Design, Synthesis, Potency and Cytoselectivity of Anticancer Agents Derived by Parallel Synthesis from alpha-Aminosuberic Acid

Chemotherapy in the last century was characterized by cytotoxic drugs that did not discriminate between cancerous and normal cell types and were consequently accompanied by toxic side effects that were often dose limiting. The ability of differentiating agents to selectively kill cancer cells or transform them to a nonproliferating or normal phenotype could lead to cell- and tissue-specific drugs without the side effects of current cancer chemotherapeutics. This may be possible for a new generation of histone deacetylase inhibitors derived from amino acids. Structure-activity relationships are now reported for 43 compounds derived from 2-aminosuberic acid that kill a range of cancer cells, 26 being potent cytotoxins against MM96L melanoma cells (IC50 20 nM-1 mu M), while 17 were between 5- and 60-fold more selective in killing MM96L melanoma cells versus normal (neonatal foreskin fibroblasts, NFF) cells. This represents a 10- to 100-fold increase in potency and up to a 10-fold higher selectivity over previously reported compounds derived from cysteine (J. Med. Chem. 2004, 47, 2984). Selectivity is also an underestimate, because the normal cells, NFF, are rarely all killed by the drugs that also induce selective blockade of the cell cycle for normal but not cancer cells. Selected compounds were tested against a panel of human cancer cell lines (melanomas, prostate, breast, ovarian, cervical, lung, and colon) and found to be both selective and potent cytotoxins (IC50 20 nM-1 mu M). Compounds in this class typically inhibit human histone deacetylases, as evidenced by hyperacetylation of histones in both normal and cancer cells, induce expression of p21, and differentiate surviving cancer cells to a nonproliferating phenotype. These compounds may be valuable leads for the development of new chemotherapeutic agents.

Detecting and sorting targeting peptides wtih neural networks and support vector machines

This paper presents a composite multi-layer classifier system for predicting the subcellular localization of proteins based on their amino acid sequence. The work is an extension of our previous predictor PProwler v1.1 which is itself built upon the series of predictors SignalP and TargetP. In this study we outline experiments conducted to improve the classifier design. The major improvement came from using Support Vector machines as a "smart gate" sorting the outputs of several different targeting peptide detection networks. Our final model (PProwler v1.2) gives MCC values of 0.873 for non-plant and 0.849 for plant proteins. The model improves upon the accuracy of our previous subcellular localization predictor (PProwler v1.1) by 2% for plant data (which represents 7.5% improvement upon TargetP).

Parallel 3D Simulation of a Fault Gouge using the Lattice Solid Model

Despite the insight gained from 2-D particle models, and given that the dynamics of crustal faults occur in 3-D space, the question remains, how do the 3-D fault gouge dynamics differ from those in 2-D? Traditionally, 2-D modeling has been preferred over 3-D simulations because of the computational cost of solving 3-D problems. However, modern high performance computing architectures, combined with a parallel implementation of the Lattice Solid Model (LSM), provide the opportunity to explore 3-D fault micro-mechanics and to advance understanding of effective constitutive relations of fault gouge layers. In this paper, macroscopic friction values from 2-D and 3-D LSM simulations, performed on an SGI Altix 3700 super-cluster, are compared. Two rectangular elastic blocks of bonded particles, with a rough fault plane and separated by a region of randomly sized non-bonded gouge particles, are sheared in opposite directions by normally-loaded driving plates. The results demonstrate that the gouge particles in the 3-D models undergo significant out-of-plane motion during shear. The 3-D models also exhibit a higher mean macroscopic friction than the 2-D models for varying values of interparticle friction. 2-D LSM gouge models have previously been shown to exhibit accelerating energy release in simulated earthquake cycles, supporting the Critical Point hypothesis. The 3-D models are shown to also display accelerating energy release, and good fits of power law time-to-failure functions to the cumulative energy release are obtained.

Parallel implementation of stochastic simulation for large scale cellular processes

Experimental and theoretical studies have shown the importance of stochastic processes in genetic regulatory networks and cellular processes. Cellular networks and genetic circuits often involve small numbers of key proteins such as transcriptional factors and signaling proteins. In recent years stochastic models have been used successfully for studying noise in biological pathways, and stochastic modelling of biological systems has become a very important research field in computational biology. One of the challenge problems in this field is the reduction of the huge computing time in stochastic simulations. Based on the system of the mitogen-activated protein kinase cascade that is activated by epidermal growth factor, this work give a parallel implementation by using OpenMP and parallelism across the simulation. Special attention is paid to the independence of the generated random numbers in parallel computing, that is a key criterion for the success of stochastic simulations. Numerical results indicate that parallel computers can be used as an efficient tool for simulating the dynamics of large-scale genetic regulatory networks and cellular processes