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Special Issue "Computational Analysis for Protein Structure and Interaction"

A special issue of Molecules (ISSN 1420-3049). This special issue belongs to the section "Bioorganic Chemistry".

Deadline for manuscript submissions: 1 November 2017

Special Issue Editor

Guest Editor
Prof. Dr. Quan Zou

School of Computer Science and Technology, Tianjin University, Tianjin 300350, China
Website | E-Mail
Interests: bioinformatics; protein structure prediction; protein-protein interaction; special protein identification; machine learning; sequence alignment; cloud computing

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 the 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. Moreover, new techniques on structure could also facilitate protein–protein interaction research.

The Guest Editor looks forward to collecting a set of recent advances in the 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 monthly 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.

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 contacts prediction

Published Papers (5 papers)

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Research

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Open AccessArticle Predicting and Interpreting the Structure of Type IV Pilus of Electricigens by Molecular Dynamics Simulations
Molecules 2017, 22(8), 1342; doi:10.3390/molecules22081342
Received: 30 June 2017 / Revised: 7 August 2017 / Accepted: 10 August 2017 / Published: 12 August 2017
PDF Full-text (4254 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Nanowires that transfer electrons to extracellular acceptors are important in organic matter degradation and nutrient cycling in the environment. Geobacter pili of the group of Type IV pilus are regarded as nanowire-like biological structures. However, determination of the structure of pili remains challenging
[...] Read more.
Nanowires that transfer electrons to extracellular acceptors are important in organic matter degradation and nutrient cycling in the environment. Geobacter pili of the group of Type IV pilus are regarded as nanowire-like biological structures. However, determination of the structure of pili remains challenging due to the insolubility of monomers, presence of surface appendages, heterogeneity of the assembly, and low-resolution of electron microscopy techniques. Our previous study provided a method to predict structures for Type IV pili. In this work, we improved on our previous method using molecular dynamics simulations to optimize structures of Neisseria gonorrhoeae (GC), Neisseria meningitidis and Geobacter uraniireducens pilus. Comparison between the predicted structures for GC and Neisseria meningitidis pilus and their native structures revealed that proposed method could predict Type IV pilus successfully. According to the predicted structures, the structural basis for conductivity in G.uraniireducens pili was attributed to the three N-terminal aromatic amino acids. The aromatics were interspersed within the regions of charged amino acids, which may influence the configuration of the aromatic contacts and the rate of electron transfer. These results will supplement experimental research into the mechanism of long-rang electron transport along pili of electricigens. Full article
(This article belongs to the Special Issue Computational Analysis for Protein Structure and Interaction)
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Open AccessArticle Neighbor Affinity-Based Core-Attachment Method to Detect Protein Complexes in Dynamic PPI Networks
Molecules 2017, 22(7), 1223; doi:10.3390/molecules22071223
Received: 28 June 2017 / Revised: 14 July 2017 / Accepted: 18 July 2017 / Published: 24 July 2017
PDF Full-text (5444 KB) | HTML Full-text | XML Full-text
Abstract
Protein complexes play significant roles in cellular processes. Identifying protein complexes from protein-protein interaction (PPI) networks is an effective strategy to understand biological processes and cellular functions. A number of methods have recently been proposed to detect protein complexes. However, most of methods
[...] Read more.
Protein complexes play significant roles in cellular processes. Identifying protein complexes from protein-protein interaction (PPI) networks is an effective strategy to understand biological processes and cellular functions. A number of methods have recently been proposed to detect protein complexes. However, most of methods predict protein complexes from static PPI networks, and usually overlook the inherent dynamics and topological properties of protein complexes. In this paper, we proposed a novel method, called NABCAM (Neighbor Affinity-Based Core-Attachment Method), to identify protein complexes from dynamic PPI networks. Firstly, the centrality score of every protein is calculated. The proteins with the highest centrality scores are regarded as the seed proteins. Secondly, the seed proteins are expanded to complex cores by calculating the similarity values between the seed proteins and their neighboring proteins. Thirdly, the attachments are appended to their corresponding protein complex cores by comparing the affinity among neighbors inside the core, against that outside the core. Finally, filtering processes are carried out to obtain the final clustering result. The result in the DIP database shows that the NABCAM algorithm can predict protein complexes effectively in comparison with other state-of-the-art methods. Moreover, many protein complexes predicted by our method are biologically significant. Full article
(This article belongs to the Special Issue Computational Analysis for Protein Structure and Interaction)
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Open AccessArticle Prediction of Drug–Target Interaction Networks from the Integration of Protein Sequences and Drug Chemical Structures
Molecules 2017, 22(7), 1119; doi:10.3390/molecules22071119
Received: 27 May 2017 / Revised: 27 June 2017 / Accepted: 3 July 2017 / Published: 5 July 2017
PDF Full-text (798 KB) | HTML Full-text | XML Full-text
Abstract
Knowledge of drug–target interaction (DTI) plays an important role in discovering new drug candidates. Unfortunately, there are unavoidable shortcomings; including the time-consuming and expensive nature of the experimental method to predict DTI. Therefore, it motivates us to develop an effective computational method to
[...] Read more.
Knowledge of drug–target interaction (DTI) plays an important role in discovering new drug candidates. Unfortunately, there are unavoidable shortcomings; including the time-consuming and expensive nature of the experimental method to predict DTI. Therefore, it motivates us to develop an effective computational method to predict DTI based on protein sequence. In the paper, we proposed a novel computational approach based on protein sequence, namely PDTPS (Predicting Drug Targets with Protein Sequence) to predict DTI. The PDTPS method combines Bi-gram probabilities (BIGP), Position Specific Scoring Matrix (PSSM), and Principal Component Analysis (PCA) with Relevance Vector Machine (RVM). In order to evaluate the prediction capacity of the PDTPS, the experiment was carried out on enzyme, ion channel, GPCR, and nuclear receptor datasets by using five-fold cross-validation tests. The proposed PDTPS method achieved average accuracy of 97.73%, 93.12%, 86.78%, and 87.78% on enzyme, ion channel, GPCR and nuclear receptor datasets, respectively. The experimental results showed that our method has good prediction performance. Furthermore, in order to further evaluate the prediction performance of the proposed PDTPS method, we compared it with the state-of-the-art support vector machine (SVM) classifier on enzyme and ion channel datasets, and other exiting methods on four datasets. The promising comparison results further demonstrate that the efficiency and robust of the proposed PDTPS method. This makes it a useful tool and suitable for predicting DTI, as well as other bioinformatics tasks. Full article
(This article belongs to the Special Issue Computational Analysis for Protein Structure and Interaction)
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Open AccessArticle High-Performance Prediction of Human Estrogen Receptor Agonists Based on Chemical Structures
Molecules 2017, 22(4), 675; doi:10.3390/molecules22040675
Received: 16 March 2017 / Revised: 16 April 2017 / Accepted: 19 April 2017 / Published: 23 April 2017
PDF Full-text (2839 KB) | HTML Full-text | XML Full-text
Abstract
Many agonists for the estrogen receptor are known to disrupt endocrine functioning. We have developed a computational model that predicts agonists for the estrogen receptor ligand-binding domain in an assay system. Our model was entered into the Tox21 Data Challenge 2014, a computational
[...] Read more.
Many agonists for the estrogen receptor are known to disrupt endocrine functioning. We have developed a computational model that predicts agonists for the estrogen receptor ligand-binding domain in an assay system. Our model was entered into the Tox21 Data Challenge 2014, a computational toxicology competition organized by the National Center for Advancing Translational Sciences. This competition aims to find high-performance predictive models for various adverse-outcome pathways, including the estrogen receptor. Our predictive model, which is based on the random forest method, delivered the best performance in its competition category. In the current study, the predictive performance of the random forest models was improved by strictly adjusting the hyperparameters to avoid overfitting. The random forest models were optimized from 4000 descriptors simultaneously applied to 10,000 activity assay results for the estrogen receptor ligand-binding domain, which have been measured and compiled by Tox21. Owing to the correlation between our model’s and the challenge’s results, we consider that our model currently possesses the highest predictive power on agonist activity of the estrogen receptor ligand-binding domain. Furthermore, analysis of the optimized model revealed some important features of the agonists, such as the number of hydroxyl groups in the molecules. Full article
(This article belongs to the Special Issue Computational Analysis for Protein Structure and Interaction)
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Review

