Decoding the Mobility and Time Scales of Protein Loops
Yina Gu1, Da-Wei Li1, Rafael Brüschweiler1
1Department of Chemistry and Biochemistry and ‡Campus Chemical Instrument Center, The Ohio State University , Columbus, Ohio 43210, United States.
Journal of Chemical Theory and Computation
|November 19, 2015
Summary
Protein loop flexibility is crucial for molecular interactions. This study developed a predictive algorithm, ToeLoop, using molecular dynamics simulations to accurately forecast protein loop dynamics and their time scales.
Area of Science:
- Biochemistry
- Computational Biology
- Structural Biology
Background:
- Protein loops are essential for molecular interactions and recognition.
- Understanding protein loop dynamics is critical for biological processes.
Purpose of the Study:
- To develop a predictive model for protein loop flexibility and dynamics.
- To classify protein loops based on their motional time scales.
Main Methods:
- Performed 500 ns molecular dynamics (MD) simulations for 38 proteins.
- Analyzed 169 protein loops, classifying them into fast, slow, and static categories.
- Developed the ToeLoop algorithm using loop descriptors and validated with NMR chemical shifts.
Main Results:
- Established three distinct categories of protein loop dynamics: fast (<10 ns), slow (10–500 ns), and static.
- The ToeLoop algorithm accurately predicts protein loop flexibility and dynamics.
- A public web server for ToeLoop is available for predicting loop dynamics.
Conclusions:
- Protein loop dynamics can be predicted rapidly and with reasonable accuracy.
- The ToeLoop algorithm aids in understanding the roles of loops in protein-protein interactions and binding.
- This predictive capability facilitates the screening of protein structures for functional insights.
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