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Advanced Research in Biomolecular Design for Medical Applications
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Advanced Research in Biomolecular Design for Medical Applications

A special issue of International Journal of Molecular Sciences (ISSN 1422-0067). This special issue belongs to the section "Molecular Informatics".

Deadline for manuscript submissions: closed (20 December 2024) | Viewed by 4490

Special Issue Editor

Dr. Binh P. Nguyen

E-Mail Website
Guest Editor
School of Mathematics and Statistics, Victoria University of Wellington, Wellington 6012, New Zealand
Interests: machine learning; data science; deep learning; biomedical image analysis; health informatics; bioinformatics; drug discovery
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue aims to investigate the rapidly growing field of biomolecular design and its ability to bring about significant changes in the field of medical science. It will explore the various applications of biomolecular design approaches, highlighting their crucial role in transforming medical diagnostics, therapies, and treatments.

Biomolecular design is an interdisciplinary field that combines biology, chemistry, and computer sciences to develop new methods for engineering and controlling molecular structures. By combining advanced technology and research methods, this sector has great potential for tackling urgent medical issues, including the creation of targeted drug delivery systems and the development of personalized medications that cater to unique patient requirements.

The main goal of this Special Issue is to present and distribute innovative research, approaches, and breakthroughs in biomolecular design that are primarily focused on medicinal applications. We seek to collect papers that provide clear insights into new tactics, cutting-edge methodologies, and revolutionary applications in biomolecular design that have the potential to greatly influence medical interventions, illness management, and healthcare outcomes.

Dr. Binh P. Nguyen
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 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. International Journal of Molecular Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. There is an Article Processing Charge (APC) for publication in this open access journal. For details about the APC please see here. 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

  • drug discovery
  • biomolecular design
  • molecular modeling
  • bioinformatics
  • medical applications

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Published Papers (3 papers)

