Advancements in artificial intelligence (AI) are dramatically transforming the field of protein science, offering powerful tools to tackle its longstanding challenges. Proteins, as essential biomolecules, are crucial for a wide range of biological functions, including enzymatic catalysis, molecular transport, and signal transduction. The prediction of protein structures, stability, dynamics, and interactions is vital for understanding biological processes and for driving progress in drug discovery, personalized medicine, and synthetic biology.

This research topic aims to explore the cutting-edge applications and methodological innovations at the intersection of AI and protein science. We seek to provide a platform for researchers to present their latest findings and methodologies that utilize AI to address complex bioinformatics challenges related to protein science. Contributors are encouraged to present novel AI techniques and demonstrate their application in real-world bioinformatics scenarios.

Subtopics of interest include, but are not limited to:

1. Protein Structure Prediction and Modeling: The latest AI methods in predicting protein tertiary and quaternary structures with high accuracy, focusing on innovations beyond established models like AlphaFold.

2. Functional Annotation and Protein Stability: AI approaches for predicting protein functions, active sites, and changes in stability induced by protein variations and their potential pathogenicity

3. Dynamic Simulations and Folding Pathways: Utilizing AI to model protein folding pathways and to simulate dynamic protein behaviours in various biological contexts.

4. Protein-Protein and Protein-Ligand Interactions: New AI-driven techniques for identifying and modeling interactions, and their implications for understanding molecular mechanisms and designing therapeutic molecules.

5. Proteome informatics: AI-assisted mass spectrometry-based proteomics to model physicochemical principles or to identify molecular drivers of a disease.

5. Integration of Multi-Omics Data: Leveraging AI to integrate proteomics with genomics, transcriptomics, and metabolomics data to gain comprehensive insights into biological systems.

6. Ethical, Technical, and Computational Challenges: Addressing the challenges associated with data quality, algorithm transparency, and reproducibility in AI-driven protein research.

7. Emerging Trends and Future Perspectives: Speculative discussions on future AI technologies and their potential to unlock new dimensions in bioinformatics research and applications.

We invite original research articles, reviews, case studies, and opinion pieces that provide novel insights, propose new methodologies, or address critical challenges in the application of AI to protein science. Through this research topic, we aim to highlight the transformative potential of AI innovations and foster discussions that will shape the future of bioinformatics research.

Are you interested in contributing a manuscript to this Research Topic?

AI in Protein Science