Biology subjects

Deane, C. M.

Publications and source records attributed to Deane, C. M..

At least 73 records · Page 4Linked to original sources

bioRxiv2021

DLAB - Deep learning methods for structure-based virtual screening of antibodies

Antibodies are one of the most important classes of pharmaceuticals, with over 80 approved molecules currently in use against a wide variety of diseases. The drug discovery process for antibody therapeutic candidates however is time-and cost-intensive and heavily reliant on in-vivo and in-vitro high throughput screens. Here, we introduce a framework for structure-based deep learning for antibodies (DLAB) which can virtually screen putative binding antibodies against antigen targets of interest. DLAB is built to be able to predict antibody-antigen binding for antigens with no known antibody binders. We demonstrate that DLAB can be used both to improve antibody-antigen docking and structure-based virtual screening of antibody drug candidates. DLAB enables improved pose ranking for antibody docking experiments as well as selection of antibody-antigen pairings for which accurate poses are generated and correctly ranked. We also show that DLAB can identify binding antibodies against specific antigens in a case study. Our results demonstrate the promise of deep learning methods for structure-based virtual screening of antibodies.

bioinformatics↗

bioRxiv2021

Humanization of antibodies using a machine learning approach on large-scale repertoire data

Monoclonal antibody therapeutics are often produced from non-human sources (typically murine), and can therefore generate immunogenic responses in humans. Humanization procedures aim to produce antibody therapeutics that do not elicit an immune response and are safe for human use, without impacting efficacy. Humanization is normally carried out in a largely trial-and-error experimental process. We have built machine learning classifiers that can discriminate between human and non-human antibody variable domain sequences using the large amount of repertoire data now available. Our classifiers consistently outperform existing best-in-class models, and our output scores exhibit a negative relationship with the experimental immunogenicity of existing antibody therapeutics. We used our classifiers to develop a novel, computational humanization tool, Hu-mAb, that suggests mutations to an input sequence to reduce its immunogenicity. For a set of existing therapeutics with known precursor sequences, the mutations suggested by Hu-mAb show significant overlap with those deduced experimentally. Hu-mAb is therefore an effective replacement for trial- and-error humanization experiments, producing similar results in a fraction of the time. Hu-mAb is freely available to use at opig.stats.ox.ac.uk/webapps/humab.

immunology↗

bioRxiv2020

The allosteric modulation of Complement C5 by knob domain peptides

To overcome limited germline combinatorial diversity, bovines have evolved a subset of antibodies with ultra-long CDRH3 regions that harbour cysteine-rich knob domains. To produce affinity-maturated peptides, we previously isolated autonomous 3-6 kDa knob domains from bovine antibodies. Here, we show that binding of four knob domain peptides elicits a range of effects on the clinically validated drug target complement C5. Allosteric mechanisms predominated, with one peptide selectively inhibiting C5 cleavage by the alternative pathway C5 convertase, revealing a targetable mechanistic difference between the classical and alternative pathway C5 convertases. Taking a hybrid biophysical approach, we present C5-knob domain co-crystal structures and, by solution methods, observed allosteric effects propagating >50 [A] from the binding sites. This study expands the therapeutic scope of C5, presents new inhibitors and introduces knob domains as new, low molecular weight antibody fragments, with therapeutic potential.

immunology↗

bioRxiv2020

Co-evolutionary Distance Prediction for Flexibility Prediction

Co-evolution analysis can be used to accurately predict residue-residue contacts from multiple sequence alignments. The introduction of machine-learning techniques has enabled substantial improvements in precision and a shift from predicting binary contacts to predicting distances between pairs of residues. These developments have significantly improved the accuracy of de novo prediction of static protein structures. Here we examine the potential of these residue-residue distance predictions to predict protein flexibility rather than static structure. We used DMPfold to predict distance distributions for every residue pair in a set of proteins that showed both rigid and flexible behaviour. Residue pairs that were in contact in at least one reference structure were considered and classified as rigid, flexible or neither. The predicted distance distribution of each residue pair was analysed for local maxima of probability indicating the most likely distance or distances between a pair of residues. The average number of local maxima per residue pair was found to be different between the sets of rigid and flexible residue pairs. Flexible residue pairs more often had multiple local maxima in their predicted distance distribution than rigid residue pairs suggesting that the shape of predicted distance distributions is predictive of rigidity or flexibility of residue pairs.

