The terminal enzymes of (bacterio)chlorophyll biosynthesis

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Abstract

(Bacterio)chlorophylls are modified tetrapyrroles that are used by phototrophic organisms to harvest solar energy, powering the metabolic processes that sustain most of the life on Earth. Biosynthesis of these pigments involves enzymatic modification of the side chains and oxidation state of a porphyrin precursor, modifications that differ by species and alter the absorption properties of the pigments. (Bacterio)chlorophylls are coordinated by proteins that form macromolecular assemblies to absorb light and transfer excitation energy to a special pair of redox-active (bacterio)chlorophyll molecules in the photosynthetic reaction centre. Assembly of these pigment–protein complexes is aided by an isoprenoid moiety esterified to the (bacterio)chlorin macrocycle, which anchors and stabilizes the pigments within their protein scaffolds. The reduction of the isoprenoid ‘tail’ and its addition to the macrocycle are the final stages in (bacterio)chlorophyll biosynthesis and are catalysed by two enzymes, geranylgeranyl reductase and (bacterio)chlorophyll synthase. These enzymes work in conjunction with photosynthetic complex assembly factors and the membrane biogenesis machinery to synchronize delivery of the pigments to the proteins that coordinate them. In this review, we summarize current understanding of the catalytic mechanism, substrate recognition and regulation of these crucial enzymes and their involvement in thylakoid biogenesis and photosystem repair in oxygenic phototrophs.

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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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Reactive oxygen species: metabolism, oxidative stress, and signal transduction.

Klaus Apel, Heribert Hirt (2004)

Several reactive oxygen species (ROS) are continuously produced in plants as byproducts of aerobic metabolism. Depending on the nature of the ROS species, some are highly toxic and rapidly detoxified by various cellular enzymatic and nonenzymatic mechanisms. Whereas plants are surfeited with mechanisms to combat increased ROS levels during abiotic stress conditions, in other circumstances plants appear to purposefully generate ROS as signaling molecules to control various processes including pathogen defense, programmed cell death, and stomatal behavior. This review describes the mechanisms of ROS generation and removal in plants during development and under biotic and abiotic stress conditions. New insights into the complexity and roles that ROS play in plants have come from genetic analyses of ROS detoxifying and signaling mutants. Considering recent ROS-induced genome-wide expression analyses, the possible functions and mechanisms for ROS sensing and signaling in plants are compared with those in animals and yeast.

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Improved protein structure prediction using potentials from deep learning

Andrew W. Senior, Richard Evans, John Jumper … (2020)

Protein structure prediction can be used to determine the three-dimensional shape of a protein from its amino acid sequence1. This problem is of fundamental importance as the structure of a protein largely determines its function2; however, protein structures can be difficult to determine experimentally. Considerable progress has recently been made by leveraging genetic information. It is possible to infer which amino acid residues are in contact by analysing covariation in homologous sequences, which aids in the prediction of protein structures3. Here we show that we can train a neural network to make accurate predictions of the distances between pairs of residues, which convey more information about the structure than contact predictions. Using this information, we construct a potential of mean force4 that can accurately describe the shape of a protein. We find that the resulting potential can be optimized by a simple gradient descent algorithm to generate structures without complex sampling procedures. The resulting system, named AlphaFold, achieves high accuracy, even for sequences with fewer homologous sequences. In the recent Critical Assessment of Protein Structure Prediction5 (CASP13)-a blind assessment of the state of the field-AlphaFold created high-accuracy structures (with template modelling (TM) scores6 of 0.7 or higher) for 24 out of 43 free modelling domains, whereas the next best method, which used sampling and contact information, achieved such accuracy for only 14 out of 43 domains. AlphaFold represents a considerable advance in protein-structure prediction. We expect this increased accuracy to enable insights into the function and malfunction of proteins, especially in cases for which no structures for homologous proteins have been experimentally determined7.

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Author and article information

Contributors

Matthew S. Proctor:

ORCID: http://orcid.org/0000-0002-1484-850X

Role: ConceptualizationRole: VisualizationRole: Writing – original draftRole: Writing – review & editing

George A. Sutherland:

ORCID: http://orcid.org/0000-0002-6319-4637

Role: VisualizationRole: Writing – review & editing

Daniel P. Canniffe:

ORCID: http://orcid.org/0000-0002-5022-0437

Role: Writing – review & editing

Andrew Hitchcock:

ORCID: http://orcid.org/0000-0001-6572-434X

Role: ConceptualizationRole: Writing – original draftRole: Writing – review & editing

Journal

Journal ID (nlm-ta): R Soc Open Sci

Journal ID (iso-abbrev): R Soc Open Sci

Journal ID (publisher-id): RSOS

Journal ID (hwp): royopensci

Title: Royal Society Open Science

Publisher: The Royal Society

ISSN (Electronic): 2054-5703

Publication date (Electronic, pub): May 4, 2022

Publication date (Electronic, collection): May 2022

Publication date PMC-release: May 4, 2022

Volume: 9

Issue: 5

Electronic Location Identifier: 211903

Affiliations

[ ¹ ] Plants, Photosynthesis and Soil, School of Biosciences, University of Sheffield, , Firth Court, Western Bank, Sheffield S10 2TN, UK

[ ² ] Biochemistry and Systems Biology, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, , Crown Street, Liverpool L69 7ZB, UK

Author notes

One contribution to the ‘Life Sciences New Talent special collection’.

Electronic supplementary material is available online at https://doi.org/10.6084/m9.figshare.c.5953393.

Author information

Matthew S. Proctor http://orcid.org/0000-0002-1484-850X

George A. Sutherland http://orcid.org/0000-0002-6319-4637

Daniel P. Canniffe http://orcid.org/0000-0002-5022-0437

Andrew Hitchcock http://orcid.org/0000-0001-6572-434X

Article

Publisher ID: rsos211903

DOI: 10.1098/rsos.211903

PMC ID: 9066304

PubMed ID: 35573041

SO-VID: ed1c6287-431f-49c4-b0f3-418f1628ad39

License:

Published by the Royal Society under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/4.0/, which permits unrestricted use, provided the original author and source are credited.

History

Date received : December 3, 2021

Date accepted : March 29, 2022

Funding

Funded by: Royal Society, http://dx.doi.org/10.13039/501100000288;

Award ID: URF\R1\191548

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The terminal enzymes of (bacterio)chlorophyll biosynthesis

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Most cited references 211

Highly accurate protein structure prediction with AlphaFold

Reactive oxygen species: metabolism, oxidative stress, and signal transduction.

Improved protein structure prediction using potentials from deep learning

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