Biology subjects

Deane, C. M.

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

At least 37 records · Page 2Linked to original sources

bioRxiv2024

Humatch - fast, gene-specific joint humanisation of antibody heavy and light chains

1Antibodies are a popular and powerful class of therapeutic due to their ability to exhibit high affinity and specificity to target proteins. However, the majority of antibody therapeutics are not genetically human, with initial therapeutic designs typically obtained from animal models. Humanisation of these precursors is essential to reduce immunogenic risks when administered to humans. Here, we present Humatch, a computational tool designed to offer experimental-like joint humanisation of heavy and light chains in seconds. Humatch consists of three lightweight Convolutional Neural Networks (CNNs) trained to identify human heavy V-genes, light V-genes, and well-paired antibody sequences with near-perfect accuracy. We show that these CNNs, alongside germline similarity, can be used for fast humanisation that aligns well with known experimental data. Throughout the humanisation process, a sequence is guided towards a specific target gene and away from others via multiclass CNN outputs and gene-specific germline data. This guidance ensures final humanised designs do not sit between genes, a trait that is not naturally observed. Humatchs optimisation towards specific genes and good VH/VL pairing increases the chances that final designs will be stable and express well and reduces the chances of immunogenic epitopes forming between the two chains. Humatchs training data and source code are provided open-source. AvailabilitySource code is freely available at github.com/oxpig/Humatch. Data can be found at doi.org/10.5281/zenodo.13764770 Contactdeane@stats.ox.ac.uk Supplementary informationSupplementary data are available at bioRxiv online.

bioinformatics↗

bioRxiv2024

Graph models of brain state in deep anaesthesia reveal sink state dynamics of reduced spatiotemporal complexity and integration

Anaesthetisia is an important surgical and explorative tool in the study of consciousness. Much work has been done to connect the deeply anaesthetised condition with decreased complexity. However, anaesthesia-induced unconsciousness is also a dynamic condition in which functional activity and complexity may fluctuate, being perturbed by internal or external (e.g. noxious) stimuli. We use fMRI data from a cohort undergoing deep propofol anaesthesia to investigate resting state dynamics using dynamic brain state models and spatiotemporal network analysis. We focus our analysis on group-level dynamics of brain state temporal complexity, functional activity, connectivity and spatiotemporal modularization in deep anaesthesia and wakefulness. We find that in contrast to dynamics in the wakeful condition, anaesthesia dynamics are dominated by a handful of sink states that act as low-complexity attractors to which subjects repeatedly return. On a subject-level, our analysis provides tentative evidence that these low complexity attractor states appear to depend on subject-specific age and anaesthesia susceptibility factors. Finally, our spatiotemporal analysis, including a novel spatiotemporal clustering of graphs representing hidden Markov models, suggests that dynamic functional organisation in anaesthesia can be characterised by mostly unchanging, isolated regional subnetworks that share some similarities with the brains underlying structural connectivity, as determined from normative tractography data.

neuroscience↗

bioRxiv2024

p-IgGen: A Paired Antibody Generative Language Model

A key challenge in antibody drug discovery is designing novel sequences that are free from developability issues - such as aggregation, polyspecificity, poor expression, or low solubility. Here, we present p-IgGen, a protein language model for paired heavylight chain antibody generation. The model generates diverse, antibody-like sequences with pairing properties found in natural antibodies. We also create a finetuned version of p-IgGen that biases the model to generate antibodies with 3D biophysical properties that fall within distributions seen in clinical-stage therapeutic antibodies.

immunology↗

bioRxiv2024

Expanding the scope of a catalogue search to bioisosteric fragment merges using a graph database approach

