Biology subjects

Orengo, C.

Publications and source records attributed to Orengo, C..

28 records · Page 2Linked to original sources

bioRxiv2022

Dissecting peripheral protein-membrane interfaces

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.

bioinformatics↗

bioRxiv2022

AlphaFold2 reveals commonalities and novelties in protein structure space for 21 model organisms

Over the last year, there have been substantial improvements in protein structure prediction, particularly in methods like DeepMinds AlphaFold2 (AF2) that exploit deep learning strategies. Here we report a new CATH-Assign protocol which is used to analyse the first tranche of AF2 models predicted for 21 model organisms and discuss insights these models bring on the nature of protein structure space. We analyse good quality models and those with no unusual structural characteristics, i.e., features rarely seen in experimental structures. For the [~]370,000 models that meet these criteria, we observe that 92% can be assigned to evolutionary superfamilies in CATH. The remaining domains cluster into 2,367 putative novel superfamilies. Detailed manual analysis on a subset of 618 of those which had at least one human relative revealed some extremely remote homologies and some further unusual features, but 26 could be confirmed as novel superfamilies and one of these has an alpha-beta propeller architectural arrangement never seen before. By clustering both experimental and predicted AF2 domain structures into distinct global fold groups, we observe that the new AF2 models in CATH increase information on structural diversity by 36%. This expansion in structural diversity will help to reveal associated functional diversity not previously detected. Our novel CATH-Assign protocol scales well and will be able to harness the huge expansion (at least 100 million models) in structural data promised by DeepMind to provide more comprehensive coverage of even the most diverse superfamilies to help rationalise evolutionary changes in their functions.

bioinformatics↗

bioRxiv2022

CATHe: Detection of remote homologues for CATH superfamilies using embeddings from protein language models

1.CATH is a protein domain classification resource that combines an automated workflow of structure and sequence comparison alongside expert manual curation to construct a hierarchical classification of evolutionary and structural relationships. The aim of this study was to develop algorithms for detecting remote homologues that might be missed by state-of-the-art HMM-based approaches. The proposed algorithm for this task (CATHe) combines a neural network with sequence representations obtained from protein language models. The employed dataset consisted of remote homologues that had less than 20% sequence identity. The CATHe models trained on 1773 largest, and 50 largest CATH superfamilies had an accuracy of 85.6+-0.4, and 98.15+-0.30 respectively. To examine whether CATHe was able to detect more remote homologues than HMM-based approaches, we employed a dataset consisting of protein regions that had annotations in Pfam, but not in CATH. For this experiment, we used highly reliable CATHe predictions (expected error rate <0.5%), which provided CATH annotations for 4.62 million Pfam domains. For a subset of these domains from homo sapiens, we structurally validated 90.86% of the predictions by comparing their corresponding AlphaFold structures with experimental structures from the CATHe predicted superfamilies.

bioinformatics↗

bioRxiv2021

Insertions in the SARS-CoV-2 Spike N-Terminal Domain May Aid COVID-19 Transmission

Coronavirus disease 2019 (COVID-19) caused by SARS-CoV-2 is an ongoing pandemic that causes significant health/socioeconomic burden. Variants of concern (VOCs) have emerged which may affect transmissibility, disease severity and re-infection risk. Most studies focus on the receptor-binding domain (RBD) of the Spike protein. However, some studies suggest that the Spike N-terminal domain (NTD) may have a role in facilitating virus entry via sialic-acid receptor binding. Furthermore, most VOCs include novel NTD variants. Recent analyses demonstrated that NTD insertions in VOCs tend to lie close to loop regions likely to be involved in binding sialic acids. We extended the structural characterisation of these putative sugar binding pockets and explored whether variants could enhance the binding to sialic acids and therefore to the host membrane, thereby contributing to increased transmissibility. We found that recent NTD insertions in VOCs (i.e., Gamma, Delta and Omicron variants) and emerging variants of interest (VOIs) (i.e., Iota, Lambda, Theta variants) frequently lie close to known and putative sugar-binding pockets. For some variants, including the recent Omicron VOC, we find increases in predicted sialic acid binding energy, compared to the original SARS-CoV-2, which may contribute to increased transmission. We examined the similarity of NTD across a range of related Betacoronaviruses to determine whether the putative sugar-binding pockets are sufficiently similar to be exploited in drug design. Despite global sequence and structure similarity, most sialic-acid binding pockets of NTD vary across related coronaviruses. Typically, SARS-CoV-2 possesses additional loops in these pockets that increase contact with polysaccharides. Our work suggests ongoing evolutionary tuning of the sugar-binding pockets in the virus. Whilst three of the pockets are too structurally variable to be amenable to pan Betacoronavirus drug design, we detected a fourth pocket that is highly structurally conserved and could therefore be investigated in pursuit of a generic drug. Our structure-based analyses help rationalise the effects of VOCs and provide hypotheses for experiments. For example, the Omicron variant, which has increased binding to sialic acids in pocket 3, has a rather unique insertion near pocket 3. Our work suggests a strong need for experimental monitoring of VOC changes in NTD.

