DGraph Clusters Flaviviruses and β-Coronaviruses According to Their Hosts, Disease Type, and Human Cell Receptors

Benjamin A Braun; Catherine H Schein; Werner Braun

doi:10.1177/11779322211020316

DGraph Clusters Flaviviruses and β-Coronaviruses According to Their Hosts, Disease Type, and Human Cell Receptors

Bioinform Biol Insights. 2021 Jun 7:15:11779322211020316. doi: 10.1177/11779322211020316. eCollection 2021.

Authors

Benjamin A Braun¹, Catherine H Schein^{2

3}, Werner Braun^{2

3}

Affiliations

¹ Department of Computer Science, Stanford University, Stanford, CA, USA.
² Department of Biochemistry and Molecular Biology, The University of Texas Medical Branch, Galveston, TX, USA.
³ Institute for Human Infections and Immunity, The University of Texas Medical Branch, Galveston, TX, USA.

Abstract

Motivation: There is a need for rapid and easy-to-use, alignment-free methods to cluster large groups of protein sequence data. Commonly used phylogenetic trees based on alignments can be used to visualize only a limited number of protein sequences. DGraph, introduced here, is an application developed to generate 2-dimensional (2D) maps based on similarity scores for sequences. The program automatically calculates and graphically displays property distance (PD) scores based on physico-chemical property (PCP) similarities from an unaligned list of FASTA files. Such "PD-graphs" show the interrelatedness of the sequences, whereby clusters can reveal deeper connectivities.

Results: Property distance graphs generated for flavivirus (FV), enterovirus (EV), and coronavirus (CoV) sequences from complete polyproteins or individual proteins are consistent with biological data on vector types, hosts, cellular receptors, and disease phenotypes. Property distance graphs separate the tick- from the mosquito-borne FV, cluster viruses that infect bats, camels, seabirds, and humans separately. The clusters correlate with disease phenotype. The PD method segregates the β-CoV spike proteins of severe acute respiratory syndrome (SARS), severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), and Middle East respiratory syndrome (MERS) sequences from other human pathogenic CoV, with clustering consistent with cellular receptor usage. The graphs also suggest evolutionary relationships that may be difficult to determine with conventional bootstrapping methods that require postulating an ancestral sequence.

Keywords: Alignment free clustering; Enterovirus classification; Physical-chemical property (PCP) consensus; Property distances (PD) of viral sequences; SARS origin.

Abstract

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