Hi everyone,
My blogpost this time around is a list of conferences popular with OPIGlets. You are highly likely to see at least one of us attending or presenting at these meetings! I’ve tried to make it as exhaustive as possible (with thanks to Fergus Imrie!), listing conferences in upcoming chronological order.
(Most descriptions are slightly modified snippets taken from the official websites.)
Author Archives: Matthew Raybould
What can you do with the OPIG Antibody Suite?
OPIG has now developed a whole range of tools for antibody analysis. I thought it might be helpful to summarise all the different tools we are maintaining (some of which are brand new, and some are not hosted at opig.stats), and what they are useful for.
Immunoglobulin Gene Sequencing (Ig-Seq/NGS) Data Analysis
1. OAS
Link: http://antibodymap.org/
Required Input: N/A (Database)
Paper: http://www.jimmunol.org/content/201/8/2502
OAS (Observed Antibody Space) is a quality-filtered, consistently-annotated database of all of the publicly available next generation sequencing (NGS) data of antibodies. Here you can:
ISMB 2018 (Chicago): Summary of Interesting Talks/Posters
Catherine’s Selection
Network approach integrates 3D structural and sequence data to improve protein structural comparison
Why: Current graph mapping in protein structural comparison ignores sequence order of residues. Residues distant in sequence but close in 3D space are more important.
How: Introduce sequence order of residues, set a sequence-distance cutoff to consider structurally important residues, count the graphlet frequency and embed into PCA space.
Results: the new method is predictive of SCOP and CATH ‘groups’. Certain graphlets are enriched in alpha and beta folds.
Link: https://www.nature.com/articles/s41598-017-14411-y
Investigating the molecular determinants of Ebola virus pathogenicity
Why: Reston virus is the only Ebola virus that is not pathogenic to human
What they do: multiple sequence alignment to look for specificity determining positions (SDPs) using s3det, then predict the effect of each individual SDP on the stability of the protein with mCSM.
Results: VP40 SDPs alter octamer formation, structure hydrophobic core. VP24 SDPs leads to impair binding to KPNA5 in human, which inhibits interferon signalling.
Impact: only a few SDPs distinguish Reston VP24 from VP24 of others. Human-pathogenic Reston viruses may emerge.
Link: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5558184/#__ffn_sectitle
Computational Analysis Highlights Key Molecular Interactions and Conformational Flexibility of a New Epitope on the Malaria Circumsporozoite Protein and Paves the Way for Vaccine Design
Why: An antibody with a strong binding affinity was found in a group of subjects. This antibody prevents cleavage of the surface protein.
What they do: They found the linear epitope, crystallise the strong and medium binders and run a molecular dynamic simulation to find out the flexibility of the structures.
Results: The strong binder is less flexible. Moreover, the strong binder is similar to the germline sequence which may mean that this antibody could have been readily formed.
Link: https://www.nature.com/articles/nm.4512
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Matt’s Selection
“Analysis of sequence and structure data to understand nanobody architectures and antigen interactions”
Laura S. Mitchell (Colwell Group)
University of Cambridge, UK
This poster detailed the work from Laura’s two most recent publications, which can be found here: https://doi.org/10.1002/prot.25497, https://doi.org/10.1093/protein/gzy017
They describe a comprehensive analysis of the binding properties of the 156 non-redundant nanobody-antigen (Nb-Ag) complexes in the PDB/SAbDab (October 2017). Their analyses include Nb sequence variability (both global and across the binding regions), contact maps of nanobody-antigen interactions by region, and the typical chemical properties of each paratope. Nb-Ag complexes are compared to a reference set of monoclonal antibody-antigen (mAb-Ag) complexes. This work is a key first step in advancing our understanding of Nb paratopes, and will aid the development of new diagnostics and therapeutics.
“OSPREY 3.0: Open-Source Protein Redesign for You, with Powerful New Features”
Jeffrey W. Martin (Donald Group)
Duke University, USA
OSPREY 3.0 (https://www.biorxiv.org/content/early/2018/04/23/306324) represents a large advance towards time-efficient continuous flexibility modelling of protein-protein interfaces.
Its new algorithms LUTE and BBK* allow for continuous rotamer flexibility searching and entropy-aware binding constant approximation in a much more efficient manner. The CATS algorithm also introduces local backbone flexibility as a long-awaited feature. This software now has a easy-to-use Python interface, and is fully Open-Source, making it an extremely attractive alternative to other proprietary protein design tools.