The efficiency of fragment-to-lead optimization could be improved by automated workflows for the design of follow-up compounds. Pipelines that are able to fully exploit the interaction opportunities identified from the crystal structures of bound fragments would greatly aid this goal. To do so, these pipelines need to require minimal intervention from the user and be computationally efficient. In this work, we describe an updated version of our fragment merging methodology, which provides several feature enhancements, primarily by expanding the chemical space searched, allowing the identification of more diverse follow-up compounds, thus maximizing the chances of finding successful hits. While the original method focused on finding perfect merges, meaning compounds that directly incorporate substructures from the original fragments, here we expand the search to what we term bioisosteric merges, involving the incorporation of substructures that replicate the pharmacophoric features of the original fragments but may not be exactly identical. Unlike existing pharmacophore and shape-based descriptors used for virtual screening, this approach combines the search for these properties with the incorporation of novelty, which is necessary when searching for ways to link together distinct substructures. Compared with perfect merging, our new approach is able to find compounds that are directly informed by structures within the original fragments but are more chemically diverse. We contrast our approach with the use of a pharmacophore-constrained docking pipeline, run in parallel for select fragment pairs, and show that our method requires between 1.1-45.9-fold less computational time for conformer generation per merging hit identified, referring to compounds that show a favourable degree of shape and colour overlap and recapitulation of original fragment interactions. Overall, our results show that our method has potential to be used to generate designs inspired by all fragments within a given pocket.

bioinformatics↗

bioRxiv2024

PLAbDab-nano: a database of camelid and shark nanobodies from patents and literature

Nanobodies are essential proteins of the adaptive immune systems of camelid and shark species, complementing conventional antibodies. Properties such as their relatively small size, solubility and high thermostability make VHH and VNAR modalities a promising therapeutic format and a valuable resource for a wide range of biological applications. The volume of academic literature and patents related to nanobodies has risen significantly over the past decade. Here, we present PLAbDab-nano, a nanobody complement to the Patent and Literature Antibody Database (PLAbDab). PLAbDab-nano is a selfupdating, searchable repository containing approximately 5000 annotated VHH and VNAR sequences. We describe the methods used to curate the entries in PLAbDab-nano, and highlight how PLAbDab-nano could be used to design diverse libraries, as well as find sequences similar to known patented or therapeutic entries. PLAbDab-nano is freely available as a searchable web server (opig.stats.ox.ac.uk/webapps/plabdab-nano/). Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=193 SRC="FIGDIR/small/604232v1_ufig1.gif" ALT="Figure 1"> View larger version (40K): org.highwire.dtl.DTLVardef@5d81d4org.highwire.dtl.DTLVardef@f69f21org.highwire.dtl.DTLVardef@1493c54org.highwire.dtl.DTLVardef@117e3c5_HPS_FORMAT_FIGEXP M_FIG C_FIG

immunology↗

bioRxiv2024

T-cell receptor structures and predictive models reveal comparable alpha and beta chain structural diversity despite differing genetic complexity

T-cell receptor (TCR) structures are currently under-utilised in early-stage drug discovery and repertoire-scale informatics. Here, we leverage a large dataset of solved TCR structures from Immunocore to evaluate the current state-of-the-art for TCR structure prediction, and identify which regions of the TCR remain challenging to model. Through clustering analyses and the training of a TCR-specific model capable of large-scale structure prediction, we find that the alpha chain VJ-recombined loop (CDRA3) is as structurally diverse and correspondingly difficult to predict as the beta chain VDJ-recombined loop (CDRB3). This differentiates TCR variable domain loops from the genetically analogous antibody loops and supports the conjecture that both TCR alpha and beta chains are deterministic of antigen specificity. We hypothesise that the larger number of alpha chain joining genes compared to beta chain joining genes compensates for the lack of a diversity gene segment. Overall, our study demonstrates that valuable structure-function relationships can lie in alpha chains despite their simpler junctions. We also provide over 1.5M predicted TCR structures to enable repertoire structural analysis and elucidate strategies towards improving the accuracy of future TCR structure predictors.

immunology↗

bioRxiv2024

The Observed T cell receptor Space database enables paired-chain repertoire mining, coherence analysis and language modelling

