Rainer König

Improving genetic algorithms for protein folding simulations by systematic crossover

To improve protein folding simulations, we investigated a new search strategy in combination with the simple genetic algorithm on a two-dimensional lattice model. This search strategy, we called systematic crossover, couples the best individuals, tests every possible crossover point, and takes the two best individuals for the next generation. We compared the standard genetic algorithm with and without this new implementation for various chain lengths and showed that this strategy finds local minima with better energy values and is significantly faster in identifying the global minimum than the standard genetic algorithm.

Evaluation of reverse phase protein array (RPPA)-based pathway-activation profiling in 84 non-small cell lung cancer (NSCLC) cell lines as platform for cancer proteomics and biomarker discovery ☆

The reverse phase protein array (RPPA) approach was employed for a quantitative analysis of 71 cancer-relevant proteins and phosphoproteins in 84 non-small cell lung cancer (NSCLC) cell lines and by monitoring the activation state of selected receptor tyrosine kinases, PI3K/AKT and MEK/ERK1/2 signaling, cell cycle control, apoptosis, and DNA damage. Additional information on NSCLC cell lines such as that of transcriptomic data, genomic aberrations, and drug sensitivity was analyzed in the context of proteomic data using supervised and non-supervised approaches for data analysis. First, the unsupervised analysis of proteomic data indicated that proteins clustering closely together reflect well-known signaling modules, e.g. PI3K/AKT- and RAS/RAF/ERK-signaling, cell cycle regulation, and apoptosis. However, mutations of EGFR, ERBB2, RAF, RAS, TP53, and PI3K were found dispersed across different signaling pathway clusters. Merely cell lines with an amplification of EGFR and/or ERBB2 clustered closely together on the proteomic, but not on the transcriptomic level. Secondly, supervised data analysis revealed that sensitivity towards anti-EGFR drugs generally correlated better with high level EGFR phosphorylation than with EGFR abundance itself. High level phosphorylation of RB and high abundance of AURKA were identified as candidates that can potentially predict sensitivity towards the aurora kinase inhibitor VX680. Examples shown demonstrate that the RPPA approach presents a useful platform for targeted proteomics with high potential for biomarker discovery. This article is part of a Special Issue entitled: Biomarkers: A Proteomic Challenge.

Refined Genetic Algorithm Simulations to Model Proteins

Abstract The advent of completely sequenced genomes is leading to an unprecedented growth of sequence information while adequate structure information is often lacking. Genetic algorithm simulations have been refined and applied as a helpful tool for this question. Modified strategies are tested first on simple lattice protein models. This includes consideration of entropy (protein adjacent water shell) and improved search strategies (pioneer search +14%, systematic recombination +50% in search efficiency). Next, extension to grid free simulations of proteins in full main chain representation is examined. Our protein main chain simulations are further refined by independent criteria such as fitness per residue to judge predicted structures obtained at the end of a simulation. Protein families and protein interactions predicted from the complete H. pylori genomic sequence demonstrate how the full main chain simulations are then applied to model new protein sequences and protein families apparent from genome analysis.

Upregulation of cell cycle genes in head and neck cancer patients may be antagonized by erufosine’s down regulation of cell cycle processes in OSCC cells

The TCGA database was analyzed to identify deregulation of cell cycle genes across 24 cancer types and ensuing effects on patient survival. Pan-cancer analysis showed that head and neck squamous cell carcinoma (HNSCC) ranks amongst the top four cancers showing deregulated cell cycle genes. Also, the median gene expression of all CDKs and cyclins in HNSCC patient samples was higher than that of the global gene expression. This was verified by IHC staining of CCND1 from HNSCC patients. When evaluating the quartiles with highest and lowest expression, increased CCND1/CDK6 levels had negative implication on patient survival. In search for a drug, which may antagonize this tumor profile, the potential of the alkylphosphocholine erufosine was evaluated against cell lines of the HNSCC subtype, oral squamous cell carcinoma (OSCC) using in-vitro and in-vivo assays. Erufosine inhibited growth of OSCC cell lines concentration dependently. Initial microarray findings revealed that cyclins and CDKs were down-regulated concentration dependently upon exposure to erufosine and participated in negative enrichment of cell cycle processes. These findings, indicating a pan-cdk/cyclin inhibition by erufosine, were verified at both, mRNA and protein levels. Erufosine caused a G2/M block and inhibition of colony formation. Significant tumor growth retardation was seen upon treatment with erufosine in a xenograft model. For the decreased cyclin D1 and CDK 4/6 levels found in tumor tissue, these proteins can serve as biomarker for erufosine intervention. The findings demonstrate the potential of erufosine as cell cycle inhibitor in HNSCC treatment, alone or in combination with current therapeutic agents.

Identifying Antimalarial Drug Targets by Cellular Network Analysis

Malaria is one of the most deadly parasitic infectious diseases and identifying novel drug targets is mandatory for the development of new drugs. To find drug targets, metabolic and signaling networks have been constructed. These networks have been investigated by graph theoretical methods. Furthermore, mechanistic models have been set up based on stoichiometric equations. At equilibrium, production and consumption of internal metabolites need to be balanced leading to a large set of flux equations, and this can be used for metabolic flux simulations to identify drug targets. Analysis of flux variability and knockout simulations were applied to detect potential drug targets whose absence reduces the predicted biomass production and hence viability of the parasite in the host cell. Furthermore, not only the parasite was studied, but also the interaction between the host and the parasite, and, based on experimental expression data, stage-specific metabolic models of the parasite were developed, particularly during the red-blood cell stage. In this chapter, these various network-based approaches for drug target prediction will be explained and summarized.