Biology subjects

Wong, W. K.

Publications and source records attributed to Wong, W. K..

13 recordsLinked to original sources

bioRxiv2025

Predicting the conformational flexibility of antibody and T-cell receptor CDRs

AbstractMany proteins are highly flexible and their ability to adapt their shape can be fundamental to their functional properties. We can now computationally predict a single, static protein structure with high accuracy. However, we are not yet able to reliably predict structural flexibility. A major factor limiting such predictions is the scarcity of suitable training data. Here, we focus on predicting the structural flexibility of the functionally important antibody and T-cell receptor CDR3 loops. We extracted a dataset of CDR3 like loop motifs from the PDB to create ALL-conformations, a dataset containing 1.2 million structures and more than 100,000 unique sequences. Using this dataset, we develop ITsFlexible a method classifying CDR3 flexibility, which outperforms all alternative approaches on our crystal structure datasets and successfully generalises to MD simulations. We also used ITsFlexible to predict the flexibility of three completely novel CDRH3 loops and experimentally determined their conformations using cryo-EM.

bioinformatics↗

bioRxiv2023

The Patent and Literature Antibody Database (PLAbDab): an evolving reference set of functionally diverse, literature-annotated antibody sequences and structures

Antibodies are key proteins of the adaptive immune system, and there exists a large body of academic literature and patents dedicated to their study and concomitant conversion into therapeutics, diagnostics, or reagents. These documents often contain extensive functional characterisations of the sets of antibodies the describe. However, leveraging these heterogeneous reports, for example to offer insights into the properties of query antibodies of interest, is currently challenging as there is no central repository through which this wide corpus can be mined by sequence or structure. Here, we present PLAbDab (the Patent and Literature Antibody Database), a self-updating repository containing over 150,000 paired antibody sequences and 3D structural models, of which over 65,000 are unique. Each entry in the database also contains the title and authors of its literature source. Here we describe the methods used to extract, filter, pair, and model the antibodies in PLAbDab, and showcase how PLAbDab can be searched by sequence, structure, or keyword. PLAbDab uses include annotating query antibodies with potential antigen information from similar entries, analysing structural models of existing antibodies to identify modifications that could improve their properties, and compiling bespoke datasets of antibody sequences/structures known to bind to a specific antigen. PLAbDab is freely available via Github (https://github.com/oxpig/PLAbDab) and as a searchable webserver (https://opig.stats.ox.ac.uk/webapps/plabdab/).

bioinformatics↗

bioRxiv2023

Improved computational epitope profiling using structural models identifies a broader diversity of antibodies that bind the same epitope

The function of an antibody is intrinsically linked to which epitope it engages. Clonal clustering methods, based on sequence identity, are commonly used to group antibodies that will bind the same epitope. However, such methods neglect the fact that antibodies with highly diverse sequences can exhibit similar binding site geometries and engage common epitopes. In a previous study we described SPACE1, a method that structurally clustered antibodies in order to predict their epitopes. This methodology was limited by the inaccuracies and incomplete coverage of template-based modelling. It was also only benchmarked at the level of domain-consistency on one virus class. Here, we present SPACE2, which uses the latest machine learning based structure prediction technology combined with a novel clustering protocol and benchmark it on binding data that has epitope level resolution. On six diverse sets of antigen specific antibodies we demonstrate that SPACE2 accurately clusters antibodies that engage common epitopes and achieves far higher data set coverage than clonal clustering and SPACE1. Furthermore, we show that the functionally consistent structural clusters identified by SPACE2 are even more diverse in sequence, genetic lineage, and species origin than those found by SPACE1. These results reiterate that structural data improves our ability to identify antibodies that bind the same epitope, adding information to sequence-based methods, especially in data sets of antibodies from diverse sources. SPACE2 is openly available on Github (https://github.com/oxpig/SPACE2).

bioinformatics↗

bioRxiv2022

ImmuneBuilder: Deep-Learning models for predicting the structures of immune proteins.