bioinformatics↗

bioRxiv2021

Characterizing disease-associated human proteins without available protein structures or homologues

Mutations in human proteins lead to diseases. The structure of these proteins can help understand the mechanism of such diseases and develop therapeutics against them. With improved deep learning techniques such as RoseTTAFold and AlphaFold, we can predict the structure of proteins even in the absence of structural homologues. We modeled and extracted the domains from 553 disease-associated human proteins without known protein structures or close homologues in the Protein Databank (PDB). We noticed that the model quality was higher and the RMSD lower between AlphaFold and RoseTTAFold models for domains that could be assigned to CATH families as compared to those which could only be assigned to Pfam families of unknown structure or could not be assigned to either. We predicted ligand-binding sites, protein-protein interfaces, conserved residues in these predicted structures. We then explored whether the disease-associated missense mutations were in the proximity of these predicted functional sites, if they destabilized the protein structure based on ddG calculations or if they were predicted to be pathogenic. We could explain 80% of these disease-associated mutations based on proximity to functional sites, structural destabilization or pathogenicity. When compared to polymorphisms a larger percentage of disease associated missense mutations were buried, closer to predicted functional sites, predicted as destabilising and/or pathogenic. Usage of models from the two state-of-the-art techniques provide better confidence in our predictions, and we explain 93 additional mutations based on RoseTTAFold models which could not be explained based solely on AlphaFold models.

bioinformatics↗

bioRxiv2021

Contrastive learning on protein embeddings enlightens midnight zone at lightning speed

Experimental structures are leveraged through multiple sequence alignments, or more generally through homology-based inference (HBI), facilitating the transfer of information from a protein with known annotation to a query without any annotation. A recent alternative expands the concept of HBI from sequence-distance lookup to embedding-based annotation transfer (EAT). These embeddings are derived from protein Language Models (pLMs). Here, we introduce using single protein representations from pLMs for contrastive learning. This learning procedure creates a new set of embeddings that optimizes constraints captured by hierarchical classifications of protein 3D structures defined by the CATH resource. The approach, dubbed ProtTucker, has an improved ability to recognize distant homologous relationships than more traditional techniques such as threading or fold recognition. Thus, these embeddings have allowed sequence comparison to step into the "midnight zone" of protein similarity, i.e., the region in which distantly related sequences have a seemingly random pairwise sequence similarity. The novelty of this work is in the particular combination of tools and sampling techniques that ascertained good performance comparable or better to existing state-of-the-art sequence comparison methods. Additionally, since this method does not need to generate alignments it is also orders of magnitudes faster. The code is available at https://github.com/Rostlab/EAT.

bioinformatics↗

bioRxiv2021

Clustering FunFams using sequence embeddings improves EC purity

MotivationClassifying proteins into functional families can improve our understanding of protein function and can allow transferring annotations within one family. For this, functional families need to be "pure", i.e., contain only proteins with identical function. Functional Families (FunFams) cluster proteins within CATH superfamilies into such groups of proteins sharing function. 11% of all FunFams (22,830 of 203,639) contain EC annotations and of those, 7% (1,526 of 22,830) have inconsistent functional annotations. ResultsWe propose an approach to further cluster FunFams into functionally more consistent sub-families by encoding their sequences through embeddings. These embeddings originate from language models transferring knowledge gained from predicting missing amino acids in a sequence (ProtBERT) and have been further optimized to distinguish between proteins belonging to the same or a different CATH superfamily (PB-Tucker). Using distances between embeddings and DBSCAN to cluster FunFams and identify outliers, doubled the number of pure clusters per FunFam compared to random clustering. Our approach was not limited to FunFams but also succeeded on families created using sequence similarity alone. Complementing EC annotations, we observed similar results for binding annotations. Thus, we expect an increased purity also for other aspects of function. Our results can help generating FunFams; the resulting clusters with improved functional consistency allow more reliable inference of annotations. We expect this approach to succeed equally for any other grouping of proteins by their phenotypes. AvailabilityCode and embeddings are available via GitHub: https://github.com/Rostlab/FunFamsClustering

bioinformatics↗

bioRxiv2020

Recurrent mutations in SARS-CoV-2 genomes isolated from mink point to rapid host-adaptation