bioinformatics↗

bioRxiv2020

Generating Property-Matched Decoy Molecules Using Deep Learning

An essential step in the development of virtual screening methods is the use of established sets of actives and decoys for benchmarking and training. However, the decoy molecules in commonly used sets are biased meaning that methods often exploit these biases to separate actives and decoys, rather than learning how to perform molecular recognition. This fundamental issue prevents generalisation and hinders virtual screening method development. We have developed a deep learning method (DeepCoy) that generates decoys to a users preferred specification in order to remove such biases or construct sets with a defined bias. We validated DeepCoy using two established benchmarks, DUD-E and DEKOIS 2.0. For all DUD-E targets and 80 of the 81 DEKOIS 2.0 targets, our generated decoy molecules more closely matched the active molecules physicochemical properties while introducing no discernible additional risk of false negatives. The DeepCoy decoys improved the Deviation from Optimal Embedding (DOE) score by an average of 81% and 66%, respectively, decreasing from 0.163 to 0.032 for DUD-E and from 0.109 to 0.038 for DEKOIS 2.0. Further, the generated decoys are harder to distinguish than the original decoy molecules via docking with Autodock Vina, with virtual screening performance falling from an AUC ROC of 0.71 to 0.63. The code is available at https://github.com/oxpig/DeepCoy. Generated molecules can be downloaded from http://opig.stats.ox.ac.uk/resources.

bioinformatics↗

bioRxiv2020

COGENT: evaluating the consistency of gene co-expression networks

Gene co-expression networks can be constructed in multiple different ways, both in the use of different measures of co-expression, and in the thresholds applied to the calculated co-expression values, from any given dataset. It is often not clear which co-expression network construction method should be preferred. COGENT provides a set of tools designed to aid the choice of network construction method without the need for any external validation data. Availability and implementationhttps://github.com/lbozhilova/COGENT

bioinformatics↗

bioRxiv2020

Robust gene coexpression networks using signed distance correlation

MotivationEven within well studied organisms, many genes lack useful functional annotations. One way to generate such functional information is to infer biological relationships between genes/proteins, using a network of gene coexpression data that includes functional annotations. However, the lack of trustworthy functional annotations can impede the validation of such networks. Hence, there is a need for a principled method to construct gene coexpression networks that capture biological information and are structurally stable even in the absence of functional information. ResultsWe introduce the concept of signed distance correlation as a measure of dependency between two variables, and apply it to generate gene coexpression networks. Distance correlation offers a more intuitive approach to network construction than commonly used methods such as Pearson correlation. We propose a framework to generate self-consistent networks using signed distance correlation purely from gene expression data, with no additional information. We analyse data from three different organisms to illustrate how networks generated with our method are more stable and capture more biological information compared to networks obtained from Pearson or Spearman correlations. Code availabilityhttps://github.com/javier-pardodiaz/sdcorGCN.

bioinformatics↗

bioRxiv2020

A computational method for immune repertoire mining that identifies novel binders from different clonotypes, demonstrated by identifying anti-Pertussis toxoid antibodies

Due to their shared genetic history, antibodies from the same clonotype often bind to the same epitope. This knowledge is used in immune repertoire mining, where known binders are used to search bulk sequencing repertoires to identify new binders. However current computational methods cannot identify epitope convergence between antibodies from different clonotypes, limiting the sequence diversity of antigen-specific antibodies which can be identified. We describe how the antibody binding site, the paratope, can be used to cluster antibodies with common antigen reactivity from different clonotypes. Our method, paratyping, uses the predicted paratope to identify these novel cross clonotype matches. We experimentally validated our predictions on a Pertussis toxoid dataset. Our results show that even the simplest abstraction of the antibody binding site, using only the length of the loops involved and predicted binding residues, is sufficient to group antigen-specific antibodies and provide additional information to conventional clonotype analysis.

