High throughput identification of peptides in databases from tandem mass spectrometry data is a key technique in modern proteomics. Common approaches to interpret large scale peptide identification results are based on the statistical analysis of average score distributions, which are constructed from the set of best scores produced by large collections of MS/MS spectra by using searching engines such as SEQUEST. Other approaches calculate individual peptide identification probabilities on the basis of theoretical models or from single-spectrum score distributions constructed by the set of scores produced by each MS/MS spectrum. In this work, we study the mathematical properties of average SEQUEST score distributions by introducing the concept of spectrum quality and expressing these average distributions as compositions of single-spectrum distributions. We predict and demonstrate in the practice that average score distributions are dominated by the quality distribution in the spectra collection, except in the low probability region, where it is possible to predict the dependence of average probability on database size. Our analysis leads to a novel indicator, the probability ratio, which takes optimally into account the statistical information provided by the first and second best scores. The probability ratio is a non-parametric and robust indicator that makes spectra classification according to parameters such as charge state unnecessary and allows a peptide identification performance, on the basis of false discovery rates, that is better than that obtained by other empirical statistical approaches. The probability ratio also compares favorably with statistical probability indicators obtained by the construction of single-spectrum SEQUEST score distributions. These results make the robustness, conceptual simplicity, and ease of automation of the probability ratio algorithm a very attractive alternative to determine peptide identification confidences and error rates in high throughput experiments. Molecular & Cellular Proteomics 7:1135-1145, 2008.Modern proteomics is mainly based on the analysis of proteins by mass spectrometry. With the increasing use of multidimensional peptide separation coupled to tandem mass spectrometry as an alternative to gel-based approaches for protein expression analysis and high throughput protein identification, there is a growing interest in the development of scoring systems for automated, large scale peptide identification.SEQUEST (1-3), one of the first and most popular scoring schemes, measures the degree of correlation between the experimentally observed and the theoretical MS/MS spectra of peptides present in protein databases and determines the peptide sequence yielding the best correlation score, or Xcorr. Another related SEQUEST parameter is the delta score, or ⌬C n , which measures the difference between the best and second best scores (1-3). SEQUEST results must be further processed to determine whether the peptide identification is correct or not. Filtering of SEQUEST...

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This work reports on the formation of different types of structures on the surface of polymer films upon UV laser irradiation. Poly(ethylene terephthalate) was irradiated with nanosecond UV pulses at 193 and 266 nm. The polarization of the laser beam and the irradiation angle of incidence were varied, giving rise to laser induced surface structures with different shapes and periodicities. The irradiated surfaces were topographically characterized by atomic force microscopy and the chemical modifications induced by laser irradiation were inspected via micro-Raman and fluorescence spectroscopies. Contact angle measurements were performed with different liquids, and the results evaluated in terms of surface free energy components. Finally, in order to test the influence of surface properties for a potential application, the modified surfaces were used for mesenchymal stem cell culture assays and the effect of nanostructure and surface chemistry on cell adhesion was evaluated.

The precise control over the interaction between cells and the surface of materials plays a crucial role in optimizing the integration of implanted biomaterials. In this regard, material surface with controlled topographic features at the micro- and nano-scales has been proved to affect the overall cell behavior and therefore the final osseointegration of implants. Within this context, femtosecond (fs) laser micro/nano machining technology was used in this work to modify the surface structure of stainless steel aiming at controlling cell adhesion and migration. The experimental results show that cells tend to attach and preferentially align to the laser-induced nanopatterns oriented in a specific direction. Accordingly, the laser-based fabrication method here described constitutes a simple, clean, and scalable technique which allows a precise control of the surface nano-patterning process and, subsequently, enables the control of cell adhesion, migration, and polarization. Moreover, since our surface-patterning approach does not involve any chemical treatments and is performed in a single step process, it could in principle be applied to most metallic materials.