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 they 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. 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 facilitating the compilation of bespoke datasets of antibody sequences/structures that 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/).

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

The function of an antibody is intrinsically linked to the epitope it engages. Clonal clustering methods, based on sequence identity, are commonly used to group antibodies that will bind to 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 modeling. In addition, it was 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 have 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 dataset 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 improve our ability to identify antibodies that bind to the same epitope, adding information to sequence-based methods, especially in datasets of antibodies from diverse sources. SPACE2 is openly available on GitHub (https://github.com/oxpig/SPACE2).

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 2.81Å, a 0.09Å 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 2.89Å, a 0.55Å 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://doi.org/10.5281/zenodo.7258553 ).

Assessing the Content Quality of Online Parental Resources about Newborn Metabolic Disease Screening: A Content Analysis.

Parents increasingly utilise the internet to obtain information on health practices, but the quality of online information about screening for inherited metabolic diseases (IMD) needs to be improved. A content analysis examined how IMD blood and urine tests were described online in local healthcare sectors between May and June 2021. Among the nine resources, four were blood test providers and five were urine test providers. All mentioned the test benefits and procedures. Other information, such as false-positive/negative or risk of pain, was infrequently mentioned. The descriptions of urine tests are advertised as outperforming blood tests and can be purchased from commercial laboratory sites without medical guidance. Two urine test providers claimed no false results were reported. A few commercial advertisements highlighted the simplicity of the urine test and potentially overstated the invasiveness of the blood test. We found that some advertisements described IMD as "silent killers" and emphasised the advantage of getting "reassurance" in controlling the child's developmental health and well-being. To better protect the parents, or broadly, the public interest, regulatory and oversight measures on the urine tests should be implemented to promote the proper use of genetic tests. Without timely regulation and oversight, the incorrect descriptions might create a public misconception about utilising these commercial laboratory tests to inform health decisions.

Peptide Centric Vß Specific Germline Contacts Shape a Specialist 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 Vß2, 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 Vß2-containing TCRs and correlates with an unusual Vα-Vß interface, CDR loop conformations, and Vß2-specific germline contacts with peptides. Vß2 and Ld may represent "specialist" components for antigen recognition that allows for particularly strong and focused T cell responses.

Epitope profiling using computational structural modelling demonstrated on coronavirus-binding antibodies.

Identifying the epitope of an antibody is a key step in understanding its function and its potential as a therapeutic. Sequence-based clonal clustering can identify antibodies with similar epitope complementarity, however, antibodies from markedly different lineages but with similar structures can engage the same epitope. We describe a novel computational method for epitope profiling based on structural modelling and clustering. Using the method, we demonstrate that sequence dissimilar but functionally similar antibodies can be found across the Coronavirus Antibody Database, with high accuracy (92% of antibodies in multiple-occupancy structural clusters bind to consistent domains). 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 is 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.

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. Here, 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-Ligity's performance to an existing tool for comparing general protein interfaces, InterComp, and showed improved performance on antibody cases achieved in a substantially reduced time. 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.

TCRBuilder: multi-state T-cell receptor structure prediction.

MOTIVATION: T-cell receptors (TCRs) are immune proteins that primarily target peptide antigens presented by the major histocompatibility complex. They tend to have lower specificity and affinity than their antibody counterparts, and their binding sites have been shown to adopt multiple conformations, which is potentially an important factor for their polyspecificity. None of the current TCR-modelling tools predict this variability which limits our ability to accurately predict TCR binding. RESULTS: We present TCRBuilder, a multi-state TCR structure prediction tool. Given a paired αßTCR sequence, TCRBuilder returns a model or an ensemble of models covering the potential conformations of the binding site. This enables the analysis of structurally driven polyspecificity in TCRs, which is not possible with existing tools. AVAILABILITY AND IMPLEMENTATION: http://opig.stats.ox.ac.uk/resources. CONTACT: deane@stats.ox.ac.uk. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

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 complementary-determining regions (CDRs) of more than 180 million human and mouse B-cell receptor (BCR) repertoire sequences. These structurally annotated CDRs 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 CDR structures and these patterns of naïve repertoire paratope usage are highly conserved across subjects. In contrast, more differentiated B-cells are more personalized in terms of CDR structure usage. Our results establish the CDR 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. The software tool for structural annotation of BCR repertoires, SAAB+, is available at https://github.com/oxpig/saab_plus.

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 ß-sandwich architecture, TCRs are specialized to recognize 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 (ß3in 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.

Ligity: A Non-Superpositional, Knowledge-Based Approach to Virtual Screening.

