Gauge symmetries and structure of proteins
We discuss the gauge field theory approach to protein structure study, which allows a natural way to introduce collective degrees of freedom and nonlinear topological structures. Local symmetry of proteins and its breaking in the medium is considered, what allows to derive Abelian Higgs model of protein backbone, correct folding of which is defined by gauge symmetry breaking due hydrophobic forces. Within this model structure of protein backbone is defined by superposition of one-dimensional topological solitons (kinks), what allows to reproduce the three-dimensional structure of the protein backbone with precision up to 1A and to predict its dynamics.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Gauge-Equivariant Graph Neural Networks for Lattice Gauge Theories
Local gauge symmetry underlies fundamental interactions and strongly correlated quantum matter, yet existing machine-learning approaches lack a general, principled framework for learning under site-dependent symmetries, …
Graph Neural NetworkDeepSymmetry : Using 3D convolutional networks for identification of tandem repeats and internal symmetries in protein structures
Motivation: Thanks to the recent advances in structural biology, nowadays three-dimensional structures of various proteins are solved on a routine basis. A large portion of these contain structural repetitions or interna…
Unification of Symmetries Inside Neural Networks: Transformer, Feedforward and Neural ODE
Understanding the inner workings of neural networks, including transformers, remains one of the most challenging puzzles in machine learning. This study introduces a novel approach by applying the principles of gauge sym…
Identifying the Group-Theoretic Structure of Machine-Learned Symmetries
Deep learning was recently successfully used in deriving symmetry transformations that preserve important physics quantities. Being completely agnostic, these techniques postpone the identification of the discovered symm…
Deep LearningGaugeQuant: Online Learning of Quantization-Optimal Bases from LLM Symmetries
Transformers are known to have internal continuous symmetries that leave outputs invariant, while modifying quantization. GaugeQuant leverages this in-training by introducing a LogSumExp term to the loss that breaks the …