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Article ; Online: Dissecting peripheral protein-membrane interfaces.

Thibault Tubiana / Ian Sillitoe / Christine Orengo / Nathalie Reuter

PLoS Computational Biology, Vol 18, Iss 12, p e

2022 Volume 1010346

Abstract: Peripheral membrane proteins (PMPs) include a wide variety of proteins that have in common to bind transiently to the chemically complex interfacial region of membranes through their interfacial binding site (IBS). In contrast to protein-protein or ... ...

Abstract	Peripheral membrane proteins (PMPs) include a wide variety of proteins that have in common to bind transiently to the chemically complex interfacial region of membranes through their interfacial binding site (IBS). In contrast to protein-protein or protein-DNA/RNA interfaces, peripheral protein-membrane interfaces are poorly characterized. We collected a dataset of PMP domains representative of the variety of PMP functions: membrane-targeting domains (Annexin, C1, C2, discoidin C2, PH, PX), enzymes (PLA, PLC/D) and lipid-transfer proteins (START). The dataset contains 1328 experimental structures and 1194 AphaFold models. We mapped the amino acid composition and structural patterns of the IBS of each protein in this dataset, and evaluated which were more likely to be found at the IBS compared to the rest of the domains' accessible surface. In agreement with earlier work we find that about two thirds of the PMPs in the dataset have protruding hydrophobes (Leu, Ile, Phe, Tyr, Trp and Met) at their IBS. The three aromatic amino acids Trp, Tyr and Phe are a hallmark of PMPs IBS regardless of whether they protrude on loops or not. This is also the case for lysines but not arginines suggesting that, unlike for Arg-rich membrane-active peptides, the less membrane-disruptive lysine is preferred in PMPs. Another striking observation was the over-representation of glycines at the IBS of PMPs compared to the rest of their surface, possibly procuring IBS loops a much-needed flexibility to insert in-between membrane lipids. The analysis of the 9 superfamilies revealed amino acid distribution patterns in agreement with their known functions and membrane-binding mechanisms. Besides revealing novel amino acids patterns at protein-membrane interfaces, our work contributes a new PMP dataset and an analysis pipeline that can be further built upon for future studies of PMPs properties, or for developing PMPs prediction tools using for example, machine learning approaches.
Keywords	Biology (General) ; QH301-705.5
Subject code	612
Language	English
Publishing date	2022-12-01T00:00:00Z
Publisher	Public Library of Science (PLoS)
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Tracing Evolution Through Protein Structures

Nicola Bordin / Ian Sillitoe / Jonathan G. Lees / Christine Orengo

Frontiers in Molecular Biosciences, Vol

Nature Captured in a Few Thousand Folds

2021 Volume 8

Abstract: This article is dedicated to the memory of Cyrus Chothia, who was a leading light in the world of protein structure evolution. His elegant analyses of protein families and their mechanisms of structural and functional evolution provided important ... ...

Abstract	This article is dedicated to the memory of Cyrus Chothia, who was a leading light in the world of protein structure evolution. His elegant analyses of protein families and their mechanisms of structural and functional evolution provided important evolutionary and biological insights and firmly established the value of structural perspectives. He was a mentor and supervisor to many other leading scientists who continued his quest to characterise structure and function space. He was also a generous and supportive colleague to those applying different approaches. In this article we review some of his accomplishments and the history of protein structure classifications, particularly SCOP and CATH. We also highlight some of the evolutionary insights these two classifications have brought. Finally, we discuss how the expansion and integration of protein sequence data into these structural families helps reveal the dark matter of function space and can inform the emergence of novel functions in Metazoa. Since we cover 25 years of structural classification, it has not been feasible to review all structure based evolutionary studies and hence we focus mainly on those undertaken by the SCOP and CATH groups and their collaborators.
Keywords	bioinformatics and computational biology ; protein structural and functional analysis ; structural bioinformatics ; protein evolution ; protein structure classification ; Biology (General) ; QH301-705.5
Subject code	612
Language	English
Publishing date	2021-05-01T00:00:00Z
Publisher	Frontiers Media S.A.
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Broad functional profiling of fission yeast proteins using phenomics and machine learning

María Rodríguez-López / Nicola Bordin / Jon Lees / Harry Scholes / Shaimaa Hassan / Quentin Saintain / Stephan Kamrad / Christine Orengo / Jürg Bähler

eLife, Vol

2023 Volume 12

Abstract: Many proteins remain poorly characterized even in well-studied organisms, presenting a bottleneck for research. We applied phenomics and machine-learning approaches with Schizosaccharomyces pombe for broad cues on protein functions. We assayed colony- ... ...

