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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: 30 September 2024 | Viewed by 539

Special Issue Editor


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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
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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.

Prof. Dr. Alexandre G. De Brevern
Guest Editor

Manuscript Submission Information

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Keywords

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

Published Papers (1 paper)

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Research

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 (registering DOI) - 18 May 2024
Viewed by 170
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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