A Review of Deep Generative Models for Distance Matrices in Protein Structure Modeling

Abstract

Deep generative models are being explored as a promising approach for generating distance matrix representations of protein tertiary structures. These models capture the complex spatial arrangement of amino acid residues, but challenges remain in ensuring the generated structures are physically realistic, diverse, and capable of reconstructing missing regions accurately. A key question is whether these models can generate protein structures that align with experimentally determined structures while preserving biological relevance. This review examines recent advancements in generative modeling for protein structures, discussing various methods, their strengths and limitations, and the evaluation metrics used. Recent studies have demonstrated significant improvements in model performance by integrating biophysical constraints, multi-scale representations, and hybrid learning strategies, offering promising directions for enhancing structure prediction accuracy, native-like structural integrity, and biological relevance. It also highlights the need for optimizing model architectures and improving validation strategies to enhance protein structure prediction and expand their role in structural biology.