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16 pages, 2354 KiB  
Article
Porter 6: Protein Secondary Structure Prediction by Leveraging Pre-Trained Language Models (PLMs)
by Wafa Alanazi, Di Meng and Gianluca Pollastri
Int. J. Mol. Sci. 2025, 26(1), 130; https://doi.org/10.3390/ijms26010130 - 27 Dec 2024
Cited by 1 | Viewed by 1068
Abstract
Accurately predicting protein secondary structure (PSSP) is crucial for understanding protein function, which is foundational to advancements in drug development, disease treatment, and biotechnology. Researchers gain critical insights into protein folding and function within cells by predicting protein secondary structures. The advent of [...] Read more.
Accurately predicting protein secondary structure (PSSP) is crucial for understanding protein function, which is foundational to advancements in drug development, disease treatment, and biotechnology. Researchers gain critical insights into protein folding and function within cells by predicting protein secondary structures. The advent of deep learning models, capable of processing complex sequence data and identifying meaningful patterns, offer substantial potential to enhance the accuracy and efficiency of protein structure predictions. In particular, recent breakthroughs in deep learning—driven by the integration of natural language processing (NLP) algorithms—have significantly advanced the field of protein research. Inspired by the remarkable success of NLP techniques, this study harnesses the power of pre-trained language models (PLMs) to advance PSSP prediction. We conduct a comprehensive evaluation of various deep learning models trained on distinct sequence embeddings, including one-hot encoding and PLM-based approaches such as ProtTrans and ESM-2, to develop a cutting-edge prediction system optimized for accuracy and computational efficiency. Our proposed model, Porter 6, is an ensemble of CBRNN-based predictors, leveraging the protein language model ESM-2 as input features. Porter 6 achieves outstanding performance on large-scale, independent test sets. On a 2022 test set, the model attains an impressive 86.60% accuracy in three-state (Q3) and 76.43% in eight-state (Q8) classifications. When tested on a more recent 2024 test set, Porter 6 maintains robust performance, achieving 84.56% in Q3 and 74.18% in Q8 classifications. This represents a significant 3% improvement over its predecessor, outperforming or matching state-of-the-art approaches in the field. Full article
(This article belongs to the Special Issue Advanced Research in Biomolecular Design for Medical Applications)
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17 pages, 6375 KiB  
Article
Designing a Candidate Multi-Epitope Vaccine against Transmissible Gastroenteritis Virus Based on Immunoinformatic and Molecular Dynamics
by Yihan Bai, Mingxia Zhou, Naidong Wang, Yi Yang and Dongliang Wang
Int. J. Mol. Sci. 2024, 25(16), 8828; https://doi.org/10.3390/ijms25168828 - 13 Aug 2024
Cited by 2 | Viewed by 1549
Abstract
Transmissible gastroenteritis virus (TGEV) is an etiological agent of enteric disease that results in high mortality rates in piglets. The economic impact of the virus is considerable, causing significant losses to the pig industry. The development of an efficacious subunit vaccine to provide [...] Read more.
Transmissible gastroenteritis virus (TGEV) is an etiological agent of enteric disease that results in high mortality rates in piglets. The economic impact of the virus is considerable, causing significant losses to the pig industry. The development of an efficacious subunit vaccine to provide promising protection against TGEV is of the utmost importance. The viral antigen, spike glycoprotein (S), is widely regarded as one of the most effective antigenic components for vaccine research. In this study, we employed immunoinformatics and molecular dynamics approaches to develop an ‘ideal’ multi-epitope vaccine. Firstly, the dominant, non-toxic, highly antigenic T (Th, CTL) and B cell epitopes predicted from the TGEV S protein were artificially engineered in tandem to design candidate subunit vaccines. Molecular docking and dynamic simulation results demonstrate that it exhibits robust interactions with toll-like receptor 4 (TLR4). Of particular significance was the finding that the vaccine was capable of triggering an immune response in mammals, as evidenced by the immune simulation results. The humoral aspect is typified by elevated levels of IgG and IgM, whereas the cellular immune aspect is capable of eliciting the robust production of interleukins and cytokines (IFN-γ and IL-2). Furthermore, the adoption of E. coli expression systems for the preparation of vaccines will also result in cost savings. This study offers logical guidelines for the development of a secure and efficacious subunit vaccine against TGEV, in addition to providing a novel theoretical foundation and strategy to prevent associated CoV infections. Full article
(This article belongs to the Special Issue Advanced Research in Biomolecular Design for Medical Applications)
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36 pages, 1578 KiB  
Perspective
Should Artificial Intelligence Play a Durable Role in Biomedical Research and Practice?
by Pierre Bongrand
Int. J. Mol. Sci. 2024, 25(24), 13371; https://doi.org/10.3390/ijms252413371 - 13 Dec 2024
Viewed by 1127
Abstract
During the last decade, artificial intelligence (AI) was applied to nearly all domains of human activity, including scientific research. It is thus warranted to ask whether AI thinking should be durably involved in biomedical research. This problem was addressed by examining three complementary [...] Read more.
During the last decade, artificial intelligence (AI) was applied to nearly all domains of human activity, including scientific research. It is thus warranted to ask whether AI thinking should be durably involved in biomedical research. This problem was addressed by examining three complementary questions (i) What are the major barriers currently met by biomedical investigators? It is suggested that during the last 2 decades there was a shift towards a growing need to elucidate complex systems, and that this was not sufficiently fulfilled by previously successful methods such as theoretical modeling or computer simulation (ii) What is the potential of AI to meet the aforementioned need? it is suggested that recent AI methods are well-suited to perform classification and prediction tasks on multivariate systems, and possibly help in data interpretation, provided their efficiency is properly validated. (iii) Recent representative results obtained with machine learning suggest that AI efficiency may be comparable to that displayed by human operators. It is concluded that AI should durably play an important role in biomedical practice. Also, as already suggested in other scientific domains such as physics, combining AI with conventional methods might generate further progress and new applications, involving heuristic and data interpretation. Full article
(This article belongs to the Special Issue Advanced Research in Biomolecular Design for Medical Applications)
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