We present Ligity, a hybrid ligand-structure-based, non-superpositional method for virtual screening of large databases of small molecules. Ligity uses the relative spatial distribution of pharmacophoric interaction points (PIPs) derived from the conformations of small molecules. These are compared with the PIPs derived from key interaction features found in protein-ligand complexes and are used to prioritize likely binders. We investigated the effect of generating PIPs using the single lowest energy conformer versus an ensemble of conformers for each screened ligand, using different bin sizes for the distance between two features, utilizing triangular sets of pharmacophoric features (3-PIPs) versus chiral tetrahedral sets (4-PIPs), fusing data for targets with multiple protein-ligand complex structures, and applying different similarity measures. Ligity was benchmarked using the Directory of Useful Decoys-Enhanced (DUD-E). Optimal results were obtained using the tetrahedral PIPs derived from an ensemble of bound ligand conformers and a bin size of 1.5 Å, which are used as the default settings for Ligity. The high-throughput screening mode of Ligity, using only the lowest-energy conformer of each ligand, was used for benchmarking against the whole of the DUD-E, and a more resource-intensive, "information-rich" mode of Ligity, using a conformational ensemble of each ligand, were used for a representative subset of 10 targets. Against the full DUD-E database, mean area under the receiver operating characteristic curve (AUC) values ranged from 0.44 to 0.99, while for the representative subset they ranged from 0.61 to 0.86. Data fusion further improved Ligity's performance, with mean AUC values ranging from 0.64 to 0.95. Ligity is very efficient compared to a protein-ligand docking method such as AutoDock Vina: if the time taken for the precalculation of Ligity descriptors is included in the comparason, then Ligity is about 20 times faster than docking. A direct comparison of the virtual screening steps shows Ligity to be over 5000 times faster. Ligity highly ranks the lowest-energy conformers of DUD-E actives, in a statistically significant manner, behavior that is not observed for DUD-E decoys. Thus, our results suggest that active compounds tend to bind in relatively low-energy conformations compared to decoys. This may be because actives-and thus their lowest-energy conformations-have been optimized for conformational complementarity with their cognate binding sites.

SCALOP: sequence-based antibody canonical loop structure annotation.

MOTIVATION: Canonical forms of the antibody complementarity-determining regions (CDRs) were first described in 1987 and have been redefined on multiple occasions since. The canonical forms are often used to approximate the antibody binding site shape as they can be predicted from sequence. A rapid predictor would facilitate the annotation of CDR structures in the large amounts of repertoire data now becoming available from next generation sequencing experiments. RESULTS: SCALOP annotates CDR canonical forms for antibody sequences, supported by an auto-updating database to capture the latest cluster information. Its accuracy is comparable to that of a standard structural predictor but it is 800 times faster. The auto-updating nature of SCALOP ensures that it always attains the best possible coverage. AVAILABILITY AND IMPLEMENTATION: SCALOP is available as a web application and for download under a GPLv3 license at opig.stats.ox.ac.uk/webapps/scalop. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Achieving Ultrahigh Carrier Mobility in Two-Dimensional Hole Gas of Black Phosphorus.

We demonstrate that a field-effect transistor (FET) made of few-layer black phosphorus (BP) encapsulated in hexagonal boron nitride (h-BN) in vacuum exhibits a room-temperature hole mobility of 5200 cm2/(Vs), being limited just by the phonon scattering. At cryogenic temperatures, the FET mobility increases up to 45 000 cm2/(Vs), which is five times higher compared to the mobility obtained in earlier reports. The unprecedentedly clean h-BN-BP-h-BN heterostructure exhibits Shubnikov-de Haas oscillations and a quantum Hall effect with Landau level (LL) filling factors down to v = 2 in conventional laboratory magnetic fields. Moreover, carrier density independent effective mass of m* = 0.26 m0 is measured, and a Landé g-factor of g = 2.47 is reported. Furthermore, an indication for a distinct hole transport behavior with up- and down-spin orientations is found.

SGO1C is a non-functional isoform of Shugoshin and can disrupt sister chromatid cohesion by interacting with PP2A-B56.

Shugoshin (SGO1) plays a pivotal role in sister chromatid cohesion during mitosis by protecting the centromeric cohesin from mitotic kinases and WAPL. Mammalian cells contain at least 6 alternatively spliced isoforms of SGO1. The relationship between the canonical SGO1A with shorter isoforms including SGO1C remains obscure. Here we show that SGO1C was unable to replace the loss of SGO1A. Instead, expression of SGO1C alone induced aberrant mitosis similar to depletion of SGO1A, promoting premature sister chromatid separation, activation of the spindle-assembly checkpoint, and mitotic arrest. In disagreement with previously published data, we found that SGO1C localized to kinetochores. However, the ability to induce aberrant mitosis did not correlate with its kinetochore localization. SGO1C mutants that abolished binding to kinetochores still triggered premature sister chromatid separation. We provide evidence that SGO1C-mediated mitotic arrest involved the sequestering of PP2A-B56 pool. Accordingly, SGO1C mutants that abolished binding to PP2A localized to kinetochores but did not induce aberrant mitosis. These studies imply that the expression of SGO1C should be tightly regulated to prevent dominant-negative effects on SGO1A and genome instability.

Direct activation of human and mouse Oct4 genes using engineered TALE and Cas9 transcription factors.

