scholarly journals Accurate prediction of protein torsion angles using evolutionary signatures and recurrent neural network

2021 ◽  
Author(s):  
Yong-Chang Xu ◽  
Tian-Jun ShangGuan ◽  
Xue-Ming Ding ◽  
Ngaam J. Cheung
Keyword(s):  
Neural Network ◽  
Protein Structure ◽  
Protein Structure Prediction ◽  
High Precision ◽  
Structure Prediction ◽  
Critical Role ◽  
Conformational Space ◽  
Tertiary Structures ◽  
Torsion Angles ◽  
Template Free

The amino acid sequence of a protein contains all the necessary information to specify its shape, which dictates its biological activities. However, it is challenging and expensive to experimentally determine the three-dimensional structure of proteins. The backbone torsion angles, as an important structural constraint, play a critical role in protein structure prediction, and accurately predicting the angles can considerably advance the tertiary structure prediction by accelerating efficient sampling of the large conformational space for low energy structures. On account of the rapid growth of protein databases and striking breakthroughs in deep learning algorithms, computational advances allow us to extract knowledge from large-scale data to address key biological questions. Here we propose evolutionary signatures that are computed from protein sequence profiles, and a deep neural network, termed ESIDEN, that adopts a straightforward architecture of recurrent neural networks with a small number of learnable parameters. The proposed ESIDEN is validated on three benchmark datasets, including D2020, TEST2016/2018, and CASPs datasets. On the D2020, using the combination of the four novel features and basic features, the ESIDEN achieves the mean absolute error (MAE) of 15.8 and 20.1 for ϕ and ψ, respectively. Comparing to the best-so-far methods, we show that the ESIDEN significantly improves the angle ψ by the MAE decrements of more than 2 degrees on both TEST2016 and TEST2018 and achieves closely approximate MAE of the angle ϕ although it adopts simple architecture and fewer learnable parameters. On fifty-nine template-free modeling targets, the ESIDEN achieves high accuracy by reducing the MAEs by about 0.4 and more than 2.5 degrees on average for the torsion angles ϕ and ψ in the CASPs, respectively. Using the predicted torsion angles, we infer the tertiary structures of four representative template-free modeling targets that achieve high precision with regard to the root-mean-square deviation and TM-score by comparing them to the native structures. The results demonstrate that the ESIDEN can make accurate predictions of the torsion angles by leveraging the evolutionary signatures compared to widely used classical features. The proposed evolutionary signatures would be also used as alternative features in predicting residue-residue distance, protein structure, and protein-ligand binding sites. Moreover, the high-precision torsion angles predicted by the ESIDEN can be used to accurately infer protein tertiary structures, and the ESIDEN would potentially pave the way to improve protein structure prediction.

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.

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.

scholarly journals FALCON2: a web server for high-quality prediction of protein tertiary structures

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Lupeng Kong ◽  
Fusong Ju ◽  
Haicang Zhang ◽  
Shiwei Sun ◽  
Dongbo Bu
Keyword(s):  
Protein Structure ◽  
Ab Initio ◽  
Protein Structure Prediction ◽  
Structure Prediction ◽  
Web Server ◽  
High Quality ◽  
Tertiary Structures ◽  
Target Proteins ◽  
High Quality Protein ◽  
Protein Functions

Abstract Background Accurate prediction of protein tertiary structures is highly desired as the knowledge of protein structures provides invaluable insights into protein functions. We have designed two approaches to protein structure prediction, including a template-based modeling approach (called ProALIGN) and an ab initio prediction approach (called ProFOLD). Briefly speaking, ProALIGN aligns a target protein with templates through exploiting the patterns of context-specific alignment motifs and then builds the final structure with reference to the homologous templates. In contrast, ProFOLD uses an end-to-end neural network to estimate inter-residue distances of target proteins and builds structures that satisfy these distance constraints. These two approaches emphasize different characteristics of target proteins: ProALIGN exploits structure information of homologous templates of target proteins while ProFOLD exploits the co-evolutionary information carried by homologous protein sequences. Recent progress has shown that the combination of template-based modeling and ab initio approaches is promising. Results In the study, we present FALCON2, a web server that integrates ProALIGN and ProFOLD to provide high-quality protein structure prediction service. For a target protein, FALCON2 executes ProALIGN and ProFOLD simultaneously to predict possible structures and selects the most likely one as the final prediction result. We evaluated FALCON2 on widely-used benchmarks, including 104 CASP13 (the 13th Critical Assessment of protein Structure Prediction) targets and 91 CASP14 targets. In-depth examination suggests that when high-quality templates are available, ProALIGN is superior to ProFOLD and in other cases, ProFOLD shows better performance. By integrating these two approaches with different emphasis, FALCON2 server outperforms the two individual approaches and also achieves state-of-the-art performance compared with existing approaches. Conclusions By integrating template-based modeling and ab initio approaches, FALCON2 provides an easy-to-use and high-quality protein structure prediction service for the community and we expect it to enable insights into a deep understanding of protein functions.