bioinformatics↗

bioRxiv2020

Novel Vβ specific germline contacts shape an elite controller T cell response

Certain CD8 T cell responses are particularly effective at controlling infection, as exemplified by elite control of HIV in individuals harboring HLA-B57. To understand the structural features that contribute to CD8 T cell elite control, we focused on a strongly protective CD8 T cell response directed against a parasite-derived peptide (HF10) presented by an atypical MHC-I molecule, H-2Ld. This response exhibits a focused TCR repertoire dominated by V{beta}2, and a representative TCR (TG6) in complex with Ld-HF10 reveals an unusual structure in which both MHC and TCR contribute extensively to peptide specificity, along with a parallel footprint of TCR on its pMHC ligand. The parallel footprint is a common feature of V{beta}2-containing TCRs and correlates with an unusual V-V{beta} interface, CDR loop conformations, and V{beta}2-specific germline contacts with peptide. V{beta}2 and Ld may represent "specialist" components for antigen recognition that allow for particularly strong and focused T cell responses.

immunology↗

bioRxiv2020

Deep sequencing of B cell receptor repertoires from COVID-19 patients reveals strong convergent immune signatures

Deep sequencing of B cell receptor (BCR) heavy chains from a cohort of 19 COVID-19 patients from the UK reveals a stereotypical naive immune response to SARS-CoV-2 which is consistent across patients and may be a positive indicator of disease outcome. Clonal expansion of the B cell memory response is also observed and may be the result of memory bystander effects. There was a strong convergent sequence signature across patients, and we identified 777 clonotypes convergent between at least four of the COVID-19 patients, but not present in healthy controls. A subset of the convergent clonotypes were homologous to known SARS and SARS-CoV-2 spike protein neutralising antibodies. Convergence was also demonstrated across wide geographies by comparison of data sets between patients from UK, USA and China, further validating the disease association and consistency of the stereotypical immune response even at the sequence level. These convergent clonotypes provide a resource to identify potential therapeutic and prophylactic antibodies and demonstrate the potential of BCR profiling as a tool to help understand and predict positive patient responses.

immunology↗

bioRxiv2020

CoV-AbDab: the Coronavirus Antibody Database

The emergence of a novel strain of betacoronavirus, SARS-CoV-2, has led to a pandemic that has been associated with hundreds of thousands of deaths. Research is ongoing around the world to create vaccines and therapies to minimise rates of disease spread and mortality. Crucial to these efforts are molecular characterisations of neutralising antibodies to SARS-CoV-2. Such antibodies would be valuable for measuring vaccine efficacy, diagnosing exposure, and developing effective biotherapeutics. Here, we describe our new database, CoV-AbDab, which already contains data on over 380 published/patented antibodies and nanobodies known to bind to at least one betacoronavirus. This database is the first consolidation of antibodies known to bind SARS-CoV-2 and other betacoronaviruses such as SARS-CoV-1 and MERS-CoV. We supply relevant metadata such as evidence of cross-neutralisation, antibody/nanobody origin, full variable domain sequence (where available) and germline assignments, epitope region, links to relevant PDB entries, homology models, and source literature. Our preliminary analysis exemplifies a spectrum of potential applications for the database, including identifying characteristic germline usage biases in receptor-binding domain antibodies and contextualising the diagnostic value of the SARS-CoV binding CDRH3s through comparison to over 500 million antibody sequences from SARS-CoV serologically naive individuals. Community submissions are invited to ensure CoV-AbDab is efficiently updated with the growing body of data analysing SARS-CoV-2. CoV-AbDab is freely available and downloadable on our website at http://opig.stats.ox.ac.uk/webapps/coronavirus.