SARS-CoV-2, the agent of the COVID-19 pandemic, can infect a wide range of mammals. Since its spread in humans, secondary host jumps of SARS-CoV-2 from humans to a variety of domestic and wild populations of mammals have been documented. The evolution of SARS-CoV-2 in different host species is of fundamental interest while also providing indication of how SARS-CoV-2 may have adapted to human hosts soon after the initial host jump, a time window for which there are no genome sequences available. Moreover, the study of SARS-CoV-2 circulating in animals is critical to assess the risk that the transmission of animal-adapted viral lineages back into humans (i.e., spillback) may pose. Here, we compared the genomic landscapes of SARS-CoV-2 isolated from animal species relative to that in humans, profiling the mutational biases indicative of potentially different selective pressures in animals. We focused on viral genomes collected in infected mink (Neovison vison) and white-tailed deer (Odocoileus virginianus) for which reports of multiple independent spillover events and subsequent animal-to-animal transmission are available. We identified six candidate mutations for animal-specific adaptation in mink (NSP9_G37E, Spike_F486L, Spike_N501T, Spike_Y453F, ORF3a_T229I, ORF3a_L219V), and one in deer (NSP3a_L1035F), though these mutations appear to confer minimal advantage for circulation in humans. Additionally, circulation of SARS-CoV-2 in mink and deer has not caused considerable changes to the evolutionary trajectory of SARS-CoV-2 thus far. Finally, our results suggest that minimal adaptation was required for human-to-animal spillover and subsequent onward transmission in mink and deer, highlighting the generalist nature of SARS-CoV-2 as a pathogen of mammalian hosts.

genomics↗

bioRxiv2018

A CATH domain functional family based approach to identify putative cancer driver genes and driver mutations

Tumour sequencing identifies highly recurrent point mutations in cancer driver genes, but rare functional mutations are hard to distinguish from large numbers of passengers. We developed a novel computational platform applying a multi-modal approach to filter out passengers and more robustly identify putative driver genes. The primary filter identifies enrichment of cancer mutations in CATH functional families (CATH-FunFams) - structurally and functionally coherent sets of evolutionary related domains. Using structural representatives from CATH-FunFams, we subsequently seek enrichment of mutations in 3D and show that these mutation clusters have a very significant tendency to lie close to known functional sites or conserved sites predicted using CATH-FunFams. Our third filter identifies enrichment of putative driver genes in functionally coherent protein network modules confirmed by literature analysis to be cancer associated.\n\nOur approach is complementary to other domain enrichment approaches exploiting Pfam families, but benefits from more functionally coherent groupings of domains. Using a set of mutations from 22 cancers we detect 151 putative cancer drivers, of which 79 are not listed in cancer resources and include recently validated cancer genes EPHA7, DCC netrin-1 receptor and zinc-finger protein ZNF479.

bioinformatics↗

bioRxiv2018

Protein structure and function analyses to understand the implication of mutually exclusive splicing

Alternative splicing (AS) has been suggested as one of the major processes expanding the diversity of proteomes in multicellular organisms. Mutually exclusive exons (MXE) provide one form of AS that is less likely to disrupt protein structure and is over-represented in the proteome compared to other forms of AS. We used domain structure information from the CATH classification to perform a systematic structural analysis of the effects of MXE splicing in high quality animal genomes (e.g. human, fly, mouse and 2 fishes) and we were able to annotate approximately 50% of MXE events with structural information. For those MXE events which can be mapped to a structure, we found that although embedded in domains, they were strongly enriched in surface exposed residues. We also demonstrated that the variable residues between splicing events lie close to known and/or predicted functional sites. We present some examples of MXE events in proteins that have important roles in cells. This work presents the first large scale systematic study of the structural/functional effects of MXE splicing using predominantly domain based modelling and functional annotation tools. Our study supports and expands on previous work in this field and helps to build a picture of how MXE events facilitate evolution of new functions.

bioinformatics↗