Jump to: Research

Open AccessReview Recent Advances in Conotoxin Classification by Using Machine Learning Methods
Molecules 2017, 22(7), 1057; doi:10.3390/molecules22071057
Received: 17 May 2017 / Revised: 12 June 2017 / Accepted: 19 June 2017 / Published: 25 June 2017
PDF Full-text (1485 KB) | HTML Full-text | XML Full-text
Abstract
Conotoxins are disulfide-rich small peptides, which are invaluable peptides that target ion channel and neuronal receptors. Conotoxins have been demonstrated as potent pharmaceuticals in the treatment of a series of diseases, such as Alzheimer’s disease, Parkinson’s disease, and epilepsy. In addition, conotoxins are
[...] Read more.
Conotoxins are disulfide-rich small peptides, which are invaluable peptides that target ion channel and neuronal receptors. Conotoxins have been demonstrated as potent pharmaceuticals in the treatment of a series of diseases, such as Alzheimer’s disease, Parkinson’s disease, and epilepsy. In addition, conotoxins are also ideal molecular templates for the development of new drug lead compounds and play important roles in neurobiological research as well. Thus, the accurate identification of conotoxin types will provide key clues for the biological research and clinical medicine. Generally, conotoxin types are confirmed when their sequence, structure, and function are experimentally validated. However, it is time-consuming and costly to acquire the structure and function information by using biochemical experiments. Therefore, it is important to develop computational tools for efficiently and effectively recognizing conotoxin types based on sequence information. In this work, we reviewed the current progress in computational identification of conotoxins in the following aspects: (i) construction of benchmark dataset; (ii) strategies for extracting sequence features; (iii) feature selection techniques; (iv) machine learning methods for classifying conotoxins; (v) the results obtained by these methods and the published tools; and (vi) future perspectives on conotoxin classification. The paper provides the basis for in-depth study of conotoxins and drug therapy research. Full article
(This article belongs to the Special Issue Computational Analysis for Protein Structure and Interaction)
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