An Iterative Framework for Generative Backmapping of Coarse Grained Proteins
This work addresses challenges in biomolecular simulation for researchers, but it is incremental as it builds on existing backmapping techniques with a multistep refinement approach.
The paper tackles the problem of inaccurate and unstable backmapping from coarse-grained to fine-grained protein representations by introducing an iterative framework using conditional VAEs and graph neural networks, resulting in improved reconstruction accuracy and more computationally efficient training for proteins with ultra-coarse representations.
The techniques of data-driven backmapping from coarse-grained (CG) to fine-grained (FG) representation often struggle with accuracy, unstable training, and physical realism, especially when applied to complex systems such as proteins. In this work, we introduce a novel iterative framework by using conditional Variational Autoencoders and graph-based neural networks, specifically designed to tackle the challenges associated with such large-scale biomolecules. Our method enables stepwise refinement from CG beads to full atomistic details. We outline the theory of iterative generative backmapping and demonstrate via numerical experiments the advantages of multistep schemes by applying them to proteins of vastly different structures with very coarse representations. This multistep approach not only improves the accuracy of reconstructions but also makes the training process more computationally efficient for proteins with ultra-CG representations.