Category Archives: Links

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:

Continue reading →

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

—

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.

Seeing the Mesoscale

There’s a range of scales that is really hard for us to see. Techniques like X-ray crystallography and increasingly, cryo-electron microscopy, let us see molecules to atomic level-of-detail. Microscopes reveal organelles in cells, but seeing the molecular ‘trees’ in the cellular ‘forest’ requires a synthesis of knowledge. David Goodsell was one of the first to show us the emergent beauty of the cell at the molecular level, and work carried out in the Molecular Graphics Laboratory at The Scripps Research Institute under the direction of Art Olson has led to a 3D molecular modeling tools like ePMV, autoPACK and cellPACK.

One of the fruits of this labor is the Visual Guide to the Cell, part of the Allen Cell Explorer. It’s well worth a look at how you can explore 3D representations of the cell in a web browser.

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.

Crystallographic programming: Super short tour of the cctbx

Two of the leading packages in crystallography are Phenix and CCP4. For most practicing crystallographers they will interact via with these to progress a single crystallographic data-set from diffraction images, through integration, merging, phasing, model building and hopefully deposition.

However, if you want to develop crystallographic software, you will likely need to decide on a framework to build upon. Phenix is built on the comprehensive cctbx library, whereas CCP4 programs are typically standlone, although common crystallographic libraries such as clipper and cctbx are utilised.

CCTBX is written mainly in python, with core crystallographic functionality written in C++. My usual starting place for understanding functionality is through the pdb parser tutorial. This introduces the concept of a hierarchy, a iterative way to represent a macromolecule:

from iotbx.pdb import hierarchy
pdb_in = hierarchy.input(file_name="model.pdb")
for chain in pdb_in.hierarchy.only_model().chains() :
  for residue_group in chain.residue_groups() :
    for atom_group in residue_group.atom_groups() :
      for atom in atom_group.atoms() :
        if (atom.element.strip().upper() == "ZN") :
          atom_group.remove_atom(atom)
      if (atom_group.atoms_size() == 0) :
        residue_group.remove_atom_group(atom_group)
    if (residue_group.atom_groups_size() == 0) :
      chain.remove_residue_group(residue_group)
f = open("model_Zn_free.pdb", "w")
f.write(pdb_in.hierarchy.as_pdb_string(
  crystal_symmetry=pdb_in.input.crystal_symmetry()))
f.close()

Although there are many ways to parse a pdb file, the introduction to iotbx.pdb, gives a view of how xray structure data can be associated to the model. The tour of the cctbx can be helpful starting place, especially for understanding how the python and c++ functionality interact through boost and the scitbx.array_family.flex. Unfortunately, documentation on cctbx tends to vary in quality and quantity throughout the modules:

Other components of the library include ways to simulate crystallographic data through simtbx, and tools for processing xfel data.

As the library is open source, github hosted source code allows exploration of previously written routines, which can be very helpful for understanding the inner workings of the library. Note that there are also bulletin boards for users and developers of phenix and cctbx respectively. A few tutorials can also be found.

Hopefully this post will give someone other than me a reminder of where to find resources to get started developing within CCTBX.

Submitting your thesis!

Writing and submitting your thesis is (almost) the final stage of completing your PhD. It can be the most stressful and unpleasant part of the process… but it can also be rewarding to see the story of your last three years’ work fall into place.

All I want for christmas is… “Piled Higher and Deeper” by Jorge Cham (www.phdcomics.com)

This post is a miscellaneous collection of advice and resources about the submission process, most of which have been passed down from the very first members of OPIG. Hopefully it will be useful to have it all in the same place for present and future members. Feel free to comment here if you have any tips I have missed!

All information and links that I’ve included are correct at the time of writing (for Oxford University Statistics students) but you should always use the university’s guidelines as your primary resource.

The very beginning: the plan

Don’t spend too long on this! But you should have an idea of your planned chapter titles and an overall story for your book. Also useful is a timeline for when you will finish drafts of chapters by. Try to be realistic with this. If you decide to change your thesis title you should fill out an application for change of thesis title form (GSO.6). Make sure you look up any restrictions (word/page limits etc.) which may apply, and confirm your hand-in date.

Starting writing

It’s a good idea to decide what you will use to write your thesis. Most OPIG members use LaTeX. There are some great thesis templates out there but the one most people tend to use is one from Cambridge’s Engineering department. You can do a fair bit of customisation within that template… changing fonts, headers, titles and more, but it’s a great starting point.