Abstract	Many proteins remain poorly characterized even in well-studied organisms, presenting a bottleneck for research. We applied phenomics and machine-learning approaches with Schizosaccharomyces pombe for broad cues on protein functions. We assayed colony-growth phenotypes to measure the fitness of deletion mutants for 3509 non-essential genes in 131 conditions with different nutrients, drugs, and stresses. These analyses exposed phenotypes for 3492 mutants, including 124 mutants of ‘priority unstudied’ proteins conserved in humans, providing varied functional clues. For example, over 900 proteins were newly implicated in the resistance to oxidative stress. Phenotype-correlation networks suggested roles for poorly characterized proteins through ‘guilt by association’ with known proteins. For complementary functional insights, we predicted Gene Ontology (GO) terms using machine learning methods exploiting protein-network and protein-homology data (NET-FF). We obtained 56,594 high-scoring GO predictions, of which 22,060 also featured high information content. Our phenotype-correlation data and NET-FF predictions showed a strong concordance with existing PomBase GO annotations and protein networks, with integrated analyses revealing 1675 novel GO predictions for 783 genes, including 47 predictions for 23 priority unstudied proteins. Experimental validation identified new proteins involved in cellular aging, showing that these predictions and phenomics data provide a rich resource to uncover new protein functions.
Keywords	cell phenotype ; functional genomics ; unknown protein ; computational prediction ; gene ontology ; machine learning ; Medicine ; R ; Science ; Q ; Biology (General) ; QH301-705.5
Subject code	006
Language	English
Publishing date	2023-10-01T00:00:00Z
Publisher	eLife Sciences Publications Ltd
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: FunFam protein families improve residue level molecular function prediction

Linus Scheibenreif / Maria Littmann / Christine Orengo / Burkhard Rost

BMC Bioinformatics, Vol 20, Iss 1, Pp 1-

2019 Volume 9

Abstract: Abstract Background The CATH database provides a hierarchical classification of protein domain structures including a sub-classification of superfamilies into functional families (FunFams). We analyzed the similarity of binding site annotations in these ... ...

Abstract	Abstract Background The CATH database provides a hierarchical classification of protein domain structures including a sub-classification of superfamilies into functional families (FunFams). We analyzed the similarity of binding site annotations in these FunFams and incorporated FunFams into the prediction of protein binding residues. Results FunFam members agreed, on average, in 36.9 ± 0.6% of their binding residue annotations. This constituted a 6.7-fold increase over randomly grouped proteins and a 1.2-fold increase (1.1-fold on the same dataset) over proteins with the same enzymatic function (identical Enzyme Commission, EC, number). Mapping de novo binding residue prediction methods (BindPredict-CCS, BindPredict-CC) onto FunFam resulted in consensus predictions for those residues that were aligned and predicted alike (binding/non-binding) within a FunFam. This simple consensus increased the F1-score (for binding) 1.5-fold over the original prediction method. Variation of the threshold for how many proteins in the consensus prediction had to agree provided a convenient control of accuracy/precision and coverage/recall, e.g. reaching a precision as high as 60.8 ± 0.4% for a stringent threshold. Conclusions The FunFams outperformed even the carefully curated EC numbers in terms of agreement of binding site residues. Additionally, we assume that our proof-of-principle through the prediction of protein binding residues will be relevant for many other solutions profiting from FunFams to infer functional information at the residue level.
Keywords	Protein function ; Protein families ; Functional families ; Binding residue prediction ; Protein binding sites ; CATH ; Computer applications to medicine. Medical informatics ; R858-859.7 ; Biology (General) ; QH301-705.5
Subject code	500
Language	English
Publishing date	2019-07-01T00:00:00Z
Publisher	BMC
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: AlphaFold2 reveals commonalities and novelties in protein structure space for 21 model organisms

Nicola Bordin / Ian Sillitoe / Vamsi Nallapareddy / Clemens Rauer / Su Datt Lam / Vaishali P. Waman / Neeladri Sen / Michael Heinzinger / Maria Littmann / Stephanie Kim / Sameer Velankar / Martin Steinegger / Burkhard Rost / Christine Orengo

Communications Biology, Vol 6, Iss 1, Pp 1-

2023 Volume 12

Abstract: A new protein domain classification protocol incorporating deep learning strategies for detecting sequence and structure similarities between domain is used to systematically study and analyse the predicted AlphaFold2 structural models for proteins of 21 ...

