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Special Issue "Computational Analysis for Protein/Gene Structure and Interaction"

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

Deadline for manuscript submissions: 31 December 2019

Special Issue Editor

Guest Editor
Prof. Dr. Quan Zou

Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu 610054, China
Interests: bioinformatics; molecular computing; sequence alignment; systems biology

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

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Molecules is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.


  • 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

Published Papers (1 paper)

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Open AccessArticle
Prediction of Protein Subcellular Localization Based on Fusion of Multi-view Features
Molecules 2019, 24(5), 919; https://doi.org/10.3390/molecules24050919
Received: 24 December 2018 / Revised: 27 February 2019 / Accepted: 28 February 2019 / Published: 6 March 2019
Cited by 1 | PDF Full-text (2133 KB) | HTML Full-text | XML Full-text
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 [...] Read more.
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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