immunology↗

bioRxiv2020

Hypergraphs for predicting essential genes using multiprotein complex data

Protein-protein interactions are crucial in many biological pathways and facilitate cellular function. Investigating these interactions as a graph of pairwise interactions can help to gain a systemic understanding of cellular processes. It is known, however, that proteins interact with each other not exclusively in pairs but also in polyadic interactions and they can form multiprotein complexes, which are stable interactions between multiple proteins. In this manuscript, we use hypergraphs to investigate multiprotein complex data. We investigate two random null models to test which hypergraph properties occur as a consequence of constraints, such as the size and the number of multiprotein complexes. We find that assortativity, the number of connected components, and clustering differ from the data to these null models. Our main finding is that projecting a hypergraph of polyadic interactions onto a graph of pairwise interactions leads to the identification of different proteins as hubs than the hyper-graph. We find in our data set that the hypergraph degree is a more accurate predictor for gene-essentiality than the degree in the pairwise graph. We find that analysing a hypergraph as pairwise graph drastically changes the distribution of the local clustering coefficient. Furthermore, using a pairwise interaction representing multiprotein complex data may lead to a spurious hierarchical structure, which is not observed in the hypergraph. Hence, we illustrate that hypergraphs can be more suitable than pairwise graphs for the analysis of multiprotein complex data.

systems biology↗

bioRxiv2020

Dataset Augmentation Allows Deep Learning-Based Virtual Screening To Better Generalise To Unseen Target Classes, And Highlight Important Binding Interactions

Current deep learning methods for structure-based virtual screening take the structures of both the protein and the ligand as input but make little or no use of the protein structure when predicting ligand binding. Here we show how a relatively simple method of dataset augmentation forces such deep learning methods to take into account information from the protein. Models trained in this way are more generalisable (make better predictions on protein-ligand complexes from a different distribution to the training data). They also assign more meaningful importance to the protein and ligand atoms involved in binding. Overall, our results show that dataset augmentation can help deep learning based virtual screening to learn physical interactions rather than dataset biases. Graphical TOC Entry O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=81 SRC="FIGDIR/small/979625v2_ufig1.gif" ALT="Figure 1"> View larger version (14K): org.highwire.dtl.DTLVardef@609f5forg.highwire.dtl.DTLVardef@17f2cbborg.highwire.dtl.DTLVardef@51e854org.highwire.dtl.DTLVardef@16c111f_HPS_FORMAT_FIGEXP M_FIG C_FIG

molecular biology↗

bioRxiv2019

Deep Generative Models for 3D Compound Design

Rational compound design remains a challenging problem for both computational methods and medicinal chemists. Computational generative methods have begun to show promising results for the design problem. However, they have not yet used the power of 3D structural information. We have developed a novel graph-based deep generative model that combines state-of-the-art machine learning techniques with structural knowledge. Our method (\"DeLinker\") takes two fragments or partial structures and designs a molecule incorporating both. The generation process is protein context dependent, utilising the relative distance and orientation between the partial structures. This 3D information is vital to successful compound design, and we demonstrate its impact on the generation process and the limitations of omitting such information. In a large scale evaluation, DeLinker designed 60% more molecules with high 3D similarity to the original molecule than a database baseline. When considering the more relevant problem of longer linkers with at least five atoms, the outperformance increased to 200%. We demonstrate the effectiveness and applicability of this approach on a diverse range of design problems: fragment linking, scaffold hopping, and proteolysis targeting chimera (PROTAC) design. As far as we are aware, this is the first molecular generative model to incorporate 3D structural information directly in the design process. Code is available at https://github.com/oxpig/DeLinker.

bioinformatics↗

bioRxiv2019

Structural Diversity of B-Cell Receptor Repertoires along the B-cell Differentiation Axis in Humans and Mice

Most current analysis tools for antibody next-generation sequencing data work with primary sequence descriptors, leaving accompanying structural information unharnessed. We have used novel rapid methods to structurally characterize the paratopes of more than 180 million human and mouse B-cell receptor (BCR) repertoire sequences. These structurally annotated paratopes provide unprecedented insights into both the structural predetermination and dynamics of the adaptive immune response. We show that B-cell types can be distinguished based solely on these structural properties. Antigen-unexperienced BCR repertoires use the highest number and diversity of paratope structures and these patterns of naive repertoire paratope usage are highly conserved across subjects. In contrast, more differentiated B-cells are more personalized in terms of paratope structure usage. Our results establish the paratope structure differences in BCR repertoires and have applications for many fields including immunodiagnostics, phage display library generation, and \"humanness\" assessment of BCR repertoires from transgenic animals.

