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Published on: January 7, 2019
Salt Bridge Builder: Using Residue Distances to Predict Salt Bridge Formation
Jason Sanchez1, Wenhan Guo2, Yuan Gao3
1Department of Pharmaceutical Sciences, School of Pharmacy, The University of Texas at El Paso, 1101 N Campbell St, El Paso, Texas 79902, United States.
We developed Salt Bridge Builder (SBB), a new software tool to engineer stabilizing salt bridges at protein interfaces. SBB uses residue distance heuristics to identify mutation sites, aiding protein design and stability prediction.
Area of Science:
- Protein engineering
- Computational biology
- Biophysics
Background:
- Salt bridges are crucial for protein stability and interactions.
- Existing tools focus on removing salt bridges, not creating them.
- Systematic methods for engineering new salt bridges are limited.
Purpose of the Study:
- Introduce Salt Bridge Builder (SBB), a software for engineering interprotein salt bridges.
- Validate the stabilizing role of salt bridges at protein interfaces.
- Develop an efficient algorithm for predicting salt bridge formation.
Main Methods:
- Utilized residue distance heuristics from structural data.
- Analyzed the SKEMPI v2 database for mutation effects.
- Benchmarked six residue distance metrics, selecting side-chain centroid distance (SCCD).
- Applied SBB to kinesin-5 and microtubule interactions.
- Employed molecular dynamics simulations and principal component analysis.
Main Results:
- SBB identifies candidate sites for novel interprotein salt bridges.
- Disrupting salt bridges significantly reduces binding free energy.
- SCCD offers optimal predictive performance and computational efficiency.
- Kinesin-5 shows enrichment in potential salt bridge sites at the microtubule interface.
- Local microenvironments influence engineered salt bridge stability.
Conclusions:
- Residue-distance-based salt bridge engineering is a viable protein engineering strategy.
- SBB provides a foundation for predicting engineered salt bridge stability.
- Future SBB extensions will incorporate microenvironment-aware stability prediction.
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