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Molecular Advances in Bioinformatics Analysis of Protein Properties
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Molecular Advances in Bioinformatics Analysis of Protein Properties

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 February 2025) | Viewed by 4945

Special Issue Editor

Dr. Alexandre G. De Brevern

E-Mail Website
Guest Editor
Department of Biological Research on the Red Blood Cells, INTS, INSERM UMR_S 1134, Université de Paris, Université de la Réunion, 75739 Paris, France
Interests: structural bioinformatics; bioinformatics; next-generation sequence; drug design; deep learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The paradigm of sequence–structure relationships was introduced more than half a century ago and has since been associated with the functions carried by proteins. However, an incomplete understanding of protein sequence–structure–function relationships leads to many difficulties for prediction methods. The highly complex nature of these relationships is a consequence of the interaction between physics and evolution, which has been studied using a wide range of experimental and theoretical techniques. In addition, this paradigm has become more complex by taking into account the dynamics of proteins, an essential element for understanding their functions, along with the pathological problems that may be associated with them.

This Special Issue will therefore deal with the study of this entire sequence–structure–function–dynamics paradigm of proteins, which can range from the most sophisticated sequence analyses to the conservation of essential residues, the dynamics of proteins through both atomistic and simplified approaches (coarse-grained, normal modes, etc.) or quantum analyses. However, the ultimate goal of these studies must be the biological question being asked and the link with experimental data.

A large number of approaches can therefore be implemented, ranging from complex phylogeny, co-evolution, coalescence, classical or accelerated dynamics approaches, docking questions with other proteins, small ligands, questions predicting properties from the sequence with deep learning approaches, etc., and this list is not exhaustive.

Dr. Alexandre G. De Brevern
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

  • protein properties
  • bioinformatics
  • structural bioinformatics
  • next-generation sequence
  • drug design
  • deep learning
  • phylogeny
  • protein functions
  • molecular modeling
  • molecular docking
  • molecular dynamics

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Published Papers (3 papers)

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Research

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15 pages, 1011 KiB  
Article
The Sequence [RRKLPVGRS] Is a Nuclear Localization Signal for Importin 8 Binding (NLS8): A Chemical Biology and Bioinformatics Study
by Athanasios A. Panagiotopoulos, Konstantina Kalyvianaki, Aikaterini Angelidaki, Dimitris Dellis, Christos A. Panagiotidis, Marilena Kampa and Elias Castanas
Int. J. Mol. Sci. 2025, 26(6), 2814; https://doi.org/10.3390/ijms26062814 - 20 Mar 2025
Viewed by 688
Abstract
Karyopherins, carrier proteins that recognize particular cargo protein patterns known as nuclear localization signals (NLSs), mediate the nuclear translocation of big proteins. In order to better understand the process of nuclear transport of proteins and create the groundwork for the development of innovative [...] Read more.
Karyopherins, carrier proteins that recognize particular cargo protein patterns known as nuclear localization signals (NLSs), mediate the nuclear translocation of big proteins. In order to better understand the process of nuclear transport of proteins and create the groundwork for the development of innovative treatments that specifically target importins, it is imperative to clarify the intricate interactions between nuclear transporters and their cargo proteins. Until recently, very few NLSs have been documented. In the current work, an in silico method was used to identify NLSs for importin 8. It was determined that the sequence RRKLPVGRS serves as a recognition motif for importin 8 binding a karyopherin that is involved in the nuclear transportation of several important proteins like AGOs, SMADs, RPL23A, and TFE3. The sequence was validated in vitro in the breast cancer cell line T47D. This work subscribes to the effort to clarify the intricate relationships between nuclear transporters and their cargo proteins, in order to better understand the mechanism of nuclear transport of proteins and lay the groundwork for the development of novel therapeutics that target particular importins and have an immediate translational impact. Full article
(This article belongs to the Special Issue Molecular Advances in Bioinformatics Analysis of Protein Properties)
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19 pages, 794 KiB  
Article
De Novo Antimicrobial Peptide Design with Feedback Generative Adversarial Networks
by Michaela Areti Zervou, Effrosyni Doutsi, Yannis Pantazis and Panagiotis Tsakalides
Int. J. Mol. Sci. 2024, 25(10), 5506; https://doi.org/10.3390/ijms25105506 - 18 May 2024
Cited by 5 | Viewed by 2175
Abstract
Antimicrobial peptides (AMPs) are promising candidates for new antibiotics due to their broad-spectrum activity against pathogens and reduced susceptibility to resistance development. Deep-learning techniques, such as deep generative models, offer a promising avenue to expedite the discovery and optimization of AMPs. A remarkable [...] Read more.
Antimicrobial peptides (AMPs) are promising candidates for new antibiotics due to their broad-spectrum activity against pathogens and reduced susceptibility to resistance development. Deep-learning techniques, such as deep generative models, offer a promising avenue to expedite the discovery and optimization of AMPs. A remarkable example is the Feedback Generative Adversarial Network (FBGAN), a deep generative model that incorporates a classifier during its training phase. Our study aims to explore the impact of enhanced classifiers on the generative capabilities of FBGAN. To this end, we introduce two alternative classifiers for the FBGAN framework, both surpassing the accuracy of the original classifier. The first classifier utilizes the k-mers technique, while the second applies transfer learning from the large protein language model Evolutionary Scale Modeling 2 (ESM2). Integrating these classifiers into FBGAN not only yields notable performance enhancements compared to the original FBGAN but also enables the proposed generative models to achieve comparable or even superior performance to established methods such as AMPGAN and HydrAMP. This achievement underscores the effectiveness of leveraging advanced classifiers within the FBGAN framework, enhancing its computational robustness for AMP de novo design and making it comparable to existing literature. Full article
(This article belongs to the Special Issue Molecular Advances in Bioinformatics Analysis of Protein Properties)
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Review