Protein structure prediction based on BN-GRU method

2020 ◽  
Vol 18 (06) ◽  
pp. 2050045
Author(s):  
Lina Yang ◽  
Pu Wei ◽  
Cheng Zhong ◽  
Xichun Li ◽  
Yuan Yan Tang
Keyword(s):  
Neural Network ◽  
Protein Structure ◽  
Spatial Structure ◽  
Protein Structure Prediction ◽  
Structure Prediction ◽  
Main Idea ◽  
Traditional Methods ◽  
Analysis Of Similarity ◽  
New Feature ◽  
Activity Mechanism

The spatial structure of the protein reflects the biological function and activity mechanism. Predicting the secondary structure of a protein is the basis content for predicting its spatial structure. Traditional methods based on statistics and sequential patterns do not achieve higher accuracy. In this paper, the application of BN-GRU neural network in protein structure prediction is discussed. The main idea is to construct a Gated Recurrent Unit (GRU) neural network. The GRU neural network can learn long-term dependencies. It can handle long sequences better than traditional methods. Based on this, BN is combined with GRU to construct a new network. Position Specific Scoring Matrix (PSSM) is used to associate with other features to build a completely new feature set. It can be proved that the application of BN on GRU can improve the accuracy of the results. The idea in this paper can also be applied to the analysis of similarity of other sequences.

scholarly journals MULTICOM2: an open-source protein structure prediction system powered by deep learning and distance prediction

2021 ◽  
Author(s):  
Tianqi Wu ◽  
Jian Liu ◽  
Zhiye Guo ◽  
Jie Hou ◽  
Jianlin Cheng
Keyword(s):  
Deep Learning ◽  
Protein Structure ◽  
Open Source ◽  
Protein Structure Prediction ◽  
Structure Prediction ◽  
Tertiary Structure ◽  
Modeling Method ◽  
Structure Modeling ◽  
Prediction System ◽  
Template Free

Abstract Protein structure prediction is an important problem in bioinformatics and has been studied for decades. However, there are still few open-source comprehensive protein structure prediction packages publicly available in the field. In this paper, we present our latest open-source protein tertiary structure prediction system - MULTICOM2, an integration of template-based modeling (TBM) and template-free modeling (FM) methods. The template-based modeling uses sequence alignment tools with deep multiple sequence alignments to search for structural templates, which are much faster and more accurate than MULTICOM1. The template-free (ab initio or de novo) modeling uses the inter-residue distances predicted by DeepDist to reconstruct tertiary structure models without using any known structure as template. In the blind CASP14 experiment, the average TM-score of the models predicted by our server predictor based on the MULTICOM2 system is 0.720 for 58 TBM (regular) domains and 0.514 for 38 FM and FM/TBM (hard) domains, indicating that MULTICOM2 is capable of predicting good tertiary structures across the board. It can predict the correct fold for 76 CASP14 domains (95% regular domains and 55% hard domains) if only one prediction is made for a domain. The success rate is increased to 3% for both regular and hard domains if five predictions are made per domain. Moreover, the prediction accuracy of the pure template-free structure modeling method on both TBM and FM targets is very close to the combination of template-based and template-free modeling methods. This demonstrates that the distance-based template-free modeling method powered by deep learning can largely replace the traditional template-based modeling method even on TBM targets that TBM methods used to dominate and therefore provides a uniform structure modeling approach to any protein. Finally, on the 38 CASP14 FM and FM/TBM hard domains, MULTICOM2 server predictors (MULTICOM-HYBRID, MULTICOM-DEEP, MULTICOM-DIST) were ranked among the top 20 automated server predictors in the CASP14 experiment. After combining multiple predictors from the same research group as one entry, MULTICOM-HYBRID was ranked no. 5. The source code of MULTICOM2 is freely available at https://github.com/multicom-toolbox/multicom/tree/multicom_v2.0.

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