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 7434
Special Issue Editors
Interests: protein bioinformatics; modelling of biological systems; applied deep learning
Special Issues, Collections and Topics in MDPI journals
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. 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
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