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Background: Particle Swarm Optimization (PSO) is an established method for parameter optimization. It represents a population-based adaptive optimization technique that is influenced by several "strategy parameters". Choosing reasonable parameter values for the PSO is crucial for its convergence behavior, and depends on the optimization task. We present a method for parameter meta-optimization based on PSO and its application to neural network training. The concept of the Optimized Particle Swarm Optimization (OPSO) is to optimize the free parameters of the PSO by having swarms within a swarm. We assessed the performance of the OPSO method on a set of five artificial fitness functions and compared it to the performance of two popular PSO implementations. Results: Our results indicate that PSO performance can be improved if meta-optimized parameter sets are applied. In addition, we could improve optimization speed and quality on the other PSO methods in the majority of our experiments. We applied the OPSO method to neural network training with the aim to build a quantitative model for predicting blood-brain barrier permeation of small organic molecules. On average, training time decreased by a factor of four and two in comparison to the other PSO methods, respectively. By applying the OPSO method, a prediction model showing good correlation with training-, test- and validation data was obtained. Conclusion: Optimizing the free parameters of the PSO method can result in performance gain. The OPSO approach yields parameter combinations improving overall optimization performance. Its conceptual simplicity makes implementing the method a straightforward task.
Unraveling the activation mechanism of taspase1 which controls the oncogenic AF4–MLL fusion protein
(2015)
We have recently demonstrated that Taspase1-mediated cleavage of the AF4–MLL oncoprotein results in the formation of a stable multiprotein complex which forms the key event for the onset of acute proB leukemia in mice. Therefore, Taspase1 represents a conditional oncoprotein in the context of t(4;11) leukemia. In this report, we used site-directed mutagenesis to unravel the molecular events by which Taspase1 becomes sequentially activated. Monomeric pro-enzymes form dimers which are autocatalytically processed into the enzymatically active form of Taspase1 (αββα). The active enzyme cleaves only very few target proteins, e.g., MLL, MLL4 and TFIIA at their corresponding consensus cleavage sites (CSTasp1) as well as AF4–MLL in the case of leukemogenic translocation. This knowledge was translated into the design of a dominant-negative mutant of Taspase1 (dnTASP1). As expected, simultaneous expression of the leukemogenic AF4–MLL and dnTASP1 causes the disappearance of the leukemogenic oncoprotein, because the uncleaved AF4–MLL protein (328 kDa) is subject to proteasomal degradation, while the cleaved AF4–MLL forms a stable oncogenic multi-protein complex with a very long half-life. Moreover, coexpression of dnTASP1 with a BFP-CSTasp1-GFP FRET biosensor effectively inhibits cleavage. The impact of our findings on future drug development and potential treatment options for t(4;11) leukemia will be discussed.