Abstract	A new protein domain classification protocol incorporating deep learning strategies for detecting sequence and structure similarities between domain is used to systematically study and analyse the predicted AlphaFold2 structural models for proteins of 21 organisms.
Keywords	Biology (General) ; QH301-705.5
Language	English
Publishing date	2023-02-01T00:00:00Z
Publisher	Nature Portfolio
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Assigning protein function from domain-function associations using DomFun

Elena Rojano / Fernando M. Jabato / James R. Perkins / José Córdoba-Caballero / Federico García-Criado / Ian Sillitoe / Christine Orengo / Juan A. G. Ranea / Pedro Seoane-Zonjic

BMC Bioinformatics, Vol 23, Iss 1, Pp 1-

2022 Volume 19

Abstract: Abstract Background Protein function prediction remains a key challenge. Domain composition affects protein function. Here we present DomFun, a Ruby gem that uses associations between protein domains and functions, calculated using multiple indices based ...

Abstract	Abstract Background Protein function prediction remains a key challenge. Domain composition affects protein function. Here we present DomFun, a Ruby gem that uses associations between protein domains and functions, calculated using multiple indices based on tripartite network analysis. These domain-function associations are combined at the protein level, to generate protein-function predictions. Results We analysed 16 tripartite networks connecting homologous superfamily and FunFam domains from CATH-Gene3D with functional annotations from the three Gene Ontology (GO) sub-ontologies, KEGG, and Reactome. We validated the results using the CAFA 3 benchmark platform for GO annotation, finding that out of the multiple association metrics and domain datasets tested, Simpson index for FunFam domain-function associations combined with Stouffer’s method leads to the best performance in almost all scenarios. We also found that using FunFams led to better performance than superfamilies, and better results were found for GO molecular function compared to GO biological process terms. DomFun performed as well as the highest-performing method in certain CAFA 3 evaluation procedures in terms of $$F_{max}$$ F max and $$S_{min}$$ S min We also implemented our own benchmark procedure, Pathway Prediction Performance (PPP), which can be used to validate function prediction for additional annotations sources, such as KEGG and Reactome. Using PPP, we found similar results to those found with CAFA 3 for GO, moreover we found good performance for the other annotation sources. As with CAFA 3, Simpson index with Stouffer’s method led to the top performance in almost all scenarios. Conclusions DomFun shows competitive performance with other methods evaluated in CAFA 3 when predicting proteins function with GO, although results vary depending on the evaluation procedure. Through our own benchmark procedure, PPP, we have shown it can also make accurate predictions for KEGG and Reactome. It performs best when using FunFams, combining Simpson index derived domain-function associations using Stouffer’s method. The tool has been implemented so that it can be easily adapted to incorporate other protein features, such as domain data from other sources, amino acid k-mers and motifs. The DomFun Ruby gem is available from https://rubygems.org/gems/DomFun . Code maintained at https://github.com/ElenaRojano/DomFun . Validation procedure scripts can be found at https://github.com/ElenaRojano/DomFun_project .
Keywords	Function prediction ; CATH ; DomFun ; Protein domains ; CAFA ; Computer applications to medicine. Medical informatics ; R858-859.7 ; Biology (General) ; QH301-705.5
Subject code	612
Language	English
Publishing date	2022-01-01T00:00:00Z
Publisher	BMC
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Transmission of SARS-CoV-2 from humans to animals and potential host adaptation

Cedric C. S. Tan / Su Datt Lam / Damien Richard / Christopher J. Owen / Dorothea Berchtold / Christine Orengo / Meera Surendran Nair / Suresh V. Kuchipudi / Vivek Kapur / Lucy van Dorp / François Balloux

Nature Communications, Vol 13, Iss 1, Pp 1-

2022 Volume 13

Abstract: Here, Tan et al. find that the rapid spread of SARS-CoV-2 in mink and deer required minimal adaptation, has only caused moderate changes to the evolutionary trajectory of the virus, and has not led to viral mutations that greatly improve human ... ...

