An Energy Landscape Treatment of Decoy Selection in Template-Free Protein Structure Prediction

Abstract Background Identifying one or more biologically-active/native decoys from millions of non-native decoys is one of the major challenges in computational structural biology. The extreme lack of balance in positive and negative samples (native and non-native decoys) in a decoy set makes the problem even more complicated. Consensus methods show varied success in handling the challenge of decoy selection despite some issues associated with clustering large decoy sets and decoy sets that do not show much structural similarity. Recent investigations into energy landscape-based decoy selection approaches show promises. However, lack of generalization over varied test cases remains a bottleneck for these methods. Results We propose a novel decoy selection method, ML-Select, a machine learning framework that exploits the energy landscape associated with the structure space probed through a template-free decoy generation. The proposed method outperforms both clustering and energy ranking-based methods, all the while consistently offering better performance on varied test-cases. Moreover, ML-Select shows promising results even for the decoy sets consisting of mostly low-quality decoys. Conclusions ML-Select is a useful method for decoy selection. This work suggests further research in finding more effective ways to adopt machine learning frameworks in achieving robust performance for decoy selection in template-free protein structure prediction.

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A glance into the evolution of template-free protein structure prediction methodologies

Biochimie ◽

10.1016/j.biochi.2020.04.026 ◽

2020 ◽

Vol 175 ◽

pp. 85-92 ◽

Cited By ~ 2

Author(s):

Surbhi Dhingra ◽

Ramanathan Sowdhamini ◽

Frédéric Cadet ◽

Bernard Offmann

Keyword(s):

Protein Structure ◽

Protein Structure Prediction ◽

Structure Prediction ◽

Free Protein ◽

Template Free

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Balancing multiple objectives in conformation sampling to control decoy diversity in template-free protein structure prediction

BMC Bioinformatics ◽

10.1186/s12859-019-2794-5 ◽

2019 ◽

Vol 20 (1) ◽

Cited By ~ 11

Author(s):

Ahmed Bin Zaman ◽

Amarda Shehu

Keyword(s):

Protein Structure ◽

Protein Structure Prediction ◽

Structure Prediction ◽

Multiple Objectives ◽

Free Protein ◽

Template Free

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Equipping Decoy Generation Algorithms for Template-free Protein Structure Prediction with Maps of the Protein Conformation Space

10.29007/j5p9 ◽

2019 ◽

Cited By ~ 1

Author(s):

Ahmed Bin Zaman ◽

Amarda Shehu

Keyword(s):

Protein Structure ◽

Protein Structure Prediction ◽

Structure Prediction ◽

Tertiary Structures ◽

Free Protein ◽

Structure Space ◽

Template Free ◽

Low Dimensional ◽

Protein Structure Space

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.

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Building maps of protein structure spaces in template-free protein structure prediction

Journal of Bioinformatics and Computational Biology ◽

10.1142/s0219720019400134 ◽

2019 ◽

Vol 17 (06) ◽

pp. 1940013

Author(s):

Ahmed Bin Zaman ◽

Amarda Shehu

Keyword(s):

Protein Structure ◽

Protein Structure Prediction ◽

Structure Prediction ◽

Protein Structures ◽

Biologically Active ◽

Tertiary Structures ◽

Free Protein ◽

Structure Space ◽

Template Free ◽

Protein Structure Space

An important goal in template-free protein structure prediction is how to control the quality of computed tertiary structures of a target amino-acid sequence. Despite great advances in algorithmic research, given the size, dimensionality, and inherent characteristics of the protein structure space, this task remains exceptionally challenging. It is current practice to aim to generate as many structures as can be afforded so as to increase the likelihood that some of them will reside near the sought but unknown biologically-active/native structure. When operating within a given computational budget, this is impractical and uninformed by any metrics of interest. In this paper, we propose instead to equip algorithms that generate tertiary structures, also known as decoy generation algorithms, with memory of the protein structure space that they explore. Specifically, we propose an evolving, granularity-controllable map of the protein structure space that makes use of low-dimensional representations of protein structures. Evaluations on diverse target sequences that include recent hard CASP targets show that drastic reductions in storage can be made without sacrificing decoy quality. The presented results make the case that integrating a map of the protein structure space is a promising mechanism to enhance decoy generation algorithms in template-free protein structure prediction.

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Graph-Based Community Detection for Decoy Selection in Template-Free Protein Structure Prediction

Molecules ◽

10.3390/molecules24050854 ◽

2019 ◽

Vol 24 (5) ◽

pp. 854 ◽

Cited By ~ 3

Author(s):

Kazi Kabir ◽

Liban Hassan ◽

Zahra Rajabi ◽

Nasrin Akhter ◽

Amarda Shehu

Keyword(s):

Protein Structure ◽

Protein Structure Prediction ◽

Community Detection ◽

Structure Prediction ◽

Protein Molecule ◽

Molecular Structures ◽

Detection Methods ◽

Tertiary Structures ◽

Free Protein ◽

Template Free

Significant efforts in wet and dry laboratories are devoted to resolving molecular structures. In particular, computational methods can now compute thousands of tertiary structures that populate the structure space of a protein molecule of interest. These advances are now allowing us to turn our attention to analysis methodologies that are able to organize the computed structures in order to highlight functionally relevant structural states. In this paper, we propose a methodology that leverages community detection methods, designed originally to detect communities in social networks, to organize computationally probed protein structure spaces. We report a principled comparison of such methods along several metrics on proteins of diverse folds and lengths. We present a rigorous evaluation in the context of decoy selection in template-free protein structure prediction. The results make the case that network-based community detection methods warrant further investigation to advance analysis of protein structure spaces for automated selection of functionally relevant structures.

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