Computational Modeling of Molecular Structures Guided by Hydrogen-Exchange Data.

Area of Science:

• Structural Biology
• Computational Biology
• Biophysics

Background:

• Hydrogen-exchange monitoring experiments have been used for decades in molecular structural studies.
• This technique offers insights into molecular dynamics in solution, complementing other experimental methods for integrative modeling.
• Current use of hydrogen-exchange data often relies on qualitative assessments of computational models.

Purpose of the Study:

• To survey and present quantitative paradigms for using hydrogen-exchange data in computational molecular modeling.
• To provide a comprehensive list of existing hydrogen-exchange prediction models.
• To guide future research towards developing improved prediction models and enhancing the synergy between hydrogen-exchange monitoring and molecular modeling.

Main Methods:

• Literature review and compilation of hydrogen-exchange prediction models.
• Categorization of models into structure-based, fractional-population, and knowledge-based approaches.
• Analysis of existing paradigms for quantitatively guiding computational molecular structure modeling.

Main Results:

• Identified a wide variety of hydrogen-exchange prediction models, ranging from purely structure-based to knowledge-based.
• Highlighted the lack of a universally accepted prediction model within the structural biology community.
• Presented the first exhaustive list of hydrogen-exchange prediction models found in the literature.

Conclusions:

• Hydrogen-exchange data holds significant potential for quantitatively guiding molecular modeling beyond qualitative assessments.
• A comprehensive understanding of existing prediction models is crucial for developing more robust and widely accepted methods.
• This work aims to serve as a resource to foster advancements in hydrogen-exchange prediction and its integration with molecular modeling.