BMC Biology is calling for submissions to our Collection on Computational inference of protein conformations and interactions.
With advancements in computational techniques and the exponential growth of available biological data, the prediction of protein conformational dynamics and interactions with diverse molecular targets has garnered significant attention. In silico tools can identify binding motifs and predict protein interactions with other proteins and small-molecule ligands (e.g., drugs, lipids, sugars, nucleotides), driving advancements in drug discovery and personalized medicine.
This Collection welcomes submissions on the prediction of protein-protein interactions, protein/small-molecule interactions, and binding motifs. Additionally, we encourage submissions focusing on conformational dynamics and intrinsically disordered regions (IDRs). We welcome manuscripts describing the application of machine learning and deep learning to address these important questions.
Topics may include, but are not limited to:
- Novel methods for predicting protein/protein and protein/small-molecule interactions
- Analyses of protein interaction networks and pathways
- Prediction of protein structure and conformational changes
- Analysis of sequence and structural binding motifs
- Reverse virtual screening approaches for identifying potential drug targets
- Prediction and characterization of Intrinsically Disordered Regions (IDRs)
Image credit: © [M] Christoph Burgstedt / Getty Images / iStock
