A COVID-19 Drug Repurposing Strategy through Quantitative Homological Similarities Using a Topological Data Analysis-Based Framework

Since its emergence in March 2020, the SARS-CoV-2 global pandemic has produced more than 116 million cases and 2.5 million deaths worldwide. Despite the enormous efforts carried out by the scientific community, no effective treatments have been developed to date. We applied a novel computational pipeline aimed to accelerate the process of identifying drug repurposing candidates which allows us to compare three-dimensional protein structures. Its use in conjunction with two in silico validation strategies (molecular docking and transcriptomic analyses) allowed us to identify a set of potential drug repurposing candidates targeting three viral proteins (3CL viral protease, NSP15 endoribonuclease, and NSP12 RNA-dependent RNA polymerase), which included rutin, dexamethasone, and vemurafenib. This is the first time that a topological data analysis (TDA)-based strategy has been used to compare a massive number of protein structures with the final objective of performing drug repurposing to treat SARS-CoV-2 infection.

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A COVID-19 Drug Repurposing Strategy Through Quantitative Homological Similarities by using a Topological Data Analysis Based Formalism

10.20944/preprints202012.0281.v1 ◽

2020 ◽

Author(s):

Raul Pérez-Moraga ◽

Jaume Forés-Martos ◽

Beatriz Suay ◽

Jean-Louis Duval ◽

Antonio Falcó ◽

...

Keyword(s):

Data Analysis ◽

Algebraic Topology ◽

Protein Structures ◽

Three Dimensional ◽

Drug Repurposing ◽

Topological Data Analysis ◽

Drug Identification ◽

Global Pandemic ◽

Candidate Drug ◽

Topological Data

Since its emergence in March 2020, the SARS-CoV-2 global pandemic has produced more than 65 million cases and one point five million deaths worldwide. Despite the enormous efforts carried out by the scientific community, no effective treatments have been developed to date. We created a novel computational pipeline aimed to speed up the process of repurposable candidate drug identification. Compared with current drug repurposing methodologies, our strategy is centered on filtering the best candidate among all selected targets focused on the introduction of a mathematical formalism motivated by recent advances in the fields of algebraic topology and topological data analysis (TDA). This formalism allows us to compare three-dimensional protein structures. Its use in conjunction with two in silico validation strategies (molecular docking and transcriptomic analyses) allowed us to identify a set of potential drug repurposing candidates targeting three viral proteins (3CL viral protease, NSP15 endoribonuclease, and NSP12 RNA-dependent RNA polymerase), which included rutin, dexamethasone, and vemurafenib among others. To our knowledge, it is the first time that a TDA based strategy has been used to compare a massive amount of protein structures with the final objective of performing drug repurposing

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Spectral and topological analysis of the cortical representation of the head position: does hypnotizability matter?

10.1101/442053 ◽

2018 ◽

Author(s):

Esther Ibanez-Marcelo ◽

Lisa Campioni ◽

Diego Manzoni ◽

Enrica L Santarcangelo ◽

Giovanni Petri

Keyword(s):

Spectral Analysis ◽

Data Analysis ◽

Large Scale ◽

Topological Analysis ◽

Head Position ◽

Topological Data Analysis ◽

Proprioceptive Information ◽

Head Positions ◽

First Time ◽

Topological Data

The aim of the study was to assess the EEG correlates of head positions, which have never been studied in humans, in participants with different psychophysiological characteristics, as encoded by their hypnotizability scores. This choice is motivated by earlier studies suggesting different processing of the vestibular/neck proprioceptive information in subjects with high (highs) and low (lows) hypnotizability scores maintaining their head rotated toward one side (RH). We analysed EEG signals recorded in 20 highs and 19 lows in basal conditions (head forward) and during RH, using spectral analysis, which captures changes localized to specific recording sites, and Topological Data Analysis (TDA), which instead describes large-scale differences in processing and representing sensorimotor information. Spectral analysis revealed significant differences related to the head position for alpha1, beta2, beta3, gamma bands, but not to hypnotizability. TDA instead revealed global hypnotizability-related differences in the strengths of the correlations among recording sites during RH. Significant changes were observed in lows on the left parieto-occipital side and in highs in right fronto-parietal region. Significant differences between the two groups were found in the occipital region, where changes were larger in lows than in highs. The study reports findings of the EEG correlates of the head posture for the first time, indicates that hypnotizability modulates its representation/processing on large-scale and that spectral and topological data analysis provide complementary results.

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Evolution of local motifs and topological proximity in self-assembled quasi-crystalline phases

Proceedings of The Royal Society A Mathematical Physical and Engineering Sciences ◽

10.1098/rspa.2020.0170 ◽

2020 ◽

Vol 476 (2241) ◽

pp. 20200170

Author(s):

Martin Cramer Pedersen ◽

Vanessa Robins ◽

Kell Mortensen ◽

Jacob J. K. Kirkensgaard

Keyword(s):

Data Analysis ◽

Self Assembly ◽

Persistent Homology ◽

Three Dimensional ◽

Topological Data Analysis ◽

Structure Identification ◽

Colloidal System ◽

Topological Proximity ◽

Topological Data ◽

Persistence Diagrams

Using methods from the field of topological data analysis, we investigate the self-assembly and emergence of three-dimensional quasi-crystalline structures in a single-component colloidal system. Combining molecular dynamics and persistent homology, we analyse the time evolution of persistence diagrams and particular local structural motifs. Our analysis reveals the formation and dissipation of specific particle constellations in these trajectories, and shows that the persistence diagrams are sensitive to nucleation and convergence to a final structure. Identification of local motifs allows quantification of the similarities between the final structures in a topological sense. This analysis reveals a continuous variation with density between crystalline clathrate, quasi-crystalline, and disordered phases quantified by ‘topological proximity’, a visualization of the Wasserstein distances between persistence diagrams. From a topological perspective, there is a subtle, but direct connection between quasi-crystalline, crystalline and disordered states. Our results demonstrate that topological data analysis provides detailed insights into molecular self-assembly.

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Topological data analysis reveals principles of chromosome structure throughout cellular differentiation

10.1101/540716 ◽

2019 ◽

Cited By ~ 1

Author(s):

Natalie Sauerwald ◽

Yihang Shen ◽

Carl Kingsford

Keyword(s):

Data Analysis ◽

Cell Lines ◽

Cellular Differentiation ◽

Structural Changes ◽

Chromosome Structure ◽

Three Dimensional ◽

Topological Data Analysis ◽

Data Sets ◽

Chromosomal Architecture ◽

Topological Data

AbstractThree-dimensional chromosome structure has a significant influence in many diverse genomic processes and has recently been shown to relate to cellular differentiation. Many methods for describing the chromosomal architecture focus on specific substructures such as topologically-associating domains (TADs) or compartments, but we are still missing a global view of all geometric features of chromosomes. Topological data analysis (TDA) is a mathematically well-founded set of methods to derive robust information about the structure and topology of data sets, making it well-suited to better understand the key features of chromosome structure. By applying TDA to the study of chromosome structure through differentiation across three cell lines, we provide insight into principles of chromosome folding generally, and observe structural changes across lineages. We identify both global and local differences in chromosome topology through differentiation, identifying trends consistent across human cell lines.AvailabilityScripts to reproduce the results from this study can be found at https://github.com/Kingsford-Group/[email protected]

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