Rational engineering of sequence specificity in R.MwoI restriction endonuclease.

R.MwoI is a Type II restriction endonucleases enzyme (REase), which specifically recognizes a palindromic interrupted DNA sequence 5'-GCNNNNNNNGC-3' (where N indicates any nucleotide), and hydrolyzes the phosphodiester bond in the DNA between the 7th and 8th base in both strands. R.MwoI exhibits remote sequence similarity to R.BglI, a REase with known structure, which recognizes an interrupted palindromic target 5'-GCCNNNNNGGC-3'. A homology model of R.MwoI in complex with DNA was constructed and used to predict functionally important amino acid residues that were subsequently targeted by mutagenesis. The model, together with the supporting experimental data, revealed regions important for recognition of the common bases in DNA sequences recognized by R.BglI and R.MwoI. Based on the bioinformatics analysis, we designed substitutions of the S310 residue in R.MwoI to arginine or glutamic acid, which led to enzyme variants with altered sequence selectivity compared with the wild-type enzyme. The S310R variant of R.MwoI preferred the 5'-GCCNNNNNGGC-3' sequence as a target, similarly to R.BglI, whereas the S310E variant preferentially cleaved a subset of the MwoI sites, depending on the identity of the 3rd and 9th nucleotide residues. Our results represent a case study of a REase sequence specificity alteration by a single amino acid substitution, based on a theoretical model in the absence of a crystal structure.

Two-stage model-based clustering for liquid chromatography mass spectrometry data analysis.

Proteomic mass spectrometry is gaining an increasing role in diagnostics and in studies on protein complexes and biological systems. This experimental technology is producing high-throughput data which is inherently noisy and may contain various errors. Mathematical processing can help in removing them.In this paper we focus on the peak alignment problem in LC-MS spectra. As an alternative to heuristic approaches to the problem, we propose a mathematically sound method which exploits a model-based clustering. In this framework experiment errors are modeled as deviations from real values and mass spectra are regarded as finite Gaussian mixtures. The advantage of such an approach is that it provides convenient techniques for adjusting parameters and selecting solutions of best quality. The method can be parameterized by assuming various constraints. In this paper we investigate and compare different classes of models. We analyze the results in terms of statistically significant biomarkers that can be identified after the alignment of spectra. The study was conducted on a dataset of plasma samples of colorectal cancer patients and healthy donors.

Modeling exopeptidase activity from LC-MS data.

Recent studies demonstrate that the peptides in the serum of cancer patients that are generated (ex vivo) as a result of tumor protease activity can be used for the detection and classification of cancer. In this paper, we propose the first formal approach to modeling exopeptidase activity from liquid chromatography-mass spectrometry (LC-MS) samples. We design a statistical model of peptidome degradation and a Metropolis-Hastings algorithm for Bayesian inference of model parameters. The model is successfully validated on a real LC-MS dataset. Our findings support the hypotheses about disease-specific exopeptidase activity, which can lead to new diagnostic approach in clinical proteomics.