ABlooper: fast accurate antibody CDR loop structure prediction with accuracy estimation

Antibodies are a key component of the immune system and have been extensively used as biotherapeutics. Accurate knowledge of their structure is central to understanding their antigen binding function. The key area for antigen binding and the main area of structural variation in antibodies is concentrated in the six complementarity determining regions (CDRs), with the most important for binding and most variable being the CDR-H3 loop. The sequence and structural variability of CDR-H3 make it particularly challenging to model. Recently deep learning methods have offered a step change in our ability to predict protein structures. In this work we present ABlooper, an end-to-end equivariant deep-learning based CDR loop structure prediction tool. ABlooper rapidly predicts the structure of CDR loops with high accuracy and provides a confidence estimate for each of its predictions. On the models of the Rosetta Antibody Benchmark, ABlooper makes predictions with an average CDR-H3 RMSD of 2.49Å, which drops to 2.05Å when considering only its 76% most confident predictions

The Conorfamide RPRFa Stabilizes the Open Conformation of Acid-Sensing Ion Channel 3 via the Nonproton Ligand–Sensing Domain

Acid-sensing ion channel 3 (ASIC3) is a proton-gated Na+ channel with important roles in pain. ASIC3 quickly desensitizes in less than a second, limiting its capacity to sense sustained acidosis during pain. RFamide neuropeptides are modulators of ASIC3 that slow its desensitization and induce a variable sustained current. The molecular mechanism of slowed desensitization and the RFamide binding site on ASIC3 are unknown. RPRFamide, a RFamide from the venom of a cone snail, has a comparatively high affinity for ASIC3 and strongly slows its desensitization. Here we show that covalent binding of a UV-sensitive RPRFamide variant to ASIC3 prevents desensitization, suggesting that RPRFamide has to unbind from ASIC3 before it can desensitize. Moreover, we show by in silico docking to a homology model of ASIC3 that a cavity in the lower palm domain, which is also known as the nonproton ligand–sensing domain, is a potential binding site of RPRFamide. Finally, using extensive mutagenesis of residues lining the nonproton ligand–sensing domain, we confirm that this domain is essential for RPRFamide modulation of ASIC3. As comparative analysis of ASIC crystal structures in the open and in the desensitized conformation suggests that the lower palm domain contracts during desensitization, our results collectively suggest that RPRFamide, and probably also other RFamide neuropeptides, bind to the nonproton ligand–sensing domain to stabilize the open conformation of ASIC3

Five computational developability guidelines for therapeutic antibody profiling

Therapeutic mAbs must not only bind to their target but must also be free from “developability issues” such as poor stability or high levels of aggregation. While small-molecule drug discovery benefits from Lipinski’s rule of five to guide the selection of molecules with appropriate biophysical properties, there is currently no in silico analog for antibody design. Here, we model the variable domain structures of a large set of post-phase-I clinical-stage antibody therapeutics (CSTs) and calculate in silico metrics to estimate their typical properties. In each case, we contextualize the CST distribution against a snapshot of the human antibody gene repertoire. We describe guideline values for five metrics thought to be implicated in poor developability: the total length of the complementarity-determining regions (CDRs), the extent and magnitude of surface hydrophobicity, positive charge and negative charge in the CDRs, and asymmetry in the net heavy- and light-chain surface charges. The guideline cutoffs for each property were derived from the values seen in CSTs, and a flagging system is proposed to identify nonconforming candidates. On two mAb drug discovery sets, we were able to selectively highlight sequences with developability issues. We make available the Therapeutic Antibody Profiler (TAP), a computational tool that builds downloadable homology models of variable domain sequences, tests them against our five developability guidelines, and reports potential sequence liabilities and canonical forms. TAP is freely available at opig.stats.ox.ac.uk/webapps/sabdab-sabpred/TAP.php

Thera-SAbDab: the Therapeutic Structural Antibody Database

The Therapeutic Structural Antibody Database (Thera-SAbDab; http://opig.stats.ox.ac.uk/webapps/therasabdab) tracks all antibody- and nanobody-related therapeutics recognized by the World Health Organisation (WHO), and identifies any corresponding structures in the Structural Antibody Database (SAbDab) with near-exact or exact variable domain sequence matches. Thera-SAbDab is synchronized with SAbDab to update weekly, reflecting new Protein Data Bank entries and the availability of new sequence data published by the WHO. Each therapeutic summary page lists structural coverage (with links to the appropriate SAbDab entries), alignments showing where any near-matches deviate in sequence, and accompanying metadata, such as intended target and investigated conditions. Thera-SAbDab can be queried by therapeutic name, by a combination of metadata, or by variable domain sequence - returning all therapeutics that are within a specified sequence identity over a specified region of the query. The sequences of all therapeutics listed in Thera-SAbDab (461 unique molecules, as of 5 August 2019) are downloadable as a single file with accompanying metadata

Five computational developability guidelines for therapeutic antibody profiling

Therapeutic mAbs must not only bind to their target but must also be free from “developability issues” such as poor stability or high levels of aggregation. While small-molecule drug discovery benefits from Lipinski’s rule of five to guide the selection of molecules with appropriate biophysical properties, there is currently no in silico analog for antibody design. Here, we model the variable domain structures of a large set of post-phase-I clinical-stage antibody therapeutics (CSTs) and calculate in silico metrics to estimate their typical properties. In each case, we contextualize the CST distribution against a snapshot of the human antibody gene repertoire. We describe guideline values for five metrics thought to be implicated in poor developability: the total length of the complementarity-determining regions (CDRs), the extent and magnitude of surface hydrophobicity, positive charge and negative charge in the CDRs, and asymmetry in the net heavy- and light-chain surface charges. The guideline cutoffs for each property were derived from the values seen in CSTs, and a flagging system is proposed to identify nonconforming candidates. On two mAb drug discovery sets, we were able to selectively highlight sequences with developability issues. We make available the Therapeutic Antibody Profiler (TAP), a computational tool that builds downloadable homology models of variable domain sequences, tests them against our five developability guidelines, and reports potential sequence liabilities and canonical forms. TAP is freely available at opig.stats.ox.ac.uk/webapps/sabdab-sabpred/TAP.php

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

Rapid Quantification of Digitoxin and Its Metabolites Using Differential Ion Mobility Spectrometry-Tandem Mass Spectrometry

This study focuses on the quantitative analysis of the cardiac glycoside drug digitoxin and its three main metabolites digitoxigenin-bisdigitoxose, digitoxigenin-monodigitoxose, and digitoxigenin using electrospray ionization-differential ion mobility spectrometry-tandem mass spectrometry (ESI-DMS-MS/MS). Despite large molecular weight differences, gas-phase separation of the four compounds in the DMS drift cell was not possible, even by utilizing additional volatile chemical modifiers. Baseline separation was achieved after adduct formation with alkali metal ions, however, and efficiency was shown to improve with increasing size of the alkali ion, reaching optimum conditions for the largest cesium ion. Subsequently, an assay was developed for quantification of digitoxin and its metabolites from human serum samples and its analytical performance assessed in a series of proof-of-concept experiments. The method was applied to spiked human serum pools with concentration levels between 2 and 80 ng/mL. After a short reversed-phase chromatographic step for desalting the sample, rapid DMS separation of the analytes was carried out, resulting in a total run time of less than 1.5 min. The instrumental method showed good repeatability; the calculated coefficients of variation ranged from 2% to 13%