Immune receptor proteins play a key role in the immune system and have shown great promise as biotherapeutics. The structure of these proteins is critical for understanding their antigen binding properties. Here, we present ImmuneBuilder, a set of deep learning models trained to accurately predict the structure of antibodies (ABodyBuilder2), nanobodies (NanoBodyBuilder2) and T-Cell receptors (TCRBuilder2). We show that ImmuneBuilder generates structures with state of the art accuracy while being far faster than AlphaFold2. For example, on a benchmark of 34 recently solved antibodies, ABodyBuilder2 predicts CDR-H3 loops with an RMSD of 2.81[A], a 0.09[A] improvement over AlphaFold-Multimer, while being over a hundred times faster. Similar results are also achieved for nanobodies, (NanoBodyBuilder2 predicts CDR-H3 loops with an average RMSD of 2.89[A], a 0.55[A] improvement over AlphaFold2) and TCRs. By predicting an ensemble of structures, ImmuneBuilder also gives an error estimate for every residue in its final prediction. ImmuneBuilder is made freely available, both to download (https://github.com/oxpig/ImmuneBuilder) and to use via our webserver (http://opig.stats.ox.ac.uk/webapps/newsabdab/sabpred). We also make available structural models for ~150 thousand non-redundant paired antibody sequences (https://zenodo.org/record/7258553).

bioinformatics↗

bioRxiv2022

Identifying human islet microRNAs associated with donor sex, age and body mass index

ObjectivesHuman islets are widely researched to understand pathophysiological mechanisms leading to diabetes. Sex, age, and body mass index (BMI) are key donor traits influencing insulin secretion. Islet function is also regulated by an intricate network of microRNAs. MethodsHere, we profiled 754 microRNAs and 58,190 potential targets in up to 131 different human islet donor preparations (without diabetes) and assessed their association with donor traits. We further performed mechanistical studies to observe the causal role of the age-associated key microRNAs on relative telomere length in human islets. ResultsMicroRNA discovery analyses identified miR-199a-5p and miR-214-3p associated with sex, age and BMI; miR-147b with sex and age; miR-378a-5p with sex and BMI; miR-542-3p, miR-34a-3p, miR-34a-5p, miR-497-5p and miR-99a-5p with age and BMI. There were 959 mRNA transcripts associated with sex (excluding those from sex-chromosomes), 940 with age and 418 with BMI. MicroRNA-199a-5p and miR-214-3p levels inversely associate with transcripts critical in islet function, metabolic regulation, and senescence. Our functional studies verified that inhibition of these two microRNAs (miR-199a-5p/-214-3p) slowed down telomere length shortening in human islet cells maintained in vitro and demonstrating cellular senescence. ConclusionsOur analyses identify human islet cell microRNAs influenced by donor traits. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=113 SRC="FIGDIR/small/512222v2_ufig1.gif" ALT="Figure 1"> View larger version (32K): org.highwire.dtl.DTLVardef@1666c2org.highwire.dtl.DTLVardef@b4695forg.highwire.dtl.DTLVardef@720e99org.highwire.dtl.DTLVardef@1c78ae_HPS_FORMAT_FIGEXP M_FIG C_FIG

cell biology↗

bioRxiv2021

Single-cell generalized trend model (scGTM): a flexible and interpretable model of gene expression trend along cell pseudotime

MotivationModeling single-cell gene expression trends along cell pseudotime is a crucial analysis for exploring biological processes. Most existing methods rely on nonparametric regression models for their flexibility; however, nonparametric models often provide trends too complex to interpret. Other existing methods use interpretable but restrictive models. Since model interpretability and flexibility are both indispensable for understanding biological processes, the single-cell field needs a model that improves the interpretability and largely maintains the flexibility of nonparametric regression models. ResultsHere we propose the single-cell generalized trend model (scGTM) for capturing a genes expression trend, which may be monotone, hill-shaped, or valley-shaped, along cell pseudotime. The scGTM has three advantages: (1) it can capture non-monotonic trends that are still easy to interpret, (2) its parameters are biologically interpretable and trend informative, and (3) it can flexibly accommodate common distributions for modeling gene expression counts. To tackle the complex optimization problems, we use the particle swarm optimization algorithm to find the constrained maximum likelihood estimates for the scGTM parameters. As an application, we analyze several single-cell gene expression data sets using the scGTM and show that it can capture interpretable gene expression trends along cell pseudotime and reveal molecular insights underlying the biological processes. Availability and implementationThe Python package scGTM is open-access and available at https://github.com/ElvisCuiHan/scGTM. Contactjli@stat.ucla.edu

bioinformatics↗

bioRxiv2021

Epitope profiling of coronavirus-binding antibodies using computational structural modelling

