Retrieving AlphaFold models from AlphaFoldDB

There are now nearly a million AlphaFold [1] protein structure predictions openly available via AlphaFoldDB [2]. This represents a huge set of new data that can be used for the development of new methods. The options for downloading structures are either in bulk (sorted by genome), or individually from the webpage for a prediction.

If you want just a few hundred or a few thousand specific structures, across different genomes, neither of these options are particularly practical. For example, if you have several thousand experimental structures for which you have their PDB [3] code, and you want to obtain the equivalent AlphaFold predictions, there is another way!

If we take the example of the PDB’s current molecule of the month, pyruvate kinase (PDB code 4FXF), this is how you can go about downloading the equivalent AlphaFold prediction programmatically.

  1. Query UniProt [4] for the corresponding accession number – an example python script is shown below:
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Ten quick tips for proofreading your work

For my blog post, I thought I’d revisit my dark past on the other side of academic publishing when I worked as a copy editor and proofreader for two years between my undergrad and Masters. During this time, I worked primarily on review papers and news content. While I don’t claim to be a great writer or editor, I thought I’d share some easy tips to help refine your writing and make it more consistent. This is by no means an exhaustive list and probably most of them will already be familiar to you!

1. Consistency is key

I think two of the most important aspects of proofreading are ensuring consistency and using your common sense. For example, instead of agonizing over how you style a word, choose what you think is most appropriate and check that you’ve applied it consistently throughout the text. Check that style matches between the main text, headings, figure legends and footnotes. Some specific things to look for include the following:

  • Capitalization
    • If you’ve capitalized headings, has this been done throughout?
  • Italicization
    • E.g., have you italicized all your mentions of ‘in silico’? 
  • Superscript and subscript
    • E.g., is ‘half-maximal inhibitory concentration (IC50)‘ the same throughout? 
  • Numbers
    • Have you mixed up numerical and spelled-out numbers?  
    • E.g., I drank five cups of coffee and 4 cups of tea. 
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Filtering molecules with long linkers

Recently I was tasked with filtering out ‘stringy’ molecules that were being produced with the fragment merging method I’m working on (that is, molecules with lots of consecutive non-ring bonds that weren’t necessarily caught with my rotatable bond filter). While this is quite a niche/specific task, through this I discovered a couple of RDKit functions that I wasn’t previously aware of but might be helpful for other people regularly looking at small molecules. The demo adapts code from this helpful blogpost on cutting a molecule into rings and linkers from ‘Is life worth living?’ (which is a useful source of cheminformatics wisdom; https://iwatobipen.wordpress.com/2020/01/23/cut-molecule-to-ring-and-linker-with-rdkit-rdkit-chemoinformatics-memo/). Obviously in practice you may be applying lots of different filters to enumerated molecules, but this is just a small example of something I found useful. 

The Jupyter Notebook can be found at: 

https://github.com/stephwills/Demo-removing-stringy-molecules/blob/main/Molecule%20filter.ipynb

Happy coding, 

Steph 

Meeko: Docking straight from SMILES string

When docking, using software like AutoDock Vina, you must prepare your ligand by protonating the molecule, generating 3D coordinates, and converting it to a specific file format (in the case of Vina, PDBQT). Docking software typically needs the protein and ligand file inputs to be written on disk. This is limiting as generating 10,000s of files for a large virtual screen can be annoying and hinder the speed at which you dock.

Fortunately, the Forli group in Scripps Research have developed a Python package, Meeko, to prepare ligands directly from SMILES or other molecule formats for docking to AutoDock 4 or Vina, without writing any files to disk. This means you can dock directly from a single file containing all the SMILES of the ligands you are investigating!

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ISMB 2022 – July 10-14 Madison, Wisconsin

Madison, Wisconsin, a place known for its superb selection of craft beverages, for having Wisconsin’s Best Cheese Curds, and, most importantly, for hosting the 2022 annual international conference on Intelligent Systems for Molecular Biology (ISMB). Fortunately, we (Lewis and Tobias) got to attend this year’s ISMB and get a taste of Madison. The 2022 conference is the 30th ISMB conference and has grown to become the world’s largest bioinformatics/computational biology conference with nearly 600 presented talks. We therefore got to hear a wide range of different and interesting talks.

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5th Artificial Intelligence in Chemistry Symposium

The lineup for the Royal Society of Chemistry’s 5th “Artificial Intelligence in Chemistry” Symposium (Thursday-Friday, 1st-2nd September 2022) is now complete for both oral and poster presentations. It really is a fantastic selection of topics and speakers and it is clear this event is now a highlight of the scientific calendar. Our very own Prof. Charlotte M. Deane, MBE will be giving a keynote.

5th RSC-BMCS/RSC-CICAG Airtificial Intelligence in Chemistry Symposium, 1st-2nd September, Churchill College, Cambridge + Zoom broadcast.