“Functional annotation of chemical libraries across diverse biological processes”
Scott Simpkins
University of Minnesota-Twin Cities, USA
This interesting talk detailed the work published in Nature Chemical Biology in September 2017 (https://doi.org/10.1038/nchembio.2436).
310 yeast gene-deletion mutants were isolated to perform chemical-genetic profile studies across six diverse small molecule high-throughput screening libraries. By studying which gene-deletion mutants were hypersensitive or resistant to each compound, the researchers could assign most members of each chemical library a probable functional annotation. Mapping back to gene-interaction profile data also allowed them to infer likely targets for some compounds. The GO annotations associated with these genes could then be used assess whether a given starting library is likely to contain promising starting-points that affect a given biological function. For example, the authors highlighted a deficiency across all libraries against the cellular processes of cytokinesis and ribosome biogenesis. Conversely, they found a large enrichment across all libraries for compounds likely to affect glycosylation or cell wall biogenesis. Compounds that target transcription and chromatin organisation were found to be enriched in certain datasets, and depleted in others. This genre of profiling provides researchers a way of judging a priori whether a given screening library is likely to contain promising lead compounds, given the functional role of the target of interest.
Helpful resources for people studying therapeutic antibodies
My work within OPIG involves studying therapeutic antibodies. It can be tough to find information about these commercial molecules, often known by unintelligible developmental names until the later stages of clinical trials. Their structures are frequently absent, as one might expect, but even their sequences are sometimes a nightmare to get hold of! Below is a list of resources that I have found particularly helpful.
IDENTITIES OF RELEVANT ANTIBODIES
1. Wikipedia (don’t judge!) is an extremely helpful resource to get started. They have the following databases:
(a) A list of FDA-approved therapeutic monoclonal antibody therapies
(b) A more general list of therapeutic, diagnostic and preventive monoclonal antibodies (includes some things that have been withdrawn)
2. The Antibody Society has list of FDA/EU approved and antibodies to watch on their website. NB: This is only available to members of the society (free for students and other concessions, standard membership is $100pa).
3. The journal ‘mAbs’ also has a series of ‘Antibodies to Watch in [Year]’ papers. Here are the ones for 2016, 2017 and 2018.
SEQUENCES
4. 137 clinical-stage (post-phase I) mAb sequences can be found in the SI of this paper by Jain et al.
5. A slightly outdated (last updated Nov 2016), but still extremely useful, resource of antibody seqeunces is this FASTA list, written by Dr Martin’s Group at UCL.
SEQUENCES & STRUCTURES
6. The IMGT monoclonal antibody database (mAb-DB) has been possibly the most helpful resource. This includes 798 entries of both therapeutics and non-therapeutics, so it’s helpful to get a list of the antibodies you are interested in first. You can search it with a wide range of parameters, including antibody name. A typical antibody result will include its mAb-DB ID, INN details, common & developmental names, species, receptor type and isotype, sequence (via the “IMGT/2Dstructure-DB” link), target, clinical trials details and – if available – the 3D structure (via the “IMGT/3Dstructure-DB” link).
7. SAbDab has a continually-updated section for all therapeutic antibody structures deposited in the PDB.
CURRENT STATUS OF THE THERAPEUTIC
8. Search the therapeutic name on AdisInsight, or Pharmacodia to see its current clinical trial status, and whether or not it has been withdrawn.
Antibody Developability: Experimental Screening Assays
[This blog post is centered around the paper “Biophysical properties of the clinical-stage antibody landscape” (http://www.pnas.org/content/114/5/944.abstract) by Tushar Jain and coworkers. It is designed as a very basic intro for computational scientists into the world of experimental biophysical assays.]
A major concern in the development of antibody therapies is being able to predict “developability issues” at the screening stage, to avoid costly developmental dead-ends. Examples of such issues include an antibody being difficult to manufacture, possessing unsuitable pharmacodynamic or pharmokinetic profiles, having a propensity to aggregate (both in storage and in vivo) and being highly immunogenic.
This post is designed to give a clear and concise summary of the principles behind some of the most common biophysical experimental assays used to assess antibody candidates for future developability issues.
1. Ease of manufacture
HEK Titre (HEKt): This assay tests the expression level of the antibody (the higher the better). The heavy and light chain sequences are subcloned into vectors (such as pcDNA 3.4+, ThermoFisher) and these vectors are subsequently transfected into a suspension of Human embryonic kidney (HEK293) cells. After a set number of days the supernatant is harvested to assess the degree of expression.