T cell activation is governed through T cell receptors (TCRs), heterodimers of two sequence-variable chains (often an alpha [] and beta [{beta}] chain) that recognise linear antigen fragments presented on the cell surfaces. Early sequencing technologies limited the study of immune repertoire TCRs to unpaired transcripts, leading to extensive analysis of {beta}-chain data alone as its greater sequence diversity suggested it should dominate antigen recognition. Over time, structural data has revealed that both and {beta} chains contribute to binding most antigens and highthroughput single-cell handling technologies have been increasingly applied to obtain samples of complete TCR variable region sequences from repertoires. Despite this, there is currently no repository dedicated to the curation of publicly available paired TCR sequence data. We have addressed this gap by creating the Observed T cell receptor Space (OTS) database, a source of consistently processed and annotated, full-length, paired-chain TCR sequencing data from 50 studies and at least 75 individuals. Currently, OTS contains 5.35M redundant (1.63M nonredundant) predominantly human TCR sequences and, based on recent data availability trends, will grow rapidly. We perform an initial analysis of OTS, leading to the identification of pairing biases, public TCRs, and distinct chain coherence patterns relative to antibodies. We also harness the data to build a publicly available paired-chain TCR language model, providing paired embedding representations and a method for residue in-filling that is conditional on the partner chain. OTS will be updated and maintained as a central community resource and is freely downloadable and available as a web application at https://opig.stats.ox.ac.uk/webapps/ots.

immunology↗

bioRxiv2024

Baselining the Buzz. Trastuzumab-HER2 Affinity, and Beyond!

Strong antibody-antigen binding is the primary consideration when developing an efficacious therapeutic antibody. In recent years, much work has been devoted to applying complex machine learning models to this cause, yet simple baselines are often lacking. Here, we show that the widely used sequence alignment method, BLOSUM, can yield diverse, binder-enriched libraries from a single starting antibody. Using Trastuzumab-HER2 as a model system, we experimentally validated 720 novel designs generated with five different computational methods using surface plasmon resonance. The BLOSUM substitution matrix outperformed all four deep learning design approaches tested, achieving an estimated minimum binder enrichment of 12.5% and producing nine sub-nanomolar binders. These results underscore the importance of comparing against simple baselines and set a benchmark to guide future computational antibody library design. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=141 SRC="FIGDIR/small/586756v2_ufig1.gif" ALT="Figure 1"> View larger version (32K): org.highwire.dtl.DTLVardef@1597ee9org.highwire.dtl.DTLVardef@9af4b6org.highwire.dtl.DTLVardef@1380e61org.highwire.dtl.DTLVardef@1380d29_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2024

Orthogonal IMiD-Degron Pairs Induce Selective Protein Degradation in Cells

Immunomodulatory imide drugs (IMiDs) including thalidomide, lenalidomide, and pomalidomide, can be used to induce degradation of a protein of interest that is fused to a short zinc finger (ZF) degron motif. These IMiDs, however, also induce degradation of endogenous neosubstrates, including IKZF1 and IKZF3. To improve degradation selectivity, we took a bump-and-hole approach to design and screen bumped IMiD analogs against 8380 ZF mutants. This yielded a bumped IMiD analog that induces efficient degradation of a mutant ZF degron, while not affecting other cellular proteins, including IKZF1 and IKZF3. In proof-of-concept studies, this system was applied to induce efficient degradation of TRIM28, a disease-relevant protein with no known small molecule binders. We anticipate that this system will make a valuable addition to the current arsenal of degron systems for use in target validation. One-Sentence SummaryEngineered zinc-finger-based degrons enable targeted protein degradation induced by selective molecular glues.

biochemistry↗

bioRxiv2023

Investigating the ability of deep learning-based structure prediction to extrapolate and/or enrich the set of antibody CDR canonical forms

Deep learning models have been shown to accurately predict protein structure from sequence, allowing researchers to explore protein space from the structural viewpoint. In this paper we explore whether "novel" features, such as distinct loop conformations can arise from these predictions despite not being present in the training data. Here we have used ABodyBuilder2, a deep learning antibody structure predictor, to predict the structures of [~]1.5M paired antibody sequences. We examined the predicted structures of the canonical CDR loops and found that most of these predictions fall into the already described CDR canonical form structural space. We also found a small number of "new" canonical clusters composed of heterogeneous sequences united by a common sequence motif and loop conformation. Analysis of these novel clusters showed their origins to be either shapes seen in the training data at very low frequency or shapes seen at high frequency but at a shorter sequence length. To evaluate explicitly the ability of ABodyBuilder2 to extrapolate, we retrained several models whilst with-holding all antibody structures of a specific CDR loop length or canonical form. These "starved" models showed evidence of generalisation across CDRs of different lengths, but they did not extrapolate to loop conformations which were highly distinct from those present in the training data. However, the models were able to accurately predict a canonical form even if only a very small number of examples of that shape were in the training data. Our results suggest that deep learning protein structure prediction methods are unable to make completely out-of-domain predictions for CDR loops. However, in our analysis we also found that even minimal amounts of data of a structural shape allow the method to recover its original predictive abilities. We have made the [~]1.5 M predicted structures used in this study available to download at https://doi.org/10.5281/zenodo.10280181.

