The MEPS server for identifying protein conformational epitopes

<p>Abstract</p> <p>Background</p> <p>One of the most interesting problems in molecular immunology is epitope mapping, i.e. the identification of the regions of interaction between an antigen and an antibody. The solution to this problem, even if approximate, would help in designing experiments to precisely map the residues involved in the interaction and could be instrumental both in designing peptides able to mimic the interacting surface of the antigen and in understanding where immunologically important regions are located in its three-dimensional structure. From an experimental point of view, both genetically encoded and chemically synthesised peptide libraries can be used to identify sequences recognized by a given antibody. The problem then arises of which region of a folded protein the selected peptides correspond to.</p> <p>Results</p> <p>We have developed a method able to find the surface region of a protein that can be effectively mimicked by a peptide, given the structure of the protein and the maximum number of side chains deemed to be required for recognition. The method is implemented as a publicly available server. It can also find and report all peptide sequences of a specified length that can mimic the surface of a given protein and store them in a database.</p> <p>The immediate application of the server is the mapping of antibody epitopes, however the system is sufficiently flexible for allowing other questions to be asked, for example one can compare the peptides representing the surface of two proteins known to interact with the same macromolecule to find which is the most likely interacting region.</p> <p>Conclusion</p> <p>We believe that the MEPS server, available at <url>http://www.caspur.it/meps</url>, will be a useful tool for immunologists and structural and computational biologists. We plan to use it ourselves to implement a database of "surface mimicking peptides" for all proteins of known structure and proteins that can be reliably modelled by comparative modelling.</p

A FIRST STEP TOWARD THE IDENTIFICATION OF MAMMALIAN SPERMATOZOA ITERACTOME.

The study of unexplained infertility of male origin is one of the most important challenges in the reproductive medicine of post genomic era. Unfortunately, despite the continuously growing effort of researchers, it remains unresolved 1. This causes the worsening of living conditions and of physical and psychological wellness of a large number of couples and determines the rising of healthcare costs for National Health Services. Recently, the availability of high throughput technologies, the so called –omics, has opened new perspective in clarifying this issue and posed new problems, related to the management of big data. On this basis, the adoption of a computational modelling-based strategy could offer a reliable tool to manage the continuously growing data on male gametes physiology 2,3. Here, we realized a computational model representing the whole ensemble of molecules present in spermatozoa, linked by their interaction. As first, we collected the data from papers indicized in PubMed referred to proteomic studies of sperm biology. Then, staring from the list of identified proteins, we carried out a pathways reconstruction analysis (Reactome FI). All the obtained pathways were used to realize a network-based computational model by using Cytoscape 3.3, called Mammalian Sperm Interactome (MSI), constituted by nodes, representing the molecules, linked by their interactions. Whole MSI is constituted by 7052 nodes, 15587 links, and 104 connected components. In particular, we identified a Main Connected Component (MCC_MSI) that accounts for 6525 nodes and 14944 links. The analysis of MCC_MSI showed that it is characterized by a scale free topology that follows the Barabasi-Albert (BA) model. The number of links per node (the node degree) follows a power law, with a negative exponent (y = a x-1.639, R2 = 0.826), and the clustering coefficient (cc), which is a measure of the network tendency to form clusters, is low (cc = 0.152) and unrelated with the node degree (R2 = 0.259). In addition, MCC_MSI displays a small world architecture: the averaged no. neighbours, which represents the mean number of connection of each node, is 3.337 and the characteristic path length, which gives the expected distance between two connected nodes, is 7.227. The analysis of MSI network topology has led us to infer important characteristics of the biological system: • the network is robust against random failure: indeed a random damage has the higher probability to affect the most frequent nodes, i.e. the less linked ones, with negligible consequences on network topology; • the messages will spread within the networks quickly and efficiently thus allowing to male gametes to adapt efficiently to the intra and extracellular stimuli. In addition, it will be possible to identify the nodes that shows a higher level of control within the networks, thus potentially offering new perspectives in the study of molecular target for diagnostics and therapeutics of male infertility of unknown origin