The newly developed transcription activator-like effector protein (TALE) and clustered regularly interspaced short palindromic repeats/Cas9 transcription factors (TF) offered a powerful and precise approach for modulating gene expression. In this article, we systematically investigated the potential of these new tools in activating the stringently silenced pluripotency gene Oct4 (Pou5f1) in mouse and human somatic cells. First, with a number of TALEs and sgRNAs targeting various regions in the mouse and human Oct4 promoters, we found that the most efficient TALE-VP64s bound around -120 to -80 bp, while highly effective sgRNAs targeted from -147 to -89-bp upstream of the transcription start sites to induce high activity of luciferase reporters. In addition, we observed significant transcriptional synergy when multiple TFs were applied simultaneously. Although individual TFs exhibited marginal activity to up-regulate endogenous gene expression, optimized combinations of TALE-VP64s could enhance endogenous Oct4 transcription up to 30-fold in mouse NIH3T3 cells and 20-fold in human HEK293T cells. More importantly, the enhancement of OCT4 transcription ultimately generated OCT4 proteins. Furthermore, examination of different epigenetic modifiers showed that histone acetyltransferase p300 could enhance both TALE-VP64 and sgRNA/dCas9-VP64 induced transcription of endogenous OCT4. Taken together, our study suggested that engineered TALE-TF and dCas9-TF are useful tools for modulating gene expression in mammalian cells.

LIF-dependent primitive neural stem cells derived from mouse ES cells represent a reversible stage of neural commitment.

Primitive neural stem cells (NSCs) define an early stage of neural induction, thus provide a model to understand the mechanism that controls initial neural commitment. In this study, we investigated primitive NSCs derived from mouse embryonic stem cells (ESCs). By genome-wide transcriptional profiling, we revealed their unique signature and depicted the molecular changes underlying critical cell fate transitions during early neural induction at a global level. Together with qRT-PCR analysis, our data illustrated that primitive NSCs retained expression of key pluripotency genes Oct4 and Nanog, while exhibiting repression of other pluripotency-related genes Zscan4, Foxp1 and Dusp9 and up-regulation of neural markers Sox1 and Hes1. The early differentiation feature in primitive NSCs was also supported by their intermediate characters on cell cycle profiles. Moreover, re-plating primitive NSCs back to ESC culture condition could reverse them back to ESC stage, as shown by reversible regulation of marker genes, cell cycle profile changes and enhanced embryoid body formation. In addition, our microarray analysis also identified genes differentially expressed in primitive NSCs, and loss-of-function analysis demonstrated that Hes1 and Ccdc141 play important function at this stage, opening up an opportunity to further understand the regulation of early neural commitment.

Decoding the Pluripotency Network: The Emergence of New Transcription Factors.

Since the successful isolation of mouse and human embryonic stem cells (ESCs) in the past decades, massive investigations have been conducted to dissect the pluripotency network that governs the ability of these cells to differentiate into all cell types. Beside the core Oct4-Sox2-Nanog circuitry, accumulating regulators, including transcription factors, epigenetic modifiers, microRNA and signaling molecules have also been found to play important roles in preserving pluripotency. Among the various regulations that orchestrate the cellular pluripotency program, transcriptional regulation is situated in the central position and appears to be dominant over other regulatory controls. In this review, we would like to summarize the recent advancements in the accumulating findings of new transcription factors that play a critical role in controlling both pluripotency network and ESC identity.

Upper-arm anthropometry: an alternative indicator of nutritional health to body mass index in unilateral lower-extremity amputees?

OBJECTIVE: To evaluate the utility of body mass index (BMI) and corrected-arm-muscle area (CAMA) as measures of nutritional health for lower-limb amputees attending prosthetics clinics. DESIGN: Cross-sectional study. SETTING: Prosthetics clinic in Australia. PARTICIPANTS: Unilateral lower-extremity amputees (N=58; age range, 21-91y; 37 transtibial, 21 transfemoral) attending a regional prosthetics clinic between May and November 2003. INTERVENTIONS: Not applicable. MAIN OUTCOME MEASURES: Weight (without prosthesis), corrected and uncorrected for the amputated limb was used with height estimated from knee height to calculate corrected BMI (cBMI) and uncorrected BMI (uBMI). CAMA was calculated using the mean of triplicate mid-upper-arm circumference (MUAC) and triceps skinfold thickness (TST) measurements. The Mini Nutritional Assessment (MNA) and Assessment of Quality of Life were administered according to recommended protocols. The Pearson correlation was used to determine the strength and significance of associations between variables, and bivariate regression analyses were performed to determine whether an association existed between the nutritional variables (BMI, CAMA, MNA) and quality of life (QOL). RESULTS: There were no statistically significant differences in the measures of nutritional health according to site (transtibial, transfemoral) of amputation. MUAC, TST, and CAMA all showed moderate to high positive correlations (r range, .541-.782) with both cBMI and uBMI. The strength of the relationship between the MNA and cBMI/uBMI was weaker (r=.383, r=.380, respectively) but remained positive and statistically significant (P=.003). QOL was not associated with cBMI or uBMI but was related to CAMA (beta=-.132; P=.030) and MNA (beta=-.561; P=.017). CONCLUSIONS: For persons with unilateral lower-extremity amputation, measurement of upper-arm anthropometry may be a more useful indicator of nutritional health and its consequences than BMI.