughput Screening, the systematic testing ("in vitro profiling") of compounds against a panel of targets covering different therapeutic areas is nowadays used to generate relevant information with respect to the in vivo behavior of drug candidates. However, the development of chemoinformatics tools required for the exploitation of such data is yet in an incipient phase. In this paper, a formalism for the analysis of activity profile vectors (describing the experimental responses of compounds in each of the considered activity tests) is introduced and applied at the study of Neighborhood Behavior (NB; the hypothesis that structurally similar compounds display similar biological properties) of molecular similarity metrics. The experimental activity profiles define an Activity Space in which more than 500 drugs and reference compounds are positioned, their coordinates being inhibitory propensities in the included tests and unambiguously characterizing a molecule in terms of its receptor binding properties. While previous studies of Neighborhood Behavior had to rely on a loose classification of compounds in terms of the therapeutic areas they were designed for, here the NB of a calculated "in silico" similarity metric has been redefined as a relationships between intermolecular dissimilarity scores in the "structural" and "activity" spaces, respectively, and expressed in terms of two quantitative criteria: "consistency" (the propensity of the metric to selectively rank activity-related compound pairs among the structurally most similar pairs) and "completeness" (monitoring the retrieval rate of activity-related compound pairs among the best ranked pairs of structural neighbors). These criteria were used to calibrate and validate a similarity metric based on Fuzzy Bipolar Pharmacophore Fingerprints.

Neighborhood behavior of in silico structural spaces with respect to in vitro activity spaces--a benchmark for neighborhood behavior assessment of different in silico similarity metrics.

In a previous work, we have introduced Neighborhood Behavior (NB) criteria for calculated molecular similarity metrics, based on the analysis of in vitro activity spaces that simultaneously monitor the responses of a compound with respect to an entire panel of biologically relevant receptors and enzymes. Now, these novel NB criteria will be used as a benchmark for the comparison of different in silico molecular similarity metrics, addressing the following topics: (1) the relative performance of 2D vs 3D descriptors, (2) the importance of the similarity scoring function for a given descriptor set, and (3) binary or Fuzzy Pharmacophore Fingerprints-can they capture the similarity of the spatial distribution of pharmacophoric groups despite different molecular connectivity? It was found that fuzzy pharmacophore descriptors (FBPA) displayed an optimal NB and, unlike their binary counterparts, were successful in evidencing pharmacophore pattern similarity independently of topological similarity. Topological FBPA, identical to the former except for the use of topological instead of 3D atom pair distances, display a somehow weaker, but significant NB. Metrics based on "classical" global 2D and 3D molecular descriptors and a Dice scoring function also performed well. The choice of the similarity scoring function is therefore as important as the choice of the appropriate molecular descriptors.