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.