Mathematical Modeling of Protein Structures: A Cohomology-Based Approach to the Flagellar Motor
- URL: http://arxiv.org/abs/2504.16941v1
- Date: Tue, 08 Apr 2025 19:21:44 GMT
- Title: Mathematical Modeling of Protein Structures: A Cohomology-Based Approach to the Flagellar Motor
- Authors: Zakaria Lamine, Abdelatif Hafid, Mohamed Rahouti,
- Abstract summary: This study presents a novel mathematical model derived from cohomology, generated by boundary classes of curves with fixed dual graphs.<n>The proposed model is utilized for protein structure analysis and prediction, with a specific application to the Flagellar Motor structure.
- Score: 2.389598109913754
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: This study presents a novel mathematical model derived from cohomology, leveraging the KEEL-proven theorem that establishes cohomology as tautological, generated by boundary classes of curves with fixed dual graphs. Simplicial complexes are constructed using skew-commutative graded algebra, and the structure theorem is applied to connect distinct homologies, enabling precise interpretations of the resulting geometric forms. The proposed model is utilized for protein structure analysis and prediction, with a specific application to the Flagellar Motor structure. This approach offers new insights into the geometric and algebraic foundations of biological macromolecular modeling, highlighting its potential for advancement in structural biology.
Related papers
- PhyloGen: Language Model-Enhanced Phylogenetic Inference via Graph Structure Generation [50.80441546742053]
Phylogenetic trees elucidate evolutionary relationships among species.<n>Traditional Markov Chain Monte Carlo methods face slow convergence and computational burdens.<n>We propose PhyloGen, a novel method leveraging a pre-trained genomic language model.
arXiv Detail & Related papers (2024-12-25T08:33:05Z) - A cohomology-based Gromov-Hausdorff metric approach for quantifying molecular similarity [0.0]
We introduce a cohomology-based Gromov-Hausdorff ultrametric method to analyze 1-dimensional and higher-dimensional (co)homology groups.<n>By incorporating geometric information, our method provides deeper insights compared to traditional persistent homology techniques.
arXiv Detail & Related papers (2024-11-21T06:58:14Z) - Generating Highly Designable Proteins with Geometric Algebra Flow Matching [1.1874952582465603]
We introduce a generative model for protein backbone design utilizing geometric products and higher order message passing.
We evaluate our architecture by incorporating it into the framework of FrameFlow, a state-of-the-art flow matching model for protein backbone generation.
arXiv Detail & Related papers (2024-11-07T23:21:36Z) - Encoding lattice structures in Quantum Computational Basis States [0.0]
We discuss an encoding methodology of lattice structures in computational basis states of qubits.
We demonstrate a specific use case of lattice models in protein structure prediction.
arXiv Detail & Related papers (2024-06-03T17:29:15Z) - A Survey of Geometric Graph Neural Networks: Data Structures, Models and Applications [71.809127869349]
This paper formalizes geometric graph as the data structure, on top of which we provide a unified view of existing models from the geometric message passing perspective.<n>We also summarize the applications as well as the related datasets to facilitate later research for methodology development and experimental evaluation.
arXiv Detail & Related papers (2024-03-01T12:13:04Z) - A Hitchhiker's Guide to Geometric GNNs for 3D Atomic Systems [87.30652640973317]
Recent advances in computational modelling of atomic systems represent them as geometric graphs with atoms embedded as nodes in 3D Euclidean space.
Geometric Graph Neural Networks have emerged as the preferred machine learning architecture powering applications ranging from protein structure prediction to molecular simulations and material generation.
This paper provides a comprehensive and self-contained overview of the field of Geometric GNNs for 3D atomic systems.
arXiv Detail & Related papers (2023-12-12T18:44:19Z) - From axioms over graphs to vectors, and back again: evaluating the
properties of graph-based ontology embeddings [78.217418197549]
One approach to generating embeddings is by introducing a set of nodes and edges for named entities and logical axioms structure.
Methods that embed in graphs (graph projections) have different properties related to the type of axioms they utilize.
arXiv Detail & Related papers (2023-03-29T08:21:49Z) - Learning Geometrically Disentangled Representations of Protein Folding
Simulations [72.03095377508856]
This work focuses on learning a generative neural network on a structural ensemble of a drug-target protein.
Model tasks involve characterizing the distinct structural fluctuations of the protein bound to various drug molecules.
Results show that our geometric learning-based method enjoys both accuracy and efficiency for generating complex structural variations.
arXiv Detail & Related papers (2022-05-20T19:38:00Z) - Dist2Cycle: A Simplicial Neural Network for Homology Localization [66.15805004725809]
Simplicial complexes can be viewed as high dimensional generalizations of graphs that explicitly encode multi-way ordered relations.
We propose a graph convolutional model for learning functions parametrized by the $k$-homological features of simplicial complexes.
arXiv Detail & Related papers (2021-10-28T14:59:41Z) - A unified diagrammatic approach to topological fixed point models [0.0]
We introduce a systematic mathematical language for describing fixed point models and apply it to the study of topological phases of matter.
The framework is reminiscent of state-sum models and lattice topological quantum field theories, but is formalised and unified in terms of tensor networks.
In contrast to existing tensor network ansatzes for the study of ground states of topologically ordered phases, the tensor networks in our formalism represent discrete path integrals in Euclidean space-time.
arXiv Detail & Related papers (2020-11-24T12:40:11Z) - Learning from Protein Structure with Geometric Vector Perceptrons [6.5360079597553025]
We introduce geometric vector perceptrons, which extend standard dense layers to operate on collections of Euclidean vectors.
We demonstrate our approach on two important problems in learning from protein structure: model quality assessment and computational protein design.
arXiv Detail & Related papers (2020-09-03T01:54:25Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.