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20 pages, 1643 KiB  
Review
Structural Bioinformatics Applied to Acetylcholinesterase Enzyme Inhibition
by María Fernanda Reynoso-García, Dulce E. Nicolás-Álvarez, A. Yair Tenorio-Barajas and Andrés Reyes-Chaparro
Int. J. Mol. Sci. 2025, 26(8), 3781; https://doi.org/10.3390/ijms26083781 - 17 Apr 2025
Cited by 1 | Viewed by 921
Abstract
Acetylcholinesterase (AChE) is a critical enzyme involved in neurotransmission by hydrolyzing acetylcholine at the synaptic cleft, making it a key target for drug discovery, particularly in the treatment of neurodegenerative disorders such as Alzheimer’s disease. Computational approaches, particularly molecular docking and molecular dynamics [...] Read more.
Acetylcholinesterase (AChE) is a critical enzyme involved in neurotransmission by hydrolyzing acetylcholine at the synaptic cleft, making it a key target for drug discovery, particularly in the treatment of neurodegenerative disorders such as Alzheimer’s disease. Computational approaches, particularly molecular docking and molecular dynamics (MD) simulations, have become indispensable tools for identifying and optimizing AChE inhibitors by predicting ligand-binding affinities, interaction mechanisms, and conformational dynamics. This review serves as a comprehensive guide for future research on AChE using molecular docking and MD simulations. It compiles and analyzes studies conducted over the past five years, providing a critical evaluation of the most widely used computational tools, including AutoDock, AutoDock Vina, and GROMACS, which have significantly contributed to the advancement of AChE inhibitor screening. Furthermore, we identify PDB ID: 4EY7, the most frequently used AChE crystal structure in docking studies, and highlight Donepezil, a well-established reference molecule widely employed as a control in computational screening for novel inhibitors. By examining these key aspects, this review aims to enhance the accuracy and reliability of virtual screening approaches and guide researchers in selecting the most appropriate computational methodologies. The integration of docking and MD simulations not only improves hit identification and lead optimization but also provides deeper mechanistic insights into AChE–ligand interactions, contributing to the rational design of more effective AChE inhibitors. Full article
(This article belongs to the Special Issue Molecular Advances in Bioinformatics Analysis of Protein Properties)
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