When the finish line’s in sight: choosing examiners

A couple of months before you are planning to submit your thesis you should discuss with your supervisor(s) potential examiners. Your supervisor can informally check with them if they are happy to examine you and then you should fill out an appointment of examiners form (GSO.3). You can also change your thesis title on this form without filling in GSO.6.

Finishing writing

Your final document is likely to be over 100 pages with thousands of words (or potential typos as you might come to call them). A great LaTeX spell checker is aspell which should already be installed on your work machine. To spell check a .tex file (ignoring all TeX notation… apart from multiple citations I found!) using a British dictionary simply type:

aspell --lang=en_GB -t -c filename.tex

You’re absolutely guaranteed to still have typos floating around but it’s a decent start. You (and others if you can get them) should manually proof-read as well!

Final Formatting

Your thesis should be set out on numbered, portrait A4 pages. It should be double spaced and the inner (bound) margin should be 3-3.5cm. For more details on the formatting required check out the university’s regulations.

Printing and binding

When you’re happy with your proof-reading (you’re still almost guaranteed to have remaining typos) you’ll have to print and bind your finished book! To comply with university guidelines you will need to submit two copies, for each of your examiners, to the exam schools. You may also like to print a copy for yourself (you will need one to take with you into your viva). Before you start, if you are printing in colour at the department make sure you have enough printer credit by emailing IT (let them know the printer and your Bod card number and they will top you up if necessary).

If you are planning to print your copies double sided you may want to buy your own paper of higher quality than that provided by the department (at least 100gsm). As of October 2014, the Oxford Print Centre was selling the cheapest packs of 100gsm paper we could find but sold out close to deadline day! Also check out WHSmiths or Ryman’s.

You might want to do a test run of a few colour pages of your thesis before you send the whole file to be printed. Printing at 1200dpi (instead of the default 600dpi) can improve the appearance of your figures considerably. You may want to stay late at the office to print so you are not disturbed by other print jobs during office hours.

Your thesis should be securely bound in either hard or soft cover. Loose-leaf or spiral binding won’t be accepted. There are several binding facilities through Oxford but I used the Oxford Print Centre just down the road, which also guarantees a one hour service for soft binding even on submission days.

Submission

Submit your completed copies to the exam schools, noting their opening hours (08.30-17:00, Monday to Friday), take the traditional photo, and bask in your newly found FREEDOM (try to forget about the viva!).

[Database] SAbDab – the Structural Antibody Database

An increasing proportion of our research at OPIG is about the structure and function of antibodies. Compared to other types of proteins, there is a large number of antibody structures publicly available in the PDB (approximately 1.8% of structures contain an antibody chain). For those of us working in the fields of antibody structure prediction, antibody-antigen docking and structure-based methods for therapeutic antibody design, this is great news!

However, we find that these data are not in a standard format with respect to antibody nomenclature. For instance, which chains are “heavy” chains and which are “light“? Which heavy and light chains pair? Is there an antigen present? If so, to which H-L pair does it bind to? Which numbering system is used … etc.

To address this problem, w e have developed SAbDab: the Structural Antibody Database. Its primary aim is for easy creation of antibody structure and antibody-antigen complex datasets for further analysis by researchers such as ourselves. These sets can be selected using a number of criteria (e.g. experimental method, species, presence of constant domains…) and redundancy filters can be applied over the sequences of both the antibody and antigen. Thanks to Jin, SAbDab now also includes associated curated affinity (Kd) values for around 190 antibody-antigen complexes. We hope this will serve as a benchmarking tool for antibody-antigen docking prediction algorithms.

Alternatively, the database can be used to inspect and compare properties of individual structures. For instance, we have recently published a method to characterise the orientation between the two antibody variable domains, VH and VL. Using the ABangle tool, users can select structures with a particular VH-VL orientation, visualise and quantify conformational changes (e.g. between bound and unbound forms) and inspect the pose of structures with certain amino acids at specific positions. Similarly, the CDR (complimentary determining region) search and clustering tools, allow for the antibody hyper-variable loops to be selected by length, type and canonical class and their structures visualised or downloaded.

SAbDab also contains features such as the template search. This allows a user to submit the sequence of either an antibody heavy or light chain (or both) and to find structures in the database that may offer good templates to use in a homology modelling protocol. Specific regions of the antibody can be isolated so that structures with a high sequence identity over, for example, the CDR H3 loop can be found. SAbDab’s weekly automatic updates ensures that it contains the latest available data. Using each method of selection, the structure, a standardised and re-numbered version of the structure, and a summary file containing information about the antibody, can be downloaded both individually or en-masse as a dataset. SAbDab will continue to develop with new tools and features and is freely available at: opig.stats.ox.ac.uk/webapps/sabdab.

