PADLOC: a web server for the identification of antiviral defence systems in microbial genomes

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Abstract

Most bacteria and archaea possess multiple antiviral defence systems that protect against infection by phages, archaeal viruses and mobile genetic elements. Our understanding of the diversity of defence systems has increased greatly in the last few years, and many more systems likely await discovery. To identify defence-related genes, we recently developed the Prokaryotic Antiviral Defence LOCator (PADLOC) bioinformatics tool. To increase the accessibility of PADLOC, we describe here the PADLOC web server (freely available at https://padloc.otago.ac.nz), allowing users to analyse whole genomes, metagenomic contigs, plasmids, phages and archaeal viruses. The web server includes a more than 5-fold increase in defence system types detected (since the first release) and expanded functionality enabling detection of CRISPR arrays and retron ncRNAs. Here, we provide user information such as input options, description of the multiple outputs, limitations and considerations for interpretation of the results, and guidance for subsequent analyses. The PADLOC web server also houses a precomputed database of the defence systems in > 230,000 RefSeq genomes. These data reveal two taxa, Campylobacterota and Spriochaetota, with unusual defence system diversity and abundance. Overall, the PADLOC web server provides a convenient and accessible resource for the detection of antiviral defence systems.

Graphical Abstract

The PADLOC web server is a one-stop resource for the identification of antiviral defence systems in microbial genomes.

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Highly accurate protein structure prediction with AlphaFold

John Jumper, Richard Evans, Alexander Pritzel … (2021)

Proteins are essential to life, and understanding their structure can facilitate a mechanistic understanding of their function. Through an enormous experimental effort 1 – 4 , the structures of around 100,000 unique proteins have been determined 5 , but this represents a small fraction of the billions of known protein sequences 6 , 7 . Structural coverage is bottlenecked by the months to years of painstaking effort required to determine a single protein structure. Accurate computational approaches are needed to address this gap and to enable large-scale structural bioinformatics. Predicting the three-dimensional structure that a protein will adopt based solely on its amino acid sequence—the structure prediction component of the ‘protein folding problem’ 8 —has been an important open research problem for more than 50 years 9 . Despite recent progress 10 – 14 , existing methods fall far short of atomic accuracy, especially when no homologous structure is available. Here we provide the first computational method that can regularly predict protein structures with atomic accuracy even in cases in which no similar structure is known. We validated an entirely redesigned version of our neural network-based model, AlphaFold, in the challenging 14th Critical Assessment of protein Structure Prediction (CASP14) 15 , demonstrating accuracy competitive with experimental structures in a majority of cases and greatly outperforming other methods. Underpinning the latest version of AlphaFold is a novel machine learning approach that incorporates physical and biological knowledge about protein structure, leveraging multi-sequence alignments, into the design of the deep learning algorithm. AlphaFold predicts protein structures with an accuracy competitive with experimental structures in the majority of cases using a novel deep learning architecture.

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MUSCLE: multiple sequence alignment with high accuracy and high throughput.

R. C. Edgar (2004)

We describe MUSCLE, a new computer program for creating multiple alignments of protein sequences. Elements of the algorithm include fast distance estimation using kmer counting, progressive alignment using a new profile function we call the log-expectation score, and refinement using tree-dependent restricted partitioning. The speed and accuracy of MUSCLE are compared with T-Coffee, MAFFT and CLUSTALW on four test sets of reference alignments: BAliBASE, SABmark, SMART and a new benchmark, PREFAB. MUSCLE achieves the highest, or joint highest, rank in accuracy on each of these sets. Without refinement, MUSCLE achieves average accuracy statistically indistinguishable from T-Coffee and MAFFT, and is the fastest of the tested methods for large numbers of sequences, aligning 5000 sequences of average length 350 in 7 min on a current desktop computer. The MUSCLE program, source code and PREFAB test data are freely available at http://www.drive5. com/muscle.

