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Recent Research of Protein Structure Prediction and Design
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Recent Research of Protein Structure Prediction and Design

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

Deadline for manuscript submissions: closed (20 March 2026) | Viewed by 4075

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,

Proteins support the majority of biological functions. Having access to partial or complete 3D structural information of proteins is therefore of major interest from basic research to pharmacological developments. Recent research in the field of protein structure prediction and design has focused on developing more accurate and efficient computational methods for predicting protein structures, i.e., proposing structural models. These developments include the use of machine learning algorithms, deep learning techniques and advanced molecular modeling approaches to improve the accuracy of protein structure predictions.

This Special Issue highlights recent breakthroughs in protein structure prediction and design. Advances in deep learning have enhanced these breakthroughs. Key achievements include AlphaFold, ESMFold and RoseTTAfold, which have significantly increased the number of proteins available for prediction, thereby expanding the structural proteome. This issue explores advances in technology, the challenges of predicting complex structures and the design of proteins for new functions. It presents interdisciplinary research with implications for drug discovery, biotechnology and synthetic biology, and provides a timely overview of advances and challenges in the field.

This Special Issue is supervised by Dr Alexandre G. de Brevern, assisted by our Guest Editor's assistant editor Dr. Joseph Rebehmed (Department of Computer Science and Mathematics, Lebanese American University).

Dr. Alexandre G. De Brevern
Guest Editor

Manuscript Submission Information

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Keywords

  • protein design
  • multidomain proteins
  • AlphaFold2
  • protein folding
  • deep learning

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Published Papers (2 papers)

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25 pages, 1136 KB  
Article
TruMPET: A New Method for Protein Secondary Structure Prediction Using Neural Networks Trained on Multiple Pre-Selected Physicochemical and Structural Features
by Yury V. Milchevskiy, Galina I. Kravatskaya and Yury V. Kravatsky
Int. J. Mol. Sci. 2025, 26(23), 11284; https://doi.org/10.3390/ijms262311284 - 21 Nov 2025
Viewed by 1324
Abstract
Protein structure prediction continues to pose multiple challenges, despite the progress made by ML. While recent deep learning models have achieved a strong performance using embeddings from protein language models, they often ignore non-canonical amino acids and rely heavily on sequence alignments or [...] Read more.
Protein structure prediction continues to pose multiple challenges, despite the progress made by ML. While recent deep learning models have achieved a strong performance using embeddings from protein language models, they often ignore non-canonical amino acids and rely heavily on sequence alignments or evolutionary profiles. Here, we present an improvement to this approach for predicting the secondary protein structure of DSSP classes solely from amino acid sequences. We suggest that ML feature sets should be generated from statistically significant mutually uncorrelated descriptors. The selection of statistically assessed descriptors, including predicting the physicochemical parameters of non-canonical amino acids, is a key component of the proposed method. The statistical significance and influence of each of the suggested features were assessed using a two-step Linear Discriminant Analysis, which permitted the evaluation of the statistical significance of each descriptor and their impact on model accuracy. We applied the set of 109 most influential statistically significant descriptors as a learning model for the two-layer Bi-LSTM network combined with ESMFold2 embeddings. Our method, TruMPET (Training upon Multiple Pre-selected Elements Technique), outperformed all other methods reported in the literature for the non-redundant datasets (CB513: DSSP Q3 = 91.36% and Q8 = 85.41%, TEST2018: DSSP Q3 = 90.64% and Q8 = 84.17%). Full article
(This article belongs to the Special Issue Recent Research of Protein Structure Prediction and Design)
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14 pages, 6812 KB  
Article
AlphaFold 3-Assisted Deciphering of the DNA Recognition by DREB1 Transcription Factors in Rice
by Wenshu Wang, Wei Cai, Jiang Zhu and Yongsheng Zhu
Int. J. Mol. Sci. 2025, 26(13), 6395; https://doi.org/10.3390/ijms26136395 - 2 Jul 2025
Cited by 2 | Viewed by 1627
Abstract
Rice genome encodes ten OsDREB1 proteins that regulate tolerance to abiotic stresses such as cold and drought. OsDREB1s can bind to the C-repeat (CRT) element, dehydration response element (DRE), and GCC-box in gene promoters for transcription regulation. However, the recognition mechanism of OsDREB1s [...] Read more.
Rice genome encodes ten OsDREB1 proteins that regulate tolerance to abiotic stresses such as cold and drought. OsDREB1s can bind to the C-repeat (CRT) element, dehydration response element (DRE), and GCC-box in gene promoters for transcription regulation. However, the recognition mechanism of OsDREB1s to these DNA elements remains unclear. Here, the structures of OsDREB1s were modelled using AlphaFold 3, which revealed a typical AP2 domain and a disordered KRP/RAGR motif adjacent to AP2 in all OsDREB1s. Structure modeling of OsDREB1A binding to CRT, DRE, and GCC-box showed that four Arg residues and a Glu (E66) from AP2 play important roles in binding to the major groove of DNA, while R40 in the KRP/RAGR motif was predicted to interact with the minor groove. The structure models revealed a few differences in the binding details for CRT, DRE, and GCC-box. Consistent with these predictions, OsDREB1A was evidenced to bind with the three DNA elements in slightly different affinities through EMSA experiments. Mutation analysis verified the key role of R40 and E66 in binding to CRT. Considering the highly conserved structure and sequence of the KRP/RAGR motif and AP2, we speculate that the DNA recognition mechanism found for OsDREB1A may be common for all OsDREB1s. Full article
(This article belongs to the Special Issue Recent Research of Protein Structure Prediction and Design)
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