ModelMaker: A Versatile Tool for Structural Modeling
Understanding the structure and function of biomolecular complexes requires powerful computational tools that integrate diverse experimental data sources. ModelMaker is an advanced computational modeling framework designed to generate atomic-level structural models of large molecular machines. By integrating data from cryo-electron microscopy (cryo-EM), X-ray crystallography, and biochemical experiments, ModelMaker refines structural hypotheses and enhances the resolution of experimental datasets.
At its core, ModelMaker employs a multi-step process that iteratively optimizes molecular structures to fit experimental constraints. The platform supports hybrid modeling approaches that combine molecular dynamics (MD) simulations and artificial intelligence (AI)-driven structure prediction methods, ensuring a comprehensive view of molecular systems in their native states. This approach allows researchers to generate high-confidence structural models that inform further experimental designs and mechanistic studies.
Bridging Scales, Methods, and Expertise: An Integrative Computational Modeling Strategy
Biological macromolecules operate across multiple timescales and spatial resolutions, necessitating an integrative approach to studying their structure, dynamics, and function. Our integrative computational modeling strategy combines quantum mechanics (QM), molecular mechanics (MM), and molecular dynamics (MD) simulations with experimental validation techniques such as vibrational spectroscopy and cryo-EM.
This multi-scale approach provides crucial insights into the mechanisms of molecular machines, including conformational changes, allosteric regulation, and functional interactions within complex cellular environments. By bridging different modeling techniques and experimental data, we enhance the predictive power of structural models and refine hypotheses about molecular mechanisms. This strategy has been successfully applied to study protein-RNA interactions, enzyme catalysis, and large macromolecular assemblies, providing a detailed understanding of biomolecular function at an unprecedented resolution.
Our work in integrative modeling serves as a critical interface between computational predictions and experimental validation, accelerating discoveries in structural biology and molecular biophysics.