SPOT‐Fold: Fragment‐Free Protein Structure Prediction Guided by Predicted Backbone Structure and Contact Map

2019 ◽  
Vol 41 (8) ◽  
pp. 745-750 ◽  
Author(s):  
Yufeng Cai ◽  
Xiongjun Li ◽  
Zhe Sun ◽  
Yutong Lu ◽  
Huiying Zhao ◽  
...  
Keyword(s):  
Protein Structure ◽  
Protein Structure Prediction ◽  
Structure Prediction ◽  
Contact Map ◽  
Free Protein ◽  
Backbone Structure

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

Biochimie ◽  
2020 ◽  
Vol 175 ◽  
pp. 85-92 ◽  
Author(s):  
Surbhi Dhingra ◽  
Ramanathan Sowdhamini ◽  
Frédéric Cadet ◽  
Bernard Offmann
Keyword(s):  
Protein Structure ◽  
Protein Structure Prediction ◽  
Structure Prediction ◽  
Free Protein ◽  
Template Free

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

10.29007/j5p9 ◽  
2019 ◽  
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.

scholarly journals Trends in template/fragment-free protein structure prediction

2010 ◽  
Vol 128 (1) ◽  
pp. 3-16 ◽  
Author(s):  
Yaoqi Zhou ◽  
Yong Duan ◽  
Yuedong Yang ◽  
Eshel Faraggi ◽  
Hongxing Lei
Keyword(s):  
Protein Structure ◽  
Protein Structure Prediction ◽  
Structure Prediction ◽  
Free Protein

scholarly journals Deep‐learning contact‐map guided protein structure prediction in CASP13

2019 ◽  
Vol 87 (12) ◽  
pp. 1149-1164 ◽  
Author(s):  
Wei Zheng ◽  
Yang Li ◽  
Chengxin Zhang ◽  
Robin Pearce ◽  
S. M. Mortuza ◽  
...  
Keyword(s):  
Deep Learning ◽  
Protein Structure ◽  
Protein Structure Prediction ◽  
Structure Prediction ◽  
Contact Map

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

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.

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

Molecules ◽  
2019 ◽  
Vol 24 (5) ◽  
pp. 854 ◽  
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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