bioinformatics↗

bioRxiv2023

Learnt representations of proteins can be used for accurate prediction of small molecule binding sites on experimentally determined and predicted protein structures

Protein-ligand binding site prediction is a useful tool for understanding the functional behaviour and potential drug-target interactions of a novel protein of interest. However, most binding site prediction methods are tested by providing crystallised ligand-bound (holo) structures as input. This testing regime is insufficient to understand the performance on novel protein targets where experimental structures are not available. An alternative option is to provide computationally predicted protein structures, but this is not commonly tested. However, due to the training data used, computationally-predicted protein structures tend to be extremely accurate, and are often biased toward a holo conformation. In this study we describe and benchmark IF-SitePred, a protein-ligand binding site prediction method which is based on the labelling of ESM-IF1 protein language model embeddings combined with point cloud annotation and clustering. We show that not only is IF-SitePred competitive with state-of-the-art methods when predicting binding sites on experimental structures, but it performs better on proxies for novel proteins where low accuracy has been simulated by molecular dynamics. Finally, IF-SitePred outperforms other methods if ensembles of predicted protein structures are generated.

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

Contextualising the developability risk of antibodies with lambda light chains using enhanced therapeutic antibody profiling

Antibodies with lambda light chains ({lambda}-antibodies) are generally considered to be less developable than those with kappa light chains ({kappa}-antibodies), leading to substantial systematic biases in drug discovery pipelines. This has contributed to kappa dominance amongst clinical-stage therapeutics. However, the identification of increasing numbers of epitopes preferentially engaged by{lambda} -antibodies shows there is a functional cost to neglecting them as potential lead candidates during discovery campaigns. Here, we update our Therapeutic Antibody Profiler (TAP) tool to use the latest data and machine learning-based structure prediction methods, and apply this new protocol to evaluate developability risk profiles for{kappa} -antibodies and{lambda} -antibodies based on their surface physicochemical properties. We find that{lambda} -antibodies are on average at a higher risk of poor developability -- as an indication, over 40% of single-cell sequenced human{lambda} -antibodies are flagged by TAP for risk-prone patches of surface hydrophobicity (PSH), compared to around 11% of human{kappa} -antibodies. Nonetheless, a substantial proportion of natural{lambda} -antibodies are assigned more moderate risk profiles by TAP and should therefore represent more tractable candidates for therapeutic development. We also analyse the populations of high and low risk antibodies, highlighting opportunities for strategic design that TAP suggests would enrich for more developable{lambda} -based candidates. Overall, we provide context to the differing developability of{kappa} - and{lambda} -antibodies, enabling a rational approach to incorporate more diversity into the initial pool of immunotherapeutic candidates.

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↗

bioRxiv2023

A comparison of the binding sites of antibodies and single-domain antibodies

Antibodies are the largest class of biotherapeutics. However, in recent years, single-domain antibodies have gained traction due to their smaller size and comparable binding affinity. Antibodies (Abs) and single-domain antibodies (sdAbs) differ in the structures of their binding sites: most significantly, single-domain antibodies lack a light chain and so have just three CDR loops. Given this inherent structural difference, it is important to understand whether Abs and sdAbs are distinguishable in how they engage a binding partner and thus, whether they are suited to different types of epitopes. In this study, we use non-redundant sequence and structural datasets to compare the paratopes, epitopes and antigen interactions of Abs and sdAbs. We demonstrate that even though sd-Abs have smaller paratopes, they target epitopes of equal size to those targeted by Abs. To achieve this, the paratopes of sdAbs contribute more interactions per residue than the paratopes of Abs. Additionally, we find that conserved framework residues are of increased importance in the paratopes of sd-Abs, suggesting that they include non-specific interactions to achieve comparable affinity. Further-more, the epitopes of sdAbs and Abs cannot be distinguished by their shape. For our datasets, sd-Abs do not target more concave epitopes than Abs: we posit that this may be explained by differences in the orientation and compaction of sdAb and Ab CDR-H3 loops. Overall, our results have important implications for the engineering and humanization of sdAbs, as well as the selection of the best modality for targeting a particular epitope.

