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Protein Structure, Function and Design

A Special Issue of International Journal of Molecular Sciences (ISSN 1422-0067) belonging to the section "Biochemistry".

Deadline for manuscript submissions: 28 February 2027 | Viewed by 747

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Guest Editor
Laboratório de Proteômica e Engenharia de Proteínas, Instituto Carlos Chagas (ICC), Fundação Oswaldo Cruz (Fiocruz Paraná), Curitiba, Brazil
Interests: protein structure, function and design; protein engineering; therapeutic proteins; integrative structural determination of target proteins; in silico structure-based prediction; structural biology and biotechnology

Special Issue Information

Dear Colleagues,

Proteins are central to virtually all biological processes, and understanding the intimate relationship between their three-dimensional structure, function and stability remains one of the great challenges of modern molecular life sciences. This Special Issue, “Protein Structure, Function and Design”, aims to gather original research and review articles that explore recent progress in elucidating, predicting and engineering protein structures. We welcome contributions covering experimental and integrative approaches to structure determination, computational and in silico structure-based prediction, molecular dynamics, and protein–ligand and protein–protein interactions, as well as rational protein design and engineering of therapeutic and industrially relevant proteins. By bringing together advances spanning structural biology, biophysics, bioinformatics and biotechnology, this Special Issue seeks to highlight how a deeper understanding of protein architecture can be translated into the design of novel molecules with enhanced function, stability and biomedical or biotechnological applications.

Dr. Tatiana de Arruda Campos Brasil de Souza
Guest Editor

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Keywords

  • protein structure
  • protein function
  • protein design
  • protein engineering
  • structural biology
  • computational prediction
  • molecular dynamics
  • protein-protein interactions
  • therapeutic proteins
  • structure-function relationship

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Published Papers (1 paper)

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Review

23 pages, 3189 KB  
Review
Diffusion-Based Protein Structure Design: Geometric Modelling, Validation Strategies, and Thermodynamic Challenges
by Wenran Li, Xavier Cadet, David Medina-Ortiz, Mehdi D. Davari, Ramanathan Sowdhamini, Miloud Bessafi, Cedric Damour, Yu Li, Alain Miranville, Alexandre G. de Brevern and Frederic Cadet
Int. J. Mol. Sci. 2026, 27(16), 7151; https://doi.org/10.3390/ijms27167151 - 10 Aug 2026
Viewed by 505
Abstract
Although deep learning has transformed protein structure prediction, the controlled generation of functional and experimentally tractable protein structures remains a major challenge in structural bioinformatics. Diffusion models offer a versatile approach to generating protein backbones, motif-conditioned scaffolds, all-atom structures and biomolecular interaction geometries, [...] Read more.
Although deep learning has transformed protein structure prediction, the controlled generation of functional and experimentally tractable protein structures remains a major challenge in structural bioinformatics. Diffusion models offer a versatile approach to generating protein backbones, motif-conditioned scaffolds, all-atom structures and biomolecular interaction geometries, while accommodating explicit structural and functional constraints. This review focuses on coordinate- and residue-frame-based diffusion approaches for generating protein structures, paying particular attention to geometric equivariance, conditioning strategies, all-atom modelling and interaction-aware design. We compare representative methods derived from RoseTTAFold, frame-diffusion architectures, and oriented-residue-cloud representations according to their molecular representation, generative objective, and validation strategy. We examine the criteria used to evaluate generated proteins, such as stereochemical quality, structural consistency, designability, novelty, diversity, computational efficiency, and experimental performance. Particular attention is given to the distinction between learned structural distributions and condition-dependent thermodynamic ensembles. Future progress will depend on the integration of generative models with molecular mechanics, conformational sampling, uncertainty estimation, free-energy methods, and experimental design–build–test–learn cycles. Within this framework, diffusion models offer candidate generation and constraint satisfaction capabilities within broader protein engineering workflows. Full article
(This article belongs to the Special Issue Protein Structure, Function and Design)
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