It marks a return to in-person meetings: it will be held at Churchill College, Cambridge, with a conference dinner at Trinity Hall.

More details are here: https://www.rscbmcs.org/events/aichem22/.

Registration for in person attendance is open until Monday 29th August 17:00 (BST).

It is also possible to register for virtual attendance; the meeting will be broadcast on Zoom.

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The evolution, evolvability and engineering of gene regulatory DNA

Catching up on the literature is one of the highlights of my job as a scientist. True, sometimes you can be overwhelmed by the amount of information you don’t have; or wonder if we really need another paper showing that protein-ligand scoring functions don’t work. And yet, sometimes you find excellent research that you can’t but regard with a mixture of awe and envy. At a recent group meeting, I discussed one such paper from the research group of Aviv Regev at MIT, where the authors perform an impressive combination of computation and experiment to consider some basic questions in gene regulation and evolution. Here is why I think it’s excellent.

The authors are interested in promoters, small sequences of DNA that precede genes, which are known to regulate how frequently their partners will be expressed. In short, these promoters are binding sites for transcription factors, a family of proteins that in turn recruit RNA polymerase to transcribe DNA to RNA. In turn, albeit not directly, the rate of gene transcription determines the rate at which a protein is produced. If this sounds simple, however, that is where our understanding stops. The human genome encodes some 1.6k different transcription factors (~6-7% of protein-coding genes) and their underworkings are still not well-understood.

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The SARS-CoV-2 protein spike glycosylation not only shields but primes binding by providing structural stability too

Yep, it is very well known that the sugar coating (aka glycosylation) of viruses makes them invisible to the immune system, a strategy so effective that like in the case of HIV, whose spike is almost entirely covered by glycans, makes it so difficult to target by the human immune system.

Unsurprisingly, coronaviruses such as SARS, MERS, and SARS-CoV-1(2) not only benefit from this evolutionary strategy but there is evidence now that sugars provide stability to their spikes to be effective binders by glueing the spike chains, hence making them infectious.

This is the major finding of this paper that introduces very interesting results from all-atom MD simulations of a fully glycosylated model of the  SARS-CoV-2 spike protein embedded in a realistic viral membrane. Researchers aimed to look into the stability of the protein spike (A, B, and C) chains in the “open” and “closed” conformation and how these changed upon key residue mutations to test how glycans sitting in the inter-chain space affect stability. It also aimed at quantifying glycans’ shielding effect from molecules ranging from 2 to 15 Angstroms, i.e., from small-sized to peptide- and antibody-sized molecules.  

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Cool ideas in Deep Learning and where to find more about them

I was planning on doing a blog post about some cool random deep learning paper that I have read in the last year or so. However, I keep finding that someone else has already written a way better blog post than what I could write. Instead I have decided to write a very brief summary of some hot ideas and then provide a link to some other page where someone describes it way better than me.

The Lottery Ticket Hypothesis

This idea has to do with pruning a model, which is when you remove a parts of your model to make it more computationally efficient while barely loosing accuracy. The lottery ticket hypothesis also has to do with how weight are initialized in neural networks and why larger models often achieve better performance.

Anyways, the hypothesis says the following: “Dense, randomly-initialized, feed-forward networks contain subnetworks (winning tickets) that—when trained in isolation—reach test accuracy comparable to the original network in a similar number of iterations.” In their analogy, the random initialization of a models weights is treated like a lottery, where some combination of a subset of these weight is already pretty close to the network you want to train (winning ticket). For a better description and a summary of advances in this field I would recommend this blog post.

SAM: Sharpness aware minimization

The key idea here has to do with finding the best optimizer to train a model capable of generalization. According to this paper, a model that has converged to a sharp minima will be less likely to generalize than one that has converged to a flatter minima. They show the following plot to provide an intuition of why this may be the case.

In the SAM paper (and ASAM for adaptive) the authors implement an optimizer that is more likely to converge to a flat minima. I found this blog post by the authors of ASAM gives a very good description of the field.

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Monoclonal antibody PRNP100 therapy for Creutzfeldt–Jakob disease

Recently, University College London Hospitals (UCLH) received a “Specials License” to allow the treatment of six patients suffering from Creutzfeldt–Jakob Disease (CJD), by way of a novel antibody known as PRN100. The results of this treatment have now been published in The Lancet.

There is currently no cure for CJD, yet over 100 people per year develop it either spontaneously or through external means including (but not limited to) growth hormones, cataract surgery or infected neurosurgical implements [1]. “There is no UK legislation which implements a compassionate use programme as set out in Article 83 of the relevant EU regulation. But the UK has implemented an exemption process known as the “Specials” in light of the requirement to be able to deal with special needs.” [2]

As there is no known cure, the request for use of PRN100 was put before the court as in Law “Some treatment decisions are so serious that the court has to make them.”

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