2. Stability of 3D structure
Melting temperature using Differential Scanning Fluorimetry (Tm with DSF) Assay: This assay tests the thermal stability of the antibody. The higher the thermal stability, the less likely the protein will spontaneously unfold and become immunogenic. The antibody is mixed with a dye that fluoresces when in contact with hydrophobic regions, such as SPYRO orange. The mixture is then taken through a range of temperatures (eg. 40°C -> 95°C at a rate of 0.5°C/2min). As the protein begins to unfold, buried hydrophobic residues will become exposed and the level of fluorescence will suddenly increase. The value of T when the increase in fluorescence intensity is greatest gives us a Tm value.
(Further reading: http://www.beta-sheet.org/resources/T22-Niesen-fingerprinting_Oxford.pdf)
3. Stickiness assays (Aggregation propensity/Low solubility/High viscosity)
Affinity-capture Self-interaction Nanoparticle Spectroscopy (AC-SINS) Assay: This assay tests how likely an antibody is to interact with itself. It uses gold nanoparticles that are coated with anti-Fc antibodies. When a dilute solution of antibodies is added, they rapidly become immobilised on the gold beads. If these antibodies subsequently attract one another, it leads to shorter interatomic distances and longer absorption wavelengths that can be detected by spectroscopy.
(Further reading: https://www.ncbi.nlm.nih.gov/pubmed/24492294)
Clone Self-interaction by Bio-layer Interferometry (CSI-BLI) Assay: A more high-throughput method that uses a label-free technology to measure self-interaction. Antibodies are loaded onto the biosensor tip and white light is shone down the instrument to yield an internal reflection interference pattern. Then the tip is inserted into a solution of the same antibody, and if self-interaction occurs, then the interference pattern shifts by an amount proportional to the change in thickness of the biological layer. Images from: http://www.fortebio.com/bli-technology.html
(Further Reading: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3896597/)
Hydrophobic Interaction Chromatography (HIC) Assay: Antibodies are mixed into a polar mobile phase and then washed over a hydrophobic column. UV-absorbance or other techniques can then be used to determine the degree of adhesion.
(Further Reading: https://www.ncbi.nlm.nih.gov/pubmed/4094424)
Standup Monolayer Chromatography (SMAC) Assay: Antibodies are injected onto a pre-packed Zenix HPLC column and their retention times are calculated. The longer the retention time, the lower their colloidal stability and the more prone they are to aggregate.
(Further Reading: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4622974/)
Size-exclusion Chromatography (SEC) Assay: Antibodies are flowed through a column consisting of spherical beads with miniscule pores. Non-aggregated antibodies are small enough to get trapped in the pores, whereas aggregated antibodies will flow through the column more rapidly. Percentage aggregation can be worked out from the concentrations of the different fractions.
4. Degree of specificity
Cross-Interaction Chromatography (CIC) Assay: This assay measures an antibody’s retention time as it flows across a column conjugated with polyclonal human serum antibodies. If an antibody takes longer to exit the column, it indicates that its surface is likely to interact with several different in vivo targets.
(Further Reading: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3896597/)
Enzyme-linked Immunosorbent Assay (ELISA) – with common antigens or Baculovirus Particles (BVPs): Common antigens or BVPs are fixed onto a solid surface and then a solution containing the antibody of interest linked to an enzyme (such as horseradish peroxidase, HRP) is washed over them. Incubation lasts for about an hour before any unreacted antibodies are washed off. When the appropriate enzyme substrate is then added, it triggers emission of a visible, fluorescent or luminescent nature, which can be detected. The intensity is proportional to the amount of antibody stuck to the surface.
(Further Reading: https://www.thermofisher.com/uk/en/home/life-science/protein-biology/protein-biology-learning-center/protein-biology-resource-library/pierce-protein-methods/overview-elisa.html)
Poly-Specificity Reagent (PSR) Binding Assay: A more high-throughput method that uses fluorescence-activated cell sorting (FACS), a type of flow cytometry. A PSR is generated by biotinylating soluble membrane proteins (from Chinese hamster ovary (CHO) cells, for example) and then is incubated with IgG-presenting yeast. After washing a secondary labeling mix is added, and flow cytometry is used to determine a median fluorescence intensity – the higher the median intensity, the greater the chance of non-specific binding.
(Further Reading: https://www.ncbi.nlm.nih.gov/pubmed/24046438)