Describing the connectivity of chemical and/or biological systems using networks is a straight gate for the introduction of mathematical tools in proteomics. Networks, in some cases even very large ones, are simple objects that are composed at least by nodes and edges. The nodes represent the parts of the system and the edges geometric and/or functional relationships between parts. In proteomics, amino acids, proteins, electrophoresis spots, polypeptidic fragments, or more complex objects can play the role of nodes. All of these networks can be numerically described using the so-called Connectivity Indices (CIs). The transformation of graphs (a picture) into CIs (numbers) facilitates the manipulation of information and the search for structure-function relationships in Proteomics. In this work, we review and comment on the challenges and new trends in the definition and applications of CIs in Proteomics. Emphasis is placed on 1-D-CIs for DNA and protein sequences, 2-D-CIs for RNA secondary structures, 3-D-topographic indices (TPGIs) for protein function annotation without alignment, 2-D-CIs and 3-D-TPGIs for the study of drug-protein or drug-RNA quantitative structure-binding relationships, and pseudo 3-D-CIs for protein surface molecular recognition. We also focus on CIs to describe Protein Interaction Networks or RNA co-expression networks. 2-D-CIs for patient blood proteome 2-DE maps or mass spectra are also covered.

A capture enzyme-linked immunosorbent assay (ELISA) using a new monoclonal antibody (mAb MM3) is reported for the detection of Fasciola hepatica excretory-secretory antigens (ESAs) in feces of infected hosts. The mAb MM3 was produced by immunization of mice with a 7- to 40-kDa purified and O-deglycosylated fraction of F. hepatica ESAs, which has previously been shown to be specific for the parasite. The specificity and sensitivity of the MM3 capture ELISA were assessed using feces from sheep and cattle. Sheep feces were obtained from a fluke-free herd (with most animals harboring other nematodes and cestodes), from lambs experimentally infected with 5-40 F. hepatica metacercariae and in some cases treated with triclabendazole at 14 wk postinfection (PI), and from uninfected control lambs. Cattle feces were collected at the slaughterhouse from adult cows naturally infected with known numbers of flukes (from 1 to 154) or free of F. hepatica infection (though in most cases harboring other helminths). The MM3 capture ELISA assay had detection limits of 0.3 (sheep) and 0.6 (cattle) ng of F. hepatica ESA per milliliter of fecal supernatant. The assay detected 100% of sheep with 1 fluke, 100% of cattle with 2 flukes, and 2 of 7 cattle with 1 fluke. The false-negative animals (5/7) were probably not detected because the F. hepatica individuals in these animals were immature (5-11 mm in length). As expected, coproantigen concentration correlated positively (r = 0.889; P < 0.001) with parasite burden and negatively (r = 0.712; P < 0.01) with the time after infection at which coproantigen was first detected. Nevertheless, even in animals with low fluke burdens (1-36 parasites), the first detection of F. hepatica-specific coproantigens by the MM3 capture ELISA preceded the first detection in egg count by 1-5 wk. In all sheep that were experimentally infected and then untreated, coproantigen remained detectable until at least 18 wk PI, whereas in sheep that were experimentally infected and then flukicide treated, coproantigen became undetectable from 1 to 3 wk after treatment. None of the fecal samples from sheep or cattle negative for fascioliasis but naturally infected with other parasites including Dicroelium dendriticum showed reactivity in the MM3 capture ELISA. These results indicate that this assay is a reliable and ultrasensitive method for detecting subnanogram amounts of F. hepatica antigens in feces from sheep and cattle, facilitating early diagnosis.

The method MARCH-INSIDE (MARkovian CHemicals IN SIlico DEsign) is a simple but efficient computational approach to the study of Quantitative Structure-Activity Relationships (QSAR) in Medicinal Chemistry. The method uses the theory of Markov Chains to generate parameters that numerically describe the chemical structure of drugs and drug targets. This approach generates two principal types of parameters Stochastic Topological Indices (sto-TIs) and stochastic 3D-Topographic Indices (sto-TPGIs). The use of these parameters allows the rapid collection, annotation, retrieval, comparison and mining of molecular and macromolecular chemical structures within large databases. In the work described here, we review and comment on the several applications of MARCH-INSIDE to the Medicinal Chemistry of Antimicrobial agents as well as their molecular targets. First we revised the use of classic sto-TIs to predict antiparasite compounds for the treatment of Fascioliasis. Next, we revised the use of chiral sto-TIs (sto-CTIs) to predict new antibacterial, antiviral and anti-coccidial compounds. After that, we review multi-target sto-TIs (mt-sto-TIs), which unifying QSAR models predicting antifungal, antibacterial, or anti-parasite drugs with multiple targets (microbial species). We also discussed the uses of mt-sto-TIs to assemble drug-drug similarity Complex Networks of antimicrobial compounds based on molecular structure. Last, we review the use of MARCH-INSIDE to generate macromolecular TIs and TPGIs for proteins or RNA targets for antimicrobial drugs.

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