108 research outputs found
Mycobacterium tuberculosis Transcriptome In Vivo Studies – A Key to Understand the Pathogen Adaptation Mechanism
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DePolymerase Predictor (DePP): a machine learning tool for the targeted identification of phage depolymerases
No full textAbstract Biofilm production plays a clinically significant role in the pathogenicity of many bacteria, limiting our ability to apply antimicrobial agents and contributing in particular to the pathogenesis of chronic infections. Bacteriophage depolymerases, leveraged by these viruses to circumvent biofilm mediated resistance, represent a potentially powerful weapon in the fight against antibiotic resistant bacteria. Such enzymes are able to degrade the extracellular matrix that is integral to the formation of all biofilms and as such would allow complementary therapies or disinfection procedures to be successfully applied. In this manuscript, we describe the development and application of a machine learning based approach towards the identification of phage depolymerases. We demonstrate that on the basis of a relatively limited number of experimentally proven enzymes and using an amino acid derived feature vector that the development of a powerful model with an accuracy on the order of 90% is possible, showing the value of such approaches in protein functional annotation and the discovery of novel therapeutic agents
ProFlex as a linguistic bridge for decoding protein dynamics in normal mode analysis
No full textArtificial intelligence is revolutionizing structural bioinformatics, with AlphaFold arguably being the most impactful development to date. The structural atlases generated by these methods present significant opportunities for unraveling biological mysteries but also pose challenges in leveraging such massive datasets effectively. In this work, we explore the dynamic landscape of hundreds of thousands of AlphaFold-predicted structures using normal mode analysis. The resulting data serve to empirically define an alphabet summarizing relative protein flexibility, termed ProFlex. Leveraging ProFlex, we describe the flexibility information space occupied by this massive dataset. We believe leveraging the data compression offered by ProFlex-like approaches opens opportunities for understanding protein function, refining structural predictions, and rendering analyses computationally tractable
Genomic hypervariability of phage Andromeda is unique among known dsDNA viruses
No full textA new lytic bacteriophage Andromeda, specific to the economically important plant pathogen Pseudomonas syringae, was isolated and characterised. It belongs to the Podoviridae family, Autographivirinae subfamily and possesses a linear dsDNA genome of 40,008 bp with four localised nicks. Crucially, Andromeda’s genome has no less than 80 hypervariable sites (SNPs), which show genome wide distribution resulting in heterogenous populations of this phage reminiscent of those of RNA virus quasispecies. Andromeda has no nucleotide sequence homology to phage phiNFS, a member of phiKMVviruses, in which a similar phenomenon was discovered. We show that Andromeda and Andromeda-related phages form a group within the Autographivirinae, designated here as the “ExophiKMVviruses”. The “ExophiKMVviruses” were revealed to share conservation of gene order with core phiKMVviruses despite their sequence-based relationship to SP6-related phages. Our findings suggest that genomic hypervariability might be a feature that occurs among various Autographivirinae groups
Genomic hypervariability of phage Andromeda is unique among known dsDNA viruses
No full textA new lytic bacteriophage Andromeda, specific to the economically important plant pathogen Pseudomonas syringae, was isolated and characterised. It belongs to the Podoviridae family, Autographivirinae subfamily and possesses a linear dsDNA genome of 40,008 bp with four localised nicks. Crucially, Andromeda’s genome has no less than 80 hypervariable sites (SNPs), which show genome wide distribution resulting in heterogenous populations of this phage reminiscent of those of RNA virus quasispecies. Andromeda has no nucleotide sequence homology to phage phiNFS, a member of phiKMVviruses, in which a similar phenomenon was discovered. We show that Andromeda and Andromeda-related phages form a group within the Autographivirinae, designated here as the “ExophiKMVviruses”. The “ExophiKMVviruses” were revealed to share conservation of gene order with core phiKMVviruses despite their sequence-based relationship to SP6-related phages. Our findings suggest that genomic hypervariability might be a feature that occurs among various Autographivirinae groups
Additional file 2 of DePolymerase Predictor (DePP): a machine learning tool for the targeted identification of phage depolymerases
No full textAdditional file 2: Table S2. Training set used to fuel the machine learning model. The table contains the sequences of the positive and negative cases along with the entire feature set generated for the downstream modelling
Additional file 1 of DePolymerase Predictor (DePP): a machine learning tool for the targeted identification of phage depolymerases
No full textAdditional file 1: Table S1. Overview of the phage depolymerase database obtained following literature search for experimentally demonstrated enzymes. The table outlines the phages and their hosts along with the target of the depolymerase. The article referencing the phage is given in full at the bottom of the table
Additional file 3 of DePolymerase Predictor (DePP): a machine learning tool for the targeted identification of phage depolymerases
No full textAdditional file 3: Table S3. Rankings of depolymerase predictions in the context of whole phage genomes. Sequences were obtained for phages containing computationally predicted depolymerases described in Pires et al.. The ranking is given relative to the total number of ORFs predicted for each of the phages presented in the table
Leveraging Structural Relationships as a Novel Mode of Viral Classification
No full text100 years have passed since the independent discovery of the humble bacteriophage (phage) by Frederick Twort and Felix d’Herelle in 1915 and 1917 respectively, and since then, it has become commonly accepted that phages represent the most abundant biological entities on Earth. Despite this fact, viral taxonomy lies in extremely treacherous waters, ever changing to accommodate the next series of phylogenetic mysteries.The utilisation of genes such as the terminase large sub-unit can in some cases provide a robust taxonomic marker, but this is often found to fail at higher taxonomic levels. In addition, the rapid evolutionary dynamics and highly modular nature of phages provide yet more phylogenetic roadblocks, necessitating additional and multifaceted approaches as a means of resolution.Here, we describe a novel approach towards the taxonomic classification of phage systems. Tools for accurately predicting the three dimensional structure of proteins are improving at an unprecedented rate due to the fact that the number of protein sequences far exceeds the number of experimentally determined structures. Our approach leverages these methods through a pipeline which compares models of phage marker genes in order to permit the inference of phylogenetic relationships based on cross model superimposition. We hope this method will supplement other approaches in providing a more holistic approach to viral classification
Leveraging structural relationships as a novel mode of viral classification
No full text100 years have passed since the independent discovery of the humble bacteriophage (phage) by Frederick Twort and Felix d’Herelle in 1915 and 1917 respectively, and since then, it has become commonly accepted that phages represent the most abundant biological entities on Earth. Despite this fact, viral taxonomy lies in extremely treacherous waters, ever changing to accommodate the next series of phylogenetic mysteries. The utilisation of genes such as the terminase large sub-unit can in some cases provide a robust taxonomic marker, but this is often found to fail at higher taxonomic levels. In addition, the rapid evolutionary dynamics and highly modular nature of phages provide yet more phylogenetic roadblocks, necessitating additional and multifaceted approaches as a means of resolution. Here, we describe a novel approach towards the taxonomic classification of phage systems. Tools for accurately predicting the three dimensional structure of proteins are improving at an unprecedented rate due to the fact that the number of protein sequences far exceeds the number of experimentally determined structures. Our approach leverages these methods through a pipeline which compares models of phage marker genes in order to permit the inference of phylogenetic relationships based on cross model superimposition. We hope this method will supplement other approaches in providing a more holistic approach to viral classification.<br/
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