Identifying the epitope of an antibody is a key step in understanding its function and its potential as a therapeutic. It is well-established in the literature that sequence-based clonal clustering can identify antibodies with similar epitope complementarity. However, there is growing evidence that antibodies from markedly different lineages but with similar structures can engage the same epitope with near-identical binding modes. Here, we describe a novel computational method for epitope profiling based on structural modelling and clustering, and show how it can identify sequence-dissimilar antibodies that engage the same epitope. We start by searching for evidence of structural conservation across the latest solved SARS-CoV-2--binding antibody crystal structures. Despite the relatively small number of solved structures, we find numerous examples of sequence-diverse but structurally-similar coronavirus-binding antibodies engaging the same epitope. We therefore developed a high-throughput structural modeling and clustering method to identify functionally-similar antibodies across the set of thousands of coronavirus-binding antibody sequences in the Coronavirus Antibody Database (CoV-AbDab). In the resulting multiple-occupancy structural clusters, 92% bind to consistent domains based on CoV-AbDab metadata. Our approach functionally links antibodies with distinct genetic lineages, species origins, and coronavirus specificities. This indicates greater convergence exists in the immune responses to coronaviruses than would be suggested by sequence-based approaches. Our results show that applying structural analytics to large class-specific antibody databases will enable high confidence structure-function relationships to be drawn, yielding new opportunities to identify functional convergence hitherto missed by sequence-only analysis.

immunology↗

bioRxiv2021

Machine learning algorithms in big data analyses identify determinants of insulin gene transcription

Machine learning (ML)-workflows enable unprejudiced/robust evaluation of complex datasets. Here, we analyzed over 490,000,000 data points to compare 10 different ML-workflows in a large (N=11,652) training dataset of human pancreatic single-cell (sc-)transcriptomes to identify genes associated with the presence or absence of insulin transcript(s). Prediction accuracy/sensitivity of each ML-workflow were tested in a separate validation dataset (N=2,913). Ensemble ML-workflows, in particular Random Forest ML-algorithm delivered high predictive power (AUC=0.83) and sensitivity (0.98), compared to other algorithms. The transcripts identified through these analyses also demonstrated significant correlation with insulin in bulk RNA-seq data from human islets. The top-10 features, (including IAPP, ADCYAP1, LDHA and SST) common to the three Ensemble ML-workflows were significantly dysregulated in scRNA-seq datasets from Ire-1{beta}-/- mice that demonstrate dedifferentiation of pancreatic {beta}-cells in a model of type 1 diabetes (T1D) and in pancreatic single cells from individuals with type 2 Diabetes (T2D). Our findings provide direct comparison of ML-workflows in big data analyses, identify key determinants of insulin transcription and provide workflows for future analyses.

genetics↗

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

Ab-Ligity: Identifying sequence-dissimilar antibodies that bind to the same epitope