GPGPUs for bioinformatics

As the clock speed in computer Central Processing Units (CPUs) began to plateau, their data and task parallelism was expanded to compensate. These days (2013) it is not uncommon to find upwards of a dozen processing cores on a single CPU and each core capable of performing 8 calculations as a single operation. Graphics Processing Units were originally intended to assist CPUs by providing hardware optimised to speed up rendering highly parallel graphical data into a frame buffer. As graphical models became more complex, it became difficult to provide a single piece of hardware which implemented an optimised design for every model and every calculation the end user may desire. Instead, GPU designs evolved to be more readily programmable and exhibit greater parallelism. Top-end GPUs are now equipped with over 2,500 simple cores and have their own CUDA or OpenCL programming languages. This new found programmability allowed users the freedom to take non-graphics tasks which would otherwise have saturated a CPU for days and to run them on the highly parallel hardware of the GPU. This technique proved so effective for certain tasks that GPU manufacturers have since begun to tweak their architectures to be suitable not just for graphics processing but also for more general purpose tasks, thus beginning the evolution General Purpose Graphics Processing Unit (GPGPU).

Improvements in data capture and model generation have caused an explosion in the amount of bioinformatic data which is now available. Data which is increasing in volume faster than CPUs are increasing in either speed or parallelism. An example of this can be found here, which displays a graph of the number of proteins stored in the Protein Data Bank per year. To process this vast volume of data, many of the common tools for structure prediction, sequence analysis, molecular dynamics and so forth have now been ported to the GPGPU. The following tools are now GPGPU enabled and offer significant speed-up compared to their CPU-based counterparts:

Application	Description	Expected Speed Up	Multi-GPU Support
Abalone	Models molecular dynamics of biopolymers for simulations of proteins, DNA and ligands	4-29x	No
ACEMD	GPU simulation of molecular mechanics force fields, implicit and explicit solvent	160 ns/day GPU version only	Yes
AMBER	Suite of programs to simulate molecular dynamics on biomolecule	89.44 ns/day JAC NVE	Yes
BarraCUDA	Sequence mapping software	6-10x	Yes
CUDASW++	Open source software for Smith-Waterman protein database searches on GPUs	10-50x	Yes
CUDA-BLASTP	Accelerates NCBI BLAST for scanning protein sequence databases	10	Yes
CUSHAW	Parallelized short read aligner	10x	Yes
DL-POLY	Simulate macromolecules, polymers, ionic systems, etc on a distributed memory parallel computer	4x	Yes
GPU-BLAST	Local search with fast k-tuple heuristic	3-4x	No
GROMACS	Simulation of biochemical molecules with complicated bond interactions	165 ns/Day DHFR	No
GPU-HMMER	Parallelized local and global search with profile Hidden Markov models	60-100x	Yes
HOOMD-Blue	Particle dynamics package written from the ground up for GPUs	2x	Yes
LAMMPS	Classical molecular dynamics package	3-18x	Yes
mCUDA-MEME	Ultrafast scalable motif discovery algorithm based on MEME	4-10x	Yes
MUMmerGPU	An open-source high-throughput parallel pairwise local sequence alignment program	13x	No
NAMD	Designed for high-performance simulation of large molecular systems	6.44 ns/days STMV 585x 2050s	Yes
OpenMM	Library and application for molecular dynamics for HPC with GPUs	Implicit: 127-213 ns/day; Explicit: 18-55 ns/day DHFR	Yes
SeqNFind	A commercial GPU Accelerated Sequence Analysis Toolset	400x	Yes
TeraChem	A general purpose quantum chemistry package	7-50x	Yes
UGENE	Opensource Smith-Waterman for SSE/CUDA, Suffix array based repeats finder and dotplot	6-8x	Yes
WideLM	Fits numerous linear models to a fixed design and response	150x	Yes

It is important to note however, that due to how GPGPUs handle floating point arithmetic compared to CPUs, results can and will differ between architectures, making a direct comparison impossible. Instead, interval arithmetic may be useful to sanity-check the results generated on the GPU are consistent with those from a CPU based system.

Good looking proteins for your publication(s)

Just came across a wonderful PyMOL gallery while creating some images for my (long overdue) confirmation report. A fantastic resource to draw sexy proteins – especially useful for posters, talks and papers (unless you are paying extra for coloured figures!).

It would be great if we had our own OPIG “pymol gallery”.

An example of one of my proteins (1tgm) with aspirin bound to it:

Oxford Protein Informatics Group

or "OPIG" to friends