Protein Inter-Residue Contacts Prediction: Methods, Performances and Applications

2019 ◽  
Vol 14 (3) ◽  
pp. 178-189 ◽  
Author(s):  
Xiaoyang Jing ◽  
Qimin Dong ◽  
Ruqian Lu ◽  
Qiwen Dong
Keyword(s):  
Machine Learning ◽  
Protein Structure ◽  
Tertiary Structure ◽  
Prediction Methods ◽  
Learning Methods ◽  
Typical Application ◽  
Machine Learning Methods ◽  
Residue Contacts ◽  
Fusion Methods ◽  
Correlated Mutations

Background:Protein inter-residue contacts prediction play an important role in the field of protein structure and function research. As a low-dimensional representation of protein tertiary structure, protein inter-residue contacts could greatly help de novo protein structure prediction methods to reduce the conformational search space. Over the past two decades, various methods have been developed for protein inter-residue contacts prediction.Objective:We provide a comprehensive and systematic review of protein inter-residue contacts prediction methods.Results:Protein inter-residue contacts prediction methods are roughly classified into five categories: correlated mutations methods, machine-learning methods, fusion methods, templatebased methods and 3D model-based methods. In this paper, firstly we describe the common definition of protein inter-residue contacts and show the typical application of protein inter-residue contacts. Then, we present a comprehensive review of the three main categories for protein interresidue contacts prediction: correlated mutations methods, machine-learning methods and fusion methods. Besides, we analyze the constraints for each category. Furthermore, we compare several representative methods on the CASP11 dataset and discuss performances of these methods in detail.Conclusion:Correlated mutations methods achieve better performances for long-range contacts, while the machine-learning method performs well for short-range contacts. Fusion methods could take advantage of the machine-learning and correlated mutations methods. Employing more effective fusion strategy could be helpful to further improve the performances of fusion methods.

scholarly journals Protein structure prediction using AI and quantum computers

2021 ◽  
Author(s):  
Ben Geoffrey A S
Keyword(s):  
Machine Learning ◽  
Protein Structure ◽  
Protein Structure Prediction ◽  
Structure Prediction ◽  
Machine Learning Algorithms ◽  
Quantum Computers ◽  
Data Set ◽  
Backbone Atoms ◽  
Protein Contact Maps

This work seeks to combine the combined advantage of leveraging these emerging areas of Artificial Intelligence and quantum computing in applying it to solve the specific biological problem of protein structure prediction using Quantum Machine Learning algorithms. The CASP dataset from ProteinNet was downloaded which is a standardized data set for machine learning of protein structure. Its large and standardized dataset of PDB entries contains the coordinates of the backbone atoms, corresponding to the sequential chain of N, C_alpha, and C' atoms. This dataset was used to train a quantum-classical hybrid Keras deep neural network model to predict the structure of the proteins. To visually qualify the quality of the predicted versus the actual protein structure, protein contact maps were generated with the experimental and predicted protein structure data and qualified. Therefore this model is recommended for the use of protein structure prediction using AI leveraging the power of quantum computers. The code is provided in the following Github repository https://github.com/bengeof/Protein-structure-prediction-using-AI-and-quantum-computers.

scholarly journals Deep Learning Approaches for Protein Structure Prediction

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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