Conformational Ensembles from Experimental Data and Computer Simulations

Conformational Ensembles from Experimental Data and Computer Simulations

Poster Abstracts

58-POS Board 18 Matching Pursuit Genetic Algorithm for Structure Characterization of Large Intrinsically Disordered Proteins Wei Liu , Daiwen Yang. National University of Singapore, Singapore, Singapore. Structure characterization of intrinsically disordered proteins (IDPs) remains a key obstacle in understanding their functional mechanism. Due to the highly dynamic feature of IDPs, structure ensembles instead of static unique structures are often derived from experimental data. Determination of a structure ensemble usually uses a combinatorial optimization strategy, which selects an optimal ensemble to fit the data from a structure pool without prior experimental information. The search space of the combinatorial optimization problem could be extremely huge, as it’s an exponential function of the ensemble size and a power function of the pool size. In such a case, conventional algorithms become less efficient to find a good solution within appropriate computational time. Here we present a matching pursuit genetic algorithm (MPGA), which uses matching pursuit (MP) for search space reduction and genetic algorithm (GA) for optimization. A sub-pool is selected from the original pool based on diverse criteria, and a structure ensemble is selected from the sub-pool by GA. Like MP, the sub-pool is sequentially adjusted according to the differences between the experimental and back-calculated data, and then utilized in next round GA. This process is iterated until the outcome converges. We demonstrate that the MPGA method outperforms other state-of-art algorithms in structure ensemble selection from a large pool (>1.3 million) of p130CasSD, an IDP with 306 amino acids.

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