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: 28 February 2025 | Viewed by 215
Special Issue Editor
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
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Keywords
- protein design
- multidomain proteins
- AlphaFold2
- protein folding
- deep learning
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