Using subpopulation EAs to map molecular structure landscapes

Abstract

The emerging view in molecular biology is that molecules are intrinsically dynamic systems rearranging themselves into different structures to interact with molecules in the cell. Such rearrangements take place on energy landscapes that are vast and multimodal, with minima housing alternative structures. The multiplicity of biologically-active structures is prompting researchers to expand their treatment of classic computational biology problems, such as the template-free protein structure prediction problem (PSP), beyond the quest for the global optimum. In this paper, we revisit subpopulation-oriented EAs as vehicles to switch the objective from classic optimization to landscape mapping. Specifically, we present two EAs, one of which makes use of subpopulation competition to allocate more computational resources to fitter subpopulations, and another of which additionally utilizes a niche preservation technique to maintain stable and diverse subpopulations. Initial assessment on benchmark optimization problems confirms that stabler subpopulations are achieved by the niche-preserving EA. Evaluation on unknown energy landscapes in the context of PSP demonstrates superior mapping performance by both algorithms over a popular Monte Carlo-based method, with the niche-preserving EA achieving superior exploration of lower-energy regions. These results suggest that subpopulation EAs hold much promise for solving important mapping problems in computational structural biology. CCS CONCEPTS • Computing methodologies → Bio-inspired approaches; • Applied computing → Molecular structural biology; Bioinformatics.

Reducing Ensembles of Protein Tertiary Structures Generated De Novo via Clustering

“…Great algorithmic advances have been made in structure generation, most remarkably by Rosetta [10], Quark [11], and others [12][13][14][15][16]. Recent works have investigated incorporating complementary information like sequence-predicted contacts and constructing new energy functions based on predicted contacts or distances of pairs of amino acids for structure generation [17][18][19].…”

“…Great progress in software and hardware have made it less costly to generate structures. For instance, algorithms operating under the umbrella of evolutionary computation can generate hundreds of thousands of structures [12,13,16,17]. Algorithms tasked with analyzing these structures now may have to additionally deal with a data size issue.…”

Decoy Ensemble Reduction in Template-free Protein Structure Prediction

“…Good advances have been made in decoy generation [10,[16][17][18][23][24][25][26]. These advances are documented in the Critical Assessment of protein Structure Prediction (CASP), which is a biennial community experiment/competition that assesses progress in PSP in several categories, including the template-free/free modeling category [9].…”

“…Great progress in software and hardware has made it less costly to generate decoys. Algorithms operating under the umbrella of evolutionary computation can generate hundreds of thousands of decoys [17,[24][25][26]. Decoy selection algorithms tasked with analyzing decoys now may have to additionally deal with a data size issue.…”

Guiding Protein Conformation Sampling with Conformation Space Maps

“…One is Rosetta's Simulated Annealing Metropolis Monte Carlo (SA-MMC) based decoy sampling algorithm. The other is a recent subpopulation EA, SP-EA + [20], that aims to prevent premature convergence and retain diversity during optimization by evolving and maintaining multiple subpopulations.…”