A key review on graph data science: The power of graphs in scientific studies

Daş, Resul; SÜLÜ>, Mücahit

doi:10.1016/j.chemolab.2023.104896

Cited by 13 publications

(2 citation statements)

References 80 publications

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“…Therefore, there is still a need to develop useful representations (order parameters) of the folding process based on fundamental laws. The present work aims to develop and test order parameters derived from graph theory [ 34 , 35 ] to contribute to the characterization of protein-disordered ensembles. The theoretical concepts developed in the present work will be tested for protein structures extracted from all-atom molecular dynamics (MD) simulations.…”

Section: Introductionmentioning

confidence: 99%

Einstein Model of a Graph to Characterize Protein Folded/Unfolded States

Tyler,

Laforge,

Guzzo

et al. 2023

Molecules

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The folded structures of proteins can be accurately predicted by deep learning algorithms from their amino-acid sequences. By contrast, in spite of decades of research studies, the prediction of folding pathways and the unfolded and misfolded states of proteins, which are intimately related to diseases, remains challenging. A two-state (folded/unfolded) description of protein folding dynamics hides the complexity of the unfolded and misfolded microstates. Here, we focus on the development of simplified order parameters to decipher the complexity of disordered protein structures. First, we show that any connected, undirected, and simple graph can be associated with a linear chain of atoms in thermal equilibrium. This analogy provides an interpretation of the usual topological descriptors of a graph, namely the Kirchhoff index and Randić resistance, in terms of effective force constants of a linear chain. We derive an exact relation between the Kirchhoff index and the average shortest path length for a linear graph and define the free energies of a graph using an Einstein model. Second, we represent the three-dimensional protein structures by connected, undirected, and simple graphs. As a proof of concept, we compute the topological descriptors and the graph free energies for an all-atom molecular dynamics trajectory of folding/unfolding events of the proteins Trp-cage and HP-36 and for the ensemble of experimental NMR models of Trp-cage. The present work shows that the local, nonlocal, and global force constants and free energies of a graph are promising tools to quantify unfolded/disordered protein states and folding/unfolding dynamics. In particular, they allow the detection of transient misfolded rigid states.

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Section: Introductionmentioning

confidence: 99%

Einstein Model of a Graph to Characterize Protein Folded/Unfolded States

Tyler,

Laforge,

Guzzo

et al. 2023

Molecules

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“…A network is the combination of a set of entities and the relationships between them [1]. Network visualization provides a visual means for analyzing the relation data and finds wide-ranging applications across multiple domains [2], spanning from transportation systems [3], social networks [4], biological networks [5,6], contact tracings [7], to academic writing analysis [8]. By nature, a significant portion of these networks is dynamic, where nodes and edges evolve over time.…”

Section: Introductionmentioning

confidence: 99%

Online Dynamic Network Visualization Based on SIPA Layout Algorithm

Wang,

Chen,

Zhou

et al. 2023

Applied Sciences

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Online dynamic network visualization is imperative for real-time network monitoring and analysis applications. It presents a significant research challenge for maintaining both layout stability and quality amid unpredictable temporal changes. While node-link diagrams are extensively utilized in online dynamic network visualization, previous node-link-diagram-based research primarily focused on stabilizing the layout by defining constraints on local node movement. However, these constraints often neglect the structural influence and its corresponding global impact, which may lead to that the representations of the network structure change significantly over time and a decrease in layout quality. To address this problem, we introduce the Structure-based Influence Propagation and Aging (SIPA) algorithm, a novel approach to preserve the stability of relative node positions and shapes of interconnected nodes (referred to as structures) between adjacent time steps. These stable structures serve as visual cues for users tracking the evolution of the network, thereby enhancing the overall layout stability. Additionally, we enhance dynamic network analysis by a highly interactive visualization system, enriching the layout result with multiple coordinated views of temporal trends, network features, animated graph diaries and snapshots. Our approach empowers users to interactively track and compare network evolution within a long-term temporal context and across multiple aspects. We demonstrate the effectiveness and performance of our approach through in-lab user studies and comparative experiments with three baseline dynamic network layout methods.

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