Background Computational approaches for the determination of biologically-active/native three-dimensional structures of proteins with novel sequences have to handle several challenges. The (conformation) space of possible three-dimensional spatial arrangements of the chain of amino acids that constitute a protein molecule is vast and high-dimensional. Exploration of the conformation spaces is performed in a sampling-based manner and is biased by the internal energy that sums atomic interactions. Even state-of-the-art energy functions that quantify such interactions are inherently inaccurate and associate with protein conformation spaces overly rugged energy surfaces riddled with artifact local minima. The response to these challenges in template-free protein structure prediction is to generate large numbers of low-energy conformations (also referred to as decoys) as a way of increasing the likelihood of having a diverse decoy dataset that covers a sufficient number of local minima possibly housing near-native conformations. Results In this paper we pursue a complementary approach and propose to directly control the diversity of generated decoys. Inspired by hard optimization problems in high-dimensional and non-linear variable spaces, we propose that conformation sampling for decoy generation is more naturally framed as a multi-objective optimization problem. We demonstrate that mechanisms inherent to evolutionary search techniques facilitate such framing and allow balancing multiple objectives in protein conformation sampling. We showcase here an operationalization of this idea via a novel evolutionary algorithm that has high exploration capability and is also able to access lower-energy regions of the energy landscape of a given protein with similar or better proximity to the known native structure than several state-of-the-art decoy generation algorithms. Conclusions The presented results constitute a promising research direction in improving decoy generation for template-free protein structure prediction with regards to balancing of multiple conflicting objectives under an optimization framework. Future work will consider additional optimization objectives and variants of improvement and selection operators to apportion a fixed computational budget. Of particular interest are directions of research that attenuate dependence on protein energy models.

Controlling the quality of tertiary structures computed for a protein molecule remains a central challenge in de-novo protein structure prediction. The rule of thumb is to generate as many structures as can be afforded, effectively acknowledging that having more structures increases the likelihood that some will reside near the sought biologically-active structure. A major drawback with this approach is that computing a large number of structures imposes time and space costs. In this paper, we propose a novel clustering-based approach which we demonstrate to significantly reduce an ensemble of generated structures without sacrificing quality. Evaluations are related on both benchmark and CASP target proteins. Structure ensembles subjected to the proposed approach and the source code of the proposed approach are publicly-available at the links provided in Section 1.

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.

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A central challenge in template-free protein structure prediction is controlling the quality of computed tertiary structures also known as decoys. Given the size, dimensionality, and inherent characteristics of the protein structure space, this is non-trivial. The current mechanism employed by decoy generation algorithms relies on generating as many decoys as can be afforded. This is impractical and uninformed by any metrics of interest on a decoy dataset. In this paper, we propose to equip a decoy generation algorithm with an evolving map of the protein structure space. The map utilizes low-dimensional representations of protein structure and serves as a memory whose granularity can be controlled. Evaluations on diverse target sequences show that drastic reductions in storage do not sacrifice decoy quality, indicating the promise of the proposed mechanism for decoy generation algorithms in template-free protein structure prediction.