immunology↗

bioRxiv2023

Investigating the Volume and Diversity of Data Needed for Generalizable Antibody-Antigen ΔΔG Prediction

Antibody-antigen binding affinity lies at the heart of therapeutic antibody development: efficacy is guided by specific binding and control of affinity. Here we present Graphinity, an equivariant graph neural network architecture built directly from antibody-antigen structures that achieves state-of-the-art performance on experimental {triangleup}{triangleup}G prediction. However, our model, like previous methods, appears to be overtraining on the few hundred experimental data points available. To test if we could overcome this problem, we built a synthetic dataset of nearly 1 million FoldX-generated {triangleup}{triangleup}G values. Graphinity achieved Pearsons correlations nearing 0.9 and was robust to train-test cutoffs and noise on this dataset. The synthetic dataset also allowed us to investigate the role of dataset size and diversity in model performance. Our results indicate there is currently insufficient experimental data to accurately and robustly predict {triangleup}{triangleup}G, with orders of magnitude more likely needed. Dataset size is not the only consideration - our tests demonstrate the importance of diversity. We also confirm that Graphinity can be used for experimental binding prediction by applying it to a dataset of >36,000 Trastuzumab variants.

bioinformatics↗

bioRxiv2023

Specific attributes of the VL domain influence both the structure and structural variability of CDR-H3 through steric effects

Antibodies, through their ability to target virtually any epitope, play a key role in driving the adaptive immune response in jawed vertebrates. The binding domains of standard antibodies are their variable light (VL) and heavy (VH) domains, both of which present analogous complementarity-determining region (CDR) loops. It has long been known that the VH CDRs contribute more heavily to the antigen-binding surface (paratope), with the CDR-H3 loop providing a major modality for the generation of diverse paratopes. Here, we provide evidence for an additional role of the VL domain as a modulator of CDR-H3 structure, using a diverse set of antibody crystal structures and a large set of molecular dynamics simulations. We show that specific attributes of the VL domain such as CDR canonical forms and genes can influence the structural diversity of the CDR-H3 loop, and provide a physical model for how this effect occurs through inter-loop contacts and packing of CDRs against each other. Our study provides insights into the interdependent nature of CDR conformations, an understanding of which is important for the rational antibody design process.

bioinformatics↗

bioRxiv2023

Exploring The Ability Of Machine Learning-Based Virtual Screening Models To Identify The Functional Groups Responsible For Binding

Many recently proposed structure-based virtual screening models appear to be able to accurately distinguish high affinity binders from non-binders. However, several recent studies have shown that they often do so by exploiting ligand-specific biases in the dataset, rather than identifying favourable intermolecular interactions in the input protein-ligand complex. In this work we propose a novel approach for assessing the extent to which machine learningbased virtual screening models are able to identify the functional groups responsible for binding. To sidestep the difficulty in establishing the ground truth importance of each atom of a large scale set of protein-ligand complexes, we propose a protocol for generating synthetic data where the label of an example is assigned by a 3-dimensional deterministic binding rule. This allows us to precisely quantify the ground truth importance of each atom and compare it to the model generated attributions. Using our generated datasets, we demonstrate that a recently proposed deep learning-based virtual screening model, PointVS, identified the most important functional groups with 39% more efficiency than a fingerprint-based random forest, suggesting that it would generalise more effectively to new examples. In addition, we found that ligand-specific biases, such as those present in widely used virtual screening datasets, substantially impaired the ability of all ML models to identify the most important functional groups. We have made our synthetic data generation framework available to facilitate the benchmarking of new virtual screening models. Code is available at https://github.com/tomhadfield95/synthVS.

bioinformatics↗