Solving the structure of an antibody-antigen complex gives atomic level information of the interactions between an antibody and its antigen, but such structures are expensive and hard to obtain. Alternative experimental sources include epitope mapping and binning experiments which can be used as a surrogate to identify key interacting residues. However, their resolution is usually not sufficient to identify if two antibodies have identical interactions. Computational approaches to this problem have so far been based on the premise that antibodies with similar sequences behave similarly. Such approaches will fail to identify sequence-distant antibodies that target the same epitope. We present Ab-Ligity, a structure-based similarity measure tailored to antibody-antigen interfaces. Using predicted paratopes on model antibody structures, we assessed its ability to identify those antibodies that target highly similar epitopes. Most antibodies adopting similar binding modes can be identified from sequence similarity alone, using methods such as clonotyping. In the challenging subset of antibodies whose sequences differ significantly, Ab-Ligity is still able to predict antibodies that would bind to highly similar epitopes (precision of 0.95 and recall of 0.69). We compared Ab-Ligitys performance to an existing tool for comparing general protein interfaces, InterComp, and showed improved performance on antibody cases alongside a significant speed-up. These results suggest that Ab-Ligity will allow the identification of diverse (sequence-dissimilar) antibodies that bind to the same epitopes from large datasets such as immune repertoires. The tool is available at http://opig.stats.ox.ac.uk/resources.

bioinformatics↗

bioRxiv2019

Megakaryocytes, erythropoietic and granulopoietic cells express CAL2 antibody in myeloproliferative neoplasms carrying CALR gene mutations

The discovery of mutated Calreticulin (CALR) in myeloproliferative neoplasms (MPN) has provided proof of clonality, diagnostic importance, and influence on prognosis of this pathology. The identification of this MPN-associated driver mutation -currently based on molecular assays- is represented as a major diagnostic criterion for essential thrombocythaemia (ET), pre-fibrotic myelofibrosis and primary myelofibrosis (PMF) in the updated World Health Organization (WHO) 2008 classification. In the present study, we validated by immunohistochemistry the diagnostic usefulness of the monoclonal CAL2 antibody. Cases of acute myeloid leukaemia (AML) and myelodysplastic/ myeloproliferative neoplasms (MDS/MPN) have been also investigated to assess the specificity of CAL2 antibody. For this purpose, the result of the CAL2 immunostaining was compared with the result of molecular assays. Additionally, we investigated by double staining whether expression of mutated CALR can also be demonstrated on cells of the erythroid and myeloid lineage. We confirmed the usefulness of the CAL2 monoclonal antibody in successfully detecting mutant CALR in bone marrow biopsies. We showed that the immune-reactivity of CAL2 was absolutely restricted to the presence of CALR mutations, which were seen only in ET and MDS/MPN biopsies, but not in AML biopsies (14/14). There was 100% concordance in biopsy specimens with the concomitant molecular results. We applied double staining technique and confirmed that a subpopulation of granulopoietic and erythropoietic cells express mutated CALR as demonstrated with the CAL2 antibody in cases of MPNs. This supports the suggestion that the CALR mutations occur in a multipotent progenitor capable of generating both myeloid and erythroid progeny with preferential expansion of megakaryocytic cell lineage as a result of CALR mutation in an immature hematopoietic stem cell.

pathology↗

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

Comparative analysis of the CDR loops of antigen receptors

The adaptive immune system uses two main types of antigen receptors: T-cell receptors (TCRs) and antibodies. While both proteins share a globally similar {beta}-sandwich architecture, TCRs are specialised to recognise peptide antigens in the binding groove of the major histocompatibility complex, while antibodies can bind an almost infinite range of molecules. For both proteins, the main determinants of target recognition are the complementarity-determining region (CDR) loops. Five of the six CDRs adopt a limited number of backbone conformations, known as the canonical classes; the remaining CDR ({beta}3 in TCRs and H3 in antibodies) is more structurally diverse. In this paper, we first update the definition of canonical forms in TCRs, build an auto-updating sequence-based prediction tool (available at http://opig.stats.ox.ac.uk/resources) and demonstrate its application on large scale sequencing studies. Given the global similarity of TCRs and antibodies, we then examine the structural similarity of their CDRs. We find that TCR and antibody CDRs tend to have different length distributions, and where they have similar lengths, they mostly occupy distinct structural spaces. In the rare cases where we found structural similarity, the underlying sequence patterns for the TCR and antibody version are different. Finally, where multiple structures have been solved for the same CDR sequence, the structural variability in TCR loops is higher than that in antibodies, suggesting TCR CDRs are more flexible. These structural differences between TCR and antibody CDRs may be important to their different biological functions.

bioinformatics↗