Protein structure prediction using deep learning distance and hydrogen‐bonding restraints in CASP14

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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DESTINI: A deep-learning approach to contact-driven protein structure prediction

Scientific Reports ◽

10.1038/s41598-019-40314-1 ◽

2019 ◽

Vol 9 (1) ◽

Cited By ~ 21

Author(s):

Mu Gao ◽

Hongyi Zhou ◽

Jeffrey Skolnick

Keyword(s):

Deep Learning ◽

Protein Structure ◽

Protein Structure Prediction ◽

Structure Prediction ◽

Learning Approach

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Deep Learning Approaches for Protein Structure Prediction

International Journal of Engineering & Technology ◽

10.14419/ijet.v7i4.5.20037 ◽

2018 ◽

Vol 7 (4.5) ◽

pp. 168

Author(s):

Khatri Chandni ◽

Prof. Mrudang Pandya ◽

Dr. Sunil Jardosh

Keyword(s):

Machine Learning ◽

Deep Learning ◽

Protein Structure ◽

Protein Structure Prediction ◽

Structure Prediction ◽

Machine Learning Techniques ◽

Great Promise ◽

Learning Approaches ◽

Learning Networks ◽

Learning Techniques

In recent years, Machine Learning techniques that are based on Deep Learning networks that show a great promise in research communities.Successful methods for deep learning involve Artificial Neural Networks and Machine Learning. Deep Learning solves severa problems in bioinformatics. Protein Structure Prediction is one of the most important fields that can be solving using Deep Learning approaches.These protein are categorized on basis of occurrence of amino acid patterns occur to extract the feature. In these paper aimed to review work based on protein structure prediction solve using Deep Learning Networks. Objective is to review motivate and facilitatethese deep learn the network for predicting protein sequences using Deep Learning.

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