Structural analysis of SNARE motifs from sea perch, Lateolabrax japonicus by computerized approaches

Three cDNA sequences encoding four SNARE (N-ethylmaleimide-sensitive fusion protein attachment protein receptors) motifs were cloned from sea perch, and the deduced peptide sequences were analyzed for structural prediction by using 14 different web servers and softwares. The "ionic layer" structure, the three dimensional extension and conformational characters of the SNARE 7S core complex by using bioinformatics approaches were compared respectively with those from mammalian X-ray crystallographic investigations. The result suggested that the formation and stabilization of fish SNARE core complex might be driven by hydrophobic association, hydrogen bond among R group of core amino acids and electrostatic attraction at molecular level. This revealed that the SNARE proteins interaction of the fish may share the same molecular mechanism with that of mammal, indicating the universality and solidity of SNARE core complex theory. This work is also an attempt to get the protein 3D structural information which appears to be similar to that obtained through X-ray crystallography, only by using computerized approaches. (C) 2007 Elsevier Ltd. All rights reserved.

Computational Intelligence and Feature Selection : Rough and Fuzzy Approaches

Computational Intelligence and Feature Selection provides a high level audience with both the background and fundamental ideas behind feature selection with an emphasis on those techniques based on rough and fuzzy sets, including their hybridizations. It introduces set theory, fuzzy set theory, rough set theory, and fuzzy-rough set theory, and illustrates the power and efficacy of the feature selections described through the use of real-world applications and worked examples. Program files implementing major algorithms covered, together with the necessary instructions and datasets, are available on the Web.

Clinical Insights Into the Biology and Treatment of Pancreatic Cancer.

Pancreatic cancer is a devastating disease with a universally poor prognosis. In 2015, it is estimated that there will be 48,960 new cases of pancreatic cancer and that 40,560 people will die of the disease. The 5-year survival rate is 7.2% for all patients with pancreatic cancer; however, survival depends greatly on the stage at diagnosis. Unfortunately, 53% of patients already have metastatic disease at diagnosis, which corresponds to a 5-year survival rate of 2.4%. Even for the 9% of patients with localized disease confined to the pancreas, the 5-year survival is still modest at only 27.1%. These grim statistics highlight the need for ways to identify cohorts of individuals at highest risk, methods to screen those at highest risk to identify preinvasive pathologic precursors, and development of effective systemic therapies. Recent clinical and translational progress has emphasized the relationship with diabetes, the role of the stroma, and the interplay of each of these with inflammation in the pathobiology of pancreatic cancer. In this article, we will discuss these relationships and how they might translate into novel management strategies for the treatment of this disease.

Population-based local search for protein folding simulation in the MJ energy model and cubic lattices

We present experimental results on benchmark problems in 3D cubic lattice structures with the Miyazawa-Jernigan energy function for two local search procedures that utilise the pull-move set: (i) population-based local search (PLS) that traverses the energy landscape with greedy steps towards (potential) local minima followed by upward steps up to a certain level of the objective function; (ii) simulated annealing with a logarithmic cooling schedule (LSA). The parameter settings for PLS are derived from short LSA-runs executed in pre-processing and the procedure utilises tabu lists generated for each member of the population. In terms of the total number of energy function evaluations both methods perform equally well, however. PLS has the potential of being parallelised with an expected speed-up in the region of the population size. Furthermore, both methods require a significant smaller number of function evaluations when compared to Monte Carlo simulations with kink-jump moves. (C) 2009 Elsevier Ltd. All rights reserved.