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!
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.
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.
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.
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.
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.
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.
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.”
With many tours (Farcical and otherwise) restricted due to Covid, 2022 celebrated the resurrection of OPIG’s glorious Tour de Farce. This year’s route was nine miles and an unusually conservative four pubs.
After listening to Lewis’ conference prep talk, we left the Statistics Department around 5pm for a leisurely trundle through Mesopotamia, The Oxford Psychopath, Old Marston and out to our first rest stop, The Victoria.
Finding a way to express the similarity of irregular and discrete molecular graphs to enable quantitative algorithmic reasoning in chemical space is a fundamental problem in data-driven small molecule drug discovery.
Virtually all algorithms that are widely and successfully used in this setting boil down to extracting and comparing (multi-)sets of subgraphs, differing only in the space of substructures they consider and the extent to which they are able to adapt to specific downstream applications.
A large body of recent work has explored approaches centred around graph neural networks (GNNs), which can often maximise both of these considerations. However, the subgraph-derived embeddings learned by these algorithms may not always perform well beyond the specific datasets they are trained on and for many generic or resource-constrained applications more traditional “non-parametric” topological fingerprints may still be a viable and often preferable choice .
This blog post gives an overview of the topological fingerprint algorithms implemented in RDKit. In general, they count the occurrences of a certain family of subgraphs in a given molecule and then represent this set/multiset as a bit/count vector, which can be compared to other fingerprints with the Jaccard/Dice similarity metric or further processed by other algorithms.
A blog post about reviewing papers and preparing papers for publication.
We start with the following premise: all papers have limitations. There is not a single paper without limitations. A method may not be generally applicable, a result may not be completely justified by the data or a theory may make restrictive assumptions. To cover all limitations would make a paper infinitely long, so we must stop somewhere.
A lot of limitations fall into the following scenario. The results or methods are presented but they could have extended them in some way. Suppose, we obtain results on a particular cell type using an immortalized cell-line. Are the results still true, if we performed the experiments on primary or patient-derived cells? If the signal from the original cells was sufficiently robust then we would hope so. However, we can not be one hundred percent sure. A similar example is a method that can be applied to a certain type of data. It may be possible to extend the method to be applied to other data types. However, this may require some new methodology. I call this flavor of limitations vertical limitations. They are vertical in the sense that they build upon an already developed result in the manuscript. For certain journals, they will require that you tackle vertical limitations by adapting the original idea or method to demonstrate broad appeal or that idea could permeate multiple fields. Most of the time, however, the premise of an approach is not to keep extending it. It works. Leave it alone. Do not ask for more. An idea done well does not need more.