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The Protein Data Bank.

H M Berman, J Westbrook, Z. Feng … (2000)

The Protein Data Bank (PDB; http://www.rcsb.org/pdb/ ) is the single worldwide archive of structural data of biological macromolecules. This paper describes the goals of the PDB, the systems in place for data deposition and access, how to obtain further information, and near-term plans for the future development of the resource.

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Author and article information

Contributors

Leighton J Payne:

ORCID: https://orcid.org/0000-0003-2305-6827

Sean Meaden

Mario R Mestre

Chris Palmer

Nicolás Toro:

ORCID: https://orcid.org/0000-0002-3614-807X

Peter C Fineran:

ORCID: https://orcid.org/0000-0002-4639-6704

Simon A Jackson:

ORCID: https://orcid.org/0000-0002-4512-3093

Journal

Journal ID (nlm-ta): Nucleic Acids Res

Journal ID (iso-abbrev): Nucleic Acids Res

Journal ID (publisher-id): nar

Title: Nucleic Acids Research

Publisher: Oxford University Press

ISSN (Print): 0305-1048

ISSN (Electronic): 1362-4962

Publication date Collection: 05 July 2022

Publication date (Electronic): 25 May 2022

Publication date PMC-release: 25 May 2022

Volume: 50

Issue: W1

Pages: W541-W550

Affiliations

Department of Microbiology and Immunology, University of Otago , Dunedin, New Zealand

Biosciences, University of Exeter , Penryn, UK

Independent Researcher , Spain

Information Technology Services Research and Teaching Group, University of Otago , Dunedin, New Zealand

Department of Soil Microbiology and Symbiotic Systems, Estación Experimental del Zaidín, Consejo Superior de Investigaciones Científicas, Structure, Dynamics and Function of Rhizobacterial Genomes, Grupo de Ecología Genética de la Rizosfera , Granada, Spain

Department of Microbiology and Immunology, University of Otago , Dunedin, New Zealand

Genetics Otago, University of Otago , Dunedin, New Zealand

Bioprotection Aotearoa, University of Otago , Dunedin, New Zealand

Maurice Wilkins Centre for Molecular Biodiscovery, University of Otago , Dunedin, New Zealand

Department of Microbiology and Immunology, University of Otago , Dunedin, New Zealand

Genetics Otago, University of Otago , Dunedin, New Zealand

Bioprotection Aotearoa, University of Otago , Dunedin, New Zealand

Maurice Wilkins Centre for Molecular Biodiscovery, University of Otago , Dunedin, New Zealand

Author notes

To whom correspondence should be addressed. Tel: +64 3 479 8428; Email: simon.jackson@ 123456otago.ac.nz

Author information

Leighton J Payne https://orcid.org/0000-0003-2305-6827

Nicolás Toro https://orcid.org/0000-0002-3614-807X

Peter C Fineran https://orcid.org/0000-0002-4639-6704

Simon A Jackson https://orcid.org/0000-0002-4512-3093

Article

Publisher ID: gkac400

DOI: 10.1093/nar/gkac400

PMC ID: 9252829

PubMed ID: 35639517

SO-VID: 96cee48c-29d5-483b-89be-7ca92c3c9355

License:

This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.

History

Date accepted : 05 May 2022

Date revision received : 24 April 2022

Date received : 03 April 2022

Page count

Pages: 10

Funding

Funded by: Royal Society of New Zealand Te Apārangi (RSNZ) Marsden;

Funded by: University of Otago, DOI 10.13039/100008247;

Funded by: Bioprotection Aotearoa;

Funded by: University of Otago Doctoral Scholarship;

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PADLOC: a web server for the identification of antiviral defence systems in microbial genomes

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Genome Integrity

Most cited references 89

Highly accurate protein structure prediction with AlphaFold

MUSCLE: multiple sequence alignment with high accuracy and high throughput.

The Protein Data Bank.

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