Computational Biology: Protein Structure Prediction
A special issue of Biomolecules (ISSN 2218-273X). This special issue belongs to the section "Biomacromolecules: Proteins".
Deadline for manuscript submissions: 31 March 2025 | Viewed by 76
Special Issue Editor
Interests: homology modeling; molecular dynamics; machine learning in protein structure prediction; computational chemistry; multi omics data integration
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Determining protein structures is essential for comprehending the intricate mechanisms that drive biological processes, with significant implications for drug discovery, enzyme design, and the treatment of various diseases. Protein structure prediction stands as a pivotal area within computational biology, uniting biological research with advanced computational methods. Despite notable progress, the accurate prediction of protein structures from their amino acid sequences remains a formidable challenge.
This Special Issue aims to gather cutting-edge research that explores novel computational methods and algorithms to predict protein structures more accurately. Our goal is to highlight advancements that can enhance our understanding of protein folding, stability, and interactions, which are crucial for developing targeted therapies and understanding cellular functions. This Special Issue aims to advance the current limitations of protein structure prediction by incorporating contributions from a wide range of fields, including machine learning, molecular dynamics, and quantum mechanics. The knowledge gained from these studies will enrich academic understanding while also offering practical applications in fields such as drug discovery, enzyme technology, and beyond.
Dr. Jayadev Joshi
Guest Editor
Manuscript Submission Information
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Keywords
- homology modeling
- ab initio prediction
- molecular dynamics
- protein folding
- structural alignment
- protein–ligand interactions
- energy minimization
- secondary structure prediction
- bioinformatics
- molecular modeling
- machine learning in protein prediction
- fold recognition
- peptide structure prediction
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