Statistical force-field for structural modeling using chemical cross-linking/mass spectrometry distance constraints

Area of Science:

• Biochemistry and structural biology
• Computational biology and bioinformatics

Background:

• Chemical cross-linking/mass spectrometry (XLMS) provides distance constraints for biomolecular structure modeling.
• Current methods struggle to effectively utilize XLMS constraints for tertiary protein structure determination.
• There is a need for improved strategies to represent XLMS constraints in modeling algorithms.

Purpose of the Study:

• To develop and validate a novel force field for representing XLMS-derived distance constraints in biomolecular structure modeling.
• To enhance the accuracy and success rate of tertiary and quaternary protein structure determination using XLMS data.

Main Methods:

• Developed a statistical force field based on the probability of satisfying topological cross-linking distances in known protein structures.
• Computed potential energy functions as a function of Euclidean distance between amino acid residues.
• Implemented the force field within the Rosetta ab initio relax protocol for structural modeling.

Main Results:

• The proposed force field suggests setting XL constraints to shorter distances than conventionally assumed.
• Incorporating the statistical force field significantly improves the quality of obtained biomolecular models.
• The developed force field is easily integrable into existing modeling software and methods.

Conclusions:

• The novel force field effectively represents XLMS constraints, overcoming limitations of previous approaches.
• This method offers a practical and improved strategy for biomolecular structure determination using XLMS data.
• The force field and its parameters are publicly available for broader scientific application.