An Iterative Framework for Generative Backmapping of Coarse Grained Proteins

2025-05-23

AI Summary

Coarse-grained (CG) to all-atom fine-grained (FG) reverse mapping of proteins suffers from low reconstruction accuracy, training instability, and physical distortions. Method: We propose a novel multi-step iterative generative framework that uniquely integrates conditional variational autoencoders (cVAEs) with graph neural networks (GNNs), establishing a physics-guided latent-space optimization mechanism for progressive refinement from minimal CG representations to atomic-resolution structures. Contribution/Results: Theoretically, we introduce a multi-scale iterative reverse-mapping paradigm that jointly enhances modeling fidelity and training robustness, overcoming key bottlenecks in ultra-CG system modeling. Extensive validation across diverse protein topologies demonstrates a 32% reduction in root-mean-square deviation (RMSD), a 2.1× improvement in training efficiency, and strict adherence of generated structures to fundamental physical constraints—including bond lengths and bond angles.

Technology Category

• Machine Learning: Deep Generative Models & Autoencoders
• Computer Vision: Generative Adversarial Networks (GANs) for Vision
• Natural Language Processing: Code Generation / Program Synthesis from Natural Language

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications

Abstract

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.

Problem

• Improving accuracy in coarse-grained to fine-grained protein backmapping
• Addressing unstable training in large-scale biomolecule reconstruction
• Enhancing physical realism in ultra-coarse protein representations

Innovation

• Iterative framework using conditional Variational Autoencoders
• Graph-based neural networks for large biomolecules
• Stepwise refinement from coarse to atomistic details