immunology↗

bioRxiv2019

Functional module detection through integration of single-cell RNA sequencing data with protein-protein interaction networks

Recent advances in single-cell RNA sequencing (scRNA-seq) have allowed researchers to explore transcriptional function at a cellular level. In this study, we present O_SCPLOWSCC_SCPLOWPPIN, a method for integrating single-cell RNA sequencing data with protein-protein interaction networks (PPINs) that detects active modules in cells of different transcriptional states. We achieve this by clustering RNA-sequencing data, identifying differentially expressed genes, constructing node-weighted PPINs, and finding the maximum-weight connected subgraphs with an exact Steiner-tree approach. As a case study, we investigate RNA-sequencing data from human liver spheroids but the techniques described here are applicable to other organisms and tissues. O_SCPLOWSCC_SCPLOWPPIN allows us to expand the output of differential expressed genes analysis with information from protein interactions. We find that different transcriptional states have different subnetworks of the PPIN significantly enriched which represent biological pathways. In these pathways, O_SCPLOWSCC_SCPLOWPPIN also identifies proteins that are not differentially expressed but have a crucial biological function (e.g., as receptors) and therefore reveals biology beyond a standard differentially expressed gene analysis.

bioinformatics↗

bioRxiv2019

RFQAmodel: Random Forest Quality Assessment to identify a predicted protein structure in the correct fold

While template-free protein structure prediction protocols now produce good quality models for many targets, modelling failure remains common. For these methods to be useful it is important that users can both choose the best model from the hundreds to thousands of models that are commonly generated for a target, and determine whether this model is likely to be correct. We have developed Random Forest Quality Assessment (RFQAmodel), which assesses whether models produced by a protein structure prediction pipeline have the correct fold. RFQAmodel uses a combination of existing quality assessment scores with two predicted contact map alignment scores. These alignment scores are able to identify correct models for targets that are not otherwise captured. Our classifier was trained on a large set of protein domains that are structurally diverse and evenly balanced in terms of protein features known to have an effect on modelling success, and then tested on a second set of 244 protein domains with a similar spread of properties. When models for each target in this second set were ranked according to the RFQAmodel score, the highest-ranking model had a high-confidence RFQAmodel score for 67 modelling targets, of which 52 had the correct fold. At the other end of the scale RFQAmodel correctly predicted that for 59 targets the highest-ranked model was incorrect. In comparisons to other methods we found that RFQAmodel is better able to identify correct models for targets where only a few of the models are correct. We found that RFQAmodel achieved a similar performance on the model sets for CASP12 and CASP13 free-modelling targets. Finally, by iteratively generating models and running RFQAmodel until a model is produced that is predicted to be correct with high confidence, we demonstrate how such a protocol can be used to focus computational efforts on difficult modelling targets.

bioinformatics↗

bioRxiv2019

Maturation of naïve and antigen-experienced B-cell receptor repertoires with age

B cells play a central role in adaptive immune processes, mainly through the production of antibodies. The maturation of the B-cell system with age is poorly studied. We extensively investigated age-related alterations of naive and antigen-experienced B-cell receptor (BCR) repertoires. The most significant changes were observed in the first 10 years of life, and were characterized by altered immunoglobulin gene usage and an increased frequency of mutated antibodies structurally diverging from their germline precursors. Older age was associated with an increased usage of downstream constant region genes and fewer antibodies with self-reactive properties. As mutations accumulated with age, the frequency of germline-encoded self-reactive antibodies decreased, indicating a possible beneficial role of self-reactive B-cells in the developing immune system. Our results suggest a continuous process of change through childhood across a broad range of parameters characterizing BCR repertoires and stress the importance of using well-selected, age-appropriate controls in BCR studies.

immunology↗