Drawing molecules on your laptop usually requires access to proprietary software such as ChemDraw (link) or free websites such as PubChem’s online sketcher (link). However, if you are feeling adventurous, you can build your personal sketcher in React/Typescript using the Ketcher package!
Ketcher is an open-source package that allows easy implementation of a molecule sketcher into a web application. Unfortunately, it does require TypeScript so the script to run it cannot be imported directly into an HTML page. Therefore we will set up a simple React app to get it working.
The sketcher is very sleek and has a vast array of functionality, such as choosing any atom from the periodic table and being able to directly import molecules from either SMILES or Mol2/SDF file format into the sketcher. These molecules can then be edited and saved to a new file in the chemical file format of your choosing.
Sometimes it can be handy to work with multiple structures in PyMOL using Python.
Here’s a snippet of code you might find useful: we iterate over all the α-carbon atoms in a protein and append to a list tuples such as (‘GLY’, 1). The dictionary, ‘reslist’, returns a list of residue names and indices for each molecule, where the key is a string containing the name of the molecule.
from pymol import cmd
# Create a list of all the objects, called 'mpls':
mols = cmd.get_object_list('*')
# Create an empty dictionary that will return a list of residues
# given the name of the molecule object
reslist = {}
# Set the dictionaries to be empty lists
for m in mols: reslist[m] = []
# Use PyMOL's iterate command to go over every α-Carbon and append
# a tuple consisting of the each residue's residue name ('resn') and
# residue index ('resi '):
for m in mols: cmd.iterate('%s and n. ca'%m, 'reslist["%s"].append((resn,int(resi)))'%m)
This script assumes you only have protein molecules loaded, and ignores things like chain ID and insertion codes.
Once you have your list of residues, you can use it with the cmd.align command, e.g., to align a particular residue to a reference structure.
Jamboree (1) a large gathering, as of a political party or the teams of a sporting league, often including a program of speeches and entertainment.; (2) a large gathering of members of the Boy Scouts or Girl Scouts, usually nationwide or international in scope
Oxford Dictionary
This October marks twenty years since our supreme leader, Charlotte Deane, came to Oxford to start the first protein informatics group in this university.
Twenty years is a really long time, and at OPIG we like to celebrate things in style. From the beginning, it was clear that we would be doing what we know best: get together, consume lots of food and drinks, and perhaps talk about science. But, frankly, that’s what we do all the time. This simply wasn’t enough to celebrate two decades of scientific production. So Charlotte entrusted several of us with an ambitious goal: to reach out to our former members, and to ask them to join us, in Oxford, to celebrate two decades of protein informatics. And that’s what we did.
For two months, we painstakingly tracked down every person that has ever been part of our group, and attempted to gather their contact details to invite them to Oxford. Attempted to, for the most part. While LinkedIn gave us some early victories, some alumni had managed to cover their tracks very well, including one person we could only found after tracking down their three previous jobs. Nevertheless, after much digging, we managed to find updated contact details for every person that has ever passed by our lab, and nearly thirty of these former alumni (almost 50% of them!) made their way to Oxford on October 8th* to hold the first OPIG Jamboree.
From the first student (Sanne Abeln, rightmost in the second row) to the most recent (Kate, whose hair can barely be seen on the leftmost third row), we are all here!Continue reading →
Nanobodies are an exciting area of increasing interest in the biotherapeutics domain. They consist only of a heavy chain variable domain so are much smaller than conventional antibodies (about 1/10th of their mass) but despite this, manage to achieve comparable affinity for their targets, in addition to being more soluble and stable – good things come in small packages! Nanobodies are not naturally produced in humans but can be derived from camelids (VHHs) or sharks (vNARs) and then engineered to humanise them. For the rest of this blog post we will skip over the science entirely and learn how to draw a llama, a great example of a camelid species.
This is my first OPIG blog! I’m going to start with a summary of the Graphormer, a Graph Neural Network (GNN) that borrows concepts from Transformers to boost performance on graph tasks. This post is largely based on the NeurIPS paper Do Transformers Really Perform Bad for Graph Representation?by Ying et. al., which introduces the Graphormer, and which we read for our last deep learning journal club. The project has now been integrated as a Microsoft Research project.
I’ll start with a cheap and cheerful summary of Transformers and GNNs before diving into the changes in the Graphormer. Enjoy!
