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Computational Analysis for Protein/Gene Structure and Interaction

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A special issue of Molecules (ISSN 1420-3049).

Deadline for manuscript submissions: closed (31 December 2019) | Viewed by 7175

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Special Issue Editor

Prof. Dr. Quan Zou

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Guest Editor

Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu 610054, China
Interests: bioinformatics; parallel computing; deep learning; protein classification; genome assembly
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Protein structure analysis is a hot topic and key issue in organic chemistry and molecular biology research. Several essential protein molecules were rebuilt with Cryo-EM (cryo-electron microscopy) and their structures were published in Nature and Science. Computational structure analysis and prediction is a key process for 3D structure reconstruction. Machine learning techniques have been employed for protein secondary and tertiary structure prediction for a long time, and it seemed to have reached a bottleneck. However, the development of the Cryo-EM technique brings new challenges and requirements to computer science. Additionally, deep learning in machine learning also seems to be powerful. Therefore, there is considerable and increasing interest in developing computational methods for protein structure analysis and prediction.

Among the manuscripts in the previous Special Issue, were several outstanding works on gene structure computational analysis and prediction. This is also an important topic, and sometimes employs similar machine learning techniques. Thus, in this SI we added gene structure and interaction to the topic scope. We also welcome works on the PPI, RNA/RNA interaction, DNA/RNA binding proteins.

The Guest Editor looks forward to collecting a set of recent advances on related topics to provide a platform for researchers and bridge the gap between computer researchers and structural chemistry researchers.

Prof. Dr. Quan Zou
Guest Editor

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Keywords

protein structure prediction
protein–protein interaction network
Cryo-EM molecule particles boxing
Cryo-EM image process
machine learning
protein disorder region
docking
protein inter-residue contact prediction

Related Special Issue

Computational Analysis for Protein Structure and Interaction in Molecules (39 articles)

Published Papers (2 papers)

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Research

16 pages, 2476 KiB

Open AccessArticle

PredPSD: A Gradient Tree Boosting Approach for Single-Stranded and Double-Stranded DNA Binding Protein Prediction

by Changgeng Tan, Tong Wang, Wenyi Yang and Lei Deng

Molecules 2020, 25(1), 98; https://doi.org/10.3390/molecules25010098 - 26 Dec 2019

Cited by 6 | Viewed by 2804

Abstract

Interactions between proteins and DNAs play essential roles in many biological processes. DNA binding proteins can be classified into two categories. Double-stranded DNA-binding proteins (DSBs) bind to double-stranded DNA and are involved in a series of cell functions such as gene expression and regulation. Single-stranded DNA-binding proteins (SSBs) are necessary for DNA replication, recombination, and repair and are responsible for binding to the single-stranded DNA. Therefore, the effective classification of DNA-binding proteins is helpful for functional annotations of proteins. In this work, we propose PredPSD, a computational method based on sequence information that accurately predicts SSBs and DSBs. It introduces three novel feature extraction algorithms. In particular, we use the autocross-covariance (ACC) transformation to transform feature matrices into fixed-length vectors. Then, we put the optimal feature subset obtained by the minimal-redundancy-maximal-relevance criterion (mRMR) feature selection algorithm into the gradient tree boosting (GTB). In 10-fold cross-validation based on a benchmark dataset, PredPSD achieves promising performances with an AUC score of 0.956 and an accuracy of 0.912, which are better than those of existing methods. Moreover, our method has significantly improved the prediction accuracy in independent testing. The experimental results show that PredPSD can significantly recognize the binding specificity and differentiate DSBs and SSBs. Full article

(This article belongs to the Special Issue Computational Analysis for Protein/Gene Structure and Interaction)

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13 pages, 2133 KiB

Open AccessArticle

Prediction of Protein Subcellular Localization Based on Fusion of Multi-view Features

by Bo Li, Lijun Cai, Bo Liao, Xiangzheng Fu, Pingping Bing and Jialiang Yang

Molecules 2019, 24(5), 919; https://doi.org/10.3390/molecules24050919 - 06 Mar 2019

Cited by 19 | Viewed by 3790

Abstract

The prediction of protein subcellular localization is critical for inferring protein functions, gene regulations and protein-protein interactions. With the advances of high-throughput sequencing technologies and proteomic methods, the protein sequences of numerous yeasts have become publicly available, which enables us to computationally predict yeast protein subcellular localization. However, widely-used protein sequence representation techniques, such as amino acid composition and the Chou’s pseudo amino acid composition (PseAAC), are difficult in extracting adequate information about the interactions between residues and position distribution of each residue. Therefore, it is still urgent to develop novel sequence representations. In this study, we have presented two novel protein sequence representation techniques including Generalized Chaos Game Representation (GCGR) based on the frequency and distributions of the residues in the protein primary sequence, and novel statistics and information theory (NSI) reflecting local position information of the sequence. In the GCGR + NSI representation, a protein primary sequence is simply represented by a 5-dimensional feature vector, while other popular methods like PseAAC and dipeptide adopt features of more than hundreds of dimensions. In practice, the feature representation is highly efficient in predicting protein subcellular localization. Even without using machine learning-based classifiers, a simple model based on the feature vector can achieve prediction accuracies of 0.8825 and 0.7736 respectively for the CL317 and ZW225 datasets. To further evaluate the effectiveness of the proposed encoding schemes, we introduce a multi-view features-based method to combine the two above-mentioned features with other well-known features including PseAAC and dipeptide composition, and use support vector machine as the classifier to predict protein subcellular localization. This novel model achieves prediction accuracies of 0.927 and 0.871 respectively for the CL317 and ZW225 datasets, better than other existing methods in the jackknife tests. The results suggest that the GCGR and NSI features are useful complements to popular protein sequence representations in predicting yeast protein subcellular localization. Finally, we validate a few newly predicted protein subcellular localizations by evidences from some published articles in authority journals and books. Full article

(This article belongs to the Special Issue Computational Analysis for Protein/Gene Structure and Interaction)

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