Advances of Protein Bioinformatics

A special issue of Processes (ISSN 2227-9717). This special issue belongs to the section "Biological Processes and Systems".

Deadline for manuscript submissions: closed (31 October 2021) | Viewed by 6269

Special Issue Editors


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Guest Editor
Department of Automatic Control and Systems Engineering, Politehnica University of Bucharest, 060042 Bucharest, Romania
Interests: protein bioinformatics; modelling of biological systems; applied deep learning
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
1. Laboratory of Structural and Computational Physical-Chemistry for Nanosciences and QSAR, Biology-Chemistry Department, West University of Timisoara, Str. Pestalozzi 16, 300115 Timisoara, Romania
2. Laboratory of Renewable Energies-Photovoltaics, R&D National Institute for Electrochemistry and Condensed Matter–INCEMC–Timisoara, Str. Dr. Aurel Podeanu 144, 300569 Timișoara, Romania
Interests: quantum physical chemistry; nanochemistry; reactivity indices and principles; electronegativity; density functional theory; path integrals; enzyme kinetics; QSAR; epistemology and philosophy of science
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Spectacular developments in experimental techniques and computing power have generated an unprecedented amount of biological data at the genomic and proteomic levels. Protein bioinformatics refers to the application of bioinformatics techniques and methodologies to the analysis of protein sequences, structures, and functions.

This Special Issue, entitled “Advances in Protein Bioinformatics”, aims to address key issues in protein bioinformatics and to highlight recent advances that can successfully complement the results obtained using traditional laboratory (“wet”) methods. The Editors seek high-quality works focusing on the latest applications of algorithmic methods, data-driven techniques, artificial intelligence, and deep learning for the analysis of the structure and function of proteins and protein interaction networks. Potential topics include, but are not limited to:

  • advances in the graphical visualization of proteins;
  • cloud computing and big data technologies for accelerated similarity searches;
  • improvements in traditional algorithmic techniques, such as heuristic algorithms, approximate algorithms, and graph algorithms, for solving protein bioinformatics problems, such as protein quantitative structure–activity relationships (QSARs), protein–ligand interactions in molecular docking, and protein structure similarity searching;
  • prediction of antibody neutralization sensitivity by a glycoprotein structure, e.g., HIV-1’s gp160;
  • deep-learning-based frameworks for studying protein–RNA interactions; and
  • other applications of deep learning in protein bioinformatics.
Prof. Dr. Catalin Buiu
Prof. Dr. Mihai V. Putz
Guest Editors

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 submissions that pass pre-check are 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. Processes 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 2400 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

  • big data
  • protein
  • protein interaction networks
  • bioinformatics
  • molecular graphics
  • quantitative structure–activity relationship (QSAR)
  • data-driven techniques
  • artificial intelligence
  • deep learning

Published Papers (2 papers)

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Research

14 pages, 1023 KiB  
Article
Machine Learning Approaches for Discriminating Bacterial and Viral Targeted Human Proteins
by Ranjan Kumar Barman, Anirban Mukhopadhyay, Ujjwal Maulik and Santasabuj Das
Processes 2022, 10(2), 291; https://doi.org/10.3390/pr10020291 - 31 Jan 2022
Viewed by 2268
Abstract
Infectious diseases are one of the core biological complications for public health. It is important to recognize the pathogen-specific mechanisms to improve our understanding of infectious diseases. Differentiations between bacterial- and viral-targeted human proteins are important for improving both prognosis and treatment for [...] Read more.
Infectious diseases are one of the core biological complications for public health. It is important to recognize the pathogen-specific mechanisms to improve our understanding of infectious diseases. Differentiations between bacterial- and viral-targeted human proteins are important for improving both prognosis and treatment for the patient. Here, we introduce machine learning-based classifiers to discriminate between the two groups of human proteins. We used the sequence, network, and gene ontology features of human proteins. Among different classifiers and features, the deep neural network (DNN) classifier with amino acid composition (AAC), dipeptide composition (DC), and pseudo-amino acid composition (PAAC) (445 features) achieved the best area under the curve (AUC) value (0.939), F1-score (94.9%), and Matthews correlation coefficient (MCC) value (0.81). We found that each of the selected top 100 of the bacteria- and virus-targeted human proteins from a candidate pool of 1618 and 3916 proteins, respectively, were part of distinct enriched biological processes and pathways. Our proposed method will help to differentiate between the bacterial and viral infections based on the targeted human proteins on a global scale. Furthermore, identification of the crucial pathogen targets in the human proteome would help us to better understand the pathogen-specific infection strategies and develop novel therapeutics. Full article
(This article belongs to the Special Issue Advances of Protein Bioinformatics)
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19 pages, 2409 KiB  
Article
Scutellaria baicalensis Flavones as Potent Drugs against Acute Respiratory Injury during SARS-CoV-2 Infection: Structural Biology Approaches
by Ana-Maria Udrea, Maria Mernea, Cătălin Buiu and Speranța Avram
Processes 2020, 8(11), 1468; https://doi.org/10.3390/pr8111468 - 16 Nov 2020
Cited by 21 | Viewed by 3075
Abstract
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection can result in severe damage to the respiratory system. With no specific treatment to date, it is crucial to identify potent inhibitors of SARS-CoV-2 Chymotrypsin-like protease (3CLpro) that could also modulate the enzymes involved in [...] Read more.
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection can result in severe damage to the respiratory system. With no specific treatment to date, it is crucial to identify potent inhibitors of SARS-CoV-2 Chymotrypsin-like protease (3CLpro) that could also modulate the enzymes involved in the respiratory damage that accompanies SARS-CoV-2 infection. Here, flavones isolated from Scutellaria baicalensis (baicalein, baicalin, wogonin, norwogonin, and oroxylin A) were studied as possible compounds in the treatment of SARS-CoV-2 and SARS-CoV-2-induced acute lung injuries. We used structural bioinformatics and cheminformatics to (i) identify the critical molecular features of flavones for their binding activity at human and SARS-CoV-2 enzymes; (ii) predict their drug-likeness and lead-likeness features; (iii) calculate their pharmacokinetic profile, with an emphasis on toxicology; (iv) predict their pharmacodynamic profiles, with the identification of their human body targets involved in the respiratory system injuries; and (v) dock the ligands to SARS-CoV-2 3CLpro. All flavones presented appropriate drug-like and kinetics features, except for baicalin. Flavones could bind to SARS-CoV-2 3CLpro at a similar site, but interact slightly differently with the protease. Flavones’ pharmacodynamic profiles predict that (i) wogonin strongly binds at the cyclooxygenase2 and nitric oxide synthase; (ii) baicalein and norwogonin could modulate lysine-specific demethylase 4D-like and arachidonate 15-lipoxygenase; and (iii) baicalein, wogonin, norwogonin, and oroxylin A bind to SARS-CoV-2 3CLpro. Our results propose these flavones as possible potent drugs against respiratory damage that occurs during SARS-CoV-2 infections, with a strong recommendation for baicalein. Full article
(This article belongs to the Special Issue Advances of Protein Bioinformatics)
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