With the advent of ABlooper, we’ve recently introduced OpenMM as a new dependency for the SAbDab-SAbPred antibody modelling platform. By far the easiest way to install the OpenMM Python API is via Conda, so we’ve moved to Conda environments for the entire platform. This has made installation of the platform much easier, but introduces complications when it comes to running its web applications under Apache. In this post, I’ll briefly explain the reason for this, and provide a basic guide for running Flask apps using Conda environments under Apache.
We have all been there, working on code that continuously fails while staring at a dull and colorless command-line. However, we are in luck, as there is a way to make the constant error messages look less depressing. By changing our shell to one which enables a colorful themed command-line and fancy features like automatic text completion and web search your code won’t just fail with ease, but also with style!
A shell is your command-line interpreter, meaning you use it to process commands and output results of the command-line. The shell therefore also holds the power to add a little zest to the command-line. The most well-known shell is bash, which comes pre-installed on most UNIX systems. However, there exist many different shells, all with different pros and cons. The one we will focus on is called Z Shell or zsh for short.
Zsh was initially only for UNIX and UNIX-Like systems, but its popularity has made it accessible on most systems now. Like bash, zsh is extremely customizable and their syntax so similar that most bash commands will work in zsh. The benefit of zsh is that it comes with additional features, plugins and options, and open-source frameworks with large communities. The framework which we will look into is called Oh My Zsh.
Graph neural networks (GNNs) have quickly become one of the most important tools in computational chemistry and molecular machine learning. GNNs are a type of deep learning architecture designed for the adaptive extraction of vectorial features directly from graph-shaped input data, such as low-level molecular graphs. The feature-extraction mechanism of most modern GNNs can be decomposed into two phases:
Message-passing: In this phase the node feature vectors of the graph are iteratively updated following a trainable local neighbourhood-aggregation scheme often referred to as message-passing. Each iteration delivers a set of updated node feature vectors which is then imagined to form a new “layer” on top of all the previous sets of node feature vectors.
Global graph pooling: After a sufficient number of layers has been computed, the updated node feature vectors are used to generate a single vectorial representation of the entire graph. This step is known as global graph readout or global graph pooling. Usually only the top layer (i.e. the final set of updated node feature vectors) is used for global graph pooling, but variations of this are possible that involve all computed graph layers and even the set of initial node feature vectors. Commonly employed global graph pooling strategies include taking the sum or the average of the node features in the top graph layer.
While a lot of research attention has been focused on designing novel and more powerful message-passing schemes for GNNs, the global graph pooling step has often been treated with relative neglect. As mentioned in my previous post on the issues of GNNs, I believe this to be problematic. Naive global pooling methods (such as simply summing up all final node feature vectors) can potentially form dangerous information bottlenecks within the neural graph learning pipeline. In the worst case, such information bottlenecks pose the risk of largely cancelling out the information signal delivered by the message-passing step, no matter how sophisticated the message-passing scheme.
The languorous, muggy heat of the Korean afternoon sun was what greeted me after 13 hour cattle-class flight from a cool, sensible Helsinki night. The goings-on in Ukraine, and associated political turmoil, meant taking the scenic route – avoiding Russia and instead passing over Turkey, Kazakstan and Mongolia – with legs contorted into unnatural positions and sleep an unattainable dream. Tired and disoriented, I relied less on Anna’s expert knowledge of the Korean language than her patience for my jet-lag-induced bad mood and brain fog. We waited an hour for a bus to take us from Incheon airport to Yongsan central station in the heart of the capital. It was 35 °C.
I’ve been here for a month. Anna has found work, starting in November; I have found the need to modify my working habits. Gone are the comfortable, temperate offices on St Giles’, replaced by an ever-changing diorama of cafés, hotel rooms and libraries. Lugging around my enormous HP Pavilion, known affectionately by some as ‘The Dominator’, proved to be unsustainable.
It’s thesis-writing time for me, so any programming I do is just tinkering and tweaking and fixing the litany of bugs that Lucy Vost has so diligently exposed. I had planned to run Ubuntu on Parallels using my MacBook Air; I discovered to my dismay that a multitude of Conda packages, including PyTorch, are not supported on Apple’s M1 chip. It has been replaced by a combination of Anna’s old Intel MacBook Pro and rewriting my codebase to install and run without a GPU – adversity is the great innovator, as the saying goes.
A few weeks ago we attended the RSC’s 5th AI in Chemistry conference at Churchill College, Cambridge. It featured research and discussions on a broad range of topics and was attended by a diverse set of more than 200 researchers from academia and industry, including six Opiglets.