Abstract	Here, Tan et al. find that the rapid spread of SARS-CoV-2 in mink and deer required minimal adaptation, has only caused moderate changes to the evolutionary trajectory of the virus, and has not led to viral mutations that greatly improve human transmission thus far.
Keywords	Science ; Q
Language	English
Publishing date	2022-05-01T00:00:00Z
Publisher	Nature Portfolio
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article: The history of the CATH structural classification of protein domains

Sillitoe, Ian / Christine Orengo / Janet Thornton / Natalie Dawson

Biochimie. 2015 Dec., v. 119

2015

Abstract: This article presents a historical review of the protein structure classification database CATH. Together with the SCOP database, CATH remains comprehensive and reasonably up-to-date with the now more than 100,000 protein structures in the PDB. We review ...

Abstract	This article presents a historical review of the protein structure classification database CATH. Together with the SCOP database, CATH remains comprehensive and reasonably up-to-date with the now more than 100,000 protein structures in the PDB. We review the expansion of the CATH and SCOP resources to capture predicted domain structures in the genome sequence data and to provide information on the likely functions of proteins mediated by their constituent domains. The establishment of comprehensive function annotation resources has also meant that domain families can be functionally annotated allowing insights into functional divergence and evolution within protein families.
Keywords	databases ; evolution ; nucleotide sequences ; protein structure ; proteins
Language	English
Dates of publication	2015-12
Size	p. 209-217.
Publishing place	Elsevier B.V.
Document type	Article
ZDB-ID	120345-9
ISSN	0300-9084
ISSN	0300-9084
DOI	10.1016/j.biochi.2015.08.004
Database	NAL-Catalogue (AGRICOLA)

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Article ; Online: ISCB's Initial Reaction to The New England Journal of Medicine Editorial on Data Sharing.

Bonnie Berger / Terry Gaasterland / Thomas Lengauer / Christine Orengo / Bruno Gaeta / Scott Markel / Alfonso Valencia

PLoS Computational Biology, Vol 12, Iss 3, p e

2016 Volume 1004816

Keywords	Biology (General) ; QH301-705.5
Language	English
Publishing date	2016-03-01T00:00:00Z
Publisher	Public Library of Science (PLoS)
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Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Novel Computational Protocols for Functionally Classifying and Characterising Serine Beta-Lactamases.

David Lee / Sayoni Das / Natalie L Dawson / Dragana Dobrijevic / John Ward / Christine Orengo

PLoS Computational Biology, Vol 12, Iss 6, p e

2016 Volume 1004926

Abstract: Beta-lactamases represent the main bacterial mechanism of resistance to beta-lactam antibiotics and are a significant challenge to modern medicine. We have developed an automated classification and analysis protocol that exploits structure- and sequence- ... ...

Abstract	Beta-lactamases represent the main bacterial mechanism of resistance to beta-lactam antibiotics and are a significant challenge to modern medicine. We have developed an automated classification and analysis protocol that exploits structure- and sequence-based approaches and which allows us to propose a grouping of serine beta-lactamases that more consistently captures and rationalizes the existing three classification schemes: Classes, (A, C and D, which vary in their implementation of the mechanism of action); Types (that largely reflect evolutionary distance measured by sequence similarity); and Variant groups (which largely correspond with the Bush-Jacoby clinical groups). Our analysis platform exploits a suite of in-house and public tools to identify Functional Determinants (FDs), i.e. residue sites, responsible for conferring different phenotypes between different classes, different types and different variants. We focused on Class A beta-lactamases, the most highly populated and clinically relevant class, to identify FDs implicated in the distinct phenotypes associated with different Class A Types and Variants. We show that our FunFHMMer method can separate the known beta-lactamase classes and identify those positions likely to be responsible for the different implementations of the mechanism of action in these enzymes. Two novel algorithms, ASSP and SSPA, allow detection of FD sites likely to contribute to the broadening of the substrate profiles. Using our approaches, we recognise 151 Class A types in UniProt. Finally, we used our beta-lactamase FunFams and ASSP profiles to detect 4 novel Class A types in microbiome samples. Our platforms have been validated by literature studies, in silico analysis and some targeted experimental verification. Although developed for the serine beta-lactamases they could be used to classify and analyse any diverse protein superfamily where sub-families have diverged over both long and short evolutionary timescales.
Keywords	Biology (General) ; QH301-705.5
Language	English
Publishing date	2016-06-01T00:00:00Z
Publisher